Enhancing Student Performance Prediction In CS1 Via In-Class Coding

• URL: http://arxiv.org/abs/2510.21848v1
• Date: Wed, 22 Oct 2025 16:40:41 GMT
• Title: Enhancing Student Performance Prediction In CS1 Via In-Class Coding
• Authors: Eric Hics, Vinhthuy Phan, Kriangsiri Malasri,
• Abstract summary: Computer science's increased recognition as a prominent field of study has attracted students with diverse academic backgrounds.<n>To address this challenge, it is essential to identify struggling students early on.<n>In-class coding exercises in these courses not only offers additional practice opportunities to students but may also reveal their abilities and help teachers identify those in need of assistance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer science's increased recognition as a prominent field of study has attracted students with diverse academic backgrounds. This has significantly increased the already high failure rates in introductory courses. To address this challenge, it is essential to identify struggling students early on. Incorporating in-class coding exercises in these courses not only offers additional practice opportunities to students but may also reveal their abilities and help teachers identify those in need of assistance. In this work, we seek to determine the extent to which the practice of using in-class coding exercises enhances the ability to predict student performance, especially early in the semester. Based on data obtained in a CS1 course taught at a mid-size American university, we found that in-class exercises could improve the prediction of students' eventual performance. In particular, we found relatively accurately predictions as early as academic weeks 3 through 5, making it possible to devise early intervention strategies. This work can benefit future studies on the impact of in-class exercises as well as intervention strategies throughout the semester.

Related papers

• Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues [48.99818550820575]
  Recent studies have shown that strategies used by tutors can have significant effects on student outcomes.<n>Few works have studied predicting tutor strategy in dialogues.<n>We investigate the ability of modern LLMs, particularly Llama 3 and GPT-4o, to predict both future tutor moves and student outcomes in dialogues.
  arXiv  Detail & Related papers  (2025-07-09T14:47:35Z)
• Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments [0.37729165787434493]
  This paper develops automated tools to predict when a student is having difficulty.<n>In a potential application, such models can aid instructors in detecting struggling students and providing targeted help.
  arXiv  Detail & Related papers  (2024-08-16T04:57:54Z)
• Learning-Augmented Algorithms with Explicit Predictors [67.02156211760415]
  Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. Prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a black box. In this work, we unpack the predictor and integrate the learning problem it gives rise for within the algorithmic challenge.
  arXiv  Detail & Related papers  (2024-03-12T08:40:21Z)
• Does Starting Deep Learning Homework Earlier Improve Grades? [63.20583929886827]
  Students who start a homework assignment earlier and spend more time on it should receive better grades on the assignment. Existing literature on the impact of time spent on homework is not clear-cut and comes mostly from K-12 education. We develop a hierarchical Bayesian model to help make principled conclusions about the impact on student success.
  arXiv  Detail & Related papers  (2023-09-30T09:34:30Z)
• Learning Predictions for Algorithms with Predictions [49.341241064279714]
  We introduce a general design approach for algorithms that learn predictors. We apply techniques from online learning to learn against adversarial instances, tune robustness-consistency trade-offs, and obtain new statistical guarantees. We demonstrate the effectiveness of our approach at deriving learning algorithms by analyzing methods for bipartite matching, page migration, ski-rental, and job scheduling.
  arXiv  Detail & Related papers  (2022-02-18T17:25:43Z)
• Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction [28.383922154797315]
  Academic performance prediction aims to leverage student-related information to predict their future academic outcomes. In this paper, we analyze students' daily behavior trajectories, which can be comprehensively tracked with campus smartcard records. We propose a novel Tri-Branch CNN architecture, which is equipped with row-wise, column-wise, and depth-wise convolution and attention operations.
  arXiv  Detail & Related papers  (2021-07-22T02:35:36Z)
• Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction [8.21303828329009]
  We propose a Graph-based Exercise- and Knowledge-Aware Learning Network for accurate student score prediction. We learn students' mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts.
  arXiv  Detail & Related papers  (2021-06-01T06:53:17Z)
• Improving Students Performance in Small-Scale Online Courses -- A Machine Learning-Based Intervention [0.0]
  We show that the data collected from an online learning management system could be well utilized in order to predict students overall performance. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students progress for the better.
  arXiv  Detail & Related papers  (2020-11-23T14:12:55Z)
• Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities [56.1344233010643]
  Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. This study aims to predict dropout early-on, from the first week, by comparing several machine-learning approaches.
  arXiv  Detail & Related papers  (2020-08-12T10:44:49Z)
• EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors [55.33024245762306]
  Early prediction of students at risk (STAR) is an effective and significant means to provide timely intervention for dropout and suicide. Existing works mostly rely on either online or offline learning behaviors which are not comprehensive enough to capture the whole learning processes. We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors.
  arXiv  Detail & Related papers  (2020-06-06T12:56:26Z)
• Academic Performance Estimation with Attention-based Graph Convolutional Networks [17.985752744098267]
  Given a student's past data, the task of student's performance prediction is to predict a student's grades in future courses. Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses. We propose a novel attention-based graph convolutional networks model for student's performance prediction.
  arXiv  Detail & Related papers  (2019-12-26T23:11:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.