本研究旨在探討自我解釋以及先備知識對程式語言IF敘述學習成效的影響，研究樣本為普通高中二年級117位學生。在教學實驗中，依據不同的自我解釋學習活動分為自我解釋問題引導、自我解釋原則提示、與不實施自我解釋等三組；先備知識則依先備知識測驗分為高先備知識與低先備知識二組。
研究結果發現：(1)在事實性問題上，自我解釋學習活動和先備知識對學習成效沒有顯著影響；(2)在程式評估問題上，自我解釋學習活動對學習成效沒有顯著影響，高先備知識組顯著優於低先備知識組；(3)在程式填充問題上，自我解釋問題引導組及控制組顯著優於自我解釋原則提示組，先備知識對學習成效沒有顯著影響；(4)在程式設計問題上，自我解釋問題引導組顯著優於自我解釋原則提示組及控制組，高先備知識組顯著優於低先備知識組；(5)在自我解釋數量上，自我解釋問題引導組顯著優於自我解釋原則提示組，先備知識對自我解釋數量沒有顯著影響；(6)在學習態度方面，自我解釋問題引導組對自我解釋學習活動的接納程度以及對自我解釋學習活動學習成效的看法上，皆顯著高於自我解釋原則提示組；兩組對自我解釋學習活動難度的看法無顯著差異。

The purpose of this study was to investigate the effects of self-explanation and prior knowledge on senior high students’ programming learning performance and attitudes. Subjects were assigned to one of the three experiment groups: the guided-question group, the principle-prompt group, or the control group. Learners were identified as high prior knowledge or low prior knowledge according to their performance on prior knowledge test.
The collected data were examined in terms of factual knowledge performance, code evaluation performance, simple code generation performance, code generation performance, the amount of self-explanation, and attitudes toward self-explanation activity. On the performance of factual knowledge, self-explanation and prior knowledge were not significant between groups. On code evaluation performance, the effect of self-explanation was not significant between groups, but high prior knowledge learners outperformed low prior knowledge learners. On the performance of simple code generation (code filling), the guided-question group and the control group outperformed the principle-prompt group, however, prior knowledge did not significantly influence the learning performance. On code generation performance, the guided-question group outperformed the principle-prompt group and the control group. And the high prior knowledge group scored significantly higher than the low prior knowledge group. On the amount of self-explanation, the guided-question group generated more self-explanations than the principle-prompt group. Finally, on the analysis of attitudes toward self-explanation activities, the guided-question group showed more positive acceptance than the principle-prompt group.

數學教育學門資源整合規劃小組(民85)。學門資源整合規劃資料：數學教育。行政院國家科學委員會。
邱美虹(民83)。從〝自我解釋〞所產生的推論探究高中生化學平衡的學習。師大學報，39，525-544。
教育部(民87)。國民教育階段九年一貫課程總綱綱要。台北：教育部。
Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22, 21-295.
Anderson, J. R., Farell, R., & Sauers, R. (1984). Learning to program in LISP. Cognitive Science, 8, 87-129.
Anderson, J. R. (1987) Skill acquisition: Compilation of weak-method problem solutions. Psychological Review, 94, 192-210.
Adelson, B. (1981). Problem solving and development of abstract categories in programming languages. Memory and Cognition, 9, 422-433.
Banikowski, A. K., & Mehring, T. A. (1999). Strategies to enhance memory based on brain-research. Focus on Exceptional Children, 32(2), 1-16.
Bayman, P., & Mayer, R. E. (1988). Using conceptual models to teach BASIC computer programming. Journal of Educational Psychology, 80(3), 291-298.
Bayman, Piraye, & Mayer R. E. (1982). Novice users’ misconceptions of Basic programming statements. (ERIC Document Reproduction Service No. ED 238 395)
Bielaczyc, K., Pirolli, P., & Brown, A. (1995). Training in self-explanation and self –regulation strategies: Investigating the effect of knowledge acquisition activities on problem-solving. Cognition and Instruction, 13, 221-253.
Bransford, J., & Stein, B. S. (1984). The IDEAL problem solver: A guide for improving thinking, learning, and creativity. New York: W.H. Freeman.
Byrne, M. D., Catrambone, R., & Stasko, J. T. (1999). Evaluation animations as student aids in learning computer algorithms. Computers & Educations, 33, 253-278.
Chi, M. T. M., Glaser, R., & Glaser, R. (1985). The Nature of Expertise.; Hillsdale, NJ: Erlbaum.
Chi, M. T. H., Bassok, M., Lewis, M., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145-182.
Chi, M. T. H., & VanLehn, K. A. (1991). The content of physics self-explanations. The Journal of the Learning Science, 1(1), 69-105.
Chi, M. T. H., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-478.
Cooke, N. J., & Schvaneveldt, R. W. (1988). Effects of computer programming experience on network representations of abstract programming concepts. International Journal of Man-Machine Studies, 29, 407-427.
Cooper, G., & Sweller, J. (1987). The effects of schema acquisition and rule automation of mathematical problem-solving transfer. Journal of Educational Psychology, 79, 347-362.
Davis, E. A. (2000). Scaffolding students' knowledge integration: Prompts for reflection in KIE. International Journal of Science Education, 20(8), 819-837.
Delclos, V. R., & Harrington, C. (1991). Effects of strategy monitoring and proactive instruction on children’s problem-solving performance. Journal of Educational Psychology, 83, 35-42.
Dewey, J. (1910). How we think. Boston: D.C. Heath.
Dochy, F. J. R. C. (1995). Prior Knowledge and Learning. In International Encyclopedia of Teaching and Teacher Education.（2ed.）Cambridge University Press, UK.
Fay, A. L. (1990). Computer programming instruction: The acquisition and transfer of design skills. Unpublished doctoral dissertation, University of California, Santa Barbara.
Ferguson-Hessler, M. G. M., & de Jong, T. (1990). Studying physics texts: Differences in study processes between good and poor solvers. Cognition and Instruction, 7, 41-54.
Gagné, R. M. (1980). The conditions of learning. New York: Holt, Rinehart, & Winston.
Gick, M. L. (1986). Problem-solving strategies. Educational Psychologist, 21, 99-120.
Green, K. E. (1985). Cognitive style: a review of the literature. (ERIC Document Reproduction Service No. ED 289 902)
Greenfield, L. B. (1987). Teaching thinking through problem solving. New Directions for Teaching and Learning, 30, 5-22.
Hunt Earl. (1994). Problem Solving. RobertJ. Sternberg. Teaching and Problem Solving.pp215~232 .San Diego，Ca. Academic press.
Hirshman, E., & Bjork, R. A. (1988). The generation effect: Support for a two-factor theory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 484-494
Kahney, H. (1986). Problem solving: a cognitive approach. Open University Press, Miton Key.
Karsten, R., & Kaparthi, S. (1998). Using dynamic explanations to enhance novice programmer instruction via the WWW. Computers & Education, 30(3/4), 195-201.
King, A. (1991). Effects of training in strategic questioning on children’s problem-solving performance. Journal of Educational Psychology, 83, 307-317.
Klahr, D. & Carver, S. M. (1988). Cognitive objects in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404.
Levin, J. R. (1986). Four cognitive principles of learning-strategy instruction. Educational Psychologist, 21 (1 & 2), 3-17
Mayer, R. E. (1981). The psychology of how novices learn computer programming. Computing Surveys, 13, 121-141.
Mayer, R. E., & Fay, A. L. (1987). A chain of cognitive changes with learning to program in logo. Journal of Educational Psychology, 79, 269-279.
Mayer, R. E. (1988). From novice to expert. In M. Helander (ED.), Handbook of human-computer interaction (pp. 569-580). Amsterdam: North-Holland.
Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59(1), 43-64.
McKendree, J., & Anderson, J. R. (1987). Effect of practice on knowledge and use of basic LISP. In J. M. Carroll (Ed.), Interfacing thought: Cognitive aspects of human-computer interaction (pp. 236-259). Cambridge, MA: Bradford Books/MIT Press.
Neuman, Y., & Schwarz, B. (2000). Substituting one mystery for another: the role of self-explanations in solving algebra word problems. Learning and Instruction, 10, 203-220.
Neuman, Y., Leibowitz, L., & Schwarz, B. (2000). Patterns of verbal mediation during problem solving: A sequential analysis of self-explanation. Journal of Experimental Education, 68(3), 197-223.
Neves, D. M., & Anderson, J. R. (1981). Knowledge compilation: Mechanisms for the automization of cognitive skills. In J. R. Anderson (Ed.), Cognitive skills and their acquisition (pp. 57-84). Jillsdale, NJ: Lawrence Erlbaum Associates.
Newell, A., & Simon, H. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall.
Oliver, R. (1993). Measuring hierarchical levels of programming knowledge. Journal of Educational Computing Research, 9(3), 299-312.
Pirolli, P. L., & Anderson, J. R. (1985). The role of learning from examples in the acquisition of recursive programming skill. Canadian Journal of Psychology, 39, 240-272.
Pirolli, P., & Recker, M. (1994). Learning strategies and transfer in the domain of programming. Cognition and Instruction, 12, 235-275.
Recker, M. M., & Pirolli, P. (1990). A model of self-explanation strategies of instructional text and examples in the acquisition of programming skills. (ERIC Document Reproduction Service No. ED 318 797)
Reder, L. M., Charney, D. H., & Morgan, K. I. (1986). The role of elaborations in learning a skill from an instructional text. Memory & Cognition, 14, 64-78.
Reimann, P., & Neubert, C. (2000). The role of self-explanation in learning to use a spreadsheet through examples. Journal of Computer Assisted Learning, 16, 316-325.
Renkl, A. (1997). Learning from worked-out examples: a study on individual differences. Cognitive Science, 21, 1-30.
Ross, B. H. (1980). Remindings and their effects in learning a cognitive skill. Cognitive Psychology, 16, 371-416.
Schwartz, & Steven. (1988). Empirical studies of a “Metacourse” to enhance the learning of BASIC. (ERIC Document Reproduction Service No. ED 305 926)
Shih, Y. F., & Alessi, M. (1994). Mental models and transfer of learning in computer programming. Journal of Research on computing in education, 26(2), 154.
Shneiderman, B., & Mayer, R. E. (1979). Syntactic/semantic interactions in programmer behavior. A model and experimental results. International Journal of Computer and Information Sciences, 8, 219-238.
Smith, P. A., & Webb, G. I. (2000). The efficacy of a low-level program visualization tool for teaching programming concepts to novice programmers. Journal of Educational Computing Research, 22(2), 187-215.
Sweller, J., & Cooper, G. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59-89.
VanLehn, K. (1989). Problem solving and cognitive skill acquisition. In M. I. Posner(Ed. ), Fundations of cognitive science. Cambridge, MA: MIT Press.
VanLehn. K. (1998) Analogy events: How examples are used during problem solving. Cognitive Science, 22, 347-388.
Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. In M.C. Wittrock (ED.), Handbook of research on teaching (3rd ed.). New York: Macmillan Publishing Company.
Wiedenbeck, S., Ramalingam, V., Sarasamma, S., & Corritore, C. L. (1999). A comparison of the comprehension of object-oriented and procedural programs by novice programmers. Interacting with Computers, 11, 255-282.
Winslow, L. E. (1996). Programming pedagogy-a psychological overview. SIGSE Bulletin, 28(3), 17-21.