![]() Inspired by educational theories and effective human feedback, my dissertation has the goal of designing and evaluating different design choices of automated support, specifically next-step hints and formative feedback, to improve students' cognitive and affective outcomes in programming classrooms. Furthermore, few, if any, prior studies have investigated how well these results can be replicated in multiple learning contexts. Additionally, while some studies show that automated support can improve students' learning, it is unclear what specific design choices make them more or less effective. While researchers have put tremendous effort into developing algorithms to generate automated programming support, few controlled studies have directly evaluated its impact on students' performance, learning and affective outcomes. However, in large programming classrooms, it is hard for instructors to provide real-time support (such as hints) for every student. Timely support is essential for students to learn and improve their performance. Investigating Best Practices in the Design of Automated Hints and Formative Feedback to Improve Students' Cognitive and Affective Outcomes.
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