Improving prediction of students\u2019 performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

Wilson Chango,Cristóbal Romero,Rebeca Cerezo,Miguel Sanchez-Santillan,Roger Azevedo

doi:10.1007/s12528-021-09298-8

Abstract

The aim of this study was to predict university students’ learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.

Highlights

The rapid growth of technology has meant that computer learning has increasingly integrated artificial intelligence techniques in order to develop more personalized educational systems
We propose applying classification algorithms, feature selection algorithms, and ensembles to data gathered from a variety of sources in order to predict the students’ final performance in the Intelligent Tutoring Systems (ITS)
This paper proposes the use of ensembles and attribute selection for improving the prediction of students’ performance from multimodal data in an ITS

Summary

Introduction

The rapid growth of technology has meant that computer learning has increasingly integrated artificial intelligence techniques in order to develop more personalized educational systems. Tracking students’ behavior is a powerful research tool used to collect data on students’ cognitive, metacognitive, affective, and motivational processes deployed during learning (Azevedo et al, 2018; Greene & Azevedo, 2010; Taub et al, 2021) These different data sources can be fused and mined to reveal learning-related information such as student performance. MLA aims to combine different sources of learning traces into a single analysis, it is a subfield of EDM related to multi-view and multi-relational data and data fusion It aims to understand and optimize learning in digital where the use of videos is currently consolidated, from traditional courses to mixed and online courses (Chan et al, 2020). We describe the data sources used in the present study

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Methods

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Discussion

Conclusion