AI SearchAnalysis of course structures and learners{u2019} engagement in MOOCs the case of Thai MOOCs
Wanlipa Thongsuntia
Analysis of course structures and learners engagement in MOOCs the case of Thai MOOCs - Pathumthani : Asian Institute of Technology, 2019 - 1 online resource
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science
Recently, Massive Open Online Courses (MOOCs) have achieved more than 100 million registered learners around the world. Based on several active research, the influential ac complishment of MOOCs is a number of learners engagement. MOOCs, nevertheless, has major issues including not only low instructional quality, but also high dropout rate. As from the above fact, the research question of this thesis is to find out how the difference in course structure design may affect learners engagement. With regard to the scope of this thesis, it focuses on Thai MOOC courses in STEM subject area. The dataset contains 28 STEM courses. The learners engagement dataset selects the top 20 of the most popular courses according to registered learners. This paper applies the learning analytics to analyze the patterns and clustering of two significant dimensions: i) course design & structure and ii) learners performance & engagement. Furthermore, this thesis explores the relationships of both dimensions by using data mining, machine learning, and visualization techniques. Regarding the course design & structure, there are four dimen sions including course length & effort, Blooms taxonomy, number of learning components, and sequence components. Regarding learning analytics outcomes, the results show that courses with medium lengths and efforts have the highest percentage of passing learners and comprehensive learners in the dimension of course length & effort. Furthermore, Applying-focus is the best group which has the highest percent not only passing learners, also comprehensive learners in Blooms Taxonomy dimension. With clustering of components, the best group of Number of com ponents is video-HTML focus. For sequence components clustering, the highest percentage of passing learners and comprehensive learners is in the group of discussion-HTML and video-HTML focus. The visualization of learning analytics are illustrated in MOOCA (Massive Open Online Course Analytics)1 . MOOCA is a web application implemented using Node.js, D3 and Plotly for visualization. In addition, MOOCA has an essential function as Recommen dation which advises users when they design the courses. The outcome of this function presents the cluster of the course. Consequently, the result shows that the course is in the group of high passing learners or not.
Analysis of course structures and learners engagement in MOOCs the case of Thai MOOCs - Pathumthani : Asian Institute of Technology, 2019 - 1 online resource
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science
Recently, Massive Open Online Courses (MOOCs) have achieved more than 100 million registered learners around the world. Based on several active research, the influential ac complishment of MOOCs is a number of learners engagement. MOOCs, nevertheless, has major issues including not only low instructional quality, but also high dropout rate. As from the above fact, the research question of this thesis is to find out how the difference in course structure design may affect learners engagement. With regard to the scope of this thesis, it focuses on Thai MOOC courses in STEM subject area. The dataset contains 28 STEM courses. The learners engagement dataset selects the top 20 of the most popular courses according to registered learners. This paper applies the learning analytics to analyze the patterns and clustering of two significant dimensions: i) course design & structure and ii) learners performance & engagement. Furthermore, this thesis explores the relationships of both dimensions by using data mining, machine learning, and visualization techniques. Regarding the course design & structure, there are four dimen sions including course length & effort, Blooms taxonomy, number of learning components, and sequence components. Regarding learning analytics outcomes, the results show that courses with medium lengths and efforts have the highest percentage of passing learners and comprehensive learners in the dimension of course length & effort. Furthermore, Applying-focus is the best group which has the highest percent not only passing learners, also comprehensive learners in Blooms Taxonomy dimension. With clustering of components, the best group of Number of com ponents is video-HTML focus. For sequence components clustering, the highest percentage of passing learners and comprehensive learners is in the group of discussion-HTML and video-HTML focus. The visualization of learning analytics are illustrated in MOOCA (Massive Open Online Course Analytics)1 . MOOCA is a web application implemented using Node.js, D3 and Plotly for visualization. In addition, MOOCA has an essential function as Recommen dation which advises users when they design the courses. The outcome of this function presents the cluster of the course. Consequently, the result shows that the course is in the group of high passing learners or not.