Elo Rating Algorithm for the Purpose of Measuring Task Difficulty in Online Learning Environments
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The Elo rating algorithm, developed for the purpose of measuring player strength in chess tournaments, has also found application in the context of educational research and has been used for the purpose of measuring both learner ability and task difficulty. The quality of the estimations performed by the Elo rating algorithm has already been subject to research, and has been shown to deliver accurate estimations in both low and high-stake testing situations. However, little is known about the performance of the Elo algorithm in the context of learning environments where multiple attempts are allowed, feedback is provided, and the learning process spans several weeks or even months. This study develops the topic of Elo algorithm use in an educational context and examines its performance on real data from an online learning environment where multiple attempts were allowed, and feedback was provided after each attempt. Its performance in terms of stability of the estimation results in two analyzed periods for two groups of learners with different initial levels of knowledge are compared with alternative difficulty estimation methods: proportion correct and learner feedback. According to the results, the Elo rating algorithm outperforms both proportion correct and learning feedback. It delivers stable difficulty estimations, with correlations in the range 0.87–0.92 for the group of beginners and 0.72–0.84 for the group of experienced learners.
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