G5 Article dissertation

Personalizing K–12 STEM Education through Technology-Enhanced Learning and Learning Analytics




AuthorsBin Qushem, Umar

Publishing placeTurku

Publication year2026

Series titleTurun yliopiston julkaisuja - Annales Universitatis Turkunesis AI

Number in series757

ISBN978-952-02-0578-2

eISBN978-952-02-0579-9

ISSN0082-7002

eISSN2343-3175

Publication's open availability at the time of reportingOpen Access

Publication channel's open availability Open Access publication channel

Web address https://urn.fi/URN:ISBN:978-952-02-0579-9


Abstract

Science, Technology, Engineering and Mathematics (STEM) education has been widely recognized as a priority area over the last decade. Despite global recognition of STEM literacy as essential for problem-solving and addressing societal challenges, traditional classrooms often struggle to address individual student’s learning needs in core subjects such as mathematics and life sciences. In addition, integrated intervention approaches for achieving STEM-skills and enhancing motivation towards STEM in K-12 settings remain underexplored compared to Higher Education applications.

The present Doctoral research, therefore, tackles a pressing challenge in K-12 STEM education: “How to effectively personalize learning for diverse student needs?”. The research develops and tests a framework that integrates Technology-Enhanced Learning (TEL) platforms with Learning Analytics (LA) to create adaptive, data-informed educational experiences for diverse groups of K-12 learners. The study employs a Mixed-Methods Research (MMR) design divided into three phases: (1) a synthesis phase (2) an intervention phase; and (3) a reflection phase. Data were collected from two interventions: one involving 720 primary and lower-secondary students (4th-6th grade) who took personalized lessons on arithmetic operations over 9 months using ViLLE-tool; and another one involving 70 upper-secondary students (10th-12th grade) who took supplementary lessons on ‘Life and Evolution’ over 5 weeks using VR-tool. Data were analyzed through exploratory data analysis and statistical methods.

The research work contributes to the field by advancing theoretical understanding of personalized education, providing empirical evidence from authentic classroom settings, and demonstrating how adaptive learning technologies and LA can foster personalized learning in K-12 STEM. The work addresses critical gaps in the literature by developing an empirically grounded, theory-informed adaptive and context-sensitive personalized learning interventions framework in addition to offering a methodological blueprint for future research in the Educational Sciences.



Last updated on 17/03/2026 08:50:33 AM