Mining educational big data to develop an early alert dynamic model of academically at-risk students: A proof of concept

IF 1.9 4区教育学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Innovations in Education and Teaching International Pub Date : 2024-08-27 DOI:10.1080/14703297.2024.2396092

Xiaoxia Chen, Xiaolong Xu

引用次数: 0

Abstract

Educational big data has great predictive power to identify academically at-risk students. Based on fully mining educational big data, this study developed an early alert dynamic model of academica...

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挖掘教育大数据，开发学困生早期预警动态模型：概念验证

教育大数据对学业风险学生的识别具有很强的预测能力。本研究在充分挖掘教育大数据的基础上，建立了学业风险学生的预警动态模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Innovations in Education and Teaching International EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

4.80

自引率

5.60%

发文量

期刊介绍： Innovations in Education and Teaching International (IETI), is the journal of the Staff and Educational Development Association (SEDA) www.seda.ac.uk. As such, contributions to the Journal should reflect SEDA"s aim to promote innovation and good practice in higher education through staff and educational development and subject-related practices. Contributions are welcomed on any aspect of promoting and supporting educational change in higher and other post-school education, with an emphasis on research, experience, scholarship and evaluation, rather than mere description of practice.