Facilitating Depth of Explanation: Utilising Reflective Feedback in Collaborative Learning Environments.
Background: Natural language processing (NLP) and machine learning technologies offer significant advantages, such as facilitating the delivery of reflective feedback in collaborative learning environments while minimising technical constraints for educators related to time and location. Recently, s...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 2; pp. 1 - 19 |
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| Autores principales: | , , |
| Formato: | computer program pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184016308&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184016308 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Apr2025 vid: 41 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 184016308 184016308 184016308 10.1111/jcal.70010 184016308 ppf: 1 ppct: 18 formats: tig: atl: Facilitating Depth of Explanation: Utilising Reflective Feedback in Collaborative Learning Environments. aug: au: Yi, Suping Sintawati, Wayan Zhang, Yibing affil: School of Education Science, Jiangsu Second Normal University, Nanjing, China sug: subj: Students, Elementary Learning Environment Computer-Assisted Instruction Natural Language Processing Feedback Methods Reflection Outcomes of Education Evaluation Human Funding Source China Male Female Child Adolescence Curriculum Student Knowledge Teaching Methods Software Design Semi-Structured Interview Student Attitudes Child: 6-12 years Adolescent: 13-18 years Male Female ab: Background: Natural language processing (NLP) and machine learning technologies offer significant advantages, such as facilitating the delivery of reflective feedback in collaborative learning environments while minimising technical constraints for educators related to time and location. Recently, scholars' interest in reflective feedback has increased scientifically. However, robust empirical evidence evaluating the impacts of feedback mechanisms and innovative NLP methods on enhancing the quality of explanations in knowledge building (KB) remains limited. Objectives: This study investigated whether reflective feedback can assist primary school students in internalising and enhancing the depth of their explanations within a KB context. Method: Employing a design‐based research methodology, this study involved 32 sixth‐grade students from a primary school in Yangzhou, China, who engaged in a 15‐week KB learning initiative. Results and Conclusions: The findings indicated that (1) students achieved significant progress in developing deep explanations, marked by improvements in logicality, consistency, convergence and structure and (2) students perceived reflective feedback as a critical factor in developing robust and profound explanations and demonstrated positively to the feedback. Implications: These findings deepened our understanding of explanatory and assessment strategies in KB and highlighted the substantial instructional, technological and practical implications of reflective feedback for educators and learners. pubtype: Academic Journal doctype: computer program pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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