JOURNAL ARTICLE
Intention to Use Web 2.0 Teaching Tools: Evidence from Italian Universities after the COVID-19 Pandemic.
Published In: International Journal of Learning in Higher Education, 2023, v. 30, n. 2. P. 121 1 of 3
Database: Education Source Ultimate 2 of 3
Authored By: Javier Miranda-Gonzalez, Francisco; Vega-Gomez, Francisco; Chamorro-Mera, Antonio; Perez-Mayo, Jesús 3 of 3
Abstract
In recent years, the progressive incorporation of teaching tools based on Web 2.0 in the face-to-face university in Italy has been an ongoing process, and, clearly, a necessary one. However, the COVID-19 pandemic has created new circumstances that have accelerated this process. The suspension of face-to-face classes and the need to find alternative ways of teaching so as not to curtail university activity have contributed fundamentally to this. In this context, we have designed and developed a study with the aim of analyzing the influence of various personal and social variables of Italian university teachers on their intention of using teaching tools based on Web 2.0. To achieve this objective, we have proposed a model of structural equations that takes into account attitude, perceived usefulness, self-efficacy, social norms, and affective commitment as antecedent variables of intention. A total of 3,792 teachers participated in the study. The main results of this study show that the five variables considered positively influence the intention to use 2.0 tools in university teaching, self-efficacy being the variable that contributes most to the explanation of this intention. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Learning in Higher Education. 2023/12, Vol. 30, Issue 2, p121
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2023
- ISSN:23277955
- DOI:10.18848/2327-7955/CGP/v30i02/121-139
- Accession Number:174289529
- Copyright Statement:Copyright of International Journal of Learning in Higher Education is the property of Common Ground Research Networks 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.)
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