Towards Better Cultivating Tomorrow’s STEM Workforce : An Investigation into Participants’ Needs, Predictors of Success, and Physics Problem-Solving Abilities in the Physics Olympiad
Student science competitions, such as the German Physics Olympiad, aim to attract potentially high-achieving students and support them in developing their STEM-related potentials, ultimately fostering their contribution to the future STEM workforce. Achieving these goals requires aligning participants' needs with the opportunities provided by these competitions. Drawing on data from the WinnerS project, this dissertation investigates this alignment in the context of the Physics Olympiad, while identifying and exploring areas for improvement.
The first study used latent profile analysis to explore the diverse needs of Physics Olympiad participants. The second study employed logistic regression to determine factors contributing to success in the competition. Both studies highlighted the necessity for enhanced problem-solving support at the competition's outset, leading to the idea of an automated feedback system for physics problem solving. To develop such a system, understanding participants' problem-solving processes was considered essential. Therefore, the third study used AI techniques to identify themes in problem-solving approaches and trained a machine learning model to detect these themes in new approaches, forming a basis for automated feedback. The fourth study applied process mining and sequence analysis to investigate the order of these themes, offering insights for sequence-based feedback to improve problem-solving abilities.
Overall, this dissertation underscores opportunities to better align the Physics Olympiad with participants' needs and advances the concept of an AI-based feedback system to support students in developing their physics problem-solving abilities.
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