Analytics of self-regulated learning strategies and scaffolding: Associations with learning performance

Self-regulated learning (SRL) encapsulates learners' abilities to control, monitor, and regulate cognitive and metacognitive processes such as motivation, emotion, and learning strategies. A crucial element of SRL proficiency lies in learners' capacity to plan and implement effective learning strategies. As such, extensive academic research has been devoted to understanding the impact of learning strategies on learning performance, and the role of instructional methods (including real-time scaffolding) in amplifying students' ability to adopt effective learning strategies. However, while numerous studies have focused on learning strategies and SRL scaffolding, few have investigated their interplay in field settings, particularly examining learning strategy use at the single task level and its dynamic interaction with scaffolding in enhancing learning outcomes. Hence, we carried out a field experiment in which learners were randomly assigned to one of three scaffolding conditions (control, fixed, and adaptive scaffolding) and asked to complete an essay writing assignment in a computer-based learning environment (CBLE). Learning log data were subsequently collected and analyzed. The findings demonstrate that learning strategies could successfully forecast learning performance as gauged by the essay score, yet neither fixed nor adaptive scaffolding individually proved effective in enhancing learning performance. Intriguingly, the study revealed that the effectiveness of adaptive scaffolding in fostering learning performance varied depending on the learning strategies employed by learners. These findings offer valuable insights for the future design of scaffolding, particularly highlighting that the adaptability of scaffolding can be enriched by taking into account learners' adopted learning strategies.

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Publication Details

Journal
Computers and Education Artificial Intelligence
Published
2025-04-24
DOI
https://doi.org/10.1016/j.caeai.2025.100410
Citations
5
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
8.47

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article

Analytics of self-regulated learning strategies and scaffolding: Associations with learning performance

Namrata Srivastava, Yizhou Fan, Dragan Gašević, Lixiang Yan et al.
5 citations
Computers and Education Artificial Intelligence
Online Learning and Analytics
8.47
article

Analytics of self-regulated learning strategies and scaffolding: Associations with learning performance

Namrata Srivastava, Yizhou Fan, Dragan Gašević, Lixiang Yan, Shaveen Singh, Sehrish Iqbal, Zachari Swiecki, Tongguang Li, Mladen Raković, Xinyu Li, Yi‐Shan Tsai
article en
5 citations

Abstract

Self-regulated learning (SRL) encapsulates learners' abilities to control, monitor, and regulate cognitive and metacognitive processes such as motivation, emotion, and learning strategies. A crucial element of SRL proficiency lies in learners' capacity to plan and implement effective learning strategies. As such, extensive academic research has been devoted to understanding the impact of learning strategies on learning performance, and the role of instructional methods (including real-time scaffolding) in amplifying students' ability to adopt effective learning strategies. However, while numerous studies have focused on learning strategies and SRL scaffolding, few have investigated their interplay in field settings, particularly examining learning strategy use at the single task level and its dynamic interaction with scaffolding in enhancing learning outcomes. Hence, we carried out a field experiment in which learners were randomly assigned to one of three scaffolding conditions (control, fixed, and adaptive scaffolding) and asked to complete an essay writing assignment in a computer-based learning environment (CBLE). Learning log data were subsequently collected and analyzed. The findings demonstrate that learning strategies could successfully forecast learning performance as gauged by the essay score, yet neither fixed nor adaptive scaffolding individually proved effective in enhancing learning performance. Intriguingly, the study revealed that the effectiveness of adaptive scaffolding in fostering learning performance varied depending on the learning strategies employed by learners. These findings offer valuable insights for the future design of scaffolding, particularly highlighting that the adaptability of scaffolding can be enriched by taking into account learners' adopted learning strategies.

Computers and Education Artificial IntelligenceVol. 8
Vanderbilt University (US), Peking University (CN), Monash University (AU)
National Natural Science Foundation of China, Jacobs Foundation, Economic and Social Research Council, Australian Research Council
Openalex Percentile: Top 3%
Online Learning and Analytics
8.47
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