IRT++: Improving Student Response Prediction With Gaussian Initialisation and Other Modifications

In this paper, we explore an extension of the Item Response Theory (IRT) model to predict student responses using dichotomous data and formulate approaches to improve the predictive accuracy of the traditional algorithm. We present a simple extension to the IRT modelling approach called IRT++, which...

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Bibliographic Details
Main Authors: Saxena, Nayan, Lodaya, Varun, Thakur, Trisha
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Data models
Education
Education Data
Gaussian Initialisation
Item Response Theory
Machine learning
Machine learning algorithms
Prediction algorithms
Predictive models
Student Response Prediction
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Summary:In this paper, we explore an extension of the Item Response Theory (IRT) model to predict student responses using dichotomous data and formulate approaches to improve the predictive accuracy of the traditional algorithm. We present a simple extension to the IRT modelling approach called IRT++, which combines both the 1-parameter and 2-parameter IRT models and modulates parameter optimisation through simple machine learning techniques like adaptive gradient descent and random-normal initialisation of parameters. By experimentation on real-world education data, we show how the IRT++ modelling framework other baselines at predicting student responses, and achieves better performance while sacrificing very little in model interpretability and rate of convergence.
ISSN:2161-377X
DOI:10.1109/ICALT52272.2021.00057