ABSTRACT

Predictive models on students’ academic performance can be built by using historical data for modelling students’ learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use predictive models to detect learning difficulties faced by students and thereby plan effective interventions to support students. In this paper, we present a systematic literature review on how predictive analytics have been applied in the higher education domain. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a literature search from 2008 to 2018 and explored current trends in building data-driven predictive models to gauge students’ performance. Machine learning techniques and strategies used to build predictive models in prior studies are discussed. Furthermore, limitations encountered in interpreting data are stated and future research directions proposed.

摘要

可以通过使用历史数据对学生的学习行为进行建模来建立学生学业成绩的预测模型。此类模型可用于教育环境，以确定新生的表现，并预测这些学生是否应被归类为有课程不及格的风险。利益相关者可以使用预测模型来检测学生面临的学习困难，从而计划有效的干预措施来支持学生。在本文中，我们对预测分析如何在高等教育领域应用进行了系统的文献综述。根据系统评价和荟萃分析的首选报告项目指南，我们进行了 2008 年至 2018 年的文献检索，并探讨了构建数据驱动的预测模型以衡量学生表现的当前趋势。讨论了先前研究中用于构建预测模型的机器学习技术和策略。此外，还阐述了解释数据时遇到的局限性并提出了未来的研究方向。