**3. Didactic models for creative computational teaching**

The timely re-entry of computational [7] 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 [8].

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**).

*Software applications* accessible on the web allow both instructors (*course makers*) and students (*users*) to manipulate course content to create, present, and store. *Learning Management Systems* (*LMS*) are prepackaged applications that act as an administrative tool to manage the online course, the students who enroll, and the professor who offers the courses. *Artificial intelligence* (AI) is the byproduct of deep learning, huge swathes of databases within a database that when meshed together gives it intelligence though within a limit, that is, you can program a robot to do certain things that will be limited to the amount of intelligence you put in it. A human is still in command and an ill-programmed robot like a biased robot gives

**Figure 2.** *Technology infrastructure to teach.*

disastrous results. *Data science* is the highest application of computational ability that can detect patterns of naturally occurring events in nature, analyze it, and provide very accurate predictions and analysis in context. *Interactive rooms/walls* that began in the mid to late 1990s, but was not very successful such as the project at Stanford University and though unsuccessful at its early attempts to merge it in the teaching and learning game, has re-emerged (*Germany Applications*) with more sophisticated screens that cover the entire walls making possible the projection of visualized data comprehensible to all learners.

*Augmented reality* when applied has the capability to freeze, slow down or speed up, and look for patterns among piles and piles of data and of events that are intended to be analyzed and deduce from. It is very useful in the science and engineering course making in particular, though also usable in other fields when observation of actions is sought. *Virtual reality or virtual environments* are computing environments otherwise called *virtual immersions* where "users are immersed in low latency high-quality visual stimuli and spatial audio amplified by motion platforms, dispersed systems, and active/passive haptic device that allow users to fill objects with more sophisticated systems equipped with gesture recognition and voice input. Success of VE systems depend on system performance matching user expectations" [9]. *Ubiquitous computing* is computing at its finest you hardly notice it. *The Clouds* are on demand delivery model that enables the synchronized delivery of computing resources such as applications, storage, servers, networks, and services (liberating software providers of low-level IT on premise infrastructure) that allows instant scale up of various types of web services [10]. *Web services* are on-demand applications, storage, and servers and sometimes called SaaS, PaaS, and IaaS enabled mostly through the cloud. *DevOps and Containerization* [11] is a systems management technique for on-demand services performed in a bundled way as a completely packaged infrastructure (*operating system and software applications*) updateable on the fly with the least hassle.

Bonk [12] aptly describes a changed e-learning ecosystem in the past two decades and summarizes it based on three themes namely *Learner Engagement*, *Pervasiveness*, and *Customizability* using the same computing technologies for document-sharing, collaborating, software, hardware, communicating, and digital resource use. Based on learner engagement, he cites, 1—mobility, 2—visual, 3—touch sensory, 4—game-based, 5—immersive, 6—collaborative, 7—social, 8—digital and resource rich, 9—adventurous, 10—hands-on and in its pervasiveness, 11—mostly online, 12—video-based, 13—global, 14—immediate, 15—access to experts, and 16—synchronous in being customizable, e-Learning is more 17—open, 18—more blended, 19—more competency-based, and 20—ubiquitous. With its customizable quality, e-learning is 21—more blended, 22—more self-directed, 23—more competency-based, 24—more on demand, 25—more massive, 26—more modular, 27—more communal, 28—more modifiable, 29—more flipped, and 30—more personal. The "more personal" thus sets the stage for personalization (PLErify) in both teaching and learning, which lends itself appropriately to the different didactic models [13] of teaching for different types of courses whether it is intended for knowledge-building, theorizing, knowledge or skills acquisition, model-building, simulation, and argumentation.

#### **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 [16] extracting memories associated with completion of task (primary and secondary memories). Primary memory are those cognitive schemas a person acquires as a result of

**55**

*Personalizing Course Design, Build and Delivery Using PLErify*

interacting with the environment stored in long-term memory which the secondary memory (*the cognitive schemas stored in short-term memory*) uses to solve problems though not without limits. Knowledge creation for working memory follows the principles of (a) "narrow limit of change," that is, within a small span of time, new information should be very limited in order for it to be stored in long-term memory. "Unlimited": it may be in the amount of information it can process, working memory follows the principle of being capable of spewing (b) "unlimited amount of information" for information retrieval. When that knowledge creation does not occur, the mind adjusts by following the principle of (c) "randomness as genesis" methods (the borrowing and reorganizing principle, that is, imitation, listening, reading, and social interaction) to compensate in the knowledge creation. Finally, when cognitive overload must be overcome to commit new knowledge in long-term working memory, the mind does what is called the principle of environmental organizing and linking, retrieving, and cycles through information already stored in long-term 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 [17]. A person's style of learning is determined by environmental factors manifested through behavioral patterns. In my 2001 Doctoral Dissertation [15] 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.

**5. State of purely online learning**

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 (*including the dark forces of the web*) of tech will possess mastery of technology for every intended purpose.

MOOCs, such as EdX, Coursera, Open CourseWare (OCW), and hybrid designs, are designed to offer free courses for poor countries (MOOC), to corporations (*Coursera* and *Udacity*), EdX (*universities*), however of late modified it to a paid model adding some validation features to address legitimacy of courses, such as granting of certificates, shortening of Master's program as the case in Georgia Tech. Student attrition remains a problem compared to regular traditional

*DOI: http://dx.doi.org/10.5772/intechopen.85414*

#### *Personalizing Course Design, Build and Delivery Using PLErify DOI: http://dx.doi.org/10.5772/intechopen.85414*

*New Innovations in Engineering Education and Naval Engineering*

visualized data comprehensible to all learners.

disastrous results. *Data science* is the highest application of computational ability that can detect patterns of naturally occurring events in nature, analyze it, and provide very accurate predictions and analysis in context. *Interactive rooms/walls* that began in the mid to late 1990s, but was not very successful such as the project at Stanford University and though unsuccessful at its early attempts to merge it in the teaching and learning game, has re-emerged (*Germany Applications*) with more sophisticated screens that cover the entire walls making possible the projection of

*Augmented reality* when applied has the capability to freeze, slow down or speed up, and look for patterns among piles and piles of data and of events that are intended to be analyzed and deduce from. It is very useful in the science and engineering course making in particular, though also usable in other fields when observation of actions is sought. *Virtual reality or virtual environments* are computing environments otherwise called *virtual immersions* where "users are immersed in low latency high-quality visual stimuli and spatial audio amplified by motion platforms, dispersed systems, and active/passive haptic device that allow users to fill objects with more sophisticated systems equipped with gesture recognition and voice input. Success of VE systems depend on system performance matching user expectations" [9]. *Ubiquitous computing* is computing at its finest you hardly notice it. *The Clouds* are on demand delivery model that enables the synchronized delivery of computing resources such as applications, storage, servers, networks, and services (liberating software providers of low-level IT on premise infrastructure) that allows instant scale up of various types of web services [10]. *Web services* are on-demand applications, storage, and servers and sometimes called SaaS, PaaS, and IaaS enabled mostly through the cloud. *DevOps and Containerization* [11] is a systems management technique for on-demand services performed in a bundled way as a completely packaged infrastructure (*operating system* 

*and software applications*) updateable on the fly with the least hassle.

on learner engagement, he cites, 1—mobility, 2—visual, 3—touch sensory,

12—video-based, 13—global, 14—immediate, 15—access to experts, and

Bonk [12] aptly describes a changed e-learning ecosystem in the past two decades and summarizes it based on three themes namely *Learner Engagement*, *Pervasiveness*, and *Customizability* using the same computing technologies for document-sharing, collaborating, software, hardware, communicating, and digital resource use. Based

4—game-based, 5—immersive, 6—collaborative, 7—social, 8—digital and resource rich, 9—adventurous, 10—hands-on and in its pervasiveness, 11—mostly online,

16—synchronous in being customizable, e-Learning is more 17—open, 18—more blended, 19—more competency-based, and 20—ubiquitous. With its customizable quality, e-learning is 21—more blended, 22—more self-directed, 23—more competency-based, 24—more on demand, 25—more massive, 26—more modular, 27—more communal, 28—more modifiable, 29—more flipped, and 30—more personal. The "more personal" thus sets the stage for personalization (PLErify) in both teaching and learning, which lends itself appropriately to the different didactic models [13] of teaching for different types of courses whether it is intended for knowledge-building, theorizing, knowledge or skills acquisition, model-building,

**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 [16] extracting memories associated with completion of task (primary and secondary memories). Primary memory are those cognitive schemas a person acquires as a result of

**54**

simulation, and argumentation.

interacting with the environment stored in long-term memory which the secondary memory (*the cognitive schemas stored in short-term memory*) uses to solve problems though not without limits. Knowledge creation for working memory follows the principles of (a) "narrow limit of change," that is, within a small span of time, new information should be very limited in order for it to be stored in long-term memory. "Unlimited": it may be in the amount of information it can process, working memory follows the principle of being capable of spewing (b) "unlimited amount of information" for information retrieval. When that knowledge creation does not occur, the mind adjusts by following the principle of (c) "randomness as genesis" methods (the borrowing and reorganizing principle, that is, imitation, listening, reading, and social interaction) to compensate in the knowledge creation. Finally, when cognitive overload must be overcome to commit new knowledge in long-term working memory, the mind does what is called the principle of environmental organizing and linking, retrieving, and cycles through information already stored in long-term 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 [17]. A person's style of learning is determined by environmental factors manifested through behavioral patterns. In my 2001 Doctoral Dissertation [15] 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 (*including the dark forces of the web*) of tech will possess mastery of technology for every intended purpose.

## **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 (*Coursera* and *Udacity*), EdX (*universities*), however of late modified it to a paid model adding some validation features to address legitimacy of courses, such as granting of certificates, shortening of Master's program as the case in Georgia Tech. Student attrition remains a problem compared to regular traditional classroom model proving that regardless of convenience, students still prefer a human teacher in the classroom indicative of what history has revealed, that is, the most valuable teacher is a human. Another acute finding is that MOOC courses are not suitable for advanced courses that can only be handled by a human.

Alternatively, MOOC courses, based on a very interesting observation of Cooper and Mehran [18], 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 [19] 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 [13] 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 (*Research Resources and Libraries Semantic Knowledgebase*), analytics (*Quantifying/Analytical engines*), authoring (*Multimedia and Multimodal*), and learning management systems (*Course Delivery Student Access Point*) are placed in a private server for the instructor where he is given the freedom to extract all sorts of information for his course and capture and store those information in an organized cataloged file directory (*on both desktop and the Private server*) purely for his own access **Figure 6**.

### **6. PLErify course design and future AI prospects**

PLErify components that are visually depicted in **Figure 3** are as follows. *Research* is a set of live databases to extract content through digital libraries for download of scholarly materials, quantitative and qualitative data. *Analytics* is a set of measurement tools to analyze datasets and extract visualization files for inclusion in the instructor's course curriculum. *Authoring tools* are a set of applications to convert text to viewable mixed media and mix it up for immediate playback as one full course. *Learning management system* (*LMS*) is described in my paper [1] "Virtualized Higher Education" as the course's administrative tool performing the tedious tasks as the container that holds a course or courses. An LMS is an application that typically contains the following: administrative features for managing content: content uploading/downloading; calendar and scheduling instructor timings; student administration; faculty/student communication tool; conferencing; and homework/project submission and grading. These processes and activities as shown in **Figure 4** are time-consuming and repetitive some of which can be made efficient through automation. Decisions to automate parts of the PLErify platform depends on the instructor who (*after updating his skills in AI, deep learning, virtual assistants, and robots*) can select which activities to augment as shown in **Figure 7**.

**57**

**Figure 4.**

**Figure 3.**

*Personalizing Course Design, Build and Delivery Using PLErify*

*The PLErify application toolset desktop version (http://elearnovate.com/plerify).*

*PLErify processes and organizing storage principles for the Mac and Windows.*

*DOI: http://dx.doi.org/10.5772/intechopen.85414*

*Personalizing Course Design, Build and Delivery Using PLErify DOI: http://dx.doi.org/10.5772/intechopen.85414*

#### **Figure 3.**

*New Innovations in Engineering Education and Naval Engineering*

gram will be part of MOOC and non-MOOC.

**6. PLErify course design and future AI prospects**

PLErify components that are visually depicted in **Figure 3** are as follows. *Research* is a set of live databases to extract content through digital libraries for download of scholarly materials, quantitative and qualitative data. *Analytics* is a set of measurement tools to analyze datasets and extract visualization files for inclusion in the instructor's course curriculum. *Authoring tools* are a set of applications to convert text to viewable mixed media and mix it up for immediate playback as one full course. *Learning management system* (*LMS*) is described in my paper [1] "Virtualized Higher Education" as the course's administrative tool performing the tedious tasks as the container that holds a course or courses. An LMS is an application that typically contains the following: administrative features for managing content: content uploading/downloading; calendar and scheduling instructor timings; student administration; faculty/student communication tool; conferencing; and homework/project submission and grading. These processes and activities as shown in **Figure 4** are time-consuming and repetitive some of which can be made efficient through automation. Decisions to automate parts of the PLErify platform depends on the instructor who (*after updating his skills in AI, deep learning, virtual assistants,* 

*and robots*) can select which activities to augment as shown in **Figure 7**.

for his own access **Figure 6**.

classroom model proving that regardless of convenience, students still prefer a human teacher in the classroom indicative of what history has revealed, that is, the most valuable teacher is a human. Another acute finding is that MOOC courses are

and Mehran [18], 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 [19] 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 [13] 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 pro-

Alternatively, MOOC courses, based on a very interesting observation of Cooper

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 (*Research Resources and Libraries Semantic Knowledgebase*), analytics (*Quantifying/Analytical engines*), authoring (*Multimedia and Multimodal*), and learning management systems (*Course Delivery Student Access Point*) are placed in a private server for the instructor where he is given the freedom to extract all sorts of information for his course and capture and store those information in an organized cataloged file directory (*on both desktop and the Private server*) purely

not suitable for advanced courses that can only be handled by a human.

**56**

*The PLErify application toolset desktop version (http://elearnovate.com/plerify).*

#### **Figure 4.**

*PLErify processes and organizing storage principles for the Mac and Windows.*
