| Autor: |
Andrew Zamecnik, Srecko Joksimovic, Vitomir Kovanovic, Georg Grossmann, Djazia Ladjal, Abelardo Pardo |
| Přispěvatelé: |
Zamecnik, Andrew, Joksimovic, Srecko, Kovanovic, Vitomir, Grossmann, Georg, Ladjal, Djazia, Pardo, Abelardo, 13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023 Arlington 13-17 March 2023 |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Popis: |
Industry supervisors play a pivotal role in ongoing learner support and guidance within a work-integrated learning context. Effective provisional feedback from industry supervisors in work-integrated learning environments is essential for increasing a team's metacognitive awareness and ability to evaluate their performance. However, research that examines the usefulness and type of feedback from industry supervisors for teams remains limited. In this study, we investigate the quality of provisional feedback by comparing the teams' helpfulness rating of the feedback from two types of industry supervisors (i.e., clients and mentors), based on the feedback type (task, process, regulatory and self-level oriented) using learning analytics. The results show that teams rated the perceived helpfulness scores of clients and mentors as very useful, with mentors providing slightly more helpful feedback. We also found that mentors provide more co-occurrences of feedback classifications than clients. The overall results show that teams perceive mentor feedback as more helpful than clients and that the mentor targets feedback that is more beneficial to the teams learning than the clients. Our findings can aid in developing guidelines that aim to validate and improve existing or new feedback quality frameworks by leveraging backward evaluation data. Refereed/Peer-reviewed |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
