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Trends and Issues of Social Annotation in Education: A Systematic Review from 2000 to 2020
- Source :
-
Journal of Computer Assisted Learning . Apr 2023 39(2):329-350. - Publication Year :
- 2023
-
Abstract
- Background: Social annotation (SA) allows users to collaboratively highlight important texts, make comments and discuss with each other on the same online document. This would not only accelerate and deepen learners' cognitive understanding of information, but also help build a sense of rapport, which is critical especially because of the worldwide shift from face-to-face class to remote education as a response to the COVID-19 pandemic. Objective: To provide a systematic review of empirical SA studies, so that current development as well as issues in SA practices and research are identified. Methods: A total of 32 studies were identified and bibliometrical, instructional, and methodological analysis were conducted. Results and Conclusions: The United States has published the most SA research and technology-related journals are most receptive of SA research; one-shot quantitative designs with a sample size between 30 and 100 have been adopted most often; there is a lack of theoretical support for SA studies; higher education settings have been more frequently researched than other educational levels; SA technological features and activities have focused more on student uses and outcomes than on those of instructors; self-designed technologies were more preferred than commercial ones; both cognitive and affective outcomes were emphasized and nearly all studies reported positive findings. Implications: Future SA studies may conduct blended designs with larger sample sizes that is grounded upon solid theoretical frameworks; more customized and affordable SA technologies that support both students and teachers should be developed. Learning analytics and emotional design may be capitalized more to meet the demand of remote education during the pandemic.
Details
- Language :
- English
- ISSN :
- 0266-4909 and 1365-2729
- Volume :
- 39
- Issue :
- 2
- Database :
- ERIC
- Journal :
- Journal of Computer Assisted Learning
- Publication Type :
- Academic Journal
- Accession number :
- EJ1367708
- Document Type :
- Journal Articles<br />Information Analyses
- Full Text :
- https://doi.org/10.1111/jcal.12764