A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality

Oshima, Jun; Oshima, Ritsuko; Fujita, Wataru

doi:10.18608/jla.2018.51.2

Cited by 33 publications

(21 citation statements)

References 24 publications

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“…Previous research has revealed the significance of shared epistemic agency in knowledge‐related collaborative inquiry (eg, Lei & Chan, ; Oshima et al , ; Yang, ), and some studies have demonstrated the positive effects of high‐level shared epistemic agency on domain understanding (Yang, ; Yang et al , ; Zhang et al , ), the productivity of collaboration (Damşa, ), collective knowledge advancements (Oshima et al , ; Yang, ), and the development of high‐level competences among students (Borge et al , ; Chen, ; Yang, ). Shared epistemic agency is essential for sustaining collaborative efforts to pursue knowledge advancement (Damşa et al , ; Scardmalia, ).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

“…Collaborative inquiry, nowadays a major research strand in education, can help students to develop these future‐ready competences, such as inquiry, collaboration, knowledge creation, agency and metacognition. However, the development of these competences relies heavily on students' enactment of high‐level shared epistemic agency (Damşa, ; Oshima, Oshima, & Fujita, ; Rappa & Tang, ; Scardamalia & Bereiter, ; Zhang et al , ). This is problematic because many students exhibit little shared epistemic agency in collaborative inquiry.…”

Section: Introductionmentioning

confidence: 99%

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Collective reflective assessment for shared epistemic agency by undergraduates in knowledge building

Yang¹,

Chen²,

Yu³

et al. 2020

Brit J Educational Tech

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We examined the facilitation of shared epistemic agency through a knowledge‐building (KB) design that included analytics‐supported collective reflective assessment (AsCRA). Forty undergraduate students taking a Liberal Studies course at a university in central China used the promising ideas tool and the knowledge building discourse explorer to self‐analyze the online discourse they had created in Knowledge Forum® (KF), an online discourse environment; 34 students in a comparison section of the same course used KF but did not use the additional tools. Both classes were taught by the same teacher and studied the same inquiry topics. Multifaceted analysis of students' interaction in and the quality of discourse on KF, an educational online platform, indicated that analytics‐supported reflective assessment helped them to develop high‐level shared epistemic agency in the KB process. Qualitative analysis showed that AsCRA helped students to focus on high‐level goals (idea negotiation, synthesis of ideas and rise above thinking) in KB, and to engage in continuous assessment, reflection, and action planning to regulate and improve their discourse. The findings have important implications for the design of technology‐rich environments to support learners, and may help teachers identify the potential uses of such environments to encourage learners to engage in productive collaborative inquiry and develop metacognition and agency.

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Section: Conceptual Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collective reflective assessment for shared epistemic agency by undergraduates in knowledge building

Yang¹,

Chen²,

Yu³

et al. 2020

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…While most studies on learning as knowledge creation have been conducted by applying qualitative approaches, it is needed to develop a new quantitative analytic framework for evaluating learning as a knowledge creation practice to handle larger and richer datasets (Martinez et al, 2003;Scardamalia et al, 2012). Both qualitative and quantitative analyses are expected to provide us with deeper insights into student learning in the mixed-methods approach (Johnson and Onwuegbuzi, 2006;Oshima et al, 2018). A promising quantitative approach that recent studies have discussed is socio-semantic network analysis (SSNA).…”

Section: Evaluation Of Learning As Knowledge Creationmentioning

confidence: 99%

“…Through visualizing the network structure of words or codes used in discourse, researchers could represent how a group of learners engage in their knowledge creation. The SSNA approach has been adopted in educational studies to analyze rotation of leadership among students in the knowledge-building community (e.g., Ma et al, 2016) and to detect productive interaction patterns in the knowledge-creation practice, such as in the jigsaw instruction (e.g., Oshima et al, 2018).…”

Section: Evaluation Of Learning As Knowledge Creationmentioning

confidence: 99%

An Assessment of Idea Emergence in Subject-Matter Collaborative Learning

2020

Self Cite

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Idea emergence is critical in learning as knowledge creation. Although recent advancements make it possible to detect emergent ideas and evaluate how students engage in knowledge creation in collaborative learning contexts, the relationship between the learning processes and final conceptual understanding has not been well-studied. One confounding factor is how much students engage in their study topic during collaboration. In this study, therefore, we propose a new procedure for evaluating idea emergence in the context of jigsaw instruction by combining a socio-semantic network analysis of discourse and a text-mining algorithm, "the term-frequency." This procedure was used to evaluate how high-school learners engaged in their social process of knowledge creation as well as how much they discuss their study topics, the human immune system. Results showed that the weight of priority on a study topic did not significantly differ in both high and low conceptual understanding groups, but high conceptual understanding groups were more engaged in sharing and discussing ideas in the early stage of their collaboration. It is suggested that students' recognition of the study topic was not different depending on the levels of their conceptual understanding in a well-structured collaborative learning context such as jigsaw instruction. However, what matters is how students discuss their ideas through collaborative discourse.

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“…A study of communal knowledge in online knowledge building discourse used temporal analytics combined with graph theory to identify ideas and their mobility over time 22 . Graph centrality has been used to explore temporal and contextual profiles of shared epistemic agency on discourse transcripts 23 , identifying pivotal points of discourse exchanges. One study emphasised the importance of temporality as the main component of learning analytics through main path analysis of Wikiversity domains to model the flow of ideas 24 .…”

Section: Introductionmentioning

confidence: 99%

Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation

Peach

Greenbury

Johnston

et al. 2021

Sci Rep

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The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners’ behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequential behaviours of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task-centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.

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A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality

Cited by 33 publications

References 24 publications

Collective reflective assessment for shared epistemic agency by undergraduates in knowledge building

Collective reflective assessment for shared epistemic agency by undergraduates in knowledge building

An Assessment of Idea Emergence in Subject-Matter Collaborative Learning

Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation

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