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Trus4ed: decoding the conditions for trust in teachers and machine learning models for 4th grade predictions
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 6.15 MB | Adobe PDF |
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Resumo(s)
Effectively predicting student failure is crucial for timely resource allocation and preventing
academic difficulties. This thesis explores conditions where machine learning model
predictions should be trusted over teachers' judgments, recognizing potential disparities.
Beginning with model building, one investigates the impact of both teachers' and students'
features on the model-teacher interplay. Contrary to expectations, teachers' features do not
significantly influence this dynamic, but a closer analysis of students' reveals the significance
of psychological factors and academic achievement levels. The model exceeds for high achieving students and those with more distinctive psychological patterns. This research
provides relevant insights into shaping reliable educational predictions in diverse academic
scenarios.
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Machine learning in education Predictive modeling Economics of education Interplay teacher-model