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

AI模型利用学习分析预测CS1学生不及格

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个预测模型，利用学习分析来识别有不及格风险的一年级计算机科学学生。该模型结合了传统的学术数据和来自学习平台（如Moodle互动日志）的数字标记。一个关键发现是，加权学术势头（一项源自作业分数的指标）是预测不及格的最强指标，特别是当与学习管理系统的参与度相结合时。 AI

影响 这项研究展示了AI如何在教育环境中主动识别学习困难的学生，从而能够及时进行干预。

排序理由 这是一篇详细介绍预测学生结果的新方法和模型的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI模型利用学习分析预测CS1学生不及格

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这是一篇详细介绍预测学生结果的新方法和模型的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Lighton Phiri, Mutune Chaibela, Ivy Chisha, David Pungwa, Danny Siabbaba, Bydon Simukoko ·

    哪些CS1学生会不及格？利用加权学术势头和交互日志从计算机系统与架构的学习分析中识别数字标记

    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. This paper investigates whether a combination of traditional and digital markers can pre…

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