Analysis of course structures and learners{u2019} engagement in MOOCs (Record no. 4668)

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035 ## - SYSTEM CONTROL NUMBER
System control number .b12368465
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Wanlipa Thongsuntia
245 10 - TITLE STATEMENT
Title Analysis of course structures and learners{u2019} engagement in MOOCs
Remainder of title the case of Thai MOOCs
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Pathumthani :
Name of publisher, distributor, etc. Asian Institute of Technology,
Date of publication, distribution, etc. 2019
300 ## - PHYSICAL DESCRIPTION
Accompanying material 1 online resource
500 ## - GENERAL NOTE
General note A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science
520 ## - SUMMARY, ETC.
Summary, etc. 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{u2019} 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{u2019} 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{u2019} 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, Bloom{u2019}s 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 Bloom{u2019}s 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 {u2018}Recommen dation{u2019} 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.
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Chutiporn Anutariya,
Relator term Chairperson
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Dailey, Mathew N.,
Relator term Examination Committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Suporn Pongnumkul,
Relator term Examination committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Royal Thai Government Fellowship,
Relator term Scholarship donor
856 ## - ELECTRONIC LOCATION AND ACCESS
Materials specified Full-Text
Uniform Resource Identifier <a href="http://203.159.5.9/ait-thesis/detail.php?q=B11699">http://203.159.5.9/ait-thesis/detail.php?q=B11699</a>
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a 240418
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Operator's initials, OID (RLIN) 0
Cataloger's initials, CIN (RLIN) 200108
First date, FD (RLIN) m
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