Work

Technology-enhanced personalized learning environments based on prior knowledge in continuing education: An experimental study

Year: 2025

Type: article

Abstract: Technology-enhanced personalized learning environments based on prior knowledge (TEPLEs-PK) aim at supporting learning processes by tailoring learning activities to individual learners’ prior knowledge. This study investigated the effects of TEPLEs-PK that allow learners to skip learning content based on their prior knowledge on learning outcomes in continuing education (CE). Participants (N = 194) were asked to complete a self-paced learning path within a 14-day learning period and were randomly assigned to one of three experimental conditions: (1) a personalized condition in which participants’ prior knowledge was assessed using self-report questions (PSRQ condition), (2) a personalized condition in which participants’ prior knowledge was assessed using knowledge questions (PKQ condition), or (3) a control condition in which no personalization occurred. While the experimental condition did not affect learning path completion and learner satisfaction, our findings indicate that TEPLEs-PK may positively affect knowledge increase in CE. Goal orientations and task value significantly moderated the effects of TEPLEs-PK on knowledge increase and learner satisfaction. Exploratory analyses revealed that participants’ interactions with TEPLEs-PK might differ depending on whether prior knowledge was assessed using self-report or knowledge questions. Our study provides a sound basis for implementing TEPLEs-PK in CE. Future research should investigate the effects of TEPLEs-PK on different types of CE and various learning paths, as well as identify additional motivational dispositions that may moderate the impact of TEPLEs-PK on learning outcomes in CE.

Source: Smart Learning Environments

DOI: 10.1186/s40561-025-00407-z

Authors: Yvonne M. Fromm, Julian Rasch, Dirk Ifenthaler

Institutions: University of Mannheim, Curtin University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $1,810