Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory

By Wanli Xing, Rui Guo, Eva Petakovic, Sean Patrick Goggins. Computers in Human Behavior, 2015

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/ace60355eb9e6320e17ccf913b72e900cc503ea8

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In Artificial Intelligence

1. M. Yağcı

In Computer Science Applications

1. M. Yağcı

In Developmental and Educational Psychology

1. M. Yağcı

Citations in the corpus

• Educational data mining: prediction of students' academic performance using machine learning algorithms (2022, methodology): “However, random forests (Zabriskie et al., 2019), genetic programming (Xing et al., 2015), and Naive Bayes algorithms (Ornelas & Ordonez, 2017) were used in recent studies.”