JOURNAL ARTICLE
From Intention to Action: How Preservice Teachers Use Technology-Enabled Learning During Student Teaching.
Published In: Journal of Teacher Education, 2025, v. 76, n. 2. P. 208 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Watson, Jessica Herring 3 of 3
Abstract
This article investigates how preservice middle-level education teachers describe, intend to use, and actually use technology-enabled learning (TEL) during their student teaching experiences. TEL is defined as the student-centered use of technology as a cognitive tool to communicate, collaborate, develop critical thinking, and solve authentic problems, grounded in social constructivist pedagogy. Using Ajzen's Theory of Planned Behavior (TPB) as a framework, the qualitative case study of four preservice teachers at a southeastern U.S. university found that positive attitudes, self-efficacy, and supportive program experiences—including a dedicated educational technology course and online learning during the COVID-19 pandemic—significantly influenced their intention and actual use of TEL. The study highlights the critical role of mentor teacher support and program-wide TEL integration in fostering preservice teachers' ability to implement flexible, relevant, and collaborative technology practices in diverse classroom settings. These findings offer insights for teacher education programs aiming to enhance TEL adoption and address digital equity through sustained, program-deep approaches.
Additional Information
- Source:Journal of Teacher Education. 2025/03, Vol. 76, Issue 2, p208
- Document Type:Article
- Subject Area:Education
- Publication Date:2025
- ISSN:0022-4871
- DOI:10.1177/00224871241268577
- Accession Number:183112172
- Copyright Statement:Copyright of Journal of Teacher Education is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.