Generic course evaluation form that can be modified.
The Course-Building technique called PLErify was developed by the author in response to the emerging roles of university faculty in the technology-driven teaching with the rising popularity of AI and deep learning. Topics that support personalized teaching and learning using technology to make it more efficient, more effective and more pragmatic. Early attempts at pedagogy and trends that pushed the personalization movement are explained. The progress of the project in a Web App format is detailed focusing on a faculty building a sample hybrid course planned for a course offering of a framework of digital resources within the app in a technology-rich smart classroom. The PLErify course-building Template is explained with methodologies to add content to it in various ways with suggestions to insert multimodal techniques, e.g., Augmented Reality, Virtual Reality and Simulation, however applicable, alongside numerical data-science-supported technologies that will comprise the most part of course presentation technique. A portion of a full course will be demonstrated using PLErify with an accompanying Course Evaluation for Professors to mull to prepare for course redesign current to improve next year’s offering of same course.
- web application
- digital resources
- personal learning environment
- course development
In a digital society, every aspect of our daily lives is interconnected and each person has an identity that is solely one’s own that is encrypted and authenticable by a system. That identity allows you to interactively access multiple parts of any platform to perform actions and obtain something as a result. In an ideal version of a digital society, we humans are interconnected as citizens (
Our ever-evolving digital society intertwines the roles of humans and robots in institutions and industries. These roles keep changing as technology advances to near capability of humans through artificial intelligence and deep learning permeating the deepest trenches of every industry, not excluding higher education. In higher education, it is hardly noticeable that the roles of main players (
2. Emerging faculty role in an AI-driven scenario.
An American philosopher and educator, John Dewey (1859–1952) gave a very powerful quote with a whole new meaning that is truer now than in his time. Truer now because the educational methods we now deal with goes beyond the chalkboard, goes beyond talking in front of students, and goes beyond doing projects in isolation using pen and paper. Not completely discounting the power and value of note-taking using pen and paper and would not advise against the method, it is important to recognize the presence of computational tools being used as part of current teaching methods he would have never imagined would exist today.
Undeniably, educational technology tools ushered the transition from passive (
2.1 Robots and professors for efficient teaching
Widely practiced in Japan, Korea, Taiwan, Singapore, and China, are robots (built in the likeness of a professor/researcher), robot applications, and robots programmed to co-teach/co-research juggling the myriad roles of the human instructor. Other foreseen creative uses of these robots involve individualizing attention to each student, thereby ensuring progress, remediation, and success (knowledgebase-driven virtual assistants). Missing in those possible roles are robots that build online courses for professors based on didactic teaching styles and student learning styles all utilizing high integrity knowledge bases with optimum performance. Past attempts at course development using course sequencing , adaptive learning paths , computational teaching, and participatory teaching  can inspire new innovations in this area. One deep-learn course building technique is an AI-based course aggregator (
2.2 The MOOC-as-course augmentation for faculty and as resource for PLErify teaching learning
In AI age, faculty must face their new roles as programmer and owner/builder of learning environments of their courses that they must update per semester. Professors who remain indifferent in the new reality of a virtualized higher education vis-a-vis their expanded roles and new responsibilities will face major challenges as industry-driven automation-driven AI persistently seek dormant or stagnant unchanging areas to automate and simplify. A faculty  from Scotland recalls his very productive sabbatical spent at Google (
Regardless, MOOC courses will continue to be made available online to anybody for free, or at a minimal cost. Boosting acceleration of acceptance by universities globally, MOOC continues an upward evolution toward a better practical higher education option for both career (
Another very encouragingly useful aspect of MOOC that is only now being realized is, they add to the personalization of learning both as a teaching resource for instructors and as an inexpensive way for students to obtain advanced degrees and lastly but more importantly to upgrade skills of work professionals. MOOC business model is being revamped and, evolving toward a more profitable version, thereby offering fee-based enrollments where certification of completion is a student’s objective. The free aspect of the MOOC model however can be used by all faculty as another teaching tool. For example, professors can require students to take the MOOC version of their course offered by other universities (
While debates and experimentation continue to grow in artificial intelligence, PLErify App (2007) remains a precursor to the above scenarios. Even though the core of educational technology research centers on academic applications, academe, ironically remains the most resistant and the slowest to adapt to a scenario of AI, Big Data and IoT which when taken as a group suddenly changes the course delivery game. Groups in the tech industry persistently hint at a future without a human teacher and professor, as computer scientists now and then flirt with the idea of adding consciousness to a computer. At this time though, a robot cannot actually augment human cognitive and emotional capabilities through what they claim as smarter machines currently experimented in other industries (automobile industry). I would simply and safely assume that use of virtual robot assistant is an easy spillover for use in higher education .
It is best to speculate that whatever happens in the corporate industry will, in some form happen in the education industry. The digital society interconnects everything, from machines and app to the software/hardware; from knowledgebase to users; from different variety and degrees of transactional computing; from the teachers to the students; from the businesses to the consumers; from the students to the universities; from the faculty to the students to the universities; and finally, from the ordinary users to everything which can occur via our desktops and our handhelds. Apocalyptic ideas have been flouted at global corporate e-learning events that hint at the idea of massification to replace traditional creative teaching without a human teacher, which may appeal to select academicians who fall into the trappings of “easy teaching,” that is, less classroom presence and letting the students watch video lectures and digitized .pdf files of the syllabus. Given that these handheld tools are now a normal part of everyday life blending the here, the now, and the future, a DIY culture for course building becomes inevitable. Embodied by the PLErify application (2007), the DIY mindset provides a solid training ground for ubiquitous computing vis-a-vis course building as it involves an interplay of a variety of cognitive skills combined with digital conversion of ideas into a viewable medium.
Today, 24/7 we carry our smartphones, iPad, and other handhelds also known as mini/microcomputers, more powerful than any computers built in the 1980s and the 1990s, with us and with these technologies we socialize, network, listen to music, share photos, financially transact, chat on live video, and much more, thereby doing tasks never before possible at the very same period of time educators were theorizing on learner styles, cognitive styles, etc. My own observation over this past decade is that while educators spent so much time researching learner styles and cognitive styles, they believed impacted learning, Big Tech simply went ahead and produced a plethora of handhelds and smartphones that rapidly jumpstarted user acquiring tech skills in turn accelerating mastery that are, fortunately, usable in both daily life and university learning but unfortunately left out those who could not keep up with the constant roll-out of new versions and models. What that phase did to each of us was it made us tech-savvy and I would argue, smarter. Now, certain tech user interactions have become ingrained for majority of us smartphone and multiple device owner and users. Majority of learner tasks to: make choices, complete learner tasks, solve problems, think about thinking (metacognition), compute, analyze have become second nature.
Indeed, technology has a very democratizing effect on its dedicated users from acquiring uniformity of skills to performing actions to obtain something back as a result; skills, which by the way, are also transferable to other domains from personal, to business, to higher education with specific attention to learners. All users get it. We can turn on the device, charge the device, download and use apps, transact, collaborate, blog, share documents, and so many other things that it is now second nature to have (
3. Didactic models for creative computational teaching
The timely re-entry of computational  tools to teach creatively befits this era of our technology-driven education. Less intervention on how students create their learning paths as they meld new learning with what they already know in working memory gives students a better grasp at how to manage their interactions and the accumulation of those interactions in a self-directed way exemplified by the constructivist didactic model used in the Virtual Mentor Project notably learning by asking LBA Project .
The actors on stage in the world of tech-based teaching and learning and their functions in the teaching learning equation are summarized with one infrastructure in common: connection to the Internet (Figure 2).
Bonk  aptly describes a changed e-learning ecosystem in the past two decades and summarizes it based on three themes namely
4. General instructional design with less focus on user learning style
Instructional design for high-performance computing [5, 7, 14, 15] focuses on the principles governing working memory vis-a-vis cognitive load  extracting memories associated with completion of task (primary and secondary memories). Primary memory are those cognitive schemas a person acquires as a result of interacting with the environment stored in long-term memory which the secondary memory (
In non-scientific, non-engineering subject matter, focus on cognitive load combined with learning theories has not been exhaustively studied. Learning styles has been linked to the effective design of course materials as it affects comprehension and overall performance . A person’s style of learning is determined by environmental factors manifested through behavioral patterns. In my 2001 Doctoral Dissertation  experimental study based on learning style effect on user performance, I found out that in a matched condition, i.e., matching concrete icons with concrete learners and matching abstract icons with abstract learners resulted in better performance on recall and memory and task completion. Concrete learners performed better overall in a matched condition. There are other learning theories besides that was used (Kolb’s) in my experiment and most are in the style of thinking and therefore behaving, extent of proficiency or lack of and style of responding to environmental triggers. Knowing fully well that style of learning in the AI-driven teaching will override the learning style consideration, platforms will be built mainly based on learner independence during the learning process. That is, they will determine the route, path, and speed at accumulation of knowledge and skills as they see fit. In the past, there was “adaptive learning” where the computer adjusts to the learner based on the speed of knowledge acquisition of the user and then readjusts the next set of materials based on that performance. If the previous task proved hard, the computer generates an easier task to complete and vice versa.
Though the idea that learner pathways must still be considered in designing personal learning environments, it is safe not to overly worry about learners and skip the time-consuming practice of hand-holding knowing that users have full control of their digital strategies and techniques to learn. It is both consoling and problematic at the same time: consoling because instructors would not need to look over learner’s shoulders during the process of mastery, yet problematic because it now forces the instructors to be on the top of every technology used by the learner. Instructors need to possess tech skills better than students. Casual everyday users (
5. State of purely online learning
MOOCs, such as EdX, Coursera, Open CourseWare (OCW), and hybrid designs, are designed to offer free courses for poor countries (MOOC), to corporations (
Alternatively, MOOC courses, based on a very interesting observation of Cooper and Mehran , have the potential utility in personalized learning in the same manner as YouTube online video courses do, that is, a place to find highly reputable learning resources for students to pre-familiarize themselves of courses they will take before they turn up at actual class lectures. This utility when applied as a “before-you-attend-a-class” feature skirts the nagging issues attached in MOOC such as validation, plagiarism, certification, and lack of richer evaluation. MOOC, in that capacity, is indeed a welcome addition to personalized learning. One monetization  possibility explored by MOOC concerns that of providing added validation about the student for employment which, to my mind is very interesting and closes the loop of education to career. In an interview  with John Hennessey by his longtime colleague Davis Patterson, John was very enthusiastic about MOOC and thought of it is a compelling solution for continuing education (skills upgrading for working professionals) with his continuing belief that Masters and PhD program will be part of MOOC and non-MOOC.
In that vein, professors in higher education institutions need to skill themselves sufficiently to be able to create a digital course only once but updated for every semester’s offering. Faculty load of work is, in truth, lightened while students carry most of the load of a course, that is, reading materials, accessing mixed modal multimedia, collaborating, project work, homework, assignments, critiquing, mid-term exams, and final exams. In the PLErify platform, AI tools, in the research (
6. PLErify course design and future AI prospects
PLErify components that are visually depicted in Figure 3 are as follows.
6.1 Sample course curriculum on learning styles
Module I: Timeline Origins and Theorists
Step 1 Research: gather literature on early theorists (background, philosophies, and teachings)
Step 2 Analytics: quantitative data from research associated with background, philosophies, teachings, and applications of learning theory in higher education
Step 3: extract visuals and moving media equivalent of content derived from steps 1 and 2 and combine them to illustrate concepts and examples
Step 4: prepare the interactive module summary for Part 1.
Module II: Belief Systems of LS (Cognition, Behaviorism, Environment)
Step 1: gather literature on LS relative to cognition, behaviorism, and environment
Step 2: extract quantitative/qualitative data on LS relative to cognition, behaviorism, and environment
Step 3: extract visuals and moving media equivalent of content derived from steps 1 and 2 and combine them to illustrate concepts and examples
Step 4: prepare interactive module summary for Part II.
Module III: Higher Education Applications of Learning Styles
Step 1: gather research on higher education use of learning styles (projects successful or unsuccessful)
Step 2: extract quantitative or qualitative info based on above.
Step 3: visuals and moving media from steps 1 and 2.
Step 4: prepare interactive module summary for Part III.
Identify simulation videos or games to illustrate LS application.
Include the URL’s of videos (simulation and VR) within the course before packaging it for export to the LMS. Package the three modules as one course and export it to the LMS enroll students taking the course.
Add a Course Evaluation (Table 1) showing a generic form freely available online at https://www.jotform.com/form-templates/course-evaluation-form-3.
6.2 Course and instructor evaluation form
Course Number Date-Month-Day Year.
Please evaluate honestly.
6.3 Student participation
The amount of effort you put into this course was:
Excellent Very Good Good Fair Poor Very Poor.
On average, how many hours a week did you spend on this course (in and out of class)?
0–2 2–5 6–10 11–14 15 Up.
What grade do you expect in this course?
A (4.5–5.0) B (3.5–4.4) C (2.5–3.4) D (1.7–2.4).
This course is best described as:
Major Minor A distribution requirement A program requirement Prerequisite Other.
Every e-learning course is organized into modules shown in Figure 5. To populate content, the method is quite straightforward starting with Module 1 but not necessarily following a linear process, that is an instructor can jump from Module 1 to other modules in no particular order depending on how they interconnect topics and ideas.
Click Module 1. Module 1 will load chapters. In the edit mode, you can replace the content with your content. Chapter 1’s format is repeated for Chapters 2–4. You can replace the content as your syllabus progresses. In each chapter, you can include datasets (from the research toolset analyzed with results presented). These analyses of presented data, or sample data can be saved in database readable format backed up in instructor’s private server and desktop for inclusion in the digital course. The content of the modules is managed as shown in Figure 6. For example, in a digital course on Learning Theories, an instructor will find the timeline data to present the history of the early to modern learning theorists. This timeline tool in the PLErify App can be dramatized through an augmented reality historical film on the significance of each era and how it influenced education at different times in the history of the modern world.
Personal learning environments or expert systems as it is sometimes called is disruptive enough to education due to its “lean to use automation.” Any AI application is still limited in capability where human skills of negotiation, detection, mobilization, and understanding of power and trust (
Assigning automation features shown in Figure 7 in PLErify in the next 5–10 years will center on course preparation, in converting a simple text to something more graphic or visual, combining the visuals into a more powerful single visual based on context, capturing real live data from a source known only to the instructor, citing the link of that source in the course materials, mastery in the use of sophisticated tech-enhanced classroom, synching course presentation of materials with the tools in the smart tech-enhanced classroom, and automating tasks in use of the LMS.
7. Reflections on the profession vis-a-vis digital society
We can entrust the ability to recognize learner styles, learner abilities, comprehension and understanding in Artificial Intelligence (AI) as it continues its ascent towards enhanced intelligence in almost all facets of our digital life in this case Higher Education and Course building. In that token, Instructor (
In this second half of this decade, AI’s recognition capability has gone far beyond its early beginnings that it is now termed the Age of the Machine. Much similar to Elon Musk’s Tesla, the machine can now build other machines. Thinking about this new reality in education also means the teaching and learning can now rid of a lot of the mundane tasks in: course creation (
Professional degree certificates (
Schneider  points out that through data analytics, these learner characteristics can be extracted automatically from user’s ongoing interaction to perform a variety of transactions (
Denning  summarizes it brilliantly that to truly survive in the age of machines where the knowledge worker conducts work on highly intelligent machines, new expectations come to the fore that requires pragmatism in belongingness, ever adapting skillsets that changes as the system changes, community building based on chosen areas of belongingness (professional, leisure, or recreational), and last but not least, willingness to mentor, to display your skills to the person that needs it so that the next learner improves the knowledge to the next and so on and so forth.
8.1 Security concerns with respect to PLErify, MOOC, and tech tools
Security breaches from China, North/South Korea, and Russia are a threat to our tech-enabled life. These countries’ very advanced cyber-surveillance and intrusion system have penetrated US cyber defense system potentially undoing major education technology advancements. The industry needs to come up with a very strong authentication system as well as cyber-blocking mechanisms beyond the obvious firewalls.
Without a strong cyber security strategy attached to all these tech innovations, any attempts at technologizing higher education would face enormous challenges. China’s breaches covered the entire hardware/software and telecommunication ecosystem (home routers included) baffling Europe, the US, and Australia. A solution that has been proposed is virtualization and containerization. If virtualization and containerization provides a guarantee for the safety and security of cumulative progress and strides made in the education sector and if we are willing to adapt to rapid changes demanded of us as educators, faculty, and students, then the future will certainly be bright.
After incorporating additional critique (style, formatting, and punctuation) from my children who are both writers in their own right, I finally produced my chapter contribution to the book 2019 Engineering Design and Innovation Methods at IntechOpen. I take this opportunity to convey my deep heartfelt appreciation to IntechOpen’s generous partial publication waiver which assisted me in pursuing further publication of my manuscript with them. Thank you.
For being my constant inspiration and for whom I am constantly reminded that we must perform our responsibility as trustful stewards of technology for the future of the young generation and the forthcoming ones thereafter, I dedicate this work to my children Avinash A. Kunnath (AB Mathematics, University of California Berkeley, 2008) and Ameeta A. Kunnath (BEngg Structures, University of California San Diego, 2015) who, along with members of their generation, will continue the work in their chosen careers and professions to improve various facets of our digital society we are all part of as technology continuously shapes and impacts our modern lives by the minute.