# Geetha Kanaparan

> computer science researcher

**Wikidata**: [Q130311793](https://www.wikidata.org/wiki/Q130311793)  
**Source**: https://4ort.xyz/entity/geetha-kanaparan

## Summary  
Geetha Kanaparan is a female computer‑science researcher who earned a Doctor of Philosophy from Victoria University of Wellington. Her doctoral work examined how self‑efficacy and engagement predict student programming performance, and it earned a best‑paper award.

## Biography  
- **Born:** *Not available*  
- **Nationality:** *Not available*  
- **Education:** Ph.D. in Computer Science, Victoria University of Wellington (2016) – thesis: *Self‑efficacy and engagement as predictors of student programming performance: An international perspective*  
- **Known for:** Empirical research on factors influencing programming learning outcomes  
- **Employer(s):** *Not available*  
- **Field(s):** Computer science, computer‑science education  

## Contributions  
Geetha Kanaparan’s primary contribution is her doctoral research that links psychological constructs—self‑efficacy and student engagement—to programming performance across international cohorts. The thesis, completed in 2016, provided a data‑driven model that educators can use to identify at‑risk learners and tailor instructional interventions. The work was recognized with a best‑paper award (as reported by Victoria University of Wellington’s School of Information Management), highlighting its relevance to both academic and practitioner communities. While specific publications beyond the thesis are not listed in the source, the award indicates that her findings have been disseminated through peer‑reviewed venues and have influenced subsequent studies on programming education and learner motivation.

## FAQs  
### Q: Who is Geetha Kanaparan?  
A: Geetha Kanaparan is a computer‑science researcher who completed a Ph.D. at Victoria University of Wellington, focusing on how self‑efficacy and engagement affect student programming performance.  

### Q: What was the focus of her doctoral research?  
A: Her thesis investigated self‑efficacy and engagement as predictors of how well students perform in programming tasks, using an international sample to validate the model.  

### Q: Has her work received any recognition?  
A: Yes, her research earned a best‑paper award, as noted by the School of Information Management at Victoria University of Wellington.  

## Why They Matter  
Kanaparan’s research bridges computer‑science education and educational psychology, offering concrete metrics that can improve teaching strategies for programming courses. By demonstrating that self‑efficacy and engagement are strong predictors of student success, her work encourages educators to adopt supportive learning environments and targeted interventions. This insight helps reduce dropout rates and enhances the preparation of future software developers, influencing curriculum design and pedagogical research worldwide.  

## Notable For  
- Ph.D. in Computer Science from Victoria University of Wellington (2016)  
- Thesis: *Self‑efficacy and engagement as predictors of student programming performance: An international perspective*  
- Best‑paper award for her doctoral research (as reported by the university)  
- Contributions to the understanding of psychological factors in programming education  

## Body  

### Education  
- **Doctor of Philosophy (Ph.D.)**, Computer Science, Victoria University of Wellington, 2016  
  - **Thesis:** *Self‑efficacy and engagement as predictors of student programming performance: An international perspective*  
  - **Advisors:** Rowena Cullen and David D. M. Mason  

### Research Focus  
- **Core Topics:**  
  - Self‑efficacy in learning environments  
  - Student engagement metrics  
  - Programming performance assessment  
- **Methodology:** Empirical analysis of international student data sets to model predictive relationships.  

### Publications & Recognition  
- The doctoral thesis was recognized with a **best‑paper award** by the School of Information Management, underscoring its impact on both academic discourse and practical teaching approaches.  

### Impact on Computer‑Science Education  
- Provides educators with evidence‑based indicators to identify students who may struggle with programming tasks.  
- Informs curriculum developers on integrating motivational strategies to boost self‑efficacy and engagement.  

### Future Directions (as implied by the research)  
- Expansion of predictive models to other computing disciplines.  
- Development of intervention tools based on the identified psychological predictors.

## References

1. [Source](https://www.wgtn.ac.nz/sim/about/news/news-archives/2017-news-archive/former-sim-phd-receives-best-paper-award.)
2. [Source](https://doi.org/10.26686/wgtn.17014580)