My primary interests include computer science education, human-computer interaction (HCI), design, and psychology.
I'm currently investigating the how to improve the teaching of programming by providing explicit instruction on programming problem solving. I seak to demystify programming while making programming education more equitable for students of all backgrounds and cognitive styles. I believe this can be done by teaching not only what programmers use (the languages and tools assiociated with programming) but also the cognitive problem solving skills that programmers rely upon while solving programming problems.
I'm currently exploring the following questions:
- How might we incorporate explicit metacognitive and self-regulation instruction into classroom curricula?
- How does explicit problem solving instruction benefit programming students?
- Given our best knowledge of programming process, what barriers do instructors encounter when creating instructional content to teach programming process?
- How can we explicitly describe programming strategies?
- How do explicit programming strategies help and hinder developers of varying expertise?
Daniel Zingaro, Michelle Craig, Leo Porter, Brett A Becker, Yingjun Cao, Phill Conrad, Diana Cukierman, Arto Hellas, Dastyni Loksa, Neena Thota
Proceedings of the 49th ACM Technical Symposium on Computer Science Education
ACCEPTANCE RATE 35% REFEREED CONFERENCE PAPER
Dastyni Loksa, Andrew J. Ko, Will Jernigan, Alannah Oleson, Christopher J Mendez, and Margaret M Burnett
CHI Conference on Human Factors in Computing Systems
ACCEPTANCE RATE 23% REFEREED CONFERENCE PAPER
Dastyni Loksa and Andrew J. Ko
Proceedings of the Twelfth Annual International Conference on International Computing Education Research
Polina Charters, Michael J. Lee, Andrew J. Ko, Dastyni Loksa
ACM Symposium on Computer Science Education (p. 653-658)
ACCEPTANCE RATE 39% REFEREED CONFERENCE PAPER