428
Views
15
CrossRef citations to date
0
Altmetric
Articles

Students’ Understanding of Loops and Nested Loops in Computer Programming: An APOS Theory Perspective

REFERENCES

  • Asiala, M., Brown, A., Devries, D.J., Dubinsky, E., Mathews, D., & Thomas, K. (1996). A framework for research and curriculum development in undergraduate mathematics education. In J. Kaput, A.H. Schoenfeld, & E. Dubinsky (Eds.), Research in collegiate mathematics education II (pp. 1–32). Providence, RI: American Mathematical Society.
  • Asiala, M., Cottrill, J., Dubinsky, E., & Schingendorf, K.E. (1997). The development of students’ graphical understanding of the derivative. Journal of Mathematical Behavior, 16(4), 399–431.
  • Beth, E.W., & Piaget, J. (1966). Mathematical epistemology and psychology. Dordrecht, The Netherlands: Reidel.
  • Breidenbach, D., Dubinsky, E., Hawks, J., & Nichols, D. (1992). Development of the process conception of function. Educational Studies in Mathematics, 23(3), 247285.
  • Buyukozturk, S., Cokluk, O., & Koklu, N. (2013). Statistics for the behavioral sciences. Ankara, Turkey: Pegem Academy.
  • Cetin, I. (2009). Students’ understanding of limit concept: An APOS perspective ( Unpublished doctoral dissertation). Middle East Technical University, Turkey.
  • Cetin, I. (2013). Visualization: A tool for enhancing students’ concept images of basic object-oriented concepts. Computer Science Education, 23(1), 1–23.
  • Christensen, K., Rundus, D., Fujinoki, H., & Davis, D. (2002). A crash course for preparing students for a first course in computing: Did it work? Journal of Engineering Education, 91(4), 409–413.
  • Corritore, C.L., & Wiedenbeck, S. (1991). What do novices learn during program comprehension? International Journal of Human–Computer Interaction, 3(2), 199–222.
  • Cottrill, J., Dubinsky, E., Nichols, D., Schwinngendorf, K., Thomas, K., & Vidakovic, D. (1996). Understanding the limit concept: Beginning with a coordinated process schema. Journal of Mathematical Behavior, 15, 167–192.
  • Creswell, J.W., & Clark, V.L. P. (2007). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage Publications.
  • Davies, S.P. (1993). Models and theories of programming strategy. International Journal of Man–Machine Studies, 39(2), 237–267.
  • Dubinsky, E. (1991). Reflective abstraction in advanced mathematical thinking. In D. Tall (Ed.), Advanced mathematical thinking (pp. 95–126). Boston, MA: Kluwer.
  • Dubinsky, E. (1993). Computers in teaching and learning discrete mathematics and abstract algebra. In D.L. Ferguson (Ed.), Advanced educational technologies for mathematics and science (pp. 525–583). New York, NY: Springer-Verlag.
  • Dubinsky, E. (1997). On learning quantification. Journal of Computers in Mathematics and Science Teaching, 16(2), 335–362.
  • Dubinsky, E., Elterman, F., & Gong, C. (1988). The students’ construction of quantification. For the Learning of Mathematics, 8(2), 44–67.
  • Dubinsky, E., & Lewin, P. (1986). Reflective abstraction and mathematics education: The genetic decomposition of induction and compactness. Journal of Mathematical Behavior, 5(1), 55–92.
  • Dubinsky, E., Weller, K., Mcdonald, M., & Brown, A. (2005). Some historical issues and paradoxes regarding the concept of infinity: An APOS analysis: Part 2. Educational Studies in Mathematics, 60(2), 253–266.
  • Dubinsky, E., Weller, K., Stenger, C., & Vidakovic, D. (2008). Infinite iterative process: The tennis ball problem. European Journal of Pure and Applied Mathematics, 1(1), 99–121.
  • Dubinsky, E., & Yiparaki, O. (2000). On student understanding of AE and EA quantification. In E. Dubinsky, A. Schoenfeld, & J. Kaput (Eds.), Research in collegiate mathematics education IV (pp. 239–289). Providence, RI: American Mathematical Society.
  • DuBoulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57–73.
  • Ebrahimi, A. (1994). Novice programmer errors: Language constructs and plan composition. International Journal of Human–Computer Studies, 41(4), 457–480.
  • Eckerdal, A., Ratcliffe, M., McCartney, R., Sanders, K., Moström, J.E., & Zander, C. (2006). Threshold concepts into context in computer science education. ACM SIGCSE Bulletin, 38(3), 103–107.
  • El-Zein, A., Langrish, T., & Balaam, N. (2009). Blended teaching and learning of computer programming skills in engineering curricula. Advances in Engineering Education, 1(3), 1–18.
  • Fix, V., Wiedenbeck, S., & Scholtz, J. (1993). Characteristics of the mental representations of novice and expert programmers: An empirical study. International Journal of Man–Machine Studies, 39(5), 793–812.
  • Ginat, D. (2004). On novice loop boundaries and range conceptions. Computer Science Education, 14(3), 165–181.
  • Guindon, R. (1990). Knowledge exploited by experts during software systems design. International Journal of Man–Machine Studies, 33(3), 279–182.
  • Hazzan, O. (2003). How students attempt to reduce abstraction in the learning of mathematics and in the learning of computer science. Computer Science Education, 13(2), 95–123.
  • Hodge, B.K., & Steele, W.G. (2002). A survey of computational paradigms in undergraduate mechanical engineering education. Journal of Engineering Education, 91(4), 415–417.
  • Kahney, H. (1989). What do novice programmers know about recursion? In E. Soloway & J.C. Sphorer (Eds.), Studying the novice programmer (pp. 209–228). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Katai, Z. (2011). Multi-sensory method for teaching–learning recursion. Computer Applications in Engineering Education, 19(2), 324–243.
  • Meyer, J.H. F., & Land, R. (2003). Threshold concepts and troublesome knowledge: Linkages to ways of thinking and practising. In C. Rust (Ed.), Improving student learning—Theory and practice ten years on (pp. 412–424). Oxford, England: Oxford Centre for Staff and Learning Development (OCSLD).
  • Miles, M.B., & Huberman, A.M. (1994). Qualitative data analysis: A sourcebook of new methods ( 2nd ed.). Thousand Oaks, CA: Sage.
  • Milne, I., & Rowe, G. (2002). Difficulties in learning and teaching programming—Views of students and tutors. Education and Information Technologies, 7(1), 55–66.
  • Pea, R.D. (1986). Language-independent conceptual “bugs” in novice programming. Journal of Educational Computing Research, 2(1), 25–36.
  • Putnam, T.R., Sleeman, D., Baxter, J.A., & Kuspa, L.K. (1986). A summary of misconceptions of high school BASIC programmers. Journal of Educational Computing Research, 2(4), 459–472.
  • Rogalski, J., & Samurcay, R. (1990). Acquisition of programming knowledge and skills. In J.M. Hoc, T.R. G. Green, R. Samurçay, & D.J. Gillmore (Eds.), Psychology of programming (pp. 157–174). London, England: Academic Press.
  • Samurcay, R. (1989). The concept of variable in programming: Its meaning and use in problem solving by novice programmers. In E. Soloway & J.C. Sphorer (Eds), Studying the novice programmer (pp. 161–178). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Sleeman, D., Putnam, R.T., Baxter, J., & Kuspa, L. (1986). Pascal and high-school students: A study of misconceptions. Journal of Educational Computing Research, 2(1), 5–23.
  • Soloway, E., Bonar, J., & Ehrlich, K. (1983). Cognitive strategies and looping constructs: An empirical study. Communications of the ACM, 26(11), 853–860.
  • Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 3–50). Thousand Oaks, CA: Sage.
  • Vessey, I. (1985). Expertise in debugging computer programs: A process analysis. International Journal of Man–Machine Studies, 23, 459–494.
  • Wiedenbeck, S., Ramalingam, V., Sarasamma, S., & Corritore, C. (1999). A comparison of the comprehension of object-oriented and procedural programs by novice programmers. Interacting With Computers, 11(3), 255–282.
  • Winslow, L.E. (1996). Programming pedagogy—A psychological overview. SIGCSE Bulletin, 28(3), 17–22.
  • Yin, R.K. (2003). Case study research: Design and methods ( 3rd ed.). Thousand Oaks, CA: Sage Publications.
  • Zendler, A., Spannagel, C., & Klaudt, D. (2011). Marrying content and process in computer science education. IEEE Transactions on Computing Education, 54(3), 387–397.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.