arXiv Machine Learning August 19, 2026 By Lighton Phiri, Mutune Chaibela, Ivy Chisha, David Pungwa, Danny Siabbaba, Bydon Simukoko

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

Read the original on arXiv Machine Learning →

arXiv:2608. 16914v1 Announce Type: cross Abstract: Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv AI

Jul 23

A Unified Survival Benchmark for Temporal Dropout Risk Prediction in Learning Analytics

arXiv:2604. 08870v3 Announce Type: replace-cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous protocols, prioritizing discrimination over temporal interpretability and calibration.

By Rafael da Silva, Jeff Eicher, Gregory Longo

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arXiv AI

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An Integrated Machine Learning and Hierarchical Variance Decomposition Pipeline for Student Performance Prediction and Metacognitive Calibration on Multi-Signal Telemetry

arXiv:2606. 28881v1 Announce Type: cross Abstract: Predicting student performance and characterizing metacognitive calibration are essential for personalization in intelligent tutoring systems.

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arXiv AI

Jul 23

An Auditable Policy-Simulation Framework for Student Dropout in Intervention-Free Data

arXiv:2604. 08874v3 Announce Type: replace-cross Abstract: This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, using LMS engagement data and administrative withdrawal records.

By Rafael da Silva, Jeff Eicher, Gregory Longo

benchmarks

arXiv Machine Learning

Jul 8

Multimodal Analytics of Cybersecurity Crisis Preparation Exercises: What Predicts Success?

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rag multimodal safety

arXiv AI

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Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning

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llms rag agents efficiency benchmarks safety data-engineering

arXiv AI

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Aligning LLM-Simulated and Human Examinees for Psychometric Calibration: A Cognitive Diagnostic Profiling Approach

arXiv:2607. 26317v1 Announce Type: cross Abstract: Psychometric calibration for educational tests typically requires costly human response data.

By Wenjie Zhou, Yunting Liu, Renjiao Tang, Mark Wilson

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A Unified Survival Benchmark for Temporal Dropout Risk Prediction in Learning Analytics

An Integrated Machine Learning and Hierarchical Variance Decomposition Pipeline for Student Performance Prediction and Metacognitive Calibration on Multi-Signal Telemetry

An Auditable Policy-Simulation Framework for Student Dropout in Intervention-Free Data

Multimodal Analytics of Cybersecurity Crisis Preparation Exercises: What Predicts Success?

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