Detecting learning in noisy data
| Autor: | Van Rynald T. Liceralde, Zuowei Wang, Nitin Madnani, J. R. Lockwood, John Sabatini, Jennifer Lentini, Binod Gyawali, Anastassia Loukina, Beata Beigman Klebanov |
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| Rok vydání: | 2020 |
| Předmět: |
media_common.quotation_subject
education 05 social sciences 050301 education Context (language use) 01 natural sciences Test (assessment) 010104 statistics & probability Book reading Fluency Reading (process) Independent reading Tracking (education) 0101 mathematics Psychology 0503 education Noisy data media_common Cognitive psychology |
| Zdroj: | LAK |
| DOI: | 10.1145/3375462.3375490 |
| Popis: | In a school context, learning is usually detected by repeated measurements of the skill of interest through a sequence of specially designed tests; in particular, this is the case with tracking improvement in oral reading fluency in elementary school children in the U.S. Results presented in this paper suggest that it is possible and feasible to detect improvement in oral reading fluency using data collected during children's independent reading of a book using the Relay Reader™ app. We are thus a step closer to the vision of having a child read for the story, not for a test, yet being able to unobtrusively assess their progress in oral reading fluency. |
| Databáze: | OpenAIRE |
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