Young Learners’ Regulation of Practice Behavior in Adaptive Learning Technologies

Abstract

Although research indicates positive effects of Adaptive Learning Technologies (ALTs) on learning, we know little about young learners' regulation intentions in this context. Learners' intentions and self-evaluation determine the signals they deduce to drive self-regulated learning. This study had a twofold approach as it investigated the effect of feed-up and feed-forward reports on practice behavior and learning and explored learners' self-evaluation of goal-attainment, performance and accuracy. In the experimental condition, learners described their goals and self-evaluated their progress in feed-up and forward reports. We found no conclusive effects of the feedup and forward reports on learners' regulation of practice behavior and learning. Furthermore, results indicated that young learners' self-evaluations of goal attainment and performance were biased. Contrary to other research, we found learners both over-and underestimated performance which was strongly associated with over-or underestimation of goal attainment. Hence the signals learners used to drive regulation were often incorrect, tending to induce over-or under-practicing. Similarly, we found a bias in self-evaluation of accuracy and accuracy attainment. Learners over-or underestimated their accuracy, which was associated with over-or underestimation of accuracy attainment, which may in turn have affected effort regulation. We concluded that goal setting and self-evaluation in feed-up and forward reports was not enough to deduce valid regulatory signals. Our results indicate that young learners needed performance feedback to support correct self-evaluation and to correctly drive regulatory actions in ATLs.

A Systematic Review of Special Educational Interventions for Student Attention: Executive Function and Digital Technology in Primary School

“…Several studies highlighted identifiable learning progress as crucial for students’ EF skills training. For example, Molenaar et al (2019) compared students’ self-reports of goal attainment with actual performance and observed typical over- and underestimations of their own performance, indicative of difficulties with EF. Further, consequences of incorrect goal attainment evaluations include “a reinforcing effect that may induce an erroneous regulation of effort”, effectively disturbing decisions regarding correct time spent practicing toward learning goals (Molenaar et al, 2019, p. 10).…”

“…For example, Molenaar et al (2019) compared students’ self-reports of goal attainment with actual performance and observed typical over- and underestimations of their own performance, indicative of difficulties with EF. Further, consequences of incorrect goal attainment evaluations include “a reinforcing effect that may induce an erroneous regulation of effort”, effectively disturbing decisions regarding correct time spent practicing toward learning goals (Molenaar et al, 2019, p. 10). Intending to prevent such detrimental effects, another article by the same first and second author emphasized adaptive learning technology that assisted both students and teachers with real-time feedback of performance, such as visual graphs that show probable associated learning gain by moment-by-moment learning curves with automated scripts as related to performance and behavior (Molenaar et al, 2021).…”

“…There are many indications that EF is important to educational performance (Meltzer, 2018;Strosnider & Sharpe, 2019), which many of the articles emphasized with positive connections. Specifically, intervention examples with strategies to extend time on tasks through methods such as mindfulness training, adaptive learning technologies, and exercises for eye movement training seemed to affect EF support positively (Chan et al, 2022;Molenaar et al, 2019;Qushem et al, 2022;Vekety et al, 2022;Zhou et al, 2021). Generally, mathematics and language were the most commonly studied curriculum topics and were sometimes discussed as directly improving EF (e.g., Ferreira et al, 2022).…”

“…The most common classroom intervention proposed in the articles relates to stand-alone apps that are game-based, i.e., have sequences of specific, simple educational activities with recorded behavioral outcomes, often installed on tablet devices. One example of an article discussing a game-based intervention with educational puzzle activities includes Molenaar et al (2019), as described above, concerning students' self-evaluated goal-attainment. The popularity of these apps may be attributable to their easy implementation in mathematics teaching, which most of them were, as mathematic tasks are often split into sequences with shorter segments and have clear solutions for curriculum assessment that computer scripts can automate for the teacher or special education practitioner.…”

Exploring Students’ Use of a Mobile Application to Support Their Self-Regulated Learning Processes

“…These findings suggest that the application was not adaptive to the needs of the students. By using adaptive technologies (e.g., Molenaar et al, 2019 ; Peng et al, 2019 ), applications such as the Study app could potentially create a more personalized way of SRL support to ameliorate these issues. Future research could investigate how more adaptive applications can be developed for supporting SRL during self-study.…”