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Optimizing Experimental Science Learning Outcomes Through the Inquiry Based Method and Team Making Using a Sociometric Software Tool

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Educating Engineers for Future Industrial Revolutions (ICL 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1328))

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Abstract

The present study investigates the interactivity between members of secondary education student groups based on three team making processes, the interpersonal relationships formed using them and their effectiveness in the students’ learning outcomes in science concepts. More specifically, the research aimed to investigate students’ opinion on three different methods used for team making, comparing groups formed based on “Group Dynamics” to groups formed based on students’ preferences and groups based on student random selection by the teacher. What is more, it aims to investigate which method they would like to form groups with and which method they think has helped them be more effective in their virtual laboratory electricity concept tasks. For the purposes of the study an intervention took place with 67 randomly selected Senior High School learners. The study employed a mixed method approach (qualitative and quantitative) using the balanced test D.I.R.E.C.T. to assess students’ learning outcomes, observation and note-taking, students’ self and peer-assessment, three student focus groups discussion and twelve students’ personal interviews to assess students’ social skills and opinion on the effectiveness of experimental teaching and sociometric techniques. The findings indicate positive results on students’ cognitive level, whereas the learners who used Group Dynmics displayed better cognitive results than the rest of the groups.

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Correspondence to Charilaos Tsihouridis .

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Tsihouridis, C., Petrou, N., Batsila, M., Vavougios, D. (2021). Optimizing Experimental Science Learning Outcomes Through the Inquiry Based Method and Team Making Using a Sociometric Software Tool. In: Auer, M.E., Rüütmann, T. (eds) Educating Engineers for Future Industrial Revolutions. ICL 2020. Advances in Intelligent Systems and Computing, vol 1328. Springer, Cham. https://doi.org/10.1007/978-3-030-68198-2_4

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