TY - JOUR
T1 - Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning
AU - Lim, Lyn
AU - Bannert, Maria
AU - van der Graaf, Joep
AU - Singh, Shaveen
AU - Fan, Yizhou
AU - Surendrannair, Surya
AU - Rakovic, Mladen
AU - Molenaar, Inge
AU - Moore, Johanna
AU - Gašević, Dragan
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2023/2
Y1 - 2023/2
N2 - Self-Regulated Learning (SRL) is related to increased learning performance. Scaffolding learners in their SRL activities in a computer-based learning environment can help to improve learning outcomes, because students do not always regulate their learning spontaneously. Based on theoretical assumptions, scaffolds should be continuously adaptive and personalized to students' ongoing learning progress in order to promote SRL. The present study aimed to investigate the effects of analytics-based personalized scaffolds, facilitated by a rule-based artificial intelligence (AI) system, on students' learning process and outcomes by real-time measurement and support of SRL using trace data. Using a pre-post experimental design, students received personalized scaffolds (n = 36), generalized scaffolds (n = 32), or no scaffolds (n = 30) during learning. Findings indicated that personalized scaffolds induced more SRL activities, but no effects were found on learning outcomes. Process models indicated large similarities in the temporal structure of learning activities between groups which may explain why no group differences in learning performance were observed. In conclusion, analytics-based personalized scaffolds informed by students’ real-time SRL measured and supported with AI are a first step towards adaptive SRL supports incorporating artificial intelligence that has to be further developed in future research.
AB - Self-Regulated Learning (SRL) is related to increased learning performance. Scaffolding learners in their SRL activities in a computer-based learning environment can help to improve learning outcomes, because students do not always regulate their learning spontaneously. Based on theoretical assumptions, scaffolds should be continuously adaptive and personalized to students' ongoing learning progress in order to promote SRL. The present study aimed to investigate the effects of analytics-based personalized scaffolds, facilitated by a rule-based artificial intelligence (AI) system, on students' learning process and outcomes by real-time measurement and support of SRL using trace data. Using a pre-post experimental design, students received personalized scaffolds (n = 36), generalized scaffolds (n = 32), or no scaffolds (n = 30) during learning. Findings indicated that personalized scaffolds induced more SRL activities, but no effects were found on learning outcomes. Process models indicated large similarities in the temporal structure of learning activities between groups which may explain why no group differences in learning performance were observed. In conclusion, analytics-based personalized scaffolds informed by students’ real-time SRL measured and supported with AI are a first step towards adaptive SRL supports incorporating artificial intelligence that has to be further developed in future research.
KW - Adaptive support
KW - Learning analytics
KW - Personalized scaffolds
KW - Process mining
KW - Self-regulated learning
KW - Trace data
UR - http://www.scopus.com/inward/record.url?scp=85140890206&partnerID=8YFLogxK
U2 - 10.1016/j.chb.2022.107547
DO - 10.1016/j.chb.2022.107547
M3 - Article
AN - SCOPUS:85140890206
SN - 0747-5632
VL - 139
JO - Computers in Human Behavior
JF - Computers in Human Behavior
M1 - 107547
ER -