Analysis of online posts to discover student learning challenges and inform targeted curriculum improvement actions
Past research on analysing end-of-term student feedback tend to result in only high-level course improvement suggestions, and some recent research even argued that student feedback is a poor indicator of teaching effectiveness and student learning. Our intelligent Q&A platform with machine learn...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2020
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/5911 https://ink.library.smu.edu.sg/context/sis_research/article/6914/viewcontent/Analysis_of_online_posts_av.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Past research on analysing end-of-term student feedback tend to result in only high-level course improvement suggestions, and some recent research even argued that student feedback is a poor indicator of teaching effectiveness and student learning. Our intelligent Q&A platform with machine learning prediction and engagement features allow students to ask self-directed questions and provide answers in an out-of-class informal setting. By analysing such high quality and truthful posts which represent the students’ queries and knowledge about the course content, we can better identify the exact course topics which the students face learning challenges. We have implemented our Q&A platform for an undergraduate spreadsheets modelling course, and analysed 1025 meaningful posts to identify the hot areas represented as topic tags, map the identified hot tags progression over time, to direct instructors towards targeted improvement actions. Our proposed approach can be applied to other courses where students’ self-directed Q&A can be implemented. |
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