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

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 misconcep...

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Main Authors: ONG, De Lin, GOTTIPATI Swapna, LO, Siaw Ling, SHANKARARAMAN, Venky
格式: text
語言:English
出版: Institutional Knowledge at Singapore Management University 2021
主題:
LDA
在線閱讀: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
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機構: Singapore Management University
語言: English
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spelling sg-smu-ink.sis_research-76402022-05-21T07:29:07Z Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard ONG, De Lin GOTTIPATI Swapna, LO, Siaw Ling SHANKARARAMAN, Venky 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. 2021-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6637 info:doi/10.1109/FIE49875.2021.9637181 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Informal self-reflections text mining content analysis GSDMM LDA Word2Vec K-Means Databases and Information Systems Educational Assessment, Evaluation, and Research Higher Education
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Informal self-reflections
text mining
content analysis
GSDMM
LDA
Word2Vec
K-Means
Databases and Information Systems
Educational Assessment, Evaluation, and Research
Higher Education
spellingShingle Informal self-reflections
text mining
content analysis
GSDMM
LDA
Word2Vec
K-Means
Databases and Information Systems
Educational Assessment, Evaluation, and Research
Higher Education
ONG, De Lin
GOTTIPATI Swapna,
LO, Siaw Ling
SHANKARARAMAN, Venky
Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard
description 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.
format text
author ONG, De Lin
GOTTIPATI Swapna,
LO, Siaw Ling
SHANKARARAMAN, Venky
author_facet ONG, De Lin
GOTTIPATI Swapna,
LO, Siaw Ling
SHANKARARAMAN, Venky
author_sort ONG, De Lin
title Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard
title_short Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard
title_full Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard
title_fullStr Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard
title_full_unstemmed Mining informal & short student self-reflections for detecting challenging topics: A learning outcomes insight dashboard
title_sort mining informal & short student self-reflections for detecting challenging topics: a learning outcomes insight dashboard
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url 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
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