Homogeneous Group Formation in Collaborative Learning Scenarios: An Approach Based on Personality Traits and Genetic Algorithms

Revelo, Oscar Sanchez; Collázos, César A.; Redondo, Miguel Á.; Pinto, Ig Ibert Bittencourt Santana

doi:10.1109/tlt.2021.3105008

Cited by 25 publications

(16 citation statements)

References 46 publications

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“…Besides behavior indicators, we plan to incorporate the consistency of the ratings, including the agreement with instructor-assigned grades and average student-assigned grades (Fukazawa, 2010), into the prior evaluation behavior indicators as antecedents. Further, considering more predictors in the model, such as learning outcomes, collaborative skills, and personality variables (Piech et al, 2013;Sánchez et al, 2021), could also enhance its effectiveness. Qualitative observations and self-reports can offer valuable insights into the reasons behind unserious patterns, and exploring how the presented EBA estimated from logs connects to the observations is another promising topic.…”

Section: Contributions and Limitationsmentioning

confidence: 99%

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

Liang,

Horikoshi,

Majumdar

et al. 2024

RPTEL

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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.

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Section: Contributions and Limitationsmentioning

confidence: 99%

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 a result, learners' prior knowledge and skills is taken into consideration when assigning them to appropriate group members [25]- [27]. Additionally, personality traits from the Big Five models, particularly the aspect of externality that reflects collaborative tendencies, have been examined to enhance collaboration and improve learning outcomes [28]. Furthermore,…”

Section: Data-driven Support For Educationmentioning

confidence: 99%

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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“…[16], [20], [21], and [25], developed automatic grouping methods that achieve collaborative learning outcomes. The phrase "cold start" was used in [13] to denote the problem of the inaccessibility of students' characteristics, such as personality traits, communication skills, and leadership capacities, at the starting point.…”

Section: Literaturementioning

confidence: 99%

An Algorithm Based on Self-balancing Binary Search Tree to Generate Balanced, Intra-homogeneous and Inter-homogeneous Learning Groups

Ammar¹,

Minalla²

2023

IJACSA

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This paper presents an algorithm, based on the self-balancing binary search tree, to form learning groups. It aims to generate learning groups that are intra-homogeneous (student performance similarity within the group), interhomogeneous (group performance similarity between groups), and of balanced size. The algorithm mainly uses the 2-3 tree and the 2-3-4 tree as two implementations of a self-balancing binary search tree to form student blocks with close GPAs (grade point averages) and balanced sizes. Then, groups are formed from those blocks in a greedy manner. The experiment showed the efficiency of the proposed algorithm, compared to traditional forming methods, in balancing the size of the groups and improving their intra-and inter-homogeneity by up to 26%, regardless of the used version of the self-balancing binary search tree (2-3 or 2-3-4). For small samples of students, the use of the 2-3-4 tree was distinguished for improving intra-and interhomogeneity compared to the 2-3 tree. As for large samples of students, experiments showed that the 2-3 tree was better than the 2-3-4 tree in improving the inter-homogeneity, while the 2-3-4 tree was distinguished in improving the intra-homogeneity.

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Homogeneous Group Formation in Collaborative Learning Scenarios: An Approach Based on Personality Traits and Genetic Algorithms

Cited by 25 publications

References 46 publications

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

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

Data-Driven Support Infrastructure for Iterative Team-Based Learning

An Algorithm Based on Self-balancing Binary Search Tree to Generate Balanced, Intra-homogeneous and Inter-homogeneous Learning Groups

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