NEWS BLOG

Walter Rodriguez 5/3/22 Walter Rodriguez 5/3/22

How to Listen

Yes, most of us, including me, need to practice our listening skills! After the introductions, ask a question not quickly answered with a yes, no, or maybe. Repeat the person's name in your mind. When the person starts to talk, hold a steady, soft, or even friendly gaze directed to their left eye--if they are right-handed. To rest your eyes, consider looking slightly away every few seconds--as needed. Try to focus on every word---bypassing your thoughts. You will have to quiet your mind and hold your opinions. That is, try not to react or interrupt the speaker immediately. Instead of analyzing what you hear, try to feel and project empathy for the person. Then, when the person has completed their answer, paraphrase what they said to ensure that you understood and the person understands that you understood. Depending on their response, you might be able to start articulating your thoughts at that time. Please let me know if these listening processes worked for you and the types of challenges you experienced. #listeningskills #empathy #react #learninganddevelopment #ideas #students #studentsuccess #personalgrowthanddevelopment #retention #facultydevelopment

Walter Rodriguez 4/3/22 Walter Rodriguez 4/3/22

Purposeful (self-inflicted) interruptions might be beneficial to work focus and learning.

Last time, I wrote about designing courses & training to maximize student attention, focus, and retention. Today, I want to address interruptions. Why? Not all interruptions are bad for learning & focus. Yes, there are good and bad interruptions.

For instance, if I hear a dog barking or call to attend an unplanned meeting, it could take me a few minutes to half-and-hour to regain my work focus and concentration on the task. [Worst case: Being interrupted by an enticing social media advertisement or video game could take me longer since I will probably fall into an eternal scrolling trap.]

Nevertheless, if I stop playing the guitar to "feel" if I am playing the right melody, that may help me focus even more on the piece; if I pause reading to re-read or review a paragraph. And ponder the content; the purposeful interruption could help me remember a lesson for a more extended period. Or if I intentionally stop what I am doing to attend a planned meeting related to my subject which can be beneficial: That can help me regain my focus and concentration quicker. And perhaps, help me make mental associations that allow me to recall the content later on.

Purposeful (self-inflicted) interruptions might be beneficial to work focus and learning.

Think about how many book chapters or video series end a chapter or episode with a cliffhanger. That is, purposely interrupting the episode and leaving you wondering about what would happen next. In this way, our mind remains engaged in the content as we try to anticipate what comes next.

I usually like to finish a class with a cliffhanger or an assignment that will make my students stay engaged on the topic after the class is over.

What if we could design course lessons that leave our students wanting to learn more about the subject? How about assigning small group assignments that require students to apply what they have learned in their daily lives or work.

Further, think about The Zeigarnik Effect, which states that people remember unfinished or incomplete tasks better than completed ones. To recall: Zeigarnik experienced that waiters in a café could place the orders they had not yet delivered better than those they had distributed.

#traininganddevelopment #learning #work #students #intructionaldesign #socialmedia #content #video

Walter Rodriguez 3/13/22 Walter Rodriguez 3/13/22

Course Design >>

Can we design our courses to maximize students' attention, focus, and retention? Or, what if we developed our instructional practices to minimize stress and student withdrawal?

For decades now, e-mail, texting, and software apps from Facebook, Twitter, Google, etcetera have been able to capture our students' attention for a few seconds or minutes at a time. Unfortunately, many of these apps often interrupt what our students are doing, intending to gain their attention (eyeballs & clicks.) But what if we use some of those techniques to earn our students' attention, focus, and retention? And therefore improve learning & student retention along the way.

Emotions such as immersion and enjoyment contribute favorably to focus and learning outcomes. Researchers have demonstrated that the perceived authenticity of a learning experience can facilitate the sense of immersion for students (Rosenbaum, Klopfer, and Perry 2007; Csikszentmihalyi 1990; Newmann 1992; Rodriguez, Angle, and Snyder 2021).

The above might create competition among other well-designed courses but all for a good purpose: improving learning.

What do you think? Any ideas? What are moral & ethical issues to consider? Do you have some well-designed courses to share?

#retentionstrategies #studentsuccess #faculty #students #design #webdevelopment #coursesonline #coursedesign #coursedevelopment #curriculumdevelopment #professors #instructionaldesign #learning #experience #google #facebook

Walter Rodriguez 3/6/22 Walter Rodriguez 3/6/22

: IMPROVING FOCUS

by Walter Rodriguez, Ph.D., P.E.

You may ask: How can I improve my team’s focus, attention & retention? Simply put, infuse each of your communications and objectives with relevant, meaningful activities! The closer the subject, exercise, or task is to your team's interest, the easier it will be to captivate their focus & attention. And the more they will retain and remember the subject. Granted: It's not an easy task but necessary to make our meetings and activities more engaging and enjoyable.

I usually start my meetings and classes by asking participants: What do you want to learn or understand a bit better today (on the subject at hand)? And why? I extemporaneously construct the meeting or lesson based on the learning outcomes (LOs) that I must address in the meeting or syllabus. In addition to helping participants focus better, it helps me feel more engaged and creative as a leader, teacher, and faculty member. If you haven't, please try the above approach and let me know how it goes.

Note: If you have helpful learning strategies you wish to share with us, please feel free to post your learning experiences in this forum or contact walter@coursewell.com

#learning #retention #students #share #leadershipdevelopment #focus #instructionaldesign #instructors #facultydevelopment #learningstrategies #educationinnovation #learninginnovation #teacher #learningoutcomes #creative

Walter Rodriguez 1/6/22 Walter Rodriguez 1/6/22

Leading Life*: "Your future depends on what you do today."

“Your future depends on what you do today.” – Mahatma Gandhi

Inspired by the wisdom of Mahatma Gandhi and other great leaders and thinkers like him, in Coursewell’s Leading Life course, you will learn how you can lead yourself (and your team) to a thriving, even blissful, work-life in challenging times. But, please act now by simply writing an email to walter@coursewell.com. [There are no application forms to complete but must indicate the reason(s) for joining our faculty, retired executives, entrepreneurs, and other interested learners like yourself.]

During this course, Tuesday live sessions at 2 PM USA Eastern time, you will learn & apply:

A more thoughtful way to learn. And, more importantly, find a better way to remember, retain and recall what you have learned. For instance, many learners and researchers have discovered that changing locations and taking a quiz before studying the subject matter can improve subsequent learning.
Develop an integrated work-life strategy for you, your family, and your organization, since work-life is not divided into compartmentalized silos. Our brains are always on, even while relaxing and dreaming.
Acquire life-work strategic leadership skills to thrive in life and work—no matter the present or future challenges. For instance, learn how to lead (influence) decision-makers and peers towards a strategic goal.
Learn a holistic methodology to design & develop a better work-life for you (or your family, or your team)—socially, emotionally, intellectually, and financially.
Work on exciting real-life business & community projects along with active faculty, retired executives, entrepreneurs, participants, and your peers.
Learn to meditate and lead a work-life of purpose, riches, and focus.

For additional information, please contact walter@coursewell.com.

Moderator

Prof. Walter Rodriguez, B.S.C.E., M. Arch., Ph.D., P.E., G.C., founded Coursewell.com and teaches (remotely) at SGMI Management Institut St. Gallen online MBA program in Switzerland---including leadership, strategy, management, marketing, MIS, operations, and finance. As a practicing architect, civil engineer, general contractor, project manager, professor, and researcher, he received the Harvard Foundation Medal "to recognize his notable contributions to American Science, Engineering and Intercultural Relations." After a Post-Doctoral Fellowship at MIT (collaborating with the Media Lab & the CEE Intelligent Engineering Systems Lab), Dr. Rodriguez acquired 24 years of managerial & technical teaching and research experience as Founding Professor and Chair in the Lutgert College of Business’ Departments of Computer Science, Information Systems, Marketing, and Operations Management at Florida Gulf Coast University. [Of course, not all at once!] He was a tenured Associate Professor in the College of Engineering at the Georgia Institute of Technology, teaching Construction Management, Computer Graphics, and Computer-Aided Design. Professor Rodriguez taught CAD and Engineering Construction Management at the University of Puerto Rico’s School of Architecture and the University of Central Florida. Also, he served as the Berger Chair Professor of Design & Construction Management at Tufts University. He has published over 120 peer-refereed research papers and developed online certification courses in analytics, IT, computer graphics, and construction project management (PM), among many others. During his tenure at FGCU, he served as Founding Director of the Institute for Technological Innovation and the multi-million-dollar DoD-sponsored MyCAA Portable Careers Project and the National Science Foundation (NSF)--sponsored Constructability Review & Evaluation for Workgroups (CREW) Project. Also, he was Director of Grants and Research; Alico Endowed Chair and Eminent Scholar in Operations Management & Strategy; and Founding Chair of the Departments of Computer Information Systems, Computer Science, Project Management, and Decision Sciences as well as Florida Engineering Education Delivery System (FEEDS) program at Florida Gulf Coast University. Walter obtained his Ph.D. in engineering project management (interdisciplinary Civil Engineering/Industrial Systems Engineering) from the University of Florida. Dr. Rodriguez founded Coursewell.com to enhance careers in MIS, information technology (IT), and project management (PM) areas.

Walter Rodriguez 11/9/21 Walter Rodriguez 11/9/21

What is Innovation?

Innovation is an academic, multidisciplinary field of study for developing groundbreaking ideas, solutions, processes, products, and services. Below are a couple of innovative solutions. And below are some examples.

Field: Construction

Problem: Freddie Mac projects a major deficit of single-family homes in the USA--especially in the lower to affordable price range. (Source: https://lnkd.in/gFghWxAh)

Challenges: (1) obtaining materials in a timely fashion (mainly, due to supply-chain bottlenecks during the pandemic); (2) difficulty in recruiting, hiring, and retaining skilled workers (trades labor); (3) lack of affordable land near the city centers; (4) regulatory issues; (5) capital needed; and (6) risks associated with uncertainty, among other factors.

Opportunity: To quickly build sustainable (economically and environmentally) houses---more inexpensively and with considerably less waste.

Solution: Build less expensive (and durable) 3-D printed houses using concrete framing.

Innovator: ICON (partnership with construction developers, like Lennar) can build house walls using semi-automated 3-D printing technology. Essentially, the machine places fresh concrete
in layers. And, since the machine work with curved walls, designers can be a bit creative.

Innovation Kudos:

> Mexico (Échale) & USA (ICON) Collaboration: "3D Printed Housing for Those Who Need It Most": Check: https://lnkd.in/gAGybMzZ

> Germany (Mense-Korte/Hous3DRuck): https://www.mense-korte.de

#innovation #collaboration #concrete #sustainable #housing #technolgy

Walter Rodriguez 10/11/21 Walter Rodriguez 10/11/21

Is there a more thoughtful way to learn?

There is a lot to learn and not enough time!

Is there a more thoughtful way to learn? And, more importantly, is there a better way to remember, retain and recall what you have learned?

For instance, many learners and researchers have discovered that taking a quiz before studying the subject matter can improve subsequent learning.

The key is to invent ways to engage with the content you are trying to learn. For that, you may want to create your exams, quizzes, or problem sets.

You may try to teach the concepts to someone else. Also, relate the content to your own experiences to develop a mnemonic.

When reading, make diagrams rather than underlining or highlighting content.

Change your routine—study in a different place, location, or environment---including some tolerable distractions.

Study while cooking or doing the laundry. Brief distractions from the subject can help--including going for a walk.

Of course, let's not forget that concentration, focus, and repetition also play a role in learning many subjects.

Finally, sleep well and try to find meaning in what you are studying.

Make everything relevant to the topic and your life activities and fun. In a recent paper (posted on LinkedIn), I discussed how machine learning could assist human learning.

What do you think? What has been your experience learning more efficiently?
#learning #content #change #sleep #training #environment #machinelearning #humanlearning #corporatelearning #certificationtraining #linkedin #experience

Walter Rodriguez 9/1/21 Walter Rodriguez 9/1/21

AI and Construction Home Improvements

If you are planning a complex home improvement construction project, like me, searching construction products (think: tools, PVC pipes, etc.), services & materials at Lowe's or Home Depot might be a secondary activity after you have your preliminary design or construction plans in place. But have you thought about the complex AI and ML problems that these building suppliers encounter to help us with our home improvement construction projects? Check what an AI/ML/data-science practitioner is thinking about at: https://lnkd.in/g_ksHRZD #artificialintelligence #machinelearning #algorithms #construction #planning #data #projects #design #architecture #projectmanagement #constructionmanagement #constructionindustry

Walter Rodriguez 8/20/21 Walter Rodriguez 8/20/21

> Improving Persistence & Retention with Timely Student-Support and Instructional Technologies >

Walter Rodriguez, Ph.D., P.E.

For the last two years, my students and I have explored how we can enhance students' persistence & retention by leveraging real-life student-support & instructional technologies.

In the process, we individually and collectively realized that while software systems & apps may provide opportunities to engage students, deciding to implement software apps for delivering fully-online student-support programs needed further analysis. Reason: Untested learning- and student-support-apps could potentially create institutional havoc if sufficient instructional- and student-support are not provided ubiquitously, in a timely, effective, and efficient way.

Rather than promoting software apps' flexibility and convenience, we explored the drawbacks of low student-persistence and how information systems could assist the process. Suitably, this article presents mobile-app constructs for facilitating student-support and student-mentor-faculty engagement. The findings have been applied to thousands of students/participants in a joint private-industry/government-sponsored, multiyear, career-certification training (not-for-credit) project and later tested in an academic (for-credit) setting. This student-faculty collaboration led to a peer-reviewed paper (Rodriguez, W. et al. 2019) co-authored with undergraduate students Taylor Bass, David Souza, Jessica Lynch, Michael Lystad, and Ashley White while they were still in college. [For your convenience, below are the excerpts from the paper, and we hope you will be able to implement some of the results and report back. For questions, or to request a copy of the paper, please contact the author at walter@coursewell.com.]

….

Introduction

Could ubiquitous-learning software applications (or apps) be used to minimize the negative impact of students studying remotely? Could mobile devices (i.e., cellular smartphones) be used to improve students' retention and persistence with reliable online support and wireless communications? Can student-support apps be used to enhance the acquisition of knowledge from anywhere as well as improve student-mentor-instructor engagement?

The research reviewed the most impactful students' retention and persistence issues. And more importantly, it illustrates the online student-support strategies and natively-designed apps (Adobe 2018; Benson et al. 2013; Rodriguez 2015) that were designed and tested during a multiyear government-sponsored project. The project successfully delivered mentor-supported, video-based training to thousands of online students/participants. Besides, the paper shows sample prototypes apps, i.e., designed by students for their peers, in response to their unique experience taking online courses and studying remotely.

We discuss the online student-support strategies implemented while delivering the above non-credit training. Further, it shows the prototype app designs & policies developed to enhance student support and persistence via mobile devices. Also, the article analyzes the impact that learning and student-support apps may have on educational institutions. For instance, how these apps could potentially create institutional havoc if sufficient instructional- and student-support staff are not provided ubiquitously, in a timely, effective, and efficient way.

The paper (Rodriguez, W. et al. 2019) foresees that ubiquitous learning software applications have the potential to transform education accessibility, delivery, and student-support for anyone with a smartphone. Rather than promoting software apps' flexibility and convenience, this study analyzes the drawbacks and challenges that may occur when the institutions do not provide adequate student support. Suitably, the paper also presents mobile-app constructs for facilitating student-support and student-faculty engagement. Although strategies and functional design-requirements are given, the app system-development and implementation are beyond the scope of this paper.

While the research demonstrates how the online support and mentoring concepts were successfully applied for the retention of thousands of participants (i.e., military spouses) registered in a pre-professional, multiyear career-training certification program, it also examines the general problems and challenges that most college students are facing, regardless of delivery modality (online, mobile, hybrid and on-campus). Then, it provides recommendations, solutions, and app design-prototypes (i.e., user-interface, sample menus, mockups) to help minimize the potential impact of studying remotely, i.e., away from the on-campus student support structure, via mobile devices.

Two characteristics are analyzed, namely, student characteristics, and institutional characteristics. Alternative solutions are presented to increase the student retention rate and help students find a balance between work and study. One of the solutions consist of developing a robust and cross-functional mobile-learning student-support application to address the key issues and dimensions of well-being impacting students' persistence in college, particularly, online/mobile students. The research may be used by software developers to design better and more responsive learning and student-support apps.

Background

Research indicates that fully-online students (including community colleges and massive open online courses) have lower graduation and persistence rates than traditional or fully on-campus students. And, not surprisingly, mobile students face the same types of issues as their campus' peers. Nevertheless, the data is extremely complex to analyze, and the issue is still being debated among scholars (Haynie 2015). To understand the complexity of the problem (or challenge) at hand, a study done by the National Longitudinal Study of Adolescent to Adult Health examined college dropouts as well as graduates on their socioeconomic success and mental health profiles. This information was examined in five dimensions of well-being: (1) socioeconomic success indicators; (2) happiness/satisfaction; (3) mastery; (4) stress, and (5) depression. The results indicated that college students who dropped out of college ranged significantly on a chart of these five dimensions of well-being. There were many different types of groups investigated---including participants: (a) who averaged out on the list; (b) had higher income with less stress; (c) had higher income with high stress; (d) low income and low stress; and so on (Faas et al. 2017). Further, students' well-being is not simply based on their financial situation but also includes subjective well-being---including emotional contentment. Students should be understood as complex beings. Therefore, when assessing dropout rates at universities, it is most important to understand that there is no simple programmatic solution. Nevertheless, technical solutions (i.e., mobile apps, deep-learning), as well as big data and predictive analytics, may help anticipate issues and design ways to address the problems before it's too late (McMurtrie 2018).

Evidence: Reasons for Dropping Out

According to the National Center for Education Statistics, the 6-year graduation rate for first time and full-time undergraduate students at a 4-year degree-granting institution in fall 2009 was 59 percent (The Condition of Education 2018). [The six-year completion for fall 2011 was 57% with some underrepresented minorities critically falling from 27% to 18% below that statistic (McMurtrie 2018).] With students dropping out of college, academic administrators and faculty are faced with a question: How can we minimize student dropout rates and maximize student persistence? Colleges are concerned with the high dropout rates, and many schools have discovered ways to increase the retention rate for at-risk students by flagging (identifying) students, providing in-class tutoring, and re-designing curriculum pathways for those students.

Public and private non-profit colleges have quite similar graduation rates, while private for-profit shows that less than thirty percent of men and women graduate through their institution. With this understanding, it's important to analyze how other schools have been able to increase their graduation rate and look at some ways that schools may be able to increase their graduation rate as well.

In 2015, the six-year graduation rates at a regional university fell to 42.9%, from 57% in 2011 (Bland 2016). Even while this finding was fairly consistent with other extended longitudinal retention surveys (Boden 2012; Van Stolk et al, 2007), it caused great disappointment. Therefore, a more detailed investigation of the problem was conducted. The research looked at "figures from five, six-year periods—2005-11, 2006-12, 2007-13, 2008-14, and 2009-15—which showed that on average, about two times as many full-time students dropped out than transferred to another college (Bland 2016). While this statistic is disheartening, a look at this university's graduation rate compared to nationwide schools shows that the problem runs deeper and broader in the USA.

Students drop out for numerous reasons. There are two big reasons why students drop out. The two groups are (1) student characteristics and (2) institutional characteristics. Student and institutional characteristics can lead to students dropping out (Chen 2012). The two categories will be broken down into further detail in the next two sub-sections to see how each impacts a student's decision on dropping out of college.

Student characteristics are a major reason why students drop out of college. These are the reasons that they feel personally and can be the factor of whether to stay or not. A study was done to find the characteristics that cause students to drop out, and it revealed that the problem has persisted for decades, for instance, a "review of twenty-five years of research on college student dropout identified the following variables that are related to drop out: (a) demographic, (b) academic, (c) motivational, (d) personality, (e) college environment, (f) financial, and (g) health" (Mashburn 2001,174). On the academic variable, first, is academics, students may find themselves merely struggling in classes or are not understanding it. Some are unable to handle all the freedom and may be skipping class or not completing their homework and assignments in a timely manner. Instead of putting an effort when they see themselves slipping, most give up, leading them to drop out. That can coincide with motivation. Students' demographic also affects student's retention rates. Students might feel out of place or do not have a lot of students that are like them (i.e., gender or race/ethnicity). And may consider transferring or drop out as they are not getting the social or cultural support. One of the biggest reasons that students drop out is because of financial reasons. Students may find that college has become too expensive and quit because they can no longer afford it. Sometimes, it's as simple as not having money to purchase textbooks. [More and more, this problem is being addressed by merely offering open-source materials.] Further, an increasing number of students have a part-time or even a full-time job and, simply, have difficulties regulating fun-work-study time in order to complete their homework by the deadline, since work might be taking up all of their time. But these are not the only reasons why students drop out (Mashburn 2001; Pantages and Creedon 1978).

Students may also drop out of college because of institution characteristics. Institution characteristics are "institutional demographics, structure, faculty resources, and financial resources" (Chen 2012, 492). Financial reasons are also part of institutional issues. Adequate institutional funding and resources are a big part of retaining students. If the institution does not have enough resources and funds to provide services to keep the students active and to better themselves, problems arise very quickly. Also, the institution might not be able to afford certain academic programs or majors that students may be interested in, or they change programs too frequently—increasing the number of years to graduate. Further, if students choose a major that is not well-designed to maximize student retention, they may not feel motivated enough to persist in their degree. Another reason is faculty resources. If the faculty does not have the resources they need to help the students, they will easily fall through the cracks. The faculty might have too many students and might be unable to assist all of them. So, they might not be able to see if a student is not doing well or monitor and provide alternative assignments if the student is having difficulties on a subject. One aspect is that "the dropout risk fluctuates by year, but the highest is in the first year (17.7%)" (Chen 2012, 495). Though the first year is the highest, students can still drop out in the next couple of years. This study looked at first-year institutions; it did find that the dropout was higher for the least selective public universities than those of any other kind (Chen 2012). This fact could be applied to universities where the acceptance rate is high. A pattern that may increase as fewer students pursue college after high school. With the acceptance rate being high, it's possible that some students who did not meet traditional academic standards may get accepted into the school, i.e. high-school prerequisite courses (Woods et al. 2018). Many students get accepted who might not have the adequate academic preparation, such as colleges serving a population where high-schools are not as demanding or are lagging behind in resources. One factor that universities may fail to recognize in a timely manner, is "the responsibility for acculturating and supporting students throughout the educational experience which might be relegated to non-academic personnel" (Stevenson 2007, 141-142). With non-academic staff being in charge of that responsibility, they might not fully understand or deeply analyze the complexity of the issues at hand. The university needs to change its views. Consider this question: With faculty being the most likely point of contact, shouldn't they be more directly involved with student retention and persistence (if they are provided adequate student-support)? Unfortunately, many schools are now delegating this responsibility to non-instructional advisors, rather than instructors.

Low graduation rates are by no means an uncommon occurrence. Schools across the nation are facing the same dilemmas, and all have attempted to implement their own solutions. According to College Factual (2018), one regional university in Florida has a higher first-year retention rate than other universities in the state, but still, it has a 52.5% six-year graduation rate. Why is that? Would it be due to aspirational reasons, where students may register at a lower-tier college and transfer to a higher-ranked college, during the second year? What can be done to retain those first-year students? (DeNicco et al. 2014). Could a virtual learning community, using mobile applications to connect everyone, help improve students' persistence? As more students are engaged with these devices, it's worth exploring ubiquitous-learning apps to engage students. But there are multiple challenges.

Ubiquitous Learning Challenge

"Nearly two-thirds of college students use their smartphones to study, and the global market for mobile learning is projected to grow by 36 percent annually. Colleges are experimenting with ways to engage students in and outside of class through their phones." (Chronicle of Higher Education 2018)

While ubiquitous-learning delivery modalities (Beckmann 2010), such as eLearning and mLearning (i.e., using mobile devices for gaining knowledge, engaging students, and tracking & assessing learning outcomes from anywhere) provide tremendous flexibility and convenience for online learners, if the offering institutions do not provide robust, engaging online student-support, these students may be quickly inclined to abandon or drop these online or mobile courses without seeking help from advisors, faculty, and campus staff support. For instance, a massive number of students, particularly online and mobile working students are taking longer to complete their degrees and, unfortunately, many never graduate for a variety of student and institutional characteristics enumerated earlier (Faas et al. 2017).

Researchers have investigated the effectiveness of ubiquitous (online, mobile learning) environments (Schwartz et al. 2017; Fozdar & Kumar 2017; Pechenkina et al. 2017). And earlier technical research---focusing on the actual development and implementation process of a non-commercial experimental mobile app---were early on impacted by the online student "retention" and "persistence" challenge (Rodriguez et al. 2015). The main issue is not necessarily about innovation or technology or the way apps are designed, as illustrated by this quote:

"The main drivers of innovation in higher education are not simply a function of what is technologically possible; they are—or should be—a function of pedagogically sound and cost–effective strategies that advance our institutional missions in ways that best serve our students, are fair to our faculty, and advance the interests of our communities." (Committee on Institutional Cooperation 2013)

Figure 1 below illustrates the user interface of an experimental (non-commercial) app developed by the first author. During the three-year beta-testing of this mobile learning app, designed for the participants in the aforementioned sponsored career certification training, it was realized that students were being challenged by issues not necessarily related to the technology itself but rather to online (off-campus) support, after traditional business hours.

This non-commercial, research experimentation helped identify the fundamental fault of focusing on technology alone (see above quote) and let to providing increasing levels of student support. The prevalent issue, while beta-testing the app, was the lack of student persistence and the resulting high dropout rates from learners that didn't proactively seek human assistance in a timely manner (i.e., advisors, instructors, and support staff), beyond the lessons, forums and programmatic self-help and video-linked tutorials provided via the experimental app (Figure 1).

Of course, this is not the only experimental app trying this mobile cellular approach. Some institutions are developing software apps to allow students to engage with faculty and their peers. For instance, one app, labeled "Hotseat," lets students ask questions, take polls, and facilitate (backchannel) in-class discussions (Chronicle of Higher Education 2018, pp. 31). Although these apps may help with engagement and student retention, they don't solve the persistence problem by themselves. And there lays the complexity of the problem.

This figure shows the user-interface and interactive features of an enabling non-commercial experimental technology, named "CourseWell." The software was developed by undergraduate students and faculty and consisted of natively-designed apps and systems. It was developed specifically by the first author and his collaborators as an experiment for enhancing ubiquitous forum discussions and student-faculty interaction via mobile devices. [Coursewell.com is an initiative of Florida Gulf Coast University's Institute for Technological Innovation.]

Figure 1. Non-Commercial Experimental mLearning Software App (Rodriguez et al. 2015)

…

Analysis and Solutions: Engaging a Community of Mobile Learners

Overall, graduation and retention issues can stem from several different factors. These factors include lack of adequate academic preparation; personal problems; work-study scheduling; and financial challenges, among many other issues. Table 1 offers some sample issues and practical solutions for online, mobile learners, as well as for the student population in general.

Nowadays, most schools use Learning Management Systems (LMS) to support most, if not all, of their course offerings. Even if these courses are taught fully on-campus or in hybrid, blended or flipped (i.e., where students study and engage with the online instructional resources and then attend real-life on-campus sessions to work on individual or, better yet, collaborative activities to deepen the students' understanding of the content).

One online solution could consist in tracking, identifying and implementing more opportunities for students at-risk of dropping out—including proactive online mentoring and tutoring---based on real-time data derived from the LMS and predictive analytics. These LMS systems record and track every single student outcome, quiz, exams, project, and forum discussion. So, faculty can easily identify students that might be struggling in their classes by simply displaying their electronic grade book on the LMS. When they discover anomalies, they can generate an electronic message alerting the student of missing assignments or projects. The first author has used this technique with great results. And, surprisingly, the students in the class have provided evidence that they appreciate the early intervention (within the allowed ethical, privacy, security framework provided by the systems' tools).

Table 1: Issues and Solutions for Preventing Dropouts

Academic Preparation

Financial Resources

Work-Study Balance

Problem

Inadequate preparation for the rigor of college, either due to poor high school education or difficulties adjusting to the college workload.

Students and their families may not be able to afford to pay for tuition and books.

Many students are unable to regulate and balance work, life, fun, and study into their schedules. Students may not possess basic time-management skills or are simply working long hours (part-time or even full-time.)

Solution

Offer online tutoring- mentoring (provided by work-study students or retirees); create and offer personalized, and alternative assignments; require to complete prerequisites before each difficult assignment, and offer online/mobile prep learning opportunities for students that may be identified as at-risk.

Increase financial assistance and initiate micro-financing accessible to students from low-income families or provide mini-scholarships for work-study students.

Provide online/mobile time management tools and just-in-time tutorials. Also, an interactive application can assist students to manage their time more effectively.

Track and intervene, based on timely data about the students' progress, performance and outcomes.

Source: Lystad 2018

Currently, many institutions with low-retention rates do not offer online tutoring and mentoring services for at-risk students, while they might already be providing mentorship programs for athletes and honor students. That is, not all students have access to or are aware of opportunities to have an online mentor. Online tutoring and mentoring can lead to a better feeling of belonging for students. In a study performed by Colvin and Ashman (2010), it was found that peer mentoring was a successful way to make students feel a sense of belonging (Colvin & Ashman, 2010). Peer mentoring was determined to be a motivating factor for students to stay and succeed at a university. In addition, tutoring is an excellent way for students to seek out help from other students who have already taken courses. Having a peer tutor helps students understand topics explained at their level. Both online peer mentoring and tutoring may be delivered cost-free (or minimal cost), as the tutors and mentors might already be compensated with service learning hours, for instance. And more work-study students might be able to participate from anywhere, anytime (even on weekends and evenings).

Whenever possible, university foundations may also help to implement further scholarships and financial assistance for students that are struggling to meet tuition rates. Many schools offer scholarships to students who excel in academics even when those students are less likely to drop out. But students who are struggling academically pose a larger impact on retention and graduation rates. Providing micro-financing tools for tuition payments and textbooks might be developed by partnering with private corporations or non-governmental organizations.

For those students who struggle with academics, the online mentors/tutors previously mentioned could help them submit applications for assistantships. Financial aid is already offered for students who cannot afford college by the university and by the state. Universities should simply make students more aware of the aid that is available and consider partnering with private companies to provide micro-financing when students are unable to qualify for current aid. More importantly, faculty could choose to utilize more open-source content. Rather than requiring expensive textbooks, professors can use online open-source textbooks and instructional materials. This would save students hundreds of dollars per semester and lead to overall improved student persistence and well-being.

TRIO (2018) Student Support Services is a federal outreach program designed to identify and provide services for individuals from disadvantaged backgrounds and offers many services including academic and career advising, tutoring, peer coaching, workshops, summer bridge programs, and computer lab to name a few. When students are accepted at a university as their college of choice, they receive an email from TRIO SSS stating that they could apply for the program. To be a part of TRIO SSS and use their service one has to either be a first-generation college student, be considered to have low income or have a disability. TRIO student support services include financial literacy, and financial workshops and some students receive a scholarship. The required advising meetings are personal, and the advisors are all equipped with knowledge of the university and are able to answer any questions. Further, they keep notes of the students on the computers and have access to the students' grades.

Since TRIO SSS is a federally funded program, statistics are gathered often, and a report showing the completion rate for student support service participants seeking bachelor's degrees who were full-time, first-time first-year students at four-year institutions went from 42 percent to 51 percent (Ginder et al. 2015). This increase may not seem significant but TRIO SSS supported 103,691 students at four-year institutions and 101,065 students at two-year institutions, and the fact that these students even through adversity are able to graduate is excellent. With further studies in the program, there should be advancements in continuing to increase the percentage of graduates. Could a similar program be implemented online for all at-risk students?

With adequate funding, a similar program could be implemented online. But the school would have to find an automated way to identify students that fall into the at-risk category (i.e., the danger of potentially dropping out). Fortunately, as mentioned earlier, universities are already using big data and predictive analytics to analyze large amounts of data from former students' records in order to identify those current students, many from low-income families, who seemed most likely to drop out of school. Although not a simple project, this could be implemented at scale by developing a machine-learning/deep-learning algorithm developed for this purpose. Of course, academic counselors might need to be retrained in order to evaluate their students and implement interventions. Proactive counselor meetings should be required every semester so that the students' well-being is evaluated in addition to their curriculum pathway.

Of course, the new system would require personnel changes across the university. Since each department at the university may be affected by the others throughout the process, clear communication between stakeholders is essential. As shown in Figure 2, whoever makes executive decisions, usually, the President, Provost, or Vice-President for Enrollment Management would have to initiate a restructuring or process re-engineering or develop the new system. Then the appropriate software would need to be developed or procured followed by extensive training. The software would be a large initial outlay of funds. But the cost may be recouped by the resulting increase in the graduation rates since many schools are receiving performance-based funding.

Figure 2. AI System Development Process

As discussed earlier, some systems and online apps (solutions) have been proposed and implemented at various institutions very efficiently and effectively. But, can we develop an app that will able to engage and link a community of learners—integrating students' life, work, and study?

App-Design: Findings and Recommendations

Since 2015, the first author and several undergraduate students have been involved in the development of mobile-learning apps to stimulate student engagement with peers, faculty, mentors, advisors as well as streamlining the student learning process (Rodriguez et al. 2015). In 2017-2018 initiative, the basic design app requirements included: (1) focus on students' engagement via mobile technology; (2) design simplified User-Interface (UI) and User Experience (UX); (3) provide connective functionality; (4) automated reminders and notifications; (5) quick load times; and (6) user-friendly team-collaboration (Table 2).

When designing the initial experimental mobile app (Figure 1), the researchers decided to implement it on various operating systems (i.e., iOS, Android) as well as provide web access. The experimental apps were available to the project's participants. For ethical/privacy/security considerations, before downloading the app, the participants were prompted with a consent form. The participants could access information; such as assignments' due dates, upcoming tasks, and graded assignments. In future versions, students will also have to provide their consent (i.e., opt-in) to have their contact information uploaded into a database on a computer server. This approach would allow only privately and securely encrypted transactions among students and their faculty/mentors. Further, there will be no "surprise" (or unqualified) data and information extracted aside from what the students would permit the researchers to aggregate.

Table 2: System Requirements

Data

Functionality

Dev

UI/UX

Displays data pulled from preexisting App, LMS

Easily Accessible, downloadable, quick sign uptime

Quick Load Times/ Page Speed

Sleek and simple design

Needs to retrieve basic information from all students.

Easily to manage notifications or messages

Need to establish an API call with LMS to pull in needed data

Design with the concept of sustainability and reliability

Source: White 2018

Next, the developers would need to utilize and integrate the app with the new version of the Learning Management System (LMS) at the sponsoring institution. Usually, LMS stores all student data; such as, assignments, due dates, upcoming assignments, and hosts an email communication system as well as contains contact information of students and instructors. Ubiquitous-learning apps should be easily navigable and downloadable. The app should have a quick process download time, as well as a quick response time when storing new student information. When the student has notifications or messages, the app should provide different user variations of how to be notified: alarm, text message, email notification, home screen notification, or simple vibration tone. In order to make the data needed easily accessible, the developers would need to set up an Application Program Interface (API) call to pull the data from the LMS. This call would update on a daily basis at midnight EST. This function would continuously update the app with new assignments, instructor communications and information (should it change, and new introductions to discussions.) As learned in the initial development effort, this function would put less stress on the system in times of extreme updates such as the beginning of add/drop week, course registration dates, etc. The development team would need to ensure that the page loading time, page speed time, and integration time is as low as possible to indicate a fast processing system. Further, as in the first version, developers would need to conduct front end-user testing, as well as back-end user-testing. In addition, they would also need to create prototypes and wireframes of the internal and external systems and test different versions using a group of randomly selected students/participants. These designs should focus on the concepts of simplicity, reliability, and sustainability. Developers would need to come up with different color themes, so students have the individuality to truly make the system their "own" app. In addition, students should be able to customize layout functions with drag and drop methods and delete sections should they not find them relevant (July Rapid 2016; Savvy Apps 2018).

Below are some recommended prototypes that would meet the above design recommendations.

Prototype: Software App for Student Time Management

For rapid development, the system starts with a time-management function and progressively increase functionality to address other issues. For instance, later on, developers could utilize advanced technologies, such as artificial intelligence (AI) and adaptive learning software. The application could use AI that learns how long it takes students to complete certain assignments and tasks and use that information to better choose how to schedule the student's day. The LMS system can be synced with the application. So, assignments and other important calendar items can be imported into the calendar of the application. Cross-communication takes the workload off the student and places it entirely on the system.

The application for student time-management should have a sleek, simple design. It should be intuitive for students to understand exactly how it works. There should be functions for the students to see tasks in a calendar form, tasks in a list form, completed tasks, and a connection to the LMS. In addition, students should be able to sort by day, week, and month for different views of tasks to be completed. A recommended prototype design for the application is shown in Figure 3. But there are some commercially available calendars that could be investigated (Kazmucha 2014).

Figure 3: Prototype Design of Application for Student Time Management

Source: Lystad 2018

An application, such as time management, can help students stay organized as well as teach them time-management skills. In addition to features for time management, the application may also connect students with mentors, tutors, and advisors, i.e., in a community of online learners. The app can link students’ right to a page that allows them to schedule an appointment with their advisor or tutor.

For students with financial needs, the application may link straight to the portal financial aid. The financial aid portal can contain the information explained previously in the financial aid section or offer micro-financing options, as discussed earlier. It is essential to make it easy for students to access financial aid if the university wants students to take advantage of it.

Of course, the answer to the persistence problem is not just one single solution. In order to meet its goals in solving the persistence problem, the university should consider which of the other non-technical solutions are feasible and economically viable to implement. Table 3 shows the pros and cons of each sample solution.

Firstly, adding more financial aid for students might be an issue for universities when its already experiencing funding challenges. Even further, financial aid may not necessarily encourage students to try to succeed and graduate in four years. A better approach for financial aid is to encourage and reward students who are academically successful by staying on track and on their selected curriculum pathway. Students that earn high grades should be eligible for scholarships and grants.

Table 3: App Solution: Pros/Cons

Possible Solutions

Pros

Cons

Online, Mobile Mentoring/Tutoring

Free: Build a sense of community or community of online learners that help each other.

Cost: Need to have access to a smartphone, cellular mobile device or computer connected to the Internet, if unable to use university labs.

Financial Aid

Helps students pay

Rewards academic success

Cost to the institution

Does not help all at-risk students

Application

Helps students manage time

Builds career skills

Used by all students

Cost to the university, although it might be developed as a collaborative class project.

Source: Lystad 2018

Aside from tutoring, the time-management application could be a lifesaver for students that have trouble managing themselves. There may be additional costs for institutions to implement a new app, but these costs may be mitigated by assigning development efforts to work-study Computer Science and Computer Information Systems (CIS) students as a class project. A developmental effort, such as this app, falls within the scope of CS/CIS students and could be effectively and efficiently accomplished---not counting the educational experience if coordinated with several software development companies. After implementation, the costs to the university would be minimal, and the benefits are anticipated to be exponential. Students will have fewer obstacles when it comes to completing schoolwork as well as budgeting time for extracurricular activities to benefit retention. Figure 4 shows the system development process for the app.

Figure 4. Life Cycle: Lynch 2018

Ethical, Privacy, and Security Issues with the App

Although the above app solution seems adequate, some critical issues may arise from the system. The first major one is dealing with privacy, security, and ethics. "Computer technologies present ethical problems that cannot, as an objective matter, be adequately resolved by recourse to existing ethical theories" (Himma 2007). With the new system recommendations, the students' privacy might be an issue of concern, particularly, if the advisor or faculty would be able to see the last time they logged in and s/he tries to micromanage the students' life, work, and study. There would have to be a balance between trying to help students and trying to micromanage students if they missed an assignment, which might cause concern among the more independently minded students. It could lead to cases against the school if students feel like they have no privacy when it comes to their school work. Therefore, the institution should create panel discussions with both students and professors to see how they feel about the system and try to work to ease potential tensions---so students do not feel their privacy invaded. Fortunately, apps and systems may be designed to be opt-in or opt-out. And, they can also be designed to be proactive (perhaps, intrusive) or simply lay out passively and let the user takes control of its functionality.

An Integrated Student-Success App

Data is everywhere. Universities collect a great amount of data about students that may be used (within the privacy/security/ethical constraints) to help address students' persistence in college. And, an integrated student-success can be designed to meet the desired goals.

Figures 5 and 6 below provide an example of how an application could "proactively" or "passively" help guide students to seek help if they come across a difficult situation and they need online assistance.

Figure 5: Student Success Interface

Figure 6: Student Success – Advisor Interface.

This mobile student success (Figures 5, 6, 7, 8, and 9) will allow students to set appointments and communicate with a support team that may be composed of career advisors, psychologists, and faculty members—in an integrated fashion. In addition, students would be able to access information (tailored specifically for them) to support their curriculum pathway and help them set goals to achieve the desired outcome (i.e., hopefully, timely graduation). The support team would be armed with a robust database that would present them with all the necessary information to guide students to graduate on time and keep them motivated.

Figures 7 and 8 below illustrate some cross-functional capabilities to help the support team. Information such as the student's GPA, the financial aid required until graduation, and the course sequence needed to graduate are all listed together under one platform. This compiled information can help students and the support team take the necessary steps to successfully address any issue they might need to solve and plan an effective success path to graduation.

Figure 7: Student Success - Profile

Source: Souza 2018

The support team would be able to customize the information that they want to analyze. In other words, the support team would be able to write queries to import data from the database to help students understand better the situation and the background information of each student. It's possible that the advisors' roles would be to proactively check if the students are registering and taking the courses required to graduate on time. However, they could also inquire about grades performance, or if they needed financial aid, or if students are happy with their selected major.

In addition, advisors or mentors could possibly ask about the student's well-being---trying to assess stress-levels indirectly or if they are feeling motivated to continue taking classes. Despite the privacy/security issues that may surely arise, these types of questions are important and must be asked. Reason: Maybe some students might feel ashamed to ask for help. In some cultures, complaining about a problem to others might make the people be viewed as complainers or negative people. If advisors, like social workers, would be allowed to ask why students' grades were so low, or if they had noticed disappointment with the class and stress levels, maybe they could better guide students to seek help and perhaps they wouldn't have dropped college.

Although there is lots of help available to college students, the immediate connection might not be readily available at critical times. So, a mobile app may be a more active, interactive and proactive approach to identifying needs and wants. Moreover, if a student needs to take a break from college, they usually have to re-apply to be placed in the college system again. Are advisors checking why a student didn't take any classes for two semesters, and more importantly, asking why students dropped out of college? So, some feedback mechanism needs to be implemented.

The above illustrates the importance of a system that would offer cross-functional capabilities---including the participation of advisors, mentors, and faculty working together to ensure that each student that needs help in getting the attention required in a timely fashion. Theoretically, if implemented well, every function would be just by the click of a button and students can reach out for assistance.

Figure 8 - Student Success Metrics

Figure 9: Student Success – Resources Source: Souza 2018

Figure 9 above, illustrates some of the resources that could be available to students via this application. These resources page could include capabilities; such as the functionality to apply to financial aid, or to access micro-financing resources; the functionality to set appointments with support team members; and the capability to seek more information to improve their overall college performance. In addition, students that work and study could ask for help by reaching out to members of the support team through the application that could provide support via chat, email, or live sessions.

Tested Apps in a Sponsored, Multi-year, Certification-Training Project

Some of the app-designs and all student-service recommendations were tested during a sponsored, multiyear pre-professional career certification project. Table 4 provides the results of this training project conducted at Florida Gulf Coast University with the sponsorship of the US Department of Defense's MyCAA (Military Career Advancement Accounts) scholarship/financial/grant assistance program in partnership with industry and the Institute for Technological Innovation.

The Critical Success Factor (CRF) for the project was the completion rate* (Table 4) attained by the participating military-spouses in the online certification-training offered during the duration of the project at FGCU.

Succinctly, the project's high completion rate (87.6%) was due to the consistent high-level of online support provided (via Internet, chats, e-mail, VoIP, video, and other telecommunication tools) to all participants (i.e., military spouses) in this project/program.

Table 4 – Participants: Completion Rate

Total Participants in the Training

3343

Successfully Completed Program

2930

Completion Rate

87.6%

Attempted Certification Exam

598

Passed Certification Exam

424

Certification Pass Rate

70.9%

….

Real-Time and Asynchronous Online Support Services

Consistent and effective online support was provided to the 3,343 military spouses (Table 4) participating in the MyCAA asynchronous online training program (i.e., on-demand videos and just-in-time assessments) offered during the duration of the MyCAA multiyear project at FGCU. The participants persisted and successfully completed their training because someone cared (an advisor, an instructor) about their success and was willing to give them one more call and one more opportunity.

Although the training completion rate was high (87.6%), the institute was unable to require participants to take the pre-professional certification exam (provided by the third-party national certification agencies) once they successfully completed their training. Therefore, the result was that few military-spouses (approximately 20%) took their pre-professional certification exam. Even though 3,343 military-spouses took and passed the required quizzes and qualification exams to obtain the Certificate of Completion at the institution. Of those participants that voluntarily took their official pre-professional certification exam, about 71% passed their exam and became certified. At this point, there is still no official requirement to take the pre-professional certification exam offered by agencies. In addition, due to privacy issues, it's not possible to collect information about employment, after completing the training and exams. Therefore, employment data on MyCAA participants is no available to the investigators (i.e., data cannot be obtained from the funding agency, due to confidentiality issues.)

Participants were strongly encouraged to take advantage of the variety of services offered by the MyCAA Project Support Team (MPST). As detailed below, MPST delivered impactful, professional, and results-driven support services, beyond what is normally provided by other institutions (i.e., online 24-7 vs. the traditional campus 40- to 60- hours per week).

Below are the online student/participant services that were available to each of the 3,343 participants. Basically, the partners, in coordination with the Project Director, created a series of timed/scheduled outreach points that were designed to notify participants of their progress----keeping them engaged all the time. If a participant felt out of pace (from where they should have been on the training), the participant would at the very least receive a phone call and an e-mail. And, for those with severe academic deficiencies, they would receive an advisor consultation where a new course schedule was designed and agreed upon based on the needs of the participant.

Table 5 – Recommended Strategies to Support Online Students/Participants

Proactive Strategies:

• New Participant Online Orientation – Private one-on-one phone orientation required for all participants

• Private Student Consultations – Based on need or request, all students had the ability to self-schedule an appointment for a consultation with an advisor, if support or guidance was needed.

• All participant progress was monitored and reported to Participant Services on a DAILY basis.

• Scheduled Outreach – Timed outreach – Participants were contacted at scheduled intervals throughout training regarding their program progress/status.

• Remedial Support – Participants who were identified as "deficient" in progress were provided a phone consultation with an advisor. A revised schedule or custom learning plan was developed based on participant needs.

• In-Activity Monitoring – If no activity for an extended period, an "outreach" by phone/email occurred.

Reactive Strategies:

• On-Demand Toll-Free Phone Support

• On-Demand Email Support

• Certification Exam Registration and Scheduling Assistance

• Registration and delivery of program-specific certification Assessment module

Subject Matter Support Strategies:

• On-Demand Chat with a Live Subject Matter Experts who hold one or more industry degrees and certifications

• One-on-One Tutoring – Based on need, or by participant request

• Assistance navigating the many resources provided to supplement the video instruction

Exam Preparation Support Strategies:

• Detailed exam preparation plan upon completion of their program.

• All participants received Exam Preparation Manuals for all eligible exams.

• Private one-on-one phone consultations (Exam Preparation Session) with a Subject Matter (i.e., expert reviews key objectives, study/exam tips, and to provide additional resources to best prepare students for certification success.)

• A dedicated Exam Registration Specialist helped each student with the pre-registration process, identifying a testing location, and served as a guide during each student's path to exam day.

Technical Support Strategies:

• Dedicated Tech Support to assist with video troubleshooting, software installation, browser issues, etc.

• Creation/Delivery of custom troubleshooting & FAQ video modules

• Available by phone, email, and chat with remote screen-share support capability

Conclusion and Future Research

As demonstrated by the above career project, ubiquitous mobile learning may positively transform course delivery and completion. But, before deciding to implement mobile courses and programs, institutions would benefit from analyzing mobile learning's strengths, weaknesses, opportunities, and threats; particularly, its effects on retention and persistence.

As discussed, mobile learning is a relatively new ubiquitous learning approach, so the advantages and disadvantages (or benefits and perils) should be researched further before deciding to utilize this form of learning delivery method. Many faculty and students are aware of the convenience and flexibility of using mobile electronic devices and its popularity is growing. However, the potential pitfalls and challenges are often ignored. Demanding mobile, online courses create both an opportunity and a challenge for remote learners. And it provides tremendous scheduling flexibility for study-work students. But, if the university doesn't have a robust student support infrastructure, mobile online students may be inclined to drop the course without seeking campus support.

As reviewed in this paper, online students are taking longer to complete their degrees and, unfortunately, many never graduate. This research examined the problems students are facing. Two characteristics were analyzed, namely, student characteristics, and institutional characteristics. Alternative app-design solutions were presented to increase the student retention rate and help students find a balance between work and study. One of the solutions consisted of developing robust and cross-functional mobile-learning application prototypes to address the key issues and dimensions of well-being impacting students' persistence in college. The mobile-friendly app prototypes presented have user-friendly pull-down menus to access an interactive assignment calendar--including functionality to set appointments and communicate with advisors, faculty, mentors, and support staff. In addition, the app prototypes incorporated ways to (1) balance work and social life; (2) display grades and warnings; (3) access micro-financing resources for paying for textbooks and tuition; (4) keep track of pending and completed assignments; and (5) find study groups and mentors, among other functions. Privacy, security, and ethical dilemmas, as well as challenges in funding these free apps, were briefly addressed, in the context of mobile learning.

Despite the challenges, the researchers have concluded that software applications could help improve persistence and retention, mainly based on the high-completion rates attained during the above research-training project duration. Nevertheless, the mobile apps must absolutely be backed by a consistent online student-support system at the palm of the students' hands—including access to online mentors to assist students just-in-time.

Further, the mobile app's institutional policies should be researched and analyzed further. For instance, should the tracking apps be required on students' devices? What are the ethical and legal implications? Should apps be offered independently or integrated within the university LMS environment? In this case, the apps will have to sync with university LMS data, so the students' courses and current information (i.e., grades, due dates, etc.) would be automatically uploaded and updated–including when their assignments are due. Actually, this feature is only one of the few that would require drawing data from the LMS as the rest of the features are more extensive. In any case, the first feature that the app should offer is a Calendar/Scheduler. This function would indicate when assignments are due. A student should also be able to edit it and add their weekly personal work-study activities to the calendar, so their assignments, class schedules, meetings, and other social activities can be found in one handy place. The calendar should have a setting where a student can look at tasks for that day or it could break it down more as it shows hour by hour activities. With the calendar comes the notification functionality. So, if a student opts-in, notifications would be sent to him/her at least one day prior to the due date. From the ethical perspective, the user should have to opt-in (rather than have to opt-out) for notifications. [First Author's Note: Many LMS have a function that allows instructors to send reminders (i.e., when an assignment is due) but it's not automated and not all faculty use it. The first author has used this function very effectively to reduce non-submittals and maximize student timely submittals as well as improve persistence.]

Continuing additional recommendations, when the user ops-in, the app should show the assignments due that day. In addition, the app should incorporate some social features. According to Smith and Bland (2019), amenities can be useful in retention efforts. Therefore, a social-tab functionality would allow clubs to post their meetings when events are happening on campus, or even if someone is looking to watch, say, a sports game with people rooting for the same team. This recommended feature is meant to get the students involved on-campus and to meet friends who have similar interests. This feature could potentially keep a student active and happily engaged which might prevent them from dropping out. Another feature could be a study group function. This could be a virtual facility where someone can post about looking to meet-up with people who are taking the same course, so they can help each other understand the materials.

During app testing, the first researcher learned that students need peer support and if they are able to easily find compatible people to study with, they might be able to reinforce what was learned in the lessons and do better in the course. Another recommended feature could be for obtaining financial aid. In this function, advisors (or those maintaining the app) could post relevant scholarship opportunities or links to apply for scholarships, so students can go to the app tab instead of having to research the university website. This would save students both time and effort. And, in this way, students can focus more on their studies, rather than worry all the time about how to finance their education. The next recommendation is a way to connect to the instructor in real-time or near real-time. This could potentially assist in mediating the effects of the relationships between faculty and students (Lenz 2014). Of course, additional research is needed to assess the effects of this connect-function on both faculty and students. Further functionality: If a student missed an assignment, a query-notification should pop up on the mobile screen. In this way, the professor would be able to know the exact reason why the student missed the deadline. Students should be able to choose from pre-written answers, such as struggling with the concept, having financial problems, mental health problems, work, other, or even rather not say—which the student will answer, and that will be sent to the professor or advisor. The instructor or mentor would be able to know what is happening, so they can reach out to students/participants and try to help them before they consider dropping out. Further, faculty should be able to see when was the last time the student logged in. [Some LMS systems already have that capability.] In this way, faculty would know that if the student has not logged in a while, s/he can try to reach out to students in class or via email. The professor should also get automated notifications if a student missed an assignment, so they can reach out to the student.

In closing, certainly, there is no app or solution that can single-handily improve student persistence and retention. It is neither the fault of the students, nor the faculty, nor administrators or advisors, but rather a culmination of years of unintended academic chaos, as universities and colleges grew without adequate student-support infrastructure and resources. But, if students were to put a higher value on engaging in class; and if universities became more willing to accommodate students' busy schedules by offering more 24-7 mobile online-support (as well as engaging students as a mobile, ubiquitous learning community), student retention and student involvement would probably increase. Furthermore, offering more student-support, tutoring and better supplementary instruction would ensure that students pass courses the first time, saving them time and money. Offering more scholarships and designing engaging apps and systems to help students stay on track with coursework may improve retention and graduation rates as well. Of course, future data will certainly tell if the above approaches are improving outcomes, as universities implement and measure tactics, such as, acceleration (Herrera et al. 2012), adaptive learning (Walkington 2013), pre-freshman prep (Wischusen et al. 2011), mentoring (Lenz 2014), predictive analytics, smart advising, wireless fingerprinting (Talaviya et al. 2013), automated degree-planning, mass personalization, structured guided curriculum pathways, in-class tutors, among many other initiatives (McMurtrie 2018). But, even without all these approaches and technologies, the faculty has a pivotal role to play (Stevenson et al. 2007). And as more faculty conduct timely low-tech interventions (i.e., by simply asking "What happened?" "Why haven't you completed the assignments?"). Succinctly, as more online mentors and instructors provide online-student-support as well as alternative, engaging, relevant assignments and projects, universities may expect improvements in students' persistence, as revealed by the aforementioned multiyear government-sponsored project.

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ABOUT THE AUTHORS

Walter Rodriguez: Professor of Information Systems and Director, Institute for Technological Innovation, ISOM, Lutgert College of Business, Florida Gulf Coast University, Fort Myers, Florida, USA

Taylor Bass: Undergraduate Student, Lutgert College of Business, Florida Gulf Coast University, Fort Myers, Florida, USA

David Souza: Undergraduate Student, Lutgert College of Business, Florida Gulf Coast University, Fort Myers, Florida, USA

Jessica Lynch: Undergraduate Student, Lutgert College of Business, Florida Gulf Coast University, Fort Myers, Florida, USA

Michael Lystad: Undergraduate Student, Lutgert College of Business, Florida Gulf Coast University, Fort Myers, Florida, USA

Ashley White: Undergraduate Student, Lutgert College of Business, Florida Gulf Coast University, Fort Myers, Florida, USA

Walter Rodriguez 8/3/21 Walter Rodriguez 8/3/21

> Can AI Enhance Students' Learning and Retention?

Walter Rodriguez, Ph.D., P.E.

Certainly! By combining cognitive science and artificial intelligence (AI), instructors can help their STEM students to learn & retain more of what they have learned, as we mentioned in our article "Can Machine Learning Enhance Human Learning in Times of Disruption?" (Rodriguez W., Angle, P., Snyder, M. 2021).

The AI/Natural Language Processing (NLP) learning & memory-retention authoring platform shown in the link below allows instructors to scan any text (or video transcription) and then the system suggests key concepts or important content that you might want to reinforce in your students and even assist with follow-up material creation. Therefore, it helps students retain more for a longer time: https://lnkd.in/g3_qdSS

…….

Can Machine Learning Enhance Human Learning in Times of Disruption? (Synopsis)

Full formatted published paper is available at https://cgscholar.com/bookstore/works/can-machine-learning-enhance-human-learning-in-times-of-disruption?category_id=cgrn&path=cgrn%2F216%2F217

Walter Rodriguez, Coursewell.com, USA

Patricia Angle, University of Oklahoma, USA

Michele Snyder, Florida Gulf Coast University, USA

Summary: In times of disruption, artificial intelligence, particularly the ubiquitous machine learning, is being rapidly integrated into online working environments in academia and industry. Primarily, these transformative technologies can help automate and perform continuous routine tasks using speed, efficiency, and effectiveness to gather and analyze massive amounts of data and information. This research illustrates ways in which machine learning can benefit learners and organizations---by making learners and workers work smarter and, more importantly, improve their ubiquitous learning environment anytime, anyplace.

Humans can learn while working and studying by integrating both reinforcement learning and supervised learning paradigms, and by augmenting, supplementing, or complementing machine learning with workers' humanness, skills, creativity, emotions, passions, and tacit knowledge. Furthermore, machine learning can track the humans learning process by learning from its experience--observing and analyzing the knowledge acquisition process, progress, and difficulties. And machine learning algorithms and agents can suggest new learning paths and approaches.

In addition to exploring if and how machine learning can enhance human learning, this paper investigates how it can assist learning without impinging on natural human and social learning development. Rather than just substituting or supplementing the human recall, machine learning can integrate and enhance work performance by minimizing---instead of supplanting---tedious tasks with automation and artificial intelligence.

Keywords: Machine Learning, Human Learning, Artificial Intelligence, Transformative Learning, Ubiquitous Learning, Reinforcement Learning, Supervised Learning, Information, and Communication Technologies

Introduction: In 2020, the COVID-19 virus was associated with a sharp increase in unemployment claims, as social (physical) distancing measures were implemented across the globe. While many employees were furloughed, some employers adapted by allowing employees to work from home using information and communication technologies. Many students and faculty were also impacted, and they too had to study and teach remotely. This paper shows how humans can learn and create value in work environments using artificial intelligence (AI) and, more specifically, machine learning (ML).

This research paper asks: Can ML enhance human learning (HL)? And explores how ML is already enhancing on-the-job training, leading to work performance improvements such as process streamlining, collaboration, and co-invention. Further, the study demonstrates how humans who embrace machine learning in the workplace and how employees who continue to learn and create value to the business environment (while being more productive, efficient, and useful) increase the chances of staying employed in times of disruptive change (i.e., automation, pandemics, hurricanes, earthquakes, environmental degradation, business, and social transformation, among others.)

Researchers envision humans and machines collaborating and being co-dependent, to bridge the gap between human learning and machine learning. To conceptualize the relationship between time and embedded constructs for humans and computers, imagine a relatively steep slope of the machine learning curve, compared with the human learning curve which illustrates the shorter time frame between the introduction of a dataset and AI learning. And, in the absence of a dataset, ML tools will learn from experience and associative rules and techniques via ML's reinforcement, supervised and unsupervised learning.

Over time, researchers theorize that the gap between associations, patterns and rules, and ML algorithms and agents will be formed. Also, associations formed by humans will grow increasingly faster. Of course, the certainty of it happening is subjective until rigorous empirical studies are conducted. Presently, humans and machines work together in what is called collaborative intelligence, but there is not yet evidence that human potential is increased by such collaboration (Daugherty and Wilson, 2018). If ML is applied in a collaborative manner with the human learner, rather than directly to perform tasks, is it possible to raise the speed of human learning and thereby decrease the gap between the two curves? Humans’ retention of information will lag behind machines since ML can be powered with cloud services and supercomputers using vast stores of knowledge that are difficult for humans to retain. Human recall can degrade due to forgetfulness even with some reinforcement assisted by AI-powered machines.

Historical Context: Machine learning is an undeniable part of human life for the past decade. The notion that automation (i.e., AI-powered robots) could be built and used to assist humans has been an idea brewing in the minds of science fiction authors since the early 1940s. Issac Asimov is the first to have published a story in which its plot revolves around robots called Runaround. Around the same time, during the Second World War, British mathematicians began developing code-breaking machines to decipher the enigma that the German soldiers used (Haenlein 2019). Almost one hundred years later, we now use computers in every specialty in the business world. But, is artificial intelligence, indeed increasing human productivity levels, cost-effectiveness, and accuracy? Is implementing artificial intelligence alongside human intelligence beneficial or a colossal mistake? Are AI's in the workforce helpful or harmful? This paper will discuss facts on the utilization of Artificial Intelligence, specifically, machine learning in work environments, and assess if overall, ML has a positive impact on the way humans and businesses function. That is, can machine learning enhance human learning? Machines have been becoming more and more prevalent in business and lives since the 1950s and 60's when IBM became the first dominant mainframe company (Bourgeois 2014). For example, the medical industry is one that uses mostly AI for day-to-day functioning. Sources claim that in the future, it is forecasted 80% of a doctor's responsibilities will be completed by some sort of machine, and the industry of medical AI will be worth $10 billion (Longoni 2019). The rate at which machine learning advance is staggering, as conceptualized in Figure 1. The figure also illustrates an industry’s leader’s certainty that as technology advances, AI and ML developments will speed up even more. At that rate of change, human learning cannot bridge the gap with machine learning. By association, for most humans, human speed of learning could not reach the level of reaction and recall time that machines have by virtue the machines’ computation time, which is on the order of milliseconds. As will be discussed later in this paper, there are ways to enhance human learning by coupling humans with machines. If not, human jobs and entire careers could potentially be lost (Bolton 2018). The authors will present a framework to bridge the learning gap and supplement human learning with cues that will allow humans to enhance their learning recall. While it's unlikely that computers will be able to replace humans in the most complex, nonstandard, or most creative tasks, job replacement is a fair concern. AI is already used to replace or augment human efforts in some routine or predictable tasks, reducing the need for human labor or necessitating shifts in human tasks (Daugherty and Wilson, 2019). In addition, such use of AI might create barriers to communication between humans and machines as machines communicate with each other (Michaels 2017). While Charles Babbage created the computer as a means of calculation, it was his translator Ada Lovelace who envisioned the new machine as a creative device, able to compose music. About one hundred years later, Alan Turing posited that a computer might be capable of original ideas, contradicting Lovelace's belief on the topic. Since Turing's work in the 1950s, machines have indeed been used to create associations and rules for a given input dataset. In the twenty-first century, however, artificial intelligence and computer-aided learning are still mostly viewed as tools for facilitating collaboration, rather than collaborators with humans. Research Questions The main research questions are: (1) Can machine learning enhance human learning in work environments? (2) Can humans and machines work and learn collaboratively on a given task or assignment---rather than just being a tool for collaboration? And, if yes, how can machine learning enhance learning in times of disruption and beyond? Using a Problem-EvidenceAnalysis-Solution (or PEAS) framework, the researchers set out to state the complex problem at the intersection of machine learning and human learning. For instance, what are the issues related to the new AI technologies, and how can humans benefit from using the available ML technologies. Research on skill-biased organizational change (SBOC) has shown that in times of rapid technological growth, the jobs replaced are those who require routine tasks. Technology tends not to decrease the need for employees who make complex decisions and those who address nonroutine tasks. However, so far, the research has focused primarily on job replacement, rather than how ML can be used to increase the skill of workers. It is essential to understand the role of ML in job enhancement, because this may provide the key to employee retention in times of disruption (Autor, D. H., Levy, F., & Murnane, R. J. 2003; Caroli, E., & Van Reenen, J. 2001). Problem Although given a negative connotation, the term "disruption" can refer to any rapid change in employment. Of course, jobs can be affected during societal and business disruptions by external environmental factors, such as AI, automation, famine, wars, hurricanes, and pandemics. But disruptive change can also be caused by internal environmental factors in businesses, such as, product obsolescence, or not adapting quickly to technological innovations, such as machine learning. Humans may find it extremely challenging to adapt to new situations and technologies since the new work environment requires specialized skills and knowledge in order to transition to new job opportunities quickly. But rather than AI becoming a threat or problem, machines can play a supportive role why individuals gain new technical knowledge. Society is impacted both economically and socially when jobs are scarce, due to any of the above factors. Some researchers fear that humans may eventually be superseded by AI in the workplace (Bolton, et al. 2018). AI is already present in daily life---in smartphones, chatbots, Grammarly, and Alexa, for instance. And humans benefit from its conveniences. However, there is uncertainty about future applications of AI which engenders worry about job replacement (Andrews 2018). Are all these innovations making us smarter or more dependent? Evidence How do humans learn? This paper focus on how ML can be used to provide supplemental learning cues to enhance learning without impinging on the natural learning process. As one of the authors commented "it will be almost like having a spouse or mentor providing cues when forgetting a family adventure or anecdote" (Personal Communication, 2020). Researchers have explained how providing environmental simulations in the work environment (i.e., variety of colors, scenery, and sounds) while humans try to learn a subject, can enhance recall (Carey, 2014). Similarly, multi-sensorial situations or multimedia experiences can be provided using ML-triggered sensory simulations in a timely fashion. Nevertheless, before venturing into the potential applications of artificial intelligence (AI) and machine learning (ML), it is useful to establish an understanding of human learning. By experimenting and reviewing the research learning literature, investigators have discovered the best ways to learn most subjects—explicitly and implicitly. Human learning occurs not only through rote memorization and experimentation but also through emotional and experiential connection with the subject matter. There is evidence that perceived competence at a task incentivizes learners to advance in ability (Covington, 1984). Complexity is also related to the desire to learn through the invocation of feelings of arousal and anxiety (Hamari et al, 2016). Other emotions such as the feeling of immersion and enjoyment contribute favorably to learning outcomes (Rosenbaum et al. 2007, Czikcentmihalyi, 1990, Newmann 1992) demonstrated that the perceived authenticity of a learning experience can facilitate the feeling of immersion for students. The neurological understanding of learning involves forming, removing, or altering neural brain connections made throughout the learning process (Bassett et al. 2017). Human learning involves two distinct processes. Humans can learn either implicitly or explicitly. Through implicit learning, humans learn first-hand by doing something or are taught either how to do something, how not to do something, or how to change how we do something. Implicit learning is much more complex, however. Through this form of learning, humans learn through their perception of the world around them. For instance, this can be by observing someone else perform a task and seeing the outcome. If humans experience failure on a specific task, it is likely they won't perform the task in the same way they did as if they perform it successfully. In this process, humans would have learned something useful from failure or success. The clear distinction between these two types of learning is that implicit learning is subconscious and explicit learning is conscious. What is Artificial Intelligence and Machine Learning? Simply put, artificial intelligence (AI) is the study of training computer systems (rather than programming computers) to accomplish the tasks that humans can perform using their cognitive abilities. AI can emulate and simulate human-like abilities and functionalities, such as "learning" from the work environment (datasets). Types of AI include reactive-machine, limited-memory, theory-of-mind, and self-awareness. Reactive-machine involves only the present information and cannot form memories. Video games are an example since they respond to different stimuli. This primitive AI is unable to learn, while limited-memory can look into the past and learn (Hintze, 2016). This category of AI comprises more sophisticated artifacts, such as chatbots and biometric authentication. Theory-of-mind and self-awareness are beyond the scope of this paper and are considered underdeveloped and, to some extent, hypothetical. Reactive-machine algorithms are best suited for tasks that are repeated and predictable. For those that require complex or changing input for which a list of if-then statements is not feasible, machine learning may be applied. Machine learning uses past outcomes to construct and change its own algorithms to minimize the difference between proposed solutions and desired outcomes, or calculated solutions and the population. Machine learning (ML) fits into the general category of AI since ML can interpret data, potentially learn from the data, and then use that acquired knowledge to adapt and achieve specific goals (Green and Green 2019). ML is the process of learning with training labels. These systems are like humans in the sense that they must learn heuristically – by iterating in problem-solving process and comparing against the given assumption. In this case, since an algorithm "learns" from a labeled dataset, it provides an answer key that it can use to evaluate the accuracy on the training data. This process is known as supervised learning or supervised machine learning. But note that humans must still label data into this ML system and monitor the learning process. ML can augment or complement human capabilities (Wilson and Daugherty, 2018, n.p.). However, AI or ML cannot fully replace the human workforce yet, given humans' tacit real-world knowledge, intuition, and experience interacting with the physical world (Emerging Technology from the arXiv 2018). Another type of ML is called reinforcement learning, or reinforcement machine learning. Video gaming is one of these types of ML where cues are provided to succeed in the target goal. In this type of ML, AI agents are trying to attain a goal or improve the effectiveness and performance of a given task. Therefore, it is an interactive process—employing trial and error to arrive at a solution of a problem. Organizations that can devote the time and resources to developing and integrating machine learning in their operations will reap the benefits of automated business functions. Companies that do not incorporate ML will be left behind (Dirican 2015). The encouraging news is that job growth is expected to offset jobs lost to automation (Lund 2017, 11). Jobs introduced by the development of machine learning will most likely be enough to offset the jobs that were taken by AI and machine learning (Lund 2017, 11). But the skillsets required from human are different. Many tasks are riddled with functions and processes that are completed by ML. Most of the benefits of ML are now commonplace - personalized ads, personalized recommendations, and optimization of websites are driven by AI and ML algorithms. The solution to improving human learning coupled with machine learning should build upon the algorithms developed for some of the above applications. It is clear that AI and machine learning have been critical in not only improving our quality of life but also improving how we work. AI can be used when it complements the creativity, emotion, and innovation of humans. There are instances when AI can threaten to replace human jobs. When AI researchers were surveyed a few years ago, the survey revealed a 50% possibility that AI will be surpassing humans in all activities in the next 45 years (Maini and Sabri 2017). Now, truck drivers and call center operators can be replaced by automation, speech recognition and deep neural network robots (Brynjolfsson, Rock, and Syverson 2017). Brynjolfsson, et al (2017) stated that AI could perform 45% of the tasks supplied by the human labor force. In times of technological change, displaced workers will possibly have to learn new skills to find different jobs. On the other hand, AI can also improve work performance and value creation. Although AI can replace some occupations, it can also bring benefit to employees and employers in the workplace environment. Evidence gathered about how humans continue to learn and create value in the workplace and how machine learning enhances on-the-job training as well as how employers' benefit from improved work performance with AI paints a different picture. Even though AI can drastically change the workplace, AI will not yet replace the human workforce, but instead can augment, support, supplement, and complement human-abilities and capabilities (Wilson and Daugherty 2018). Today, humans still learn things that machines cannot because of their background knowledge and real-world experience (Emerging Technology from the arXiv 2018). With AI freeing workers from everyday responsibilities like administrative work, employees can now focus more on strategic tasks (Wellers, Elliott, and Noga 2017). There are many human skills that AI cannot yet emulate or perform, such as social networking, staff development and coaching, and collaboration (Kolbjornsrud, Amico, and Thomas, n.d.). But AI can aid the process. More importantly, AI does not understand the potential consequences for mistakes or moral implications of decisions (Green and Green 2019). Hence, humans can continue to add value in the workplace by focusing on skills that machines are incapable of, such as creativity, collaboration, empathy, and making ethical judgments calls (Kolbjornsrud, Amico, and Thomas, n.d.). Since Machine Learning analyzes data through mathematical processes, known as algorithms, humans are still essential in reviewing this information to make smart and informed decisions (Finlay 2018). For example, AI chatbots blend past customer service data and natural language processing, along with algorithms that are continually learning through exchanges, to answer customer questions (Wellers, Elliott, and Noga 2017). These chatbots still need to learn from human workers for when exceptions occur. Humans provide the labels and meaning to AI with their experience from everyday life (Green and Green 2019). Fortunately, human time saved from routine customer service calls, workers can focus on creative strategic-planning and strengthening customer retention, for instance. Without human-supervision, AI's success is limited. Humans and AI in the workplace can work together to enhance each other's strengths and compensate for each other's weaknesses. People are needed to explain the AI system's predictions, outputs, and possible errors to others who are not familiar with AI (Green and Green 2019). Workers must also inspect algorithms for fairness rather than biased results. Humans must capitalize on traits such as their positive motives, societal values, and good decision-making skills (Green and Green 2019). Workers who embrace an opportunity to collaborate with ML can also learn from the interaction with machines and continue to add value in the workplace. For instance, when playing a game of Chess or Go with an AI machine, humans can gain unique insights not easily replicated when playing even with a master. How Can Machine Learning Enhance Human Learning (On-the-Job Training)? Research gives evidence that ML can improve on-the-job training, particularly through just-intime e-learning. Just-in-time e-learning offers knowledge to workers with teaching, learning, and training at their workplace fingertips (Lokaiczyk, et al. 2007). This ML process learns the employee's work tasks and then knows how and when to supply pinpointed training (Lokaiczyk, et al. 2007). Theories and software programs such as Advanced Process-Oriented Self-Directed Learning Environment (APOSDLE), heighten on the job training by improving worker's knowledge and abilities, ultimately increasing productivity (Lokaiczyk, et al. 2007). In addition to personalized coaching, ML provides reinforced training, measurable effectiveness, and Return on Investment (ROI) along with access to an infinite amount of information (Riebli 2018). Research shows that humans need training reinforcement (i.e., repetition) to retain the information learned; and AI can provide this environment (Riebli 2018). Through personalized reinforcement learning process, ML can provide recommendations on what an employee needs to learn next. Training procedures can be improved with AI measuring results and enabling adjustments as required, providing more cost-effective, effective, and proficient training (Riebli 2018). With ML enriching employee training and development, supervisors can allot more time to interact with employees as well as improving employee relations, retention, and innovation (Riebli 2018). Table 1 shows ways in which machine learning can enhance human learning. In short, ML enhances human learning through personalization, customization, engagement of senses, challenge, and correction. AI in the workplace automates routine tasks allowing for employees to be more creative and productive (Lee 2018). Presently, top companies are utilizing ML tools and devices for smart decision-making automation (Wellers, Elliott, Noga, 2017). Intelligent decision making directly affects company costs and revenue. Statistics show that data-driven companies are 5% more productive and 6% more profitable (McAfee and Brynjolfsson 2012). Businesses are also using AI for digital transformation (Wellers, Elliott, and Noga 2017). By 2025, AI is predicted to become a $100 billion market, drastically altering the way business is handled and impacting the workplace (Wellers, Elliott, and Noga 2017). Businesses can benefit from AI by enhancing employee work performance to add value and competitive advantage to organizations. The Society of Human Resource Management (SHRM) describes that AI technology, together with machine learning and cloud-based analytic platforms, can analyze today's employees and their work performance, enabling higher quality outcomes (Romeo 2019). Table 1: Ways in Which Machine Learning Can Enhance Human Learning Means Reason Source Likelihood Unique and efficient learning Intelligent interfaces allow for customization to meet each learner's requirements. Real-time feedback (Riebli 2018) High offered during training can fix mistakes early and prevent reinforcement of negative behaviors. Improved training retention Intelligent systems reinforce the main points of lessons. (Riebli 2018) High Quantified value & ROI The capability of monitoring training leads to adjustments as necessary. Collaboration potential increases. Human learning proficiency is improved. (Riebli 2018) High Access to a virtually infinite amount of information Big Data equates to more information and potentially better decisions. Big Data Analytics may increase productivity and profitability. Decisions can be made based on evidence, rather than "feel." (McAfee and Brynjolfsson 2012) Medium-high Analyze and diagnose human difficulties learning the material. Mistakes can be recognized immediately, and corrective behavior can be prescribed. (Authors 2020) Medium-high Make recommendations to speed up the learning process. ML may recognize aspects of a task that have already been mastered. (Authors 2020) Medium-high Sense human frustration with the material and propose alternative learning paths. ML may possess multiple workflow paths, and it may suggest alternate paths based upon the learner's responses. (Authors 2020) Medium-high Trigger simulation of a variety of geographic locations to help neurons create more connections with the material The ML software may follow learning paths that require movement of the learner. (Authors 2020) Medium-high Add music, color, or other sensorystimulating backgrounds that would simulate the test environment. ML can incorporate multiple senses to build triggers for recall. Enhance human recall. (Authors 2020) (Carey 2014) Medium-high Adjust the difficulty of the task such that the learner is challenged but not overwhelmed ML can create task difficulty that avoids instant mastery and cultivates learning. This is demonstrated in 'just-in-time elearning.' Detecting training as it is needed. (Authors 2020) (Lokaiczyk, et al. 2007). (Carey 2014) Medium-high Detect fatigue and suggest work breaks ML can suggest breaks when the learner appears to be completing tasks more slowly or with more errors. (Authors 2020) Medium-high With human supervision: "Detect inductive biases which make it possible for a ML can challenge inductive biases and target the learner to analyze differences (Griffiths et al 2008). Low-high, depending on the training dataset. learner to choose between hypotheses that are equally consistent with the observed data." between the choices. With human supervision: Compare models "that differ in their inductive biases to the performance of human participants solving an inductive problem in the laboratory, it becomes possible to evaluate which set of inductive biases most closely matches those of human learners." ML can learn where inductive biases are likely to exist, through training. (Griffiths et al 2008). Low-high, depending on the training dataset. Source: Developed by the authors with adaptions from the tabulated references. Pattern recognition and monitoring patterns can also enhance employee productivity and work performance (Romeo 2019). The planning process is an excellent example of this AI system capability. AI can aid in scheduling and planning for higher efficiency, which results in boosted employee morale, productivity, and increasing cost saving for the employer (Romeo 2019). AI and ML increase efficiency and effectiveness throughout an organization, generating a competitive advantage in the global economy (Romeo 2019). Further, machine learning is allowing businesses to streamline procedures, boosting employee morale, increasing production, and enriching customer relations (Wellers, Elliott, and Noga 2017). Analysis Even routine tasks imbue the employee with tacit knowledge that is complementary to AI. As mentioned earlier, research indicates that businesses that replace human workers with technologies, such as AI and ML, may experience short-term productivity gains (Wilson and Daugherty 2018, n.p.). The evidence presented here suggests that there may yet be untapped opportunity for government and industry to harness human capital within the organization. This might occur, for example, by equipping employees with the skills they would need to adjust to and unite with the AI interface. Further, it would allow smart organizations to capitalize on the collaboration of machine learning and human learning. Companies that embrace AI and ML while cultivating a focus on employee learning may realize significant competitive advantage. An alternative offered to those humans who fear being replaced by machines comes from the Historian Yuval Noah Harari. Harari urges humans to focus on building emotional intelligence and mental balance (Skipper 2018). If people want to stay employed in the AI era, they must continually reinvent themselves. Harari believes that the most valuable employability asset in the future will be psychological flexibility. Artificial Intelligence will continue to evolve, forcing workers into continued learning, education, and training for new jobs. Change is stressful. When specific jobs become extinct, people need adaptability to learn new skills and take on innovative jobs. Since it is human nature to resist change, humans must practice managing transition. With awareness and self-knowledge, workers can repeatedly reinvent themselves, preserving employment throughout the Artificial Intelligence Revolution (Skipper 2018). A second alternative, Dr. Kai-Fu Lee suggests, is for people to espouse their human traits. Humans possess compassion, love, and creativity that no machine can acquire. Lee believes that since AI has alleviated ordinary daily tasks, humans can now get in touch with what is truly important in life, formulating a new plan for life. Dr. Lee encourages humans to change the mentality that work defines them. Create a new life that emphasizes the importance of human values and prioritization, embracing time spent with loved ones. Lee claims that AI will replace various human jobs over the next fifteen years. Yet, new occupations can focus on humanity and helping others such as social workers, caregivers, and teachers. Lee recommends capitalizing on human brains and mortal hearts. The coexistence of AI and humans' ability to love can allow humanity to retain value in the face of more productivity-enhancing technologies (Lee 2018). Solution Allowing employees to retain jobs even as the skills change, with the help of AI/ML. After appraisal of the above analysis, the researchers suggest integrating both of Dr. Lee's suggestions where coexistence is the key function. Employer and employee synchronization, coupled with worker agility and adaptability, will allow the human workforce to endure in the face of the threat of AI replacement. And, better yet, machine learning can enhance human learning and vice versa. Collaborative intelligence exemplifies the requirement for this collective effort. Companies and workers must work together towards continuous learning and re-creation of self. Psychological flexibility with self-awareness is an essential skill needed for successful employability and continuous learning in the AI revolution. Coevolving with AI and re-creating a life built on personal values, priorities, and love is a solution to having rewarding work. Below are the components and benefits of the recommended solution. Improving Learning ML-based learning platforms should simulate the emotional, audiovisual, tactile, and experiential factors which serve as incentivizing mechanisms for students. Through feedback from the individual learner, such technology can adjust strategies "on the fly" to induce the best outcomes for that person, with that subject matter, at that time. Benedict Carey (2014) researched how humans learn as well as when, where, and why it happens. Carey found that studying at different locations (rather than the same study place) enhances learning, since subject matter will have more place and sounds associations in the learners' neural network. Researchers have gone digital to provide a variety of simulated environments incurring the costs of on-site training. AI and ML can generate new photorealistic environments to simulate medical “hot zones,” for instance. An example of the symbiosis of human and machine learning is in the field of dermatology. Convolutional neural networks can be used to identify cancerous skin lesions (Esteva et al, 2017). Training such networks relies on human classification of a known set of images. Subsequently, new images can be classified automatically. The accuracy of AI classification relies upon the inclusion of all known types of cancerous lesions, so as new types are discovered, there must be human intervention to add them to the training data set. Students may test their ability to identify skin cancers using an AI “teacher” that adjusts image difficulty to match the learning rate of the student. Improving Processes Employees in the workplace are involved in a number of routine processes. Implementing ML into routine processes can help workers devote more time to improving processes. For instance, an employee in human resources may use ML for data gathering or use it to mine customer actions and transactions. Further, social sentiment data can be used to identify customers who are at high risk of withdrawing from the business relationship. Combined with profitability data, this would allow organizations to optimize for what is called next-best-action strategies as well as to personalize the end-to-end customer experience (Wellers, 2017). In addition, using ML to improve customer service is a great way to enhance human learning. The ML can sort and organize the up-to-date data and information to help employees perform better and assist the workers learn and adapt to giving the customers the best service possible. Further, ML can rapidly identify key requirements in job candidates' applications and select candidates who have the credentials that are most likely to achieve success at the company. Of course, AI software will have to be coupled with human supervision to combat human bias that might be embedded on the dataset. And can automatically flag biased language in job descriptions or detect highly qualified candidates who might have been overlooked because they didn't fit cultural expectations (Wellers, 2017). When recruiting job applicants, ML can again speed up the review process and eliminate unqualified workers. Routine processes performed by human can be tiring and/or confusing. While a recruiter can quickly scan and read multiple applications, ML never gets tired and can supply important cues to the human reviewer. Since ML can monitor existing processes and learn to recognize patterns and context, it can significantly increase the number of documents (i.e., invoices) that can be matched automatically. This would help reduce the amount of work outsourced to global service centers and, more importantly, allows the employees to focus on strategic tasks (Wellers 2017). Managing finances can be difficult for most workers due to how it can be a difficult and/or confusing task to deal with. By using ML, companies can have software set up and organize the company's finances in a way to help workers learn more efficient ways to organize it themselves. Beyond the limited scope of this paper, it should be noted that advanced research is needed on Rademacher Complexity, which measures the ability to assimilate data into a known context or form a pattern with it. While this may support the notion that a high Rademacher Complexity indicates a facility with learning, it also indicates the possibility of overfitting the data (Zhu et al. 2009). In addition, ML can help staffers in the implementation of incremental business tasks, by analyzing datasets and evaluating current systems and platforms (Craven 2019). Using specific software to aid in managing resources can help businesses with resource and data management. Further, workers can learn from ML in their workplace by managing inventory better and, in general, improving budgets allocation. Conclusion This research has illustrated how machine learning can enhance human learning in work environments. When companies recognize employees' fear of being replaced by AI, employers must provide incentives for employee engagement with machine learning to alleviate anxiety and ensure success (Wellers, Elliott and Noga, 2017). Research points that embracing AI technology in the workplace can even result in the production of new jobs (Bolton, et al. 2018). Through creating innovative, functional work relationships between AI and humans, workers can be more productive, allowing companies to capitalize on the wave of AI (Bolton, et al. 2018). The research presented here is limited to a theoretical discussion of ML and human learning. It could be further enhanced by an experimental study that compares job accuracy between subjects who train with ML and those who do not. We also do not attempt to hypothesize differences by industry. This is another avenue for future research. Not only may ML have industry-differentiated effects, but there may be contexts in which machine learning is not desirable. For instance, the market for hand-crafted goods such as quilts, knitted items, hand-built tables, and other antique-store fare signals the value of traditional familial learning methods. On the other hand, the growth of craft breweries in the 2000s indicates the increase in consumer preference for small-batch items. In both cases, uniqueness is valued. Machine learning may be a more valuable asset in the latter case, in which taste regional preferences can be used to optimize flavor profiles for local breweries. As analyzed, explained, and tabulated in this paper, machine learning can enhance human learning in multiple ways—namely: (1) ML intelligent interfaces can easily allow for personalization and customization to meet each learner's requirements; (2) ML can offer realtime feedback during training and can help fix mistakes early and prevent reinforcement of negative behaviors; (3) ML intelligent systems can reinforce main points of lessons; (4) ML can provide capability for monitoring training leads and adapt training as necessary (5) Human learning and ML collaboration can increase, as datasets are nourished; (6) human learning proficiency can improve from the collaborative interaction with ML; (7) access to big data repositories signify more information and potentially better decisions; (8) Big Data analytics can increase productivity and profitability; (9) arrangements can be made collaboratively based on both objective evidence and subjective human abilities, such as ethical and social impacts; (10) mistakes can be recognized immediately, and the corrective behavior can be prescribed; (11) ML can recognize aspects of a task that have already been mastered and help learners move to the next assignment; (12) since ML may possess multiple workflow paths, it can suggest alternate routes based on the learner's responses; (13) The ML system can follow learning paths that require specific location and movement of the learner; (14) ML can trigger simulations of a variety of geographic locations to help neurons create more connections with the material; (15) ML speech emotion recognition systems can sense human frustration with the content and propose alternative learning paths; (16) ML can create task difficulty that avoid instant mastery and cultivate challenging learning, as demonstrated in just-in-time e-learning, by detecting training as needed; (17) ML can incorporate multiple senses to build triggers for recall and, therefore, enhance and reinforce lessons learnt; (18) ML can add music, color or other sensory stimulating backgrounds that would simulate a given work or test environment; (19) ML can detect the learners' fatigue and suggest work-breaks, or even suggest breaks when the learner appears to be completing tasks more slowly or with more errors; (20) with human supervision, ML can detect inductive biases. 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Patricia Angle: Assistant Professor of Management Information Systems, University of Oklahoma, Norman, Oklahoma, USA. Formerly, Assistant Professor of Information Systems at the Lutgert College of Business, Florida Gulf Coast University, Fort Myers, Florida, USA. Michele Snyder: Undergraduate Student, Lutgert College of Business, Florida Gulf Coast