Linking prediction with personality traits: a learning analytics approach

ABSTRACT

Open, flexible and distance learning has become part of mainstream education in China. Using a blended learning program in a Chinese high school as the case, this study adopted data-mining approaches to establish predictive models using personality traits. Results showed that, for students with high OE and low extraversion, and students who are low on both of these constructs, the number of postings in digest (NPD) and average score of after-class test (SCT) were significant predictors of their achievement. For students with low OE and high extraversion, time spent on viewing course resources and number of answers provided in the format of text were significant predictors. For those with high OE and low extraversion, time spent on learning online and number of questions raised in the format of hypermedia, NPD and SCT were significant. Furthermore, deep belief networks performed best in identifying at-risk students at each stage.

KEYWORDS:

• open, flexible, and distance learning (OFDL)
• data mining
• personality traits
• significant predictors
• achievement prediction

Acknowledgments

This work was funded by 2019 Comprehensive Discipline Construction Fund of Faculty of Education, Beijing Normal University.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Fati Wu

Fati Wu is Dean of the School of Educational Technology, Faculty of Education, Beijing Normal University. He has participated in many Chinese national programs as the main researcher. His research interests include knowledge mapping, learning analytics, and e-learning system research and design.

Song Lai

Song Lai is currently working toward his PhD in distance education at Beijing Normal University, China. His research interests include machine learning, data mining, and learning analytics.