Using Targeted Feedback to Address Common Student Misconceptions in Introductory Programming
A Data-Driven Approach
Bibliographic Data
| ID | 16913630 |
|---|---|
| Authors | Yizhou Qian (0000-0003-2551-9581, Jiangnan University, corresponding author), James Daniel Lehman (0000-0002-5072-3747, Purdue University West Lafayette) |
| Year | 2019 |
| Volume | 9 |
| Issue | 4 |
| Publication date | 2019-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/2158244019885136 |
| OpenAlex | W2981974458 |
| Language | EN |
| Citations received | 6 |
| References cited | 64 |
With the expansion of computer science (CS) education, CS teachers in K-12 schools should be cognizant of student misconceptions and be prepared to help students establish accurate understanding of computer science and programming. Digital tools, such as automated assessment systems, can be useful and supportive in teaching CS courses. This two-stage design-based research (DBR) study investigated the effects of targeted feedback in an automated assessment system for addressing common misconceptions of high school students in a Java-based introductory programming course. Based on students’ common errors and underlying misconceptions, targeted feedback messages were designed and provided for students. The quantitative analysis found that with targeted feedback students were more likely to correct the errors in their code. The qualitative analysis of students’ solutions revealed that when improving the code, students receiving feedback made fewer intermediate incorrect solutions. In other words, the targeted feedback messages may help to promote conceptual change and facilitate learning. Although the findings of this exploratory study showed evidence of the power of digital tools, more research is needed to make technology benefit more CS teachers
Exploratory research · Java · Mathematics education · Medical education · Programming language · Qualitative property · Computer Science · Educational Games and Gamification · Innovative Teaching and Learning Methods · Medicine · Psychology · Teaching and Learning Programming
Exploring the quality and effectiveness of AI-generated feedback in introductory programming
The Impact of Narrative Feedback, E-Learning Modules and Realistic Video and the Reduction of Misconception
Dialogue alongside or within lecturing videos for teaching debugging
A multidimensional comparative analysis of human expert vs. AI-driven feedback approaches on learner-centered and collaborative groups
A review of automated feedback systems for learners
Integrating Computational Thinking Diagnostic Mechanism and Reflective Learning
Toward an Epistemology of Physics
Scratch
Misconceptions Reconceived
The Instructional Effect of Feedback in Test-Like Events
Feedback in written instruction
Capturing and modeling the process of conceptual change
Accommodation of a scientific conception
Feedback and Self-Regulated Learning
Conceptual change
Focus on Formative Feedback
Those Who Understand
Design-Based Research
The Power of Feedback
An Overview of Conceptual Change Theories
| Citations per year | 1.2 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |