2026-09-19T10:36:17Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1513392023-08-01T03:03:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Abdelhafez, Mohamed
Zaman, Azreen
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:32:30Z
2023-07-31T19:32:30Z
2023-06
2023-06-06T16:34:39.708Z
https://hdl.handle.net/1721.1/151339
There is a large variation in the educational background and purpose of incoming university students. To improve the overall learning experience of these students, we can utilize natural language processing such as topic modeling and sentiment analysis to facilitate common student misconception analysis. This project aims to develop an algorithm via natural language processing that extracts specific topics and common errors that students struggle with in class from online feedback semi-automatically to allow instructors to adjust lesson plans and place emphasis on topics of concern. Using these tools, we can conduct study on the effect on student grades when instructors take into account the information extracted by the model in their lesson plans. This project is aimed at MIT freshmen taking two semesters of physics.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Using Natural Language Processing to Facilitate Common Student Misconception Analysis
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>There is a large variation in the educational background and purpose of incoming university students. To improve the overall learning experience of these students, we can utilize natural language processing such as topic modeling and sentiment analysis to facilitate common student misconception analysis. This project aims to develop an algorithm via natural language processing that extracts specific topics and common errors that students struggle with in class from online feedback semi-automatically to allow instructors to adjust lesson plans and place emphasis on topics of concern. Using these tools, we can conduct study on the effect on student grades when instructors take into account the information extracted by the model in their lesson plans. This project is aimed at MIT freshmen taking two semesters of physics.</Abstract>
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