Prescribing Deep Attentive Score Prediction Attracts Improved Student Engagement

by   Youngnam Lee, et al.

Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their current position. Via the expected score provided by the ITS, a student can instantaneously compare one's expected score to one's actual score, which directly corresponds to the reliability that the ITS can instill. In other words, refining the precision of predicted scores strictly correlates to the level of confidence that a student may have with an ITS, which will evidently ensue improved student engagement. However, previous studies have solely concentrated on improving the performance of a prediction model, largely lacking focus on the benefits generated by its practical application. In this paper, we demonstrate that the accuracy of the score prediction model deployed in a real-world setting significantly impacts user engagement by providing empirical evidence. To that end, we apply a state-of-the-art deep attentive neural network-based score prediction model to Santa, a multi-platform English ITS with approximately 780K users in South Korea that exclusively focuses on the TOEIC (Test of English for International Communications) standardized examinations. We run a controlled A/B test on the ITS with two models, respectively based on collaborative filtering and deep attentive neural networks, to verify whether the more accurate model engenders any student engagement. The results conclude that the attentive model not only induces high student morale (e.g. higher diagnostic test completion ratio, number of questions answered, etc.) but also encourages active engagement (e.g. higher purchase rate, improved total profit, etc.) on Santa.


AI-Driven Interface Design for Intelligent Tutoring System Improves Student Engagement

An Intelligent Tutoring System (ITS) has been shown to improve students'...

A Deep Neural Network-Based Prediction Model for Students’ Academic Performance

Education providers are increasingly using artificial techniques for pre...

Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction

Predicting student performance is a fundamental task in Intelligent Tuto...

Individual and Group-wise Classroom Seating Experience: Effects on Student Engagement in Different Courses

Seating location in the classroom can affect student engagement, involve...

The Wits Intelligent Teaching System: Detecting Student Engagement During Lectures Using Convolutional Neural Networks

To perform contingent teaching and be responsive to students' needs duri...

Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits

We present an adaptive learning Intelligent Tutoring System, which uses ...

A framework for predicting, interpreting, and improving Learning Outcomes

It has long been recognized that academic success is a result of both co...