**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).

#### **Figure 5.**

*PLErify course creation in modules.*

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 (*much like the gut instinct humans have to sense danger and change*) is required. In other words, regardless of whether it is continually built to be smarter and smarter and contradicted by Krakovsky [20] who has a more optimistic view, AI as predicted may not develop a true "sense of self." Her research to a certain extent can be useful for building the intelligent robot as instructor assistants that will be tasked to meet students, answer course-related concerns, substitute the human instructor who is on research travel, and track student progress as described in Section 2.

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.

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metacognition.

**Figure 7.**

*Personalizing Course Design, Build and Delivery Using PLErify*

**7. Reflections on the profession vis-a-vis digital society**

*Areas in PLErify that may be automated highlighted in red circle.*

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 (*as Designers*) and Technologists can look into Chris Stary's [13] research on the Scholion Project as one guide looking into the design and implementation of his constructivist-based project that aids learners taking into account learners' mental models, cognition and

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

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

**Figure 6.** *Course module management.*

*New Innovations in Engineering Education and Naval Engineering*

different times in the history of the modern world.

student progress as described in Section 2.

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

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 (*much like the gut instinct humans have to sense danger and change*) is required. In other words, regardless of whether it is continually built to be smarter and smarter and contradicted by Krakovsky [20] who has a more optimistic view, AI as predicted may not develop a true "sense of self." Her research to a certain extent can be useful for building the intelligent robot as instructor assistants that will be tasked to meet students, answer course-related concerns, substitute the human instructor who is on research travel, and track

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

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**Figure 6.**

*Course module management.*

use of the LMS.

**Figure 5.**

*PLErify course creation in modules.*

**Figure 7.** *Areas in PLErify that may be automated highlighted in red circle.*
