Peer Evaluation System in Team Work Skills Assessment

Ismail, Nur’Ain; Tharim, Asniza Hamimi Abdul; Mohd, Thuraiya; Othman, Nor A’aini; Nasrudin, Nur Hasni; Jaffar, Noraidawati; Shuib, Mohammad Nasharudine; Kurdi, Mohd Khairi; Ismail, Yusof

doi:10.1007/978-981-287-664-5_47

Cited by 4 publications

(2 citation statements)

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“…In parallel, online evaluation systems enable participants to provide feedback to their partners, thus contributing to the modeling of their group work and task experiences [40]. The anonymity offered by online evaluation collectors enhances the reliability of the feedback provided by participants [69]. With the increasing adoption of self-reported evaluation and immediate feedback from actual participants in TBL contexts, coupled with attributes of the group learning process from the previous phase, all this evaluation and feedback data can be synchronized with the student model and subsequently used for various algorithmic grouping purposes within LA [70].…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have introduced strategies to enhance the reliability of peer assessment. These strategies include focusing on privacy protection [69], providing group awareness support as decision-making assistance [18], [21], and implementing interactive peer evaluation platforms with backward feedback mechanisms [87].…”

Section: Rater Reliability In Peer Evaluationmentioning

confidence: 99%

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Data-Driven Support Infrastructure for Iterative Team-Based Learning

Liang,

Majumdar,

Horikoshi

et al. 2024

IEEE Access

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Iterative team-based learning (TBL) is a common educational strategy for collaborative learning that involves sequential phases of individual and group learning activities. The advent of digital learning platforms, with the accumulation of learning log data, presents an opportunity to leverage datadriven techniques to enhance TBL practices. However, applying data-driven approaches in iterative TBL scenarios has received limited exploration in existing literature. Through a review of initial studies in this domain, data-driven iterative TBL emerges as a promising area. To explore this topic, we introduce a novel framework, drawing from the GLOBE framework for group learning, aimed at integrating datadriven designs into iterative TBL settings. This framework is proposed to guide data and activity design within iterative TBL, comprising four phases of group learning activity workflow and three essential steps of data flow. Additionally, we present two authentic instances supported by empirical evidence, offering insights into how educators can implement data-driven designs across different phases of TBL. Within the data-driven environment, we also uncover potential impacts and challenges of data-driven iterative TBL, to identify avenues for future research that can further expand our understanding of the possibilities in this domain.

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Section: Discussionmentioning

confidence: 99%