Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs

• URL: http://arxiv.org/abs/2608.16914v1
• Date: Thu, 16 Jul 2026 08:32:54 GMT
• Title: Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs
• Abstract summary: Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early.<n>This paper investigates whether a combination of traditional and digital markers can predict failure in a first-year CS1 course.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early. This paper investigates whether a combination of traditional and digital markers can predict failure in a first-year CS1 course (Computer Systems and Architecture) with sufficient recall to enable timely intervention. Using data from four cohorts (2017-2021, N=284) at a large public university in sub-Saharan Africa, we conducted a mixed-methods stakeholder elicitation to identify ten candidate factors. These were operationalised into a comprehensive feature set spanning demographics, self-reported surveys, Moodle interaction logs, and continuous assessment scores. A systematic ablation study using logistic regression with 5-fold cross-validation and SMOTE+ENN resampling revealed that the most predictive feature subset was Base + Demo + LMS: weighted academic momentum (M = 0.1Q1 + 0.15Q2 + 0.2Q3 + 0.55T1), basic demographics (gender, sponsorship, COVID-19 cohort), and a binary indicator of any LMS activity. On a held-out test set, logistic regression achieved 74.7% accuracy, 0.742 macro F1, and an AUC of 0.800. At the default threshold of 0.5, the model identified 87% of failing students (recall = 0.87) with a 41% false positive rate. SHAP analysis confirmed that weighted academic momentum is the strongest predictor, followed by its interaction with LMS engagement. These results demonstrate that simple digital markers can power a practical early-warning system by the fifth week of the semester. Our main contributions are: (1) a multi-source dataset and a stakeholder-guided methodology; (2) an ablation study quantifying feature group contributions; and (3) an interpretable, high-recall model ready for deployment.

Related papers

• Personalized AI Practice Replicates Learning Rate Regularity at Scale [0.0]
  Students exhibit consistent learning rates across diverse educational contexts.<n>We test these findings using a dataset of 1.8 million (366k post-filtering) student interactions from the digital platform Campus AI.
  arXiv  Detail & Related papers  (2026-03-09T13:33:28Z)
• A Rubric-Supervised Critic from Sparse Real-World Outcomes [87.11204512676193]
  Real-world coding agents operate with humans in the loop, where success signals are typically noisy, delayed, and sparse.<n>We propose a process to learn a "critic" model from sparse and noisy interaction data, which can then be used both as a reward model for either RL-based training or inference-time scaling.
  arXiv  Detail & Related papers  (2026-03-04T07:23:54Z)
• Machine Learning Grade Prediction Using Students' Grades and Demographics [0.0]
  This study introduces a unified machine learning framework that simultaneously predicts pass/fail outcomes and continuous grades.<n>Using academic and demographic data from 4424 secondary school students, classification models achieved accuracies of up to 96%.<n>Results confirm the feasibility of early, data-driven identification of at-risk students.
  arXiv  Detail & Related papers  (2026-02-28T11:56:39Z)
• Predicting Student Success with Heterogeneous Graph Deep Learning and Machine Learning Models [0.0]
  We propose a framework that integrates heterogeneous graph deep learning models to enhance early and continuous student performance prediction.<n>Our approach outperforms top machine learning models by 4.7% in validation F1 score during the critical early 7% of the semester.
  arXiv  Detail & Related papers  (2026-01-11T00:46:44Z)
• AI-Driven Personalized Learning: Predicting Academic Per-formance Through Leadership Personality Traits [0.0]
  The study explores the potential of AI technologies in personalized learning.<n>It suggests the prediction of academic success through leadership personality traits and machine learning modelling.
  arXiv  Detail & Related papers  (2025-10-22T18:47:30Z)
• SCAN: Self-Denoising Monte Carlo Annotation for Robust Process Reward Learning [76.61439010634872]
  Process reward models (PRMs) facilitate deeper reasoning processes in large language models (LLMs)<n>PRMs are challenging to develop due to the high cost and limited scalability of human-annotated data.<n>We propose Self-Denoising Monte Carlo CAN (SCAN), an efficient data synthesis and noise-tolerant learning framework.
  arXiv  Detail & Related papers  (2025-09-20T06:19:55Z)
• Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability [53.51560766150442]
  Critical tokens are elements within reasoning trajectories that significantly influence incorrect outcomes.<n>We present a novel framework for identifying these tokens through rollout sampling.<n>We show that identifying and replacing critical tokens significantly improves model accuracy.
  arXiv  Detail & Related papers  (2024-11-29T18:58:22Z)
• Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study [71.04084063541777]
  Counterfactual learning to rank has attracted extensive attention in the IR community.<n>Models can be theoretically unbiased when the user behavior assumption is correct and the propensity estimation is accurate.<n>Their effectiveness is usually empirically evaluated via simulation-based experiments due to a lack of widely available, large-scale, real click logs.
  arXiv  Detail & Related papers  (2024-04-04T10:54:38Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation [2.169383034643496]
  In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns. This paper explores a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions.
  arXiv  Detail & Related papers  (2022-10-27T14:08:25Z)
• Learning from Heterogeneous Data Based on Social Interactions over Graphs [58.34060409467834]
  This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting.
  arXiv  Detail & Related papers  (2021-12-17T12:47:18Z)
• Estimating and Improving Fairness with Adversarial Learning [65.99330614802388]
  We propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We evaluate our framework on a large-scale public-available skin lesion dataset.
  arXiv  Detail & Related papers  (2021-03-07T03:10:32Z)

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.