Literaturnachweis - Detailanzeige
| Autor/inn/en | Bauer, Aaron; Flatten, Jeff; Zoran Popovic |
|---|---|
| Titel | Analysis of Problem-Solving Behavior in Open-Ended Scientific-Discovery Game Challenges [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017). |
| Quelle | (2017), (8 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Problem Solving; Science Instruction; Cooperative Learning; Visualization; Scientific Principles; Discovery Processes; Educational Games; Puzzles; Teaching Methods; Student Behavior |
| Abstract | Problem-solving skills in creative, open-ended domains are both important and little understood. These domains are generally ill-structured, have extremely large exploration spaces, and require high levels of specialized skill in order to produce quality solutions. We investigate problem-solving behavior in one such domain, the scientific-discovery game "Foldit". Our goal is to discover differentiating patterns and understand what distinguishes high and low levels of problem-solving skill. To address the challenges posed by the scale, complexity, and ill-structuredness of "Foldit" solver behavior data, we devise an iterative visualization-based methodology and use this methodology to design a concise, meaning-rich visualization of the problem-solving process in "Foldit". We use this visualization to identify key patterns in problem-solving approaches, and report how these patterns distinguish high-performing solvers in this domain. [For the full proceedings, see ED596512.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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