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Data-informed Decision-making in TEFA Processes: An Empirical Study of a Process Derived from Peer-Instruction

Abstract : When formative assessment involves a large number of learners, Technology-Enhanced Formative Assessments are one of the most popular solutions. However, current TEFA processes lack data-informed decision-making. By analyzing a dataset gathered from a formative assessment tool, we provide evidence about how to improve decision-making in processes that ask learners to answer the same question before and after a confrontation with peers. Our results suggest that learners' understanding increases when the proportion of correct answers before the confrontation is close to 50%, or when learners consistently rate peers' rationales. Furthermore, peer ratings are more consistent when learners' confidence degrees are consistent. These results led us to design a decision-making model whose benefits will be studied in future works.
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https://hal-univ-tlse3.archives-ouvertes.fr/hal-03289228
Contributor : Rialy Andriamiseza Connect in order to contact the contributor
Submitted on : Friday, July 16, 2021 - 5:24:29 PM
Last modification on : Tuesday, October 19, 2021 - 2:26:17 PM
Long-term archiving on: : Sunday, October 17, 2021 - 7:46:18 PM

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Rialy Andriamiseza, Franck Silvestre, Jean-Francois Parmentier, Julien Broisin. Data-informed Decision-making in TEFA Processes: An Empirical Study of a Process Derived from Peer-Instruction. 8th ACM Conference on Learning @ Scale (L@S 2021), ACM, Jun 2021, Virtual Event, Germany. pp.259-262, ⟨10.1145/3430895.3460153⟩. ⟨hal-03289228⟩

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