Students’ Performance Prediction Using Multi-Channel Decision Fusion

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 524)

Abstract

A teacher or an artificial instructor, embedded in an intelligent tutoring system, is interested in predicting the performance of his/her students to better adjust the educational materials and strategies throughout the learning process. In this chapter, a multi-channel decision fusion approach, based on using the performance in “assignment categories”, such as homework assignments, is introduced to determine the overall performance of a student. In the proposed approach, the data gathered are used to determine four classes of “expert”, “good”, “average”, and “weak” performance levels. This classification is conducted on both overall performance and the performance in assignment categories. Then, a mapping from the performances in “assignment categories” is learned, and is used to predict the overall performance. The main advantage of the proposed approach is in its capability to estimate students’ performance after a few assignments. Consequently, it can help the instructors better manage their class and adjust educational materials to prevent underachievement.

Keywords

Learning analytics Student performance prediction Educational data mining Decision fusion Assignment categories 

Abbreviations

BKT

Bayesian knowledge tracing

CART

Classification and regression tree

CHAID

Chi-square automatic interaction detection

EDM

Educational data mining

ITS

Intelligent tutoring systems

LMS

Learning management system

Notes

Acknowledgements

The authors like to thank Babak Araabi and Maryam Mirian for their feedback on this chapter. Furthermore, the authors like to thank the ITS group at the Advanced Robotics and Intelligent Systems Laboratory for their help throughout this research.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of ECEUniversity of TehranTehranIran
  2. 2.Department of Psychology and Special EducationUniversity of TehranTehranIran

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