Personalized learning policies and strategies in K–12 education: Where, when, and for what?

Abstract Scant information exists about the distribution of national personalized learning policies/strategies worldwide and their focus. To address this research gap, this study conducts a narrative review of personalized learning policies/strategies in the globe and analysis the identified personalization parameters and components. Particularly, twenty policies were identified and reviewed. The obtained results revealed that most of these policies addressed personalized learning in a very superficial way, which could be due to the lack of understanding this concept. Additionally, while several personalization parameters were identified (e.g., age, culture, etc.), limited attention was paid to learners’ psychological and cognitive differences, calling for more focus on these individual differences. The analysis also reveals that personalized learning policies/strategies need to define assessment mechanisms for personalization and for the policy/strategy itself to assess the effectiveness of personalized learning. The findings of this study can contribute to the literature by highlighting to various stakeholder, including policymakers and educators, the areas of improvement in terms of writing personalized learning policies/strategies and also in terms of implementing them.

Authors

Institutions

Publication Details

Journal
Smart Learning Environments
Published
2025-12-23
DOI
https://doi.org/10.1186/s40561-025-00425-x
Primary Topic
E-Learning and COVID-19
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Personalized learning policies and strategies in K–12 education: Where, when, and for what?

Mouna Denden, Mourad Abed
Smart Learning Environments
E-Learning and COVID-19
article

Personalized learning policies and strategies in K–12 education: Where, when, and for what?

Mouna Denden, Mourad Abed
article en

Abstract

Abstract Scant information exists about the distribution of national personalized learning policies/strategies worldwide and their focus. To address this research gap, this study conducts a narrative review of personalized learning policies/strategies in the globe and analysis the identified personalization parameters and components. Particularly, twenty policies were identified and reviewed. The obtained results revealed that most of these policies addressed personalized learning in a very superficial way, which could be due to the lack of understanding this concept. Additionally, while several personalization parameters were identified (e.g., age, culture, etc.), limited attention was paid to learners’ psychological and cognitive differences, calling for more focus on these individual differences. The analysis also reveals that personalized learning policies/strategies need to define assessment mechanisms for personalization and for the policy/strategy itself to assess the effectiveness of personalized learning. The findings of this study can contribute to the literature by highlighting to various stakeholder, including policymakers and educators, the areas of improvement in terms of writing personalized learning policies/strategies and also in terms of implementing them.

Smart Learning EnvironmentsVol. 12(1)
Centre National de la Recherche Scientifique (FR), Institut National des Sciences Appliquées de Rennes (FR), INSA Hauts-de-France (FR), Université Polytechnique Hauts-de-France (FR)
Agence Nationale de la Recherche, CHIST-ERA
Quality Education
Openalex Percentile: Top 19%
E-Learning and COVID-19
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.