Name
Abstract | ||
|---|---|---|
The feedback given to novice programmers can be substantially improved by delivering advice focused on learners' cognitive misconceptions contextualized to the instruction. Building on this idea, we present Misconception-Driven Feedback (MDF); MDF uses a cognitive student model and program analysis to detect mistakes and uncover underlying misconceptions. To evaluate the impact of MDF on student learning, we performed a quasi-experimental study of novice programmers that compares conventional run-time and output check feedback against MDF over three semesters. Inferential statistics indicates MDF supports significantly accelerated acquisition of conceptual knowledge and practical programming skills. Additionally, we present descriptive analysis from the study indicating the MDF student model allows for complex analysis of student mistakes and misconceptions that can suggest improvements to the feedback, the instruction, and to specific students.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3230977.3231002 | ICER |
Keywords | Field | DocType |
CS Education, Immediate Feedback, Student Model, Misconception | Descriptive statistics,Computer science,Knowledge management,Mathematics education,Statistical inference,Program analysis,Cognition,Student learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5628-2 | 2 | 0.38 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luke Gusukuma | 1 | 6 | 2.25 |
| Austin Cory Bart | 2 | 25 | 7.21 |
| Dennis G. Kafura | 3 | 745 | 134.03 |
| Jeremy Ernst | 4 | 7 | 1.85 |