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Shin-Yu Kim; Inseong Jeon; Seong-Joo Kang – Journal of Chemical Education, 2024
Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds…
Descriptors: Chemistry, Science Instruction, Laboratory Experiments, Artificial Intelligence
David P. Reid; Timothy D. Drysdale – IEEE Transactions on Learning Technologies, 2024
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment…
Descriptors: Learning Analytics, Laboratory Experiments, Electronic Learning, Feedback (Response)
Margaret K. Meadows – Journal of Chemical Education, 2024
This paper describes the design, implementation, and evaluation of an inquiry-based laboratory for sophomore-level organic chemistry into a mostly cookbook, existing laboratory curriculum. For the laboratory, students were required to generate their own procedures and use their own recorded data from prior laboratory assignments to identify an…
Descriptors: Active Learning, Inquiry, Laboratory Experiments, Chemistry
Adrian Adams; Lauren Barth-Cohen – CBE - Life Sciences Education, 2024
In undergraduate research settings, students are likely to encounter anomalous data, that is, data that do not meet their expectations. Most of the research that directly or indirectly captures the role of anomalous data in research settings uses post-hoc reflective interviews or surveys. These data collection approaches focus on recall of past…
Descriptors: Undergraduate Students, Physics, Science Instruction, Laboratory Experiments