Peer feedback to support collaborative knowledge improvement: What kind of feedback feed-forward?

Tan, Jesmine Sio Hwee; Chen, Wenli

doi:10.1016/j.compedu.2022.104467

Cited by 38 publications

(16 citation statements)

References 56 publications

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“…Overall, it can be concluded that the optimized mechanism was helpful for motivating online learners to acquire knowledge and interact effectively. The finding is compatible with prior work [ 18 , 42 , 43 , 44 ], which indicated that personalized feedback and peer assessment enabled online learners to be more involved in collaboration and knowledge building. Considering the special challenge of online collaborative learning [ 21 ], we deduce that the results can be attributed to the support that the experimental group received, including learning diagnosis and collaborative guidance, which helped students feel connected with teachers in the isolated online settings, and assisted them in advancing their knowledge level and collaborative engagement.…”

Section: Discussionsupporting

confidence: 90%

A Data-Driven Optimized Mechanism for Improving Online Collaborative Learning: Taking Cognitive Load into Account

Zhang

Wang

et al. 2022

IJERPH

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Research on online collaborative learning has explored various methods of collaborative improvement. Recently, learning analytics have been increasingly adopted for ascertaining learners’ states and promoting collaborative performance. However, little effort has been made to investigate the transformation of collaborative states or to consider cognitive load as an essential factor for collaborative intervention. By bridging collaborative cognitive load theory and system dynamics modeling methods, this paper revealed the transformation of online learners’ collaborative states through data analysis, and then proposed an optimized mechanism to ameliorate online collaboration. A quasi-experiment was conducted with 91 college students to examine the potential of the optimized mechanism in collaborative state transformation, awareness of collaboration, learning achievement, and cognitive load. The promising results demonstrated that students learning with the optimized mechanism performed significantly differently in collaboration and knowledge acquisition, and no additional burden in cognitive load was noted.

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Section: Discussionsupporting

confidence: 90%

A Data-Driven Optimized Mechanism for Improving Online Collaborative Learning: Taking Cognitive Load into Account

Zhang

Wang

et al. 2022

IJERPH

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“…Swart et al ( 2019 ) found that feedback design, especially elaborated feedback, moderated the effectiveness of feedback and that elaborated feedback yielded the best effect. Elaborated suggestive feedback contributes to fostering deep learning and collaborative knowledge improvement (Tan & Chen, 2022 ). Furthermore, Resendes et al ( 2015 ) stated that the provision of group-level formative feedback promoted collaborative knowledge building since feedback can enhance learners’ knowledge-building abilities.…”

Section: Discussionmentioning

confidence: 99%

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

Zheng

Long

Niu

et al. 2023

Intern. J. Comput.-Support. Collab. Learn

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Learning engagement has gained increasing attention in the field of education. Previous studies have adopted conventional methods to analyze learning engagement, but these methods cannot provide timely feedback for learners. This study analyzed automated group learning engagement via deep neural network models in a computer-supported collaborative learning (CSCL) context. A quasi-experimental research design was implemented to examine the effects of the automated group learning engagement analysis and feedback approach on collaborative knowledge building, group performance, socially shared regulation, and cognitive load. In total, 120 college students participated in this study; they were assigned to 20 experimental groups and 20 control groups of three students each. The students in the experimental groups adopted the automated group learning engagement analysis and feedback approach, whereas those in the control groups used the traditional online collaborative learning approach. Both quantitative and qualitative data were collected and analyzed in depth. The results indicated significant differences in group learning engagement, group performance, collaborative knowledge building, and socially shared regulation between the experimental and control groups. The proposed approach did not increase the cognitive load for the experimental groups. The implications of the findings can potentially contribute to improving group learning engagement and group performance in CSCL.

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“…Another point that makes students have positive attitude to the aspect of evaluation and feedback in IEC program is that lecturers always provide corrective and constructive feedback to the students' works. Feedback is crucial aspect in teaching learning process since it could help develop and refining students' metacognitive knowledge (Tan & Chen, 2022), and improves students' creativity (Zhang et al, 2021). Corrective feedback was also discovered to be very powerful in affecting students' learning behavior.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Effectiveness of Intensive English Course in Islamic Higher Education

Ningrum¹,

Toyyibah²

2022

JEELS

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In the English Education department of the State Islamic Institute (IAIN) Kediri, there is a course so-called an Intensive English Course (IEC) program offered at the first semester. This course is designed as a preparatory program to prepare the students who come from various English proficiency backgrounds. This research is intended to evaluate the effectiveness of the IEC program. Employing a mixed-method approach, interviews, questionnaires, pre-test and post-test were administered to four Intensive English Course lecturers and 126 students. The findings of this study revealed that the program is effective as it is able to improve students’ performance, and the course material, teaching methods, and assessment conducted are appropriate. The interesting point to note is that, despite the fact that everything is well-prepared and the program has the potential to significantly improve the students' abilities, the improvement is still below par.

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Peer feedback to support collaborative knowledge improvement: What kind of feedback feed-forward?

Cited by 38 publications

References 56 publications

A Data-Driven Optimized Mechanism for Improving Online Collaborative Learning: Taking Cognitive Load into Account

A Data-Driven Optimized Mechanism for Improving Online Collaborative Learning: Taking Cognitive Load into Account

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

Evaluating the Effectiveness of Intensive English Course in Islamic Higher Education

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