Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard

• Main Authors: ONG, De Lin, GOTTIPATI Swapna, LO, Siaw Ling, SHANKARARAMAN, Venky
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Informal self-reflections; text mining; content analysis; GSDMM; LDA; Word2Vec; K-Means; Databases and Information Systems; Educational Assessment, Evaluation, and Research; Higher Education
• Online Access: https://ink.library.smu.edu.sg/sis_research/6637; https://ink.library.smu.edu.sg/context/sis_research/article/7640/viewcontent/LoSiawLing_2021_Mining_informal___short_student_self_reflections_for_detecting_challenging_topics.pdf
• Institution: Singapore Management University

Having students write short self-reflections at the end of each weekly session enables them to reflect on what they have learnt in the session and topics they find challenging. Analysing these self-reflections provides instructors with insights on how to address the missing conceptions and misconceptions of the students and appropriately plan and deliver the next session. Currently, manual methods adopted to analyse these student reflections are time consuming and tedious. This paper proposes a solution model that uses content mining and NLP techniques to automate the analysis of short self-reflections. We evaluate the solution model by studying its implementation in an undergraduate Information Systems course through a comparison of three different content mining techniques namely LDA–bigrams, GSDMM-bigrams, and Word2Vec based Clustering models. The evaluation involves both qualitative and quantitative methods. The results show that the proposed techniques are useful in discovering insights from the self-reflections, though the performance varied across the three methods. We provide insights into comparisons of the perspectives, which are useful to instructors.