Young learners’ motivation, self-regulation and performance in personalized learning

Personalized learning, a topic that has garnered significant attention in education, is known for its potential to cater to student's unique needs and improve educational outcomes. However, most large-scale longitudinal studies on personalized learning have primarily focused on middle school students and above (age ≥11). This study, in contrast, delves into the uncharted territory of how personalized learning affects younger students (ages 7–12), a domain largely overlooked by large-scale studies. To understand the effect of PL on young learners’ academic performance, metacognitive awareness, and motivation. Multidisciplinary design teams embedded personalized learning in eight participating elementary schools, resulting in personalized learning interventions tailored to each school in four subjects. The effects were measured over three years among 588 students and 82 teachers and analyzed using a Bayesian Gaussian regression with random intercept models and nested groups. We found significant evidence that the personalized learning interventions fostered academic performance in two of the four subjects: math and spelling. Regarding spelling, we found that the schools in which metacognitive skills were explicitly trained improved their students' spelling performance significantly compared to other schools. We found significant evidence suggesting that student ICT skills improved metacognitive awareness, intrinsic motivation, and math performance. We also found significant evidence that teachers' ICT skills support student metacognitive awareness. However, we could not confirm the theorized effect of personalized learning on metacognitive awareness or students’ intrinsic motivation. Our study provides evidence-based recommendations for implementing personalized learning interventions in elementary schools, particularly for math and spelling. Finally, improving ICT skills among students and teachers benefits students in math and in their metacognitive skills. • Personalized learning boosted academic performance in math and spelling. • School explicitly training in metacognition improved spelling performance. • Student digital literacy improved metacognition evaluation and intrinsic motivation. • Teacher digital literacy supported student metacognition knowledge.

Authors

Institutions

Publication Details

Journal
Computers & Education
Published
2024-11-23
DOI
https://doi.org/10.1016/j.compedu.2024.105208
Citations
9
Primary Topic
Innovative Teaching and Learning Methods
Type
article
Field-Weighted Citation Impact
7.12
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Young learners’ motivation, self-regulation and performance in personalized learning

Gino Camp, Marijke Kral, Anne‐Marieke van Loon, Kevin Ackermans et al.
9 citations
Computers & Education
Innovative Teaching and Learning Methods
7.12
article

Young learners’ motivation, self-regulation and performance in personalized learning

Gino Camp, Marijke Kral, Anne‐Marieke van Loon, Kevin Ackermans, Marjoke Bakker
article en
9 citations

Abstract

Personalized learning, a topic that has garnered significant attention in education, is known for its potential to cater to student's unique needs and improve educational outcomes. However, most large-scale longitudinal studies on personalized learning have primarily focused on middle school students and above (age ≥11). This study, in contrast, delves into the uncharted territory of how personalized learning affects younger students (ages 7–12), a domain largely overlooked by large-scale studies. To understand the effect of PL on young learners’ academic performance, metacognitive awareness, and motivation. Multidisciplinary design teams embedded personalized learning in eight participating elementary schools, resulting in personalized learning interventions tailored to each school in four subjects. The effects were measured over three years among 588 students and 82 teachers and analyzed using a Bayesian Gaussian regression with random intercept models and nested groups. We found significant evidence that the personalized learning interventions fostered academic performance in two of the four subjects: math and spelling. Regarding spelling, we found that the schools in which metacognitive skills were explicitly trained improved their students' spelling performance significantly compared to other schools. We found significant evidence suggesting that student ICT skills improved metacognitive awareness, intrinsic motivation, and math performance. We also found significant evidence that teachers' ICT skills support student metacognitive awareness. However, we could not confirm the theorized effect of personalized learning on metacognitive awareness or students’ intrinsic motivation. Our study provides evidence-based recommendations for implementing personalized learning interventions in elementary schools, particularly for math and spelling. Finally, improving ICT skills among students and teachers benefits students in math and in their metacognitive skills. • Personalized learning boosted academic performance in math and spelling. • School explicitly training in metacognition improved spelling performance. • Student digital literacy improved metacognition evaluation and intrinsic motivation. • Teacher digital literacy supported student metacognition knowledge.

Computers & EducationVol. 226
HAN University of Applied Sciences (NL), Fontys University of Applied Sciences (NL), Open University of the Netherlands (NL)
Openalex Percentile: Top 3%
Innovative Teaching and Learning Methods
7.12
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.