One of the major issues of teaching computer programming is concerned with determining pedagogical factors that contribute to students' learning and instructional improvement. Classroom teachers are the keys to making learning happen as well as improving teaching in schools. Teachers' perspectives on teaching affect how teachers perceive the nature of the subject, the problems of instruction and how they adopt solutions or ways to improve instruction. Teachers' views of knowledge have great impact on teachers' perspectives and their pedagogical practices. Over the years, two predominant computational theories of mind have emerged from the study of artificial intelligence, namely, the symbolic and the connectionist models. In this article, I argue, based on a qualitative study of 12 computer studies teachers in Hong Kong, that the connectionist view provides an alternative way of thinking about understanding and knowledge which leads to better insight on teaching and facilitates pedagogical change.
BarnesD. (1992). The significance of teachers' frames for teaching. In RussellT.MunbyH. (Eds.), Teachers and teaching: From classroom to reflection (pp. 9–32). London: Falmer Press.
2.
Ben-PeretzM. (1984). Kelly's theory of personal constructs as a paradigm for investigating teacher thinking. In HalkesR.OlsonJ.K. (Eds.), Teacher thinking: A new perspective on persisting problems in education (pp. 103–111). Lisse: Swets & Zeitlinger.
3.
BereiterC.ScardamaliaM. (1996). Rethinking learning. In OlsonD.R.TorranceN. (Eds.), The handbook of education and human development: New models of learning, teaching and schooling (pp. 485–513). Cambridge, MA: Basil Blackwell.
4.
BereiterC. (1991). Implications of connectionism for thinking about rules. Educational Researcher, 20, 10–16.
5.
BerryK. (1999). Destabilizing educational thought and practice: Post-formal pedagogy. In SteinbergS.R.KincheloeJ.L.HincheyP.H. (Eds.), The post-formal reader: Cognition and education (pp. 331–348). New York: Falmer Press.
6.
BodenM.A. (1988). Computer models of mind. Cambridge: Cambridge University Press.
7.
CaseR. (1996). Changing views of knowledge and their impact on educational research and practice. In OlsonD.R.TorranceN. (Eds.), The handbook of education and human development: New models of learning, teaching and schooling (pp. 75–99). Cambridge, MA: Blackwell.
8.
Darling-HammondL. (1998). Policy and change: Getting beyond bureaucracy. In HargreavesA. (Eds.), International handbook of educational change (pp. 642–667). Dordrecht: Kluwer Academic Publishers.
9.
DedeC. (Ed.). (1998). Association for Supervision and Curriculum Development 1998 Yearbook: Learning with technology. Alexandria, VA: ASCD.
10.
ElbazF. (1983). Teacher thinking: A study of practical knowledge. London: Croom Helm.
11.
GlaserB.StraussA. (1967). The discovery of grounded theory. Chicago: Aldine.
12.
GuimaraesM.A.M.de LucenaC.J.P. (1995). A system environment for teaching introductory algorithms. SIGCUE OUTLOOK, 23(3), 2–11.
13.
JonassenD. H. (1996). Computers in the classroom. Englewood Cliffs, NJ: Prentice-Hall.
14.
KannC.LindemanR. W.HellerR. (1997). Integrating algorithm animation into a learning environment. Computers Education, 28(4), 223–228.
15.
KorfR.E. (1991). Artificial intelligence as information science. Information Sciences, No. 57–58, pp. 131–134.
16.
KyriacouC. (1995). Direct teaching. In DesforgesC. (Ed.), An introduction to teaching (pp. 118–131). Oxford: Basil Blackwell.
17.
MayerR.E. (1988). Introduction to research on teaching and learning computer programming. In MayerR.E. (Ed.), Teaching and learning computing programming (pp. 1–12). Hillsdale, NJ: Lawrence Erlbaum Associates.
18.
McBeathR. (1995). Facilitating transitions in learning and teaching. In Improving university teaching: Twentieth international conference, 10–13 July 1995, Hong Kong, pp. 420–428.
19.
MinskyM. (1990). Logical vs. analogical or symoblic vs. connectionist or neat vs. scruffy. In WinstonP.H.ShellardS.A. (Eds.), Artificial intelligence at MIT: Expanding frontiers (pp. 219–243). Cambridge, MA: MIT Press.
20.
MoursundD. (1999). Project-based learning using information technology. Eugene, OR: International Society for Technology in Education.
21.
NewellA.SimonH. (1976). Computer science as empirical inquiry: Symbols and search. Communication of the ACM, 19(3), 113–126.
22.
NewellA.SimonH.A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
23.
NickersonR.S.PerkinsD.N.SmithE.E. (1985). The teaching of thinking. Hillsdale, NJ: Lawrence Erlbaum Associates.
24.
NormanD.A.SpohrerJ.C. (1996). Learner-centered education. Communication of ACM, 39(4), 24–27.
25.
PapertS. (1980). Mindstorms: Children, computers and powerful ideas. New York: Basic Books.
26.
PerkinsD.N.SchwartzS.SimmonsR. (1988). Instructional strategies for the problems of novice programmers. In MayerR.E. (Ed.), Teaching and learning computing programming (pp. 153–178). Hillsdale, NJ: Lawrence Erlbaum.
27.
PopeM.L.ScottE.M. (1984). Teachers' epistemology and practice. In HalkesR.OlsonJ.K. (Eds.), Teacher thinking: A new perspective on persisting problems in education (pp. 112–122). Lisse: Swets & Zeitlinger.
28.
RumelhartD.E. (1989). The architecture of mind: A connectionist approach. In PosnerM.I. (Ed.), Foundation of cognitive science (pp. 133–159). Cambridge, MA: MIT Press.
29.
SloaneK.D.LinnM.C. (1988). Instructional conditions in Pascal programming classes. In MayerR.E. (Ed.), Teaching and learning computing programming (pp. 207–236). Hillsdale, NJ: Lawrence Erlbaum Associates.
30.
SmithP.A.WebbG.I. (2000). The efficacy of a low-level program visualization tool for teaching programming concepts to novice C programmers. Journal of Educational Computing Research, 22(2), 187–215.
31.
StraussA. (1987). Qualitative analysis for social scientists. Cambridge: Cambridge University Press.
32.
StraussC.QuinnN. (1997). A cognitive theory of cultural meaning. Cambridge, MA: Cambridge University Press.
33.
TabulawaR. (1998). Teachers' perspectives on classroom practice in Botswana: Implications for pedagogical change. Qualitative Studies in Education, 11(2), 249–268.
34.
ThomasR.A.UpahS. C.Jr. (1996). Give programming instruction a chance. Journal of Research on Computing in Education, 29(1), 96–107.
35.
von GlasersfeldE. (1998). Cognition, construction of knowledge, and teaching. In MatthewsM.R. (Ed.), Constructivism in science education: A philosophical examination (pp. 11–30). Dordrecht: Kluwer Academic Publishers.
36.
WebbN.M.LewisS. (1988). The social context of learning computer programming. In MayerR.E. (Ed.), Teaching and learning computing programming (pp. 179–206). Hillsdale, NJ: Lawrence Erlbaum.
37.
YoungR.E.A. (1981). Study of teacher epistemologies. Australian Journal of Education, 25(2), 194–208.
38.
YuenH.K. (1999). Traffic light simulation system: A learner-centered multimedia tool for learning algorithm design. In ChowW.S. (Ed.), Multimedia information systems in practice (pp. 244–250). Singapore: Springer-Verlag.
