Collaborate like expert designers: An exploratory study of the role of individual preparation activity on students' collaborative learning

Lyu, Qianru; Chen, Wenli; Su, Junzhu; Heng, Kok Hui

doi:10.1016/j.iheduc.2023.100920

Cited by 5 publications

(2 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further analysis on consistency indicators suggests that individuals with higher reading engagement in individual reading tend to demonstrate higher reliability in peer evaluation, as they provide scores closer to the average level. This result also reflects the significance of the individual preparation phase on the performance of subsequent TBL phases, as shown in Lyu et al (2023). In summary, the prediction model is expected to empower instructors to provide remedial instructions or give automatic nudges to these at-risk students, improving the reliability of peer evaluation as a formative assessment in TBL.…”

Section: Predictive Learner Model Indicators For Detection (Rq3)mentioning

confidence: 59%

Rater behaviors in peer evaluation: Patterns and early detection with learner model

Liang,

Horikoshi,

Majumdar

et al. 2024

RPTEL

View full text Add to dashboard Cite

Peer evaluation is a common practice in team-based learning (TBL) designs, which can cover the assessment of individual or group work. However, the integrity of peer evaluation can be compromised by unserious raters—individuals who do not earnestly engage in the evaluation process. These raters may exhibit behaviors like consistently assigning the same score, rushing through evaluations, or evaluating before or long after the target presentations. This study delves into the issue of unserious peer evaluation in group presentations, with a specific focus on understanding the behavior patterns in the digital system. Utilizing evaluation behavior analysis (EBA) indicators, we identify patterns linked to unserious raters during the peer evaluation process. Meanwhile, we also connect these patterns to rating consistency and actual course performance, underscoring the significance of behavior patterns. Further, we conduct a preliminary analysis to explore the application of learner model data available before the peer evaluation starts for the early detection of unserious raters. This finding can assist teachers in providing personalized prompts and interventions before the peer evaluation stage, hence enhancing the evaluation quality through targeted interventions in a timely manner.

show abstract

Section: Predictive Learner Model Indicators For Detection (Rq3)mentioning

confidence: 59%

Rater behaviors in peer evaluation: Patterns and early detection with learner model

Liang,

Horikoshi,

Majumdar

et al. 2024

RPTEL

View full text Add to dashboard Cite

show abstract

“…As an indispensable preparation phase in flipped learning design, individual preparation holds paramount importance. [39] has pointed out that the individual ideation phase before the group learning starts is significant and can improve the quality of subsequent collaboration. In the team-based learning design, individual learning activities cover pre-reading the learning materials, and pre-test for readiness [5].…”

Section: Individual Learning Phasementioning

confidence: 99%

Data-Driven Support Infrastructure for Iterative Team-Based Learning

Liang,

Majumdar,

Horikoshi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract