ABSTRACT
Computer Science students struggle to develop fundamental programming skills and software development processes. Crucial to successful mastery is the development of discipline specific cognitive and metacognitive skills, including self-regulation. We can assist our students in the process of reflection and self-regulation by identifying and articulating successful self-regulated learning strategies for specific discipline contexts. However, in order to do so, we must develop an understanding of those discipline-specific strategies that are successful and can be readily adopted by students.
In this paper, we analyse student reflections from an introductory software development course, identifying the usage of self-regulated learning strategies that are either specific to the software development domain, or articulated in that context. This study assists in the understanding of how Computer Science students develop learning skill within the discipline, and provides examples to guide the development of scaffolding activities to assist learning development.
- C. Allwood. Novices on the computer: a review of the literature. International Journal of Man-Machine Studies, 25:633--658, 1986. Google ScholarDigital Library
- S. Bergin, R. Reilly, and D. Traynor. Examining the role of self-regulated learning on introductory programming performance. In Proceedings of ICER'05, pages 81--86, 2005. Google ScholarDigital Library
- R. Cantwell and P. Moore. The development of measures of individual differences in self-regulatory control and their relationship to academic performance. Contemporary Educational Psychology, 21:500--517, 1996.Google ScholarCross Ref
- T. Caruso, N. Hill, T. VanDeGrift, and B. Simon. Experience report: Getting novice programmers to think about improving their software development process. In Proceedings of SIGCSE'11, pages 493--498, 2011. Google ScholarDigital Library
- B. Hanks, L. Murphy, B. Simon, R. McCauley, and C. Zander. Cs1 students speak: Advice for students by students. In Proceedings of SIGCSE'09, pages 19--23, 2009. Google ScholarDigital Library
- E. Lichtinger and A. Kaplan. Self-Regulated Learning, chapter Purpose of engagement in academic self-regulation, pages 9--19. Jossey-Bass, 2011.Google Scholar
- R. Lister, E. Adams, S. Fitzgerald, W. Fone, J. Hamer, M. Lindholm, R. McCartney, E. Mostrom, K. Sanders, O. Seppala, B. Simon, and L. Thomas. A multi-national study of reading and tracing skillls in novice programmers. SIGCSE Bulletin, 36(4):119--150, December 2004. Google ScholarDigital Library
- M. McCracken, V. Almstrum, D. Diaz, M. Guzdial, D. Hagen, Y. Kolikant, C. Laxer, L. Thomas, I. Utting, and T. Wiusz. A multi-national, multi-institutional study of assessment of programming skills of first-year cs students. SIGCSE Bulletin, 33(4):125--140, 2001. Google ScholarDigital Library
- S. Paris and J. Turner. Student Motivation, Cognition and Learning: Essays in Honor of Wilbert J. McKeachie, chapter Situated Motivation, pages 213--237. Hillsdale, N.J.: Erlbaum, 1994.Google Scholar
- V. Ramalingam, D. LaBelle, and S. Wiedenbeck. Self-efficacy and mental models in learning to program. In Proceedings of ITiCSE'04, pages 171--175, 2004. Google ScholarDigital Library
- P. N. Robillard. The role of knowledge in software development. Communications of the ACM, 42(1), January 1999. Google ScholarDigital Library
- M. Sandelowski. Sample size in qualitative research. Research in Nursing & Health, 18(2):179--183, 1995.Google ScholarCross Ref
- D. Schon. The Reflective Practitioner: How Professionals Think in Action. Basic Books, 1983.Google Scholar
- G. Schraw, K. Crippen, and K. Hartley. Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36(1--2):111--139, 2006.Google Scholar
- J. Sheard, Simon, M. Hamilton, and J. Lönnberg. Analysis of research into the teaching and learning of programming. In Proceedings of ICER'09, pages 93--104, 2009. Google ScholarDigital Library
- M. Veenman, J. Elshout, and J. Meijer. The generality vs domain-specificity of metacognitive skills in novice learning across domains. Learning and Instruction, 7(2):187--209, 1997.Google ScholarCross Ref
- S. Violet. Modelling and coaching of relevant metacognitive strategies for enhancing university students' learning. Learning and Instruction, 1:319--336, 1991.Google ScholarCross Ref
- P. Winne. Inherent details in self-regulated learning. Educational Psychologist, 80:284--290, 1995.Google Scholar
- Y. Zhang and B. Wildemuth. Application of social research methods to questions in information and library science. Westport Conn: Libraries Unlimited, 2009.Google Scholar
- B. Zimmerman. A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3):329--339, 1989.Google ScholarCross Ref
Index Terms
- Identifying computer science self-regulated learning strategies
Recommendations
Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance
With the rapid development of Internet technologies, the conventional computer-assisted learning (CAL) is gradually moving toward to web-based learning. Additionally, instructors typically base their teaching methods to simultaneously interact with all ...
Motivational active learning for computer science education (abstract only)
SIGCSE '14: Proceedings of the 45th ACM technical symposium on Computer science educationMotivational Active Learning (MAL) is an innovative pedagogical approach based on MIT's teaching format TEAL (Technology-Enabled Active Learning) combined with advanced motivational strategies based on gamification design aspects. The main idea of MAL ...
Promoting self-regulated learning in web-based learning environments
Self-regulated learning with the Internet or hypermedia requires not only cognitive learning strategies, but also specific and general meta-cognitive strategies. The purposes of the Study2000 project, carried out at the TU Dresden, were to develop and ...
Comments