Abstract
Distance Learning suffers from a high dropout rate. Several works propose to use machine learning techniques to create predictive models that can identify students at risk of dropping out and, thus, reduce this index. However, predictive models can stop making accurate predictions over time and even contain cultural and prejudiced bias. Therefore, educators need to be included in the loop of modeling and simulating predictive models to identify problems and requirements that may not be easily identified by just accessing the outcomes. Our work proposes a prototype to simulate scenarios without changing the predictive model so that educators can evaluate the results of AI. After the development of the prototype and three different predictive models, educators carried out a formative evaluation on the prototype. The findings indicate that this type of scenario simulation can be useful for questioning the results of an AI, even with people without technical knowledge about predictive models.
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Acknowledgement
The authors want to thank the Brazilian funding agencies that support this project in different ways: CAPES, CNPq and FAPERJ. They would also like to express their gratitude to the volunteers who participated in the study.
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Freitas, R., Salgado, L. (2020). Educators in the Loop: Using Scenario Simulation as a Tool to Understand and Investigate Predictive Models of Student Dropout Risk in Distance Learning. In: Degen, H., Reinerman-Jones, L. (eds) Artificial Intelligence in HCI. HCII 2020. Lecture Notes in Computer Science(), vol 12217. Springer, Cham. https://doi.org/10.1007/978-3-030-50334-5_17
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