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iTalk–iSee: A participatory visual learning analytical tool for productive peer talk

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Abstract

Productive peer talk moves have a fundamental role in structuring group discussions and promoting peer interactions. However, there is a lack of comprehensive technical support for developing young learners’ skills in using productive peer talk moves. To address this, we designed iTalk–iSee, a participatory visual learning analytical tool that supports students’ learning and their use of productive peer talk moves in dialogic collaborative problem-solving (DCPS). This paper discusses aspects of the design of iTalk–iSee, including its underlying theoretical framework, visualization, and the learner agency it affords. Informed by the theory of Bakhtinian dialogism, iTalk–iSee maps productive peer talk moves onto learning goals in DCPS. It applies well-established visualization design principles to connect with students, hold and direct their attention, and enhance their understanding. It also follows a three-step (code → visualize → reflect) macro-script to strengthen students’ agency in analyzing and interpreting their talk. This paper also discusses the progressive modifications of iTalk–iSee and evaluates its usability in a field study. We present the implications of essential design features of iTalk–iSee and the challenges of using it (relating to, for example, teacher guidance, data collection, transcription, and coding). We also provide suggestions and directions for future research.

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Notes

  1. Check http://demo.italkisee.com/ for the demo of iTalk–iSee.

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Acknowledgements

This work was supported by International Research Institute for the Learning Sciences (#EDT/2020/1/1) and Hong Kong Research Grants Council, University Grants Committee (Grants No. 17608318 and No. 17605221).

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  1. Faculty of Education, The University of Hong Kong, Pokfulam, Hong Kong, China

    Liru Hu, Jiajun Wu & Gaowei Chen

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  1. Liru Hu
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Hu, L., Wu, J. & Chen, G. iTalk–iSee: A participatory visual learning analytical tool for productive peer talk. Intern. J. Comput.-Support. Collab. Learn 17, 397–425 (2022). https://doi.org/10.1007/s11412-022-09374-w

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