Publication
Recommending Mathematical Tasks Based on Reinforcement Learning and Item Response Theory
Matteo Orsoni; Alexander Pögelt; Nghia Duong-Trung; Mariagrazia Benassi; Milos Kravcik; Martin Grüttmüller
In: Claude Frasson; Phivos Mylonas; Christos Troussas (Hrsg.). Augmented Intelligence and Intelligent Tutoring Systems. International Conference on Intelligent Tutoring Systems (ITS-2023), 19th, June 2-5, Corfu, Greece, Pages 16-28, Lecture Notes in Computer Science (LNCS), No. 13891, ISBN 978-3-031-32882-4, Springer, Cham, 5/2023.
Abstract
Recommending challenging and suitable exercises to students
in an online learning environment is important, as it helps to stimulate
their engagement and motivation. This requires considering their individual
goals to improve learning efficiency on one side and on the other to provide
tasks with an appropriate difficulty for the particular person. Apparently,
this is not a trivial issue, and various approaches have been investigated
in the areas of adaptive assessment and dynamic difficulty adjustment.
Here, we present a solution for the domain of mathematics that rests
on two pillars: Reinforcement Learning (RL) and Item Response Theory
(IRT). Specifically, we investigated the effectiveness of two RL algorithms
in recommending mathematical tasks to a sample of 125 first-year Bachelor’s
students of computer science. Our recommendation was based on
the Estimated Total Score (ETS) and item difficulty estimates derived
from IRT. The results suggest that this method allowed for personalized
and adaptive recommendations of items within the user-selected threshold
while avoiding those with an already achieved target score. Experiments
were performed on a real data set to demonstrate the potential of this approach
in domains where task performance can be rigorously measured.