A lecture aims to explain a topic. This topic is composed of several concepts and sub-concepts. These concepts and sub-concepts can be grouped to form a knowledge point (KP) structure. These KPs are further arranged in the form of a hierarchy called KP-Hierarchy. The order in which teachers explain these KPs to the students is called the traversal order. A traversal order plays a significant role in deciding the success of a lecture because different traversal orders suit different types of students. While lecturing, experienced teachers tune their lectures by finding appropriate traversal orders based on the class response. Whereas new or inexperienced teachers struggle hard to identify different traversal orders to teach different types of students. Can we automate the traversal order identification process concerning different types of students? This research provides a handle to address this problem. This research proposes a KP-Traversal scheme that automates the traversal order generation process. We believe that this research helps the education industry to develop pedagogically effective personalized learning systems.

The teaching trend is shifting towards online learning due to the COVID-19 pandemic. Due to this, micro-lectures (MIL) are gaining popularity. Generally, assignments follow MIL to make learners comfortable with the concept explained in the MIL. So, the traditional micro-lecturing process (MILP) aims to enhance students’ concept understanding skills. However, according to Bloom’s taxonomy, understanding, application, and error handling are three fundamental skills required for students’ overall academic development. Hence, traditional MILs fail to nurture the other two skills. So, is it possible to integrate application and error handling skills in the traditional micro-lecturing process? This research provides a handle to address this research question by proposing the micro-tutorial strategy. This strategy modifies the traditional MILP by doping it with micro-tutorials (MTUT). This modified MILP is called the micro-tutorial-based MILP. This process can simultaneously enhance students’ understanding, application, and error handling skills. To prove the validity of the proposed strategy, we floated a ‘C’ programming course in which we lectured students using the proposed strategy and observed fantastic results. We believe that this research opens new dimensions for designing pedagogically effective MILs.

The micro-prompt* strategy balances several complex pedagogical tradeoffs hence found helpful in designing assignment free micro-lectures (MIL). Manual implementation of this strategy is time-consuming and may introduce human errors. This research is the answer to these problems. This research presents a desktop application entitled "AutoPrompt". This application helps in designing assignment free micro-lectures using the micro-prompt strategy by automating the MIL design process. The complete reference architecture of the proposed application is discussed in detail in this paper. To judge our application’s quality, we conducted a software testing program and found satisfying results, including some precious feedback. The details of the testing program, results and feedbacks are also presented in this paper. We believe that the reference architecture proposed in this paper helps researchers and software developers to design more sophisticated applications for online educators and learners.

Micro-lectures (MIL) help learners to learn using learning by doing (LBD) approach. Micro-lectures use assignments to implement LBD. Carefully crafted micro-lectures and their associated assignments induce learning curiosity in learners, resulting in improved learning quality. However, too many assignments deviate learners from learning resulting in the degraded learning quality. Thus, it is hard to manage the tradeoff between the number of micro-lectures and the number of associated assignments in online micro-lecturing, resulting in poor learning quality. This research is the answer to this problem. This research proposes a framework which uses micro-prompts for crafting micro-lectures. Micro-prompts' purpose is to embed assignment elements in the micro-lectures body. This coerces learners to cover assignment elements during the micro-lecturing process, hence eliminating/reducing the need for external assignments resulting in improved learning quality. To prove our proposed framework's validity, we conducted a fifteen-days long C programming course for beginners. We lectured them with micro-prompt embedded micro-lectures designed using our proposed framework and observed fantastic results. This paper presents these results. We believe that study of this kind helps online educators to design effective learning ecosystem.

Natural languages (NL) are tools for establishing communication among humans. NL's suffer from linguistic-ambiguities (LA) like statement interpretation problem. To eliminate LA's several organizations are using controlled natural languages (CNL's) for doing their work. In this research, we proposed a desktop application, and it's reference architecture which allows naive/beginner programmers to code using Native-CNL. Then we study the effect of using NativeCNL's in learning real programming languages. To study the effect we surveyed with 72 naive/beginner programmers. These programmers used our application to learn coding in NativeCNL. Then after acquiring programming knowledge, they were asked to program using real programming languages like C and tradeoffs between several programming parameters were analyzed.

In traditional classroom-based teaching, course instructors interact with students. They use theories given by Webb, Bloom and others to tune parameters like classroom behavior, depth of knowledge (DOK), knowledge points (KP), and others for designing quality lectures. Lecture designing for online education systems become complex because (i) students are having differences in their learning ecosystem and (ii) there is very less interaction between students and course instructors. This research proposes a strategy named "lecture-breakup" which allows online educators to design quality lectures. The validity of "lecture-breakup" is proved using a survey. We believe that this study helps online educators to design pedagogically effective lectures.

Learning by Imitation (LBI) is the most natural way of learning natural languages. The designers of online courses focus on approaches like learning by doing, adaptive learning, and so on for designing online education systems like Massive Open Online Courses (MOOC) and Intelligent Tutoring System (ITS). They don't consider LBI as an essential parameter while designing courses and MOOC/ITS for naive programmers. The purpose of this research is to arrive at a framework that helps in designing pedagogically effective MOOC/ITS for naive programmers which reflects LBI approach. We conducted an online survey where 130 students participated. Online lectures were designed using our reference framework. A desktop App was developed for naive programmers allowing them to code at different levels of abstractions. The Lectures plus App provides a learning environment reflecting LBI. We conducted pre and post-tests and found a remarkable increase in programming performance of these participants.