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Not All Pain Leads to Gain: The Role of Learner Engagement in Adaptive Flashcard Training

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Adaptive Instructional Systems (HCII 2023)

Abstract

Adaptive training methods have been designed to enhance students’ learning outcomes by tailoring the educational content based on the learner’s performance during training. In the present study, we examined different adaptive sequencing methods for a flashcard-based trainer. One sequencing method, the Adaptive Response Time-based Sequencing (ARTS) algorithm presents cards based on an individual learner’s accuracy and reaction time, such that incorrectly identified cards are prioritized over correctly identified cards. Although previous research has suggested that ARTS is more efficient and effective than other forms of flashcard sequencing, recent research was unable to replicate these findings. To that end, the current experiment compared ARTS to an adaptive control condition that reversed the ARTS algorithm and investigated if learner engagement plays a role in adaptive flashcard-based training. A sample of 50 college students learned to identify African countries in one of two adaptive flashcard sequencing conditions – ARTS and control. Engagement was measured using the flow state scale for occupational tasks and training effectiveness was determined by calculating immediate and delayed learning gains. Results revealed no statistically significant differences between ARTS and the control on immediate and delayed gains. Further, the ARTS group reported significantly lower engagement levels than the control group. A mediation analysis revealed that the relationship between the training and the learning gains was significantly mediated by engagement in an inverse format, suggesting that training reduced the levels of engagement which in turn canceled out the learning gains. Based on these findings, we present the theoretical and practical implications.

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Namukasa, M. et al. (2023). Not All Pain Leads to Gain: The Role of Learner Engagement in Adaptive Flashcard Training. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science, vol 14044. Springer, Cham. https://doi.org/10.1007/978-3-031-34735-1_2

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