Optimizing learning return on investment: Identifying learning strategies based on user behavior characteristic in language learning applications

Abstract

AbstractBegan with Computer-Assisted Language Learning (CALL) in the 1960s and extended to the widespread use of various Mobile-Assisted Language Learning (MALL) tools in education, language learning has embraced technology early on, achieved noticeable results, and found extensive practical use. However, due to the challenges in accessing user data from various language learning platforms, the measurement and assessment of language-related variables continue to rely on self-reporting and peer evaluations. This reliance hampers researchers to observe language learning from alternative perspectives, especially when it comes to analyzing raw behavioral data. To explore potential correlations between different learning modes, this study analyzed 2 million samples from Chinese students using an English language learning application. The study quantified the effectiveness of English vocabulary learning using the economic concept of return on investment (ROI) as an evaluation metric and identified four distinct learning strategies. It observed significant differences in learning ROI among learners who adopted different strategies. Based on this analysis, we recommend the following suggestions for improving language learning ROI: when memorizing new vocabulary, investing excessive amounts of time may be counterproductive; a more effective approach is to "eat less but more often," which means arranging review sessions at a reasonable pace and shortening the interval between each review.