Toward Understanding and Enhancing Novice Students' Mental Models in Computer Science
Doctoral Consortium Abstract
Zu finden in: ICER 2019, 2019
The CS1 classroom is filled with ideas that may be difficult for novice students. Flawed transfer and knowledge gaps appear in mistakes such as incorrect syntax. Students may recognize core ideas while details confuse them. Students must work through these problems to grow, but instructor intervention can assist appropriate restructuring of their mental models.
Student inquiries and submissions may exhibit misconceptions. We can use this insight into student mental model inconsistencies to better understand these models and to specifically explore analogies as a tool for intervention. Novice students have no background, so analogies relying on the "real world" may make implementation details more memorable and thus programming more approachable. The use of analogies may help to improve an incorrect mental model. Analogy usage is not new to computer science, but use for basic constructs or with "smaller" errors is largely unexplored. I aim to develop a detailed, viable collection of CS1 analogies. A subset will be studied in the classroom, particularly focused on how delivery time affects value and concept retention. This can offer insight into the intervention's impact on a student's mental model long term.
My goal is to illustrate a multifaceted view of understanding students' mental model development.
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|Sabita Acharya, Mehrdad Alizadeh, Omar AlZoubi, Barbara Di Eugenio, Edsger W. Dijkstra, John D. Ferguson, Davide Fossati, Nick E. Green, Rachel Harsley, Steve Kollmansberger, Linxiao Ma, Marc Roper, Isla Ross, Murray Wood|
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|Informatikcomputer science, Programmierenprogramming|
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