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Using Social Annotations to Augment the Learning Space and Learner Experience

Published: 02 July 2019 Publication History

Abstract

Learning has always been considered a social and collaborative activity. From the social constructivism perspective, students learn through the process of sharing experiences and build knowledge and understanding through discussion. This pilot study demonstrates the successful integration of an online social annotation tool in a Python programming workshop that allows students to discuss learning material and code segments with their fellow classmates. We investigate how the students leveraged the tool to improve their learning and to log their sentiments while reading the material and attempting the programming activities. We find that those students who had access to the annotation tool spent an above average amount of time on the material. They also had a higher completion rate and performance compared to the control group who did not have the annotation feature available to them. Feedback from the students who used the tool was very positive. Finally, we delve deeper into analysing the annotation patterns and explore how instructors can make use of fine-grained annotation data to inform and adapt the design of CS teaching and learning materials in online or blended contexts.

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Cited By

View all
  • (2023)Why do students reply? Uncovering the socio-semantic entanglement in web annotation activitiesEducation and Information Technologies10.1007/s10639-023-12187-529:8(9747-9771)Online publication date: 19-Sep-2023
  • (2022)Intelligent Support for All?Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499418(996-1002)Online publication date: 22-Feb-2022
  • (2021)Using process mining to analyse self-regulated learning: a systematic analysis of four algorithmsLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448171(333-343)Online publication date: 12-Apr-2021
  • Show More Cited By

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          cover image ACM Conferences
          ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education
          July 2019
          583 pages
          ISBN:9781450368957
          DOI:10.1145/3304221
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          Published: 02 July 2019

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          Author Tags

          1. annotations
          2. digital education
          3. engagement
          4. situated cognition

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          View all
          • (2023)Why do students reply? Uncovering the socio-semantic entanglement in web annotation activitiesEducation and Information Technologies10.1007/s10639-023-12187-529:8(9747-9771)Online publication date: 19-Sep-2023
          • (2022)Intelligent Support for All?Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499418(996-1002)Online publication date: 22-Feb-2022
          • (2021)Using process mining to analyse self-regulated learning: a systematic analysis of four algorithmsLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448171(333-343)Online publication date: 12-Apr-2021
          • (2020)UserFlow: A Tool for Visualizing Fine-grained Contextual Analytics in Teaching DocumentsProceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3387410(384-390)Online publication date: 15-Jun-2020
          • (2019)Leveraging Student Self-reports to Predict Learning OutcomesArtificial Intelligence in Education10.1007/978-3-030-23207-8_73(398-403)Online publication date: 21-Jun-2019

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