Using Natural Language Processing to Facilitate Common Student Misconception Analysis
Name
zaman-azreen-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
501.77 KB
Format
Adobe PDF
Checksum (MD5)
0c92f88820243b2b965690db91db362e
Author(s)
Zaman, Azreen
Advisor(s)
Abdelhafez, Mohamed
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
