Knowledge-Integration Processes and Learning Outcomes Associated with a Self-Diagnosis Activity: the Case of 5th-Graders Studying Simple Fractions

Article

Abstract

I examined how well a self-diagnosis activity engages students in knowledge-integration processes, and its impact on students’ mathematical achievements. The self-diagnosis activity requires students to self-diagnose their solutions to problems that they have solved on their own—namely, to identify where they went wrong and to explain the nature of their own errors—and self-score them, aided by a rubric demonstrating how to solve each problem step by step. I also examined knowledge-integration processes and the impact on students’ achievements in a traditional activity in which teachers solve, together with their students, problems that students have solved on their own. The two activities can provide students with opportunities to reflect on their own errors, which is assumed to promote learning. Two 5th-grade classes studying simple fractions with the same teacher participated. A pre-test/intervention/post-test design was employed. In the intervention, one class was assigned to the self-diagnosis activity and the other to the traditional activity. Results suggested that at least for a teacher who is not competent in managing argumentative class discussions, the self-diagnosis activity is more effective than the traditional activity in engaging students in knowledge-integration processes and enhancing their achievements. I account for these results and suggest possible directions for future research.

Keywords

Class discussion Learning from errors Self-assessment Self-diagnosis Simple fractions 

Notes

Acknowledgements

I would especially like to thank David Fortus for his valuable support and comments. I would also like to thank Edit Yerushalmi for her contribution to the current study. I appreciate the support of the Academic Arab College for Education in Israel—Haifa.

References

  1. Anderson, J. R. (2010). Cognitive psychology and its implications (7th ed.). New York: Worth Publishers.Google Scholar
  2. Andrade, H. (2010). Students as the definitive source of formative assessment: Academic self-assessment and the self-regulation of learning. In H. Andrade & G. Cizek (Eds.), Handbook of formative assessment (pp. 90–105). New York: Routledge.Google Scholar
  3. Andrade, H., & Valtcheva, A. (2009). Promoting learning and achievement through self-assessment. Theory Into Practice, 48, 12–19.CrossRefGoogle Scholar
  4. Ayalon, M., & Even, R. (2016). Factors shaping students’ opportunities to engage in argumentative activity. International Journal of Science and Mathematics Education, 14(3), 575–601.CrossRefGoogle Scholar
  5. Bennett, R. E. (2011). Formative assessment: A critical review. Assessment in Education: Principles, Policy, & Practice, 18, 5–25.CrossRefGoogle Scholar
  6. Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2004). Working inside the black box: Assessment for learning in the classroom. Phi Delta Kappan, 8(1), 8–21.CrossRefGoogle Scholar
  7. Black, P., & Wiliam, D. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80, 139–148.Google Scholar
  8. Borasi, R. (1996). Reconceiving mathematics instruction: A focus on errors (Issues in curriculum theory, policy, and research series). Norwood: Ablex Publishing Corporation.Google Scholar
  9. Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6, 271–315.CrossRefGoogle Scholar
  10. Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology (pp. 161–238). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  11. Chi, M. T. H., Bassok, M., Lewis, M. H., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.CrossRefGoogle Scholar
  12. Chi, M. T. H., de Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.Google Scholar
  13. Clark, I. (2012). Formative assessment: Assessment is for self-regulated learning. Educational Psychology Review, 24(2), 205–249.CrossRefGoogle Scholar
  14. Craig, S., Chi, M. T. H., & VanLehn, K. (2009). Improving classroom learning by collaboratively observing human tutoring videos while problem solving. Journal of Educational Psychology, 101, 779–789.CrossRefGoogle Scholar
  15. Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22, 206–214.CrossRefGoogle Scholar
  16. Eilam, B. (2002). Passing through a western-democratic teacher education: The case of Israeli-Arab teachers. Teacher College Record, 104(8), 1656–1701.CrossRefGoogle Scholar
  17. Gick, M. L., & Holyack, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 1–38.CrossRefGoogle Scholar
  18. Große, C. S., & Renkl, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction, 17, 612–634.CrossRefGoogle Scholar
  19. Hattie, J. (1999). Influences on student learning. Retrieved October 17, 2016, from http://projectlearning.org/blog/wp-content/uploads/2014/02/Influences-on-Student-Learning-John-Hattie.pdf.
  20. Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger, & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417–424). Amsterdam: Ios Press.Google Scholar
  21. Heemsoth, T., & Heinze, A. (2014). The impact of incorrect examples on learning fractions: A field experiment with 6th grade students. Instructional Science, 42(4), 639–657.CrossRefGoogle Scholar
  22. Heemsoth, T., & Heinze, A. (2016). Secondary school students learning from reflections on the rationale behind self-made errors: A field experiment. The Journal of Experimental Education, 84(1), 98–118.CrossRefGoogle Scholar
  23. Keith, N., & Frese, M. (2005). Self-Regulation in error management training: Emotion control and metacognition as mediators of performance effects. Journal of Applied Psychology, 90, 677–691.CrossRefGoogle Scholar
  24. Linn, M. C., & Eylon, B. S. (2006). Science education: Integrating views of learning and instruction. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 511–544). Mahwah: Erlbaum.Google Scholar
  25. Panadero, E., & Romero, M. (2014). To rubric or not to rubric? The effects of self-assessment on self-regulation, performance and self-efficacy. Assessment in Education: Principles, Policy & Practice, 21(2), 133–148.CrossRefGoogle Scholar
  26. Puterkovsky, M. (2013). Knowledge integration processes and development of conceptual understanding within web-based diagnostic activities focused on common alternative conceptions in introductory physics. (Unpublished doctoral dissertation). The Weizmann Institute of Science, Rehovot, Israel.Google Scholar
  27. Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13, 273–304.CrossRefGoogle Scholar
  28. Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18, 119–144.CrossRefGoogle Scholar
  29. Safadi, R., & Yerushalmi, E. (2013). Students' self-diagnosis using worked-out examples. Creative Education, 4, 205–216. doi: 10.4236/ce.2013.43031.
  30. Safadi, R., & Yerushalmi, E. (2014). Problem solving vs. troubleshooting tasks: The case of sixth-grade students studying simple electric circuits. International Journal of Science and Mathematics Education, 12(6), 1341–1366.Google Scholar
  31. Schwartz, D. L., Bransford, J. D., & Sears, D. (2005). Efficiency and innovation in transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 1–51). Mahwah: Erlbaum.Google Scholar
  32. Schwarz, B. B., Hershkowitz, R., & Prusak, N. (2010). Argumentation and mathematics. In C. Howe & K. Littleton (Eds.), Educational dialogues: Understanding and promoting productive interaction (pp. 115–141). London: Routledge.Google Scholar
  33. Siegler, R. S. (2002). Microgenetic studies of self-explanation. In N. Garnott & J. Parziale (Eds.), Microdevelopment: A process-oriented perspective for studying development and learning (pp. 31–58). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  34. Veenman, M. V. J., Wilhelm, P., & Beishuizen, J. (2004). The relation between intellectual and metacognitive skills from a developmental perspective. Learning and Instruction, 14, 89–109.CrossRefGoogle Scholar
  35. Vygotsky, L. S. (1978). Mind in society. Cambridge: Harvard University Press.Google Scholar
  36. Wells, G. (1993). Reevaluating the IRF sequence: A proposal for the articulation of theories of activity and discourse for the analysis of teaching and learning in the classroom. Linguistics and Education, 5, 1–37.CrossRefGoogle Scholar
  37. Yackel, E. (2002). What we can learn from analyzing the teacher’s role in collective argumentation. Journal of Mathematical Behavior, 21, 423–440.CrossRefGoogle Scholar
  38. Yerushalmi, E., Cohen, E., Mason, A., & Singh, C. (2012). What do students do when asked to diagnose their mistakes? Does it help them? II. A more typical quiz context. Physical Review Special Topics-Physics Education Research, 8(2), 020110.CrossRefGoogle Scholar
  39. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41, 64–72.CrossRefGoogle Scholar

Copyright information

© Ministry of Science and Technology, Taiwan 2017

Authors and Affiliations

  1. 1.The Academic Arab College for Education in IsraelHaifaIsrael

Personalised recommendations