Peer-graded individualised student homework in a single-instructor undergraduate engineering course

Cleynen, Olivier; Santa-Maria, Germán; Magdowski, Mathias; Thévenin, Dominique

doi:10.25304/rlt.v28.2339

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…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%

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%

Data-Driven Support Infrastructure for Iterative Team-Based Learning

Liang,

Majumdar,

Horikoshi

et al. 2024

IEEE Access

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“…Results revealed that both experimental groups who used paper-based or online homework were more successful than the control group that not used any kind of homework. Results of Cleynena et al (2020) who studied the effects of a homework program on engineering students and discovered positive effects of homework are also supported by Yavich and Davidovitch (2020), but Yavich and Davidovitch (2020) also signify the long term benefits of homework such as teaching time-management and increasing problem-solving abilities. Karatabak et al (2020), who researched the distance education with and without homework, found that homework creates positive differences in distance education.…”

Section: Introductionmentioning

confidence: 88%

Students Perceptions of Homework as a Formal Assessment Method

Mutlu

2021

International E-Journal of Educational Studies

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Whether it is formative or summative, an assessment which is explained in Oxford Advanced Learner's Dictionary (2015) as "an opinion or judgement about sb/sth that has been thought about" (p.75) plays a significant role in completing the language education process because of its ability to provide feedback. Choosing the right assessment method is crucial for language teachers since most classical assignments create anxiety and make the process problematic for the students. Whether the classical assignments evaluate the students' knowledge, or they understand that their grammar level for a definite time is another discrepancy to clarify. By taking into consideration those, this qualitative study focuses on the problems of classical exams and tries to reveal how beneficial and desirable can a difficult homework be as a formal assessment method. An unstructured interview form was used to collect data from 40 prep-class students who were selected using a convenience sampling method to understand their perceptions about homework as a formal assessment method. Results showed that although 32 of those students had difficulties while preparing the homework, most of them liked the process as well and think that this kind of homework is more beneficial than classical exams. Moreover, more than half of the participants' desire to have such homework instead of classical exams despite homework's difficulties, and they also express that this homework increased their language knowledge.

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“…Nowadays, digital systems facilitate in-class peer evaluation activities promptly and anonymously (Cleynen et al, 2020). This provides teachers with greater flexibility in integrating peer evaluation into their class design.…”

Section: Peer Evaluation In Team-based Learning (Tbl)mentioning

confidence: 99%

“…Peer evaluation systems offer learners a scaffold to evaluate their group members and receive real-time feedback with reduced bias, enhanced individualism and privacy protection (Cleynen et al, 2020;Ismail et al, 2019). In online evaluation systems, both the evaluation outputs and the evaluation processes of raters can be traced, providing valuable data for learning analytics applications as part of the learner model attribute.…”

Section: Data-driven Peer Evaluation With Learner Modelmentioning

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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Peer-graded individualised student homework in a single-instructor undergraduate engineering course

Cited by 7 publications

References 13 publications

Data-Driven Support Infrastructure for Iterative Team-Based Learning

Data-Driven Support Infrastructure for Iterative Team-Based Learning

Students Perceptions of Homework as a Formal Assessment Method

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

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