Abstract
In this paper, we illustrate the successful implementation of a social annotation tool within a content authoring platform, that allows students to discuss learning material with their fellow classmates, and to self-report on their cognitive, metacognitive and affective states-by self-coding the annotations as they journey through the learning material. We explore the predictive potential of such self- reports in reading material against the students completion rate and assessment scores, and also examine how visualisation of these annotation classifications can help instructors easily identify issues and adapt their teaching approach and learning material.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education |
| Editors | Arnold Pears, Mihaela Sabin |
| Place of Publication | New York NY USA |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 247-248 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450368957 |
| ISBN (Print) | 9781450363013 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | Annual Conference on Innovation and Technology in Computer Science Education 2019 - Aberdeen, United Kingdom Duration: 12 Jul 2019 → 17 Jul 2019 Conference number: 24th https://iticse.acm.org/ https://dl.acm.org/doi/proceedings/10.1145/3304221 (Proceedings) |
Publication series
| Name | Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE |
|---|---|
| Publisher | Association for Computing Machinery (ACM) |
| ISSN (Print) | 1942-647X |
Conference
| Conference | Annual Conference on Innovation and Technology in Computer Science Education 2019 |
|---|---|
| Abbreviated title | ITiCSE 2019 |
| Country/Territory | United Kingdom |
| City | Aberdeen |
| Period | 12/07/19 → 17/07/19 |
| Internet address |
Keywords
- Annotations
- E-learning
- Learning analytics
- Prediction
- Self-report