David Y.J. Kim

David Y.J. Kim

Software Developer@MIT appinventor

Posts by Collection

portfolio

publications

Inferring student comprehension from highlighting patterns in digital textbooks: An exploration in an authentic learning platform.

Published in Second Workshop on Intelligent Textbooks, Springer., 2020

Abstract : The study examines whether student comprehension and knowledge retention can be predicted from the material they choose to highlight in their textbooks. We found that the specific pattern of highlights made by students can explain about 13% of the variation in quiz scores. A low-dimensional logistic principal component based vector was the most effective input for a ridge regression model. Overall, highlights provide a strong signal of a student’s knowledge state.

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Using Semantics of Textbook Highlights to Predict Student Comprehension and Knowledge Retention

Published in Third Workshop on Intelligent Textbooks, Springer., 2021

Abstract : This study uses students’ highlights in textbooks to predict their performance on quiz questions, and constructs a semantic representation using deep-learning sentence embedding technique (SBERT) to capture content-based similarity. We built regression models that include highlighting features and found that they reliably boost model performance. The highlighting features improved models for questions at all levels of the Bloom taxonomy. However, the generalization was not strong for held-out questions.

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Speak your mind : Introducing APTLY, the software platform that turns ideas into working apps

Published in 15th annual International Conference of Education, Research and Innovation, 2022

Abstract : MIT Aptly is a tool that automatically generates mobile apps from written or spoken natural language descriptions using OpenAI’s Codex. It lets people create programs without coding or knowledge of programming, and its app generation is based on example pairs and few-shot prompts. Aptly’s performance depends on the input given to OpenAI’s Codex, and it poses challenges for research in computational thinking education. The presentation will demonstrate Aptly’s preliminary performance and review its implementation.

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talks

teaching

Graduate Student Assistant

Teaching, University of Colorado at Boulder, Computer Science Department, 2019

Graduate Student Assistant for CSCI3104 - Algorithms.