An evaluation model based on procedural behaviors for predicting MOOC learning performance: students' online learning behavior analytics and algorithms construction

IF 3.5 Q1 EDUCATION & EDUCATIONAL RESEARCH
Yao Tong, Zehui Zhan
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引用次数: 2

Abstract

Purpose The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART). Design/methodology/approach Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance. Findings The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%). Originality/value This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.
基于程序行为的MOOC学习绩效预测评价模型:学生在线学习行为分析与算法构建
本研究的目的是通过分析大规模开放网络课程(MOOC)学习者的在线学习行为,并比较多元线性回归(MLR)、多层感知器(MLP)和分类回归树(CART)三种算法,建立预测MOOC学习绩效的评价模型。设计/方法/途径通过文献综述和原始数据库数据相关性分析,构建了包含26种行为的在线学习行为指标框架。从学习者的系统交互行为、资源交互行为、社会交互行为和自主学习行为四个方面分析与最终学习绩效的相关程度。抽取与学习绩效高度相关的12种行为作为主要指标,采用MLR法、MLP法和CART法作为评价学习者MOOC学习绩效的典型算法。研究结果本研究构建的行为指标框架能够有效分析学习者的学习情况,使用MLP方法(89.91%)和CART方法(90.29%)构建的评价模型比使用MLR方法(83.64%)更能实现对MOOC学习者学习绩效的预测。本研究探讨了不同学习行为之间的模式和特征,构建了MOOC学习者学习绩效的有效预测模型,有助于教师了解学习者的学习状态,及时发现学习困难的学习者,适时提供有针对性的教学干预,提高教学质量。
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来源期刊
Interactive Technology and Smart Education
Interactive Technology and Smart Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
12.00
自引率
2.30%
发文量
30
期刊介绍: Interactive Technology and Smart Education (ITSE) is a multi-disciplinary, peer-reviewed journal, which provides a distinct forum to specially promote innovation and participative research approaches. The following terms are defined, as used in the context of this journal: -Interactive Technology refers to all forms of digital technology, as described above, emphasizing innovation and human-/user-centred approaches. -Smart Education "SMART" is used as an acronym that refers to interactive technology that offers a more flexible and tailored approach to meet diverse individual requirements by being “Sensitive, Manageable, Adaptable, Responsive and Timely” to educators’ pedagogical strategies and learners’ educational and social needs’. -Articles are invited that explore innovative use of educational technologies that advance interactive technology in general and its applications in education in particular. The journal aims to bridge gaps in the field by promoting design research, action research, and continuous evaluation as an integral part of the development cycle of usable solutions/systems.
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