Toward Semi-Automatic Misconception Discovery Using Code Embeddings

Understanding students' misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can address these challenges by highlighting patterns in student data, which domain experts can then inspect to identify misconceptions. In this work, we present a novel method for the semi-automated discovery of problem-specific misconceptions from students' program code in computing courses, using a state-of-the-art code classification model. We trained the model on a block-based programming dataset and used the learned embedding to cluster incorrect student submissions. We found these clusters correspond to specific misconceptions about the problem and would not have been easily discovered with existing approaches. We also discuss potential applications of our approach and how these misconceptions inform domain-specific insights into students' learning processes.

Early Performance Prediction using Interpretable Patterns in Programming Process Data

Instructors have limited time and resources to help struggling students, and these resources should be directed to the students who most need them. To address this, researchers have constructed models that can predict students' final course performance early in a semester. However, many predictive models are limited to static and generic student features (e.g. demographics, GPA), rather than computing-specific evidence that assesses a student's progress in class. Many programming environments now capture complete time-stamped records of students' actions during programming. In this work, we leverage this rich, fine-grained log data to build a model to predict student course outcomes. From the log data, we extract patterns of behaviors that are predictive of students' success using an approach called differential sequence mining. We evaluate our approach on a dataset from 106 students in a block-based, introductory programming course. The patterns extracted from our approach can predict final programming performance with 79% accuracy using only the first programming assignment, outperforming two baseline methods. In addition, we show that the patterns are interpretable and correspond to concrete, effective -- and ineffective -- novice programming behaviors. We also discuss these patterns and their implications for classroom instruction.