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Articles

Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1

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Pages 174-198 | Received 15 Nov 2014, Accepted 16 Jan 2015, Published online: 05 May 2015

References

  • Arnold, K. E., & Pistilli, M. D. (2012, May). Course signals at purdue: Using learning analytics to increase student success. In Proceedings of the second international conference on learning analytics and knowledge – LAK’12 (pp. 267–270). New York, NY: ACM. 10.1145/2330601
  • Askew, S., & Lodge, C. (2000). Gifts, ping-pong and loops – Linking feedback and learning. In S. Askew (Ed.), Feedback for learning (pp. 1–18). London: RoutledgeFalmer.
  • Barber, R., & Sharkey, M. (2012, May). Course correction: Using analytics to predict course success. In Proceedings of the second international conference on learning analytics and knowledge – LAK’12 (pp. 259–262). New York, NY: ACM.10.1145/2330601
  • Bergin, S., & Reilly, R. (2005). Programming: Factors that influence success. ACM SIGCSE Bulletin, 37, 411–415.10.1145/1047124
  • Bergin, S., Reilly, R., & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. ICER’05, 81–86.
  • Brasseur, L. (2005). Florence Nightingale’s visual rhetoric in the rose diagrams. Technical Communication Quarterly, 14, 161–182.
  • Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245–281.10.3102/00346543065003245
  • Carless, D. (2006). Differing perceptions in the feedback process. Studies in Higher Education, 31, 219–233.10.1080/03075070600572132
  • Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18, 683–695.10.1080/13562517.2013.827653
  • Dietz-Uhler, B., & Hurn, J. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12, 17–26.
  • Essa, A., & Ayad, H. (2012). Student success system: Risk analytics and data visualization using ensembles of predictive models. In Proceedings of the 2nd international conference on learning analytics and knowledge, LAK’12 (pp. 158–161). New York, NY: ACM.10.1145/2330601
  • Falkner, N., & Falkner, K. (2012). A fast measure for identifying at-risk students in computer science. In ICER’12 (pp. 55–62). New York, NY: ACM.
  • Fenwick, J. B., Norris, C., Barry, F., Rountree, J., Spicer, C., & Cheek, S. (2009). Another look at the behaviors of novice programmers. In ACM SIGCSE (pp. 296–300). New York, NY: ACM.
  • Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Burlingame, CA: Analytics Press.
  • Gibbs, J. (2010). Analytic induction [ Video]. Retrieved from www.youtube.com/watch?v=SizaG3KKAp4
  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77, 81–112.10.3102/003465430298487
  • Hounsell, D. (2007). Towards more sustainable feedback to students. In D. Boud & N. Falchikov (Eds.), Rethinking assessment in higher education: Learning for the longer term (pp. 101–113). London: Routledge.
  • Jessop, T., El Hakim, Y., & Gibbs, G. (2013). The whole is greater than the sum of its parts: A large-scale study of students’ learning in response to different programme assessment patterns. Assessment & Evaluation in Higher Education, 39, 73–88.
  • Müller, N., & Faltin, N. (2011). IT-support for self-regulated learning and reflection on the learning process. In Proceedings of the 11th international conference on knowledge management and knowledge technologies (i-KNOW ‘11) (p. 6). New York, NY: ACM.
  • Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31, 199–218.10.1080/03075070600572090
  • Ott, C. (2013). Development and Evaluation of an Infographic to support Students’ Self-regulated Learning in COMP160 ( Technical Report). Unpublished Manuscript. Higher Education Development Centre, University of Otago.
  • Preece, J., Rogers, Y., & Sharp, H. (2002). Interaction design: Beyond human–computer interaction (3rd ed.). Chichester: John Wiley & Sons.
  • Price, M., Handley, K., & Millar, J. (2011). Feedback: Focusing attention on engagement. Studies in Higher Education, 36, 879–896.10.1080/03075079.2010.483513
  • Rountree, N., Rountree, J., & Robins, A. (2004). Interacting factors that predict success and failure in a CS1 course. ACM SIGCSE Bulletin, 36, 101–104.10.1145/1041624
  • Simon, B., Lister, R., & Fincher, S. (2006). Multi-institutional computer science education research: A review of recent studies of novice understanding. In Proceedings of Frontiers in Education 36th Annual Conference (FIE) (pp. 12–17). San Diego, CA: IEEE.
  • Tabanao, E. S., Rodrigo, M. M. T., & Jadud, M. C. (2011). Predicting at-risk novice Java programmers through the analysis of online protocols. In Proceedings of the seventh international workshop on computing education research – ICER’11 (pp. 85–92). New York, NY: ACM. 10.1145/2016911
  • Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.
  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57, 1500–1509.10.1177/0002764213479363
  • Watson, C., Li, F. W. B., & Godwin, J. L. (2014). No tests required: Comparing traditional and dynamic predictors of programming success. In Proceedings of the 45th ACM technical symposium on computer science education – SIGCSE’14 (pp. 469–474). New York, NY: ACM.10.1145/2538862
  • Wilson, B. C., & Shrock, S. (2001). Contributing to success in an introductory computer science course. ACM SIGCSE Bulletin, 33, 184–188.10.1145/366413
  • Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1–19). New York, NY: Guilford Press.
  • Zingaro, D. (2014). Peer instruction contributes to self-efficacy in CS1. In Proceedings of the 45th ACM technical symposium on computer science education – SIGCSE’14 (pp. 373–378). New York, NY: ACM. 10.1145/2538862

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