科學實驗在科學教育中扮演著十分重要的角色，而虛擬實驗室是一種具有許多優點、例如可隨時重複、暫停、及安全的科學實驗教育工具，因而漸被廣泛使用，成為中小學實驗教學的趨勢。實驗教學中的一大挑戰是讓學習者主動進行科學探究，並提供讓學生能深入自我評估和調整想法的機會。在這樣的背景下，利用自我解釋策略幫助學生思考對科學實驗的觀察來建立正確的科學概念是一個可以嘗試的選項。而相關研究指出，開放式問答的自我解釋，比提示選項式的自我解釋較佳。但在使用放式問答的自我解釋策略時，學習者往往會回答不正確的自我解釋，此時如果沒有即時回饋教材來修正學習者的概念，往往會讓學習者建構不正確的概念，可能阻礙學習者後續的學習。因一個使用自我解釋的學習系統必須要針對使用者自我解釋中的迷思概念提供相對應的回饋才能有效地幫助使用者自我學習。
本研究建置一結合自我解釋學習策略適合國小程度之線上自然科學虛擬實驗室學習系統。此系統可讓學生隨時操作虛擬實驗室，觀察實驗中的自然現象變化，並於實驗過後回答自我解釋問題，在回答的過程中思考驗證自己的觀察是否有錯誤或不足之處。本研究並經由實際的先導實驗來蒐集學習者的自我解釋內容與經常犯錯的錯誤類型，經整理分析並且建構出自我解釋分類機制。透過自然語言處理及分類機制自動診斷學習者自我解釋內容正確與否，並且適時回饋引導教材。實驗設計找國小四年級53位學生實際使用本系統來操作自然科實驗，透過學生操作實驗後填寫自我解釋問題並紀錄其回答內容和系統回饋結果，後續再由人工分析系統準確率。本研究所開發的研究方法平均準確率達到84.45%與人工判斷的準確率相同。由此可以證明，本系統所開發的答案分類機制和系統架構運用於開放式自我解釋策略有著良好的表現。

Scientific experiments play an essential role in science education. As a widely used laboratory education tool of many advantages, e.g. cheap, repeatable, suspendable, and safe, virtual laboratory has gradually become an experimental tool in current elementary and high school. A major challenge in science education is how to initiate students on scientific inquiry and ensure multiple opportunities for their formative self-assessment and revision. Using self-explanation strategy in science education is a possible way to help learners think about the observed results of science experiments and build correct scientific concepts. Researches point out open-ended questions is better than multiple-choice questions for self-explanation. But when using open-ended question self-explanation strategy, without proper prior knowledge, a student may go wrong in deduction and result in constructing misconceptions, unless proper guidance is present. Misconception will become obstacles in further knowledge constructions. Therefore, a learning system that uses open-ended self-explanation strategy should provide proper feedbacks in order to help learners build correct concepts when in self-learning mode.

To help students operating in virtual science experiments and constructing correct concepts from observed results this study constructs an online virtual laboratory learning system with self-explanation strategy and personalize feedbacks for natural science course of primary school. The system uses natural language processing (NLP) technology to analyze students’ self-explanation strings, compares the results with the classification rules, established by an expert from reference explanations, to check the correctness of the strings and possible misconceptions in them, and gives proper learning material, as feedbacks, for the students to revise possible misconceptions. In the final experiment, the system records and checks all self-explanation strings from 53 students and gives them proper feedbacks, which reaches an average accuracy of 84.45% after the expert verify the results.

中文
中央研究院詞庫小組. (2003). 廣義知網中文詞知識庫. Retrieved from http://rocling.iis.sinica.edu.tw/CKIP/conceptnet.htm
李志卿. (2005). 利用編序教學法之學習診斷系統-以商職數學為例.
林育聖. (2002). 自我解釋對程式語言 IF 敘述學習的影響: 國立台灣師範大學資訊教育研究所碩士論文.
林明德. (2009). 一個應用於自然語言理解的知識工程工具. 國立雲林科技大學資訊工程研究所碩士班.
徐綺穗. (2010). 自我解釋學習策略對學生社會學習領域學習統整影響之研究. 教育研究學報, 44(1), 49-72.
黃蔓婷.(2007).自我解釋學習策略運用於國小四年級學童摘取大意之行動研究.
英文
Abdulwahed, M., & Nagy, Z. K. (2011). The TriLab, a novel ICT based triple access mode laboratory education model. Computers and Education, 56(1), 262-274.
Ahlum-Heath, M. E., & Di Vesta, F. J. (1986). The effect of conscious controlled verbalization cognitive strategy on transfer in problem solving. Memory and Cognition, 14(3), 281-285.
Ainsworth, S., & Loizou, A. T. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27(4), 669-681.
Aleven, V. A., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2), 147-179.
Barak, M., Ashkar, T., & Dori, Y. J. (2011). Learning science via animated movies: Its effect on students’ thinking and motivation. Computers and Education, 56(3), 839-846.
Barak, M., & Dori, Y. J. (2005). Enhancing undergraduate students' chemistry understanding through project‐based learning in an IT environment. Science Education, 89(1), 117-139.
Barak, M., & Dori, Y. J. (2009). Enhancing higher order thinking skills among inservice science teachers via embedded assessment. Journal of Science Teacher Education, 20(5), 459-474.
Berardi-Coletta, B., Buyer, L. S., Dominowski, R. L., & Rellinger, E. R. (1995). Metacognition and problem solving: A process-oriented approach. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(1), 205.
Bereiter, C., & Scardamalia, M. (1985). Cognitive coping strategies and the problem of “inert knowledge”. Thinking and Learning Skills, 2, 65-80.
Berry, D. C. (1983). Metacognitive experience and transfer of logical reasoning. The Quarterly Journal of Experimental Psychology, 35(1), 39-49.
Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221-252.
Brown, A. L., & Campione, J. C. (1994). Guided Discovery in a Community of Learners: The MIT Press.
Calin-Jageman, R. J., & Horn Ratner, H. (2005). The role of encoding in the self-explanation effect. Cognition and Instruction, 23(4), 523-543.
Cambria, E., Hupont, I., Hussain, A., Cerezo, E., & Baldassarri, S. (2011). Sentic avatar: Multimodal affective conversational agent with common sense Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues (pp. 81-95): Springer.
Cancilla, D. A. (2004). Initial design and development of an integrated laboratory network: a new approach for the use of instrumentation in the undergraduate curriculum. Journal of Chemical Education, 81(12), 1809.
Capuano, N., Marsella, M., & Salerno, S. (2000). ABITS: An Agent Based Intelligent Tutoring System for Distance Learning. Paper presented at the Proceedings of the International Workshop on Adaptive and Intelligent Web-Based Education Systems, ITS.
CHANG, P., & FENG, N. (2012). A Co-occurrence based Vector Space Model for Document Indexing. Journal of Chinese Information Processing, 1, 009.
Chao, C.-J., Lin, H.-C. K., Lin, J.-W., & Tseng, Y.-C. (2012). An Affective Learning Interface with an Interactive Animated Agent. Paper presented at the Digital Game and Intelligent Toy Enhanced Learning (DIGITEL), 2012 IEEE Fourth International Conference on.
Chen, C.-H., & Howard, B. C. (2010). Effect of Live Simulation on Middle School Students' Attitudes and Learning toward Science. Educational Technology & Society, 13(1), 133-139.
Chi, M. T. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. Advances in Instructional Psychology, 5, 161-238.
Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145-182.
Chi, M. T., Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self‐explanations improves understanding. Cognitive Science, 18(3), 439-477.
Chi, M. T., & VanLehn, K. A. (1991). The content of physics self-explanations. The Journal of the Learning Sciences, 1(1), 69-105.
Chou, C.-Y., & Liang, H.-T. (2009). Content-free computer supports for self-explaining: Modifiable typing interface and prompting. Journal of Educational Technology & Society, 12(1), 121-133.
Cognition, T., & Vanderbilt, T. G. a. (1990). Anchored instruction and its relationship to situated cognition. Educational Researcher, 2-10.
Crowley, K., & Siegler, R. S. (1999). Explanation and generalization in young children's strategy learning. Child Development, 70(2), 304-316.
Dalgarno, B., Bishop, A. G., Adlong, W., & Bedgood Jr, D. R. (2009). Effectiveness of a Virtual Laboratory as a preparatory resource for Distance Education chemistry students. Computers and Education, 53(3), 853-865. doi: http://dx.doi.org/10.1016/j.compedu.2009.05.005
De Bruin, A. B., Rikers, R. M., & Schmidt, H. G. (2007). The effect of self-explanation and prediction on the development of principled understanding of chess in novices. Contemporary Educational Psychology, 32(2), 188-205.
De Jong, T., & Van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179-201.
De Koning, B. B., Tabbers, H. K., Rikers, R. M., & Paas, F. (2011). Improved effectiveness of cueing by self‐explanations when learning from a complex animation. Applied Cognitive Psychology, 25(2), 183-194.
De Leeuw, N. (2000). How students construct knowledge from multiple sources. Unpublished manuscript, Learning Research and Development Center, University of Pittsburgh.
Dennis, J. R., & Kansky, R. J. (1984). Instructional Computing. An Action Guide for Educators: ERIC.
Dong, Z., & Dong, Q. (2006). HowNet and the Computation of Meaning: World Scientific.
Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education.
Feisel, L. D., & Rosa, A. J. (2005). The role of the laboratory in undergraduate engineering education. Journal of Engineering Education, 94(1), 121-130.
Ferguson-Hessler, M. G., & de Jong, T. (1990). Studying physics texts: Differences in study processes between good and poor performers. Cognition and Instruction, 7(1), 41-54.
Fiore, L., & Ratti, G. (2007). Remote laboratory and animal behaviour: An interactive open field system. Computers and Education, 49(4), 1299-1307.
Gagne, R. M., & Smith Jr, E. C. (1962). A study of the effects of verbalization on problem solving. Journal of Experimental Psychology, 63(1), 12.
Gibbons, N. J., Evans, C., Payne, A., Shah, K., & Griffin, D. K. (2004). Computer simulations improve university instructional laboratories. Cell Biology Education, 3(4), 263-269.
Gladwin, R. P., Margerison, D., & Walker, S. M. (1992). Computer-assisted learning in chemistry. Computers and Education, 19(1), 17-25.
Gonzalez, V., Musa, A., & Shadaram, M. (2005). Development of a communications course integrating a virtual laboratory and complex simulations. Paper presented at the Proceedings of the 2005 ASEE Annual Conference.
Gunstone, R. (1991). Reconstructing theory from practical experience. Practical Science, 67-77.
Hausmann, R. G., & Chi, M. H. (2002). Can a computer interface support self-explaining. Cognitive Technology, 7(1), 4-14.
Hirshman, E., & Bjork, R. A. (1988). The generation effect: Support for a two-factor theory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(3), 484.
Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty‐first century. Science Education, 88(1), 28-54.
Hsu, C.-Y., & Tsai, C.-C. (2013). Examining the effects of combining self-explanation principles with an educational game on learning science concepts. Interactive Learning Environments, 21(2), 104-115.
Hwang, G.-J., Yang, L.-H., & Wang, S.-Y. (2013). A concept map-embedded educational computer game for improving students' learning performance in natural science courses. Computers and Education, 69, 121-130.
Jamieson, D. G. (1984). Microcomputers in the instructional laboratory. Computers and Education, 8(4), 415-417.
Johnson, C. R., & Weinstein, D. M. (2006). Biomedical computing and visualization. Paper presented at the Proceedings of the 29th Australasian Computer Science Conference-Volume 48.
Kenny, C., & Pahl, C. (2009). Intelligent and adaptive tutoring for active learning and training environments. Interactive Learning Environments, 17(2), 181-195.
King, A. (1990). Enhancing peer interaction and learning in the classroom through reciprocal questioning. American Educational Research Journal, 27(4), 664-687.
King, A. (1994). Guiding knowledge construction in the classroom: Effects of teaching children how to question and how to explain. American Educational Research Journal, 31(2), 338-368.
Klein, D., & Manning, C. D. (2003). Accurate unlexicalized parsing. Paper presented at the Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1.
Kneebone, R. (2005). Evaluating clinical simulations for learning procedural skills: a theory-based approach. Academic Medicine, 80(6), 549-553.
Kwon, K., Kumalasari, C. D., & Howland, J. L. (2011). Self-explanation prompts on problem-solving performance in an interactive learning environment. Journal of Interactive Online Learning, 10(2), 96-112.
Ma, W.-Y., & Chen, K.-J. (2003). Introduction to CKIP Chinese word segmentation system for the first international Chinese Word Segmentation Bakeoff. Paper presented at the Proceedings of the second SIGHAN workshop on Chinese language processing-Volume 17.
Markman, A. B., & Gentner, D. (2001). Thinking. Annual review of psychology, 52(1), 223-247.
Martinez-Jimenez, P., Pontes-Pedrajas, A., Polo, J., & Climent-Bellido, M. S. (2003). Learning in chemistry with virtual laboratories. Journal of Chemical Education, 80(3), 346-352.
Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41.
Mwangi, W., & Sweller, J. (1998). Learning to solve compare word problems: The effect of example format and generating self-explanations. Cognition and Instruction, 16(2), 173-199.
Neuman, Y., Leibowitz, L., & Schwarz, B. (2000). Patterns of verbal mediation during problem solving: A sequential analysis of self-explanation. The Journal of Experimental Education, 68(3), 197-213.
Neuman, Y., & Schwarz, B. (1998). Is self‐explanation while solving problems helpful? The case of analogical problem‐solving. British Journal of Educational Psychology, 68(1), 15-24.
Newell, A., & Simon, H. A. (1972). Human problem solving (Vol. 104): Prentice-Hall Englewood Cliffs, NJ.
Nickerson, R. S. (1995). Can technology help teach for understanding. Software goes to school: Teaching for Understanding with New Technologies, 7-22.
Ning, C., Wang, R., Chen, Z., & Lu, B. (2011). An efficient similarity measure algorithm of Chinese sentence. Paper presented at the Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on.
Quan, H., Hu, J., & Fang, X. (2011). The research on collocation networks of relation words in modern Chinese language. Paper presented at the Computer Science and Education (ICCSE), 2011 6th International Conference on.
Renkl, A. (1997). Learning from worked‐out examples: A study on individual differences. Cognitive Science, 21(1), 1-29.
Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and instruction, 12(5), 529-556.
Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective. Educational Psychologist, 38(1), 15-22.
Renkl, A., Atkinson, R. K., & Große, C. S. (2004). How fading worked solution steps works–a cognitive load perspective. Instructional Science, 32(1-2), 59-82.
Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998). Learning from worked-out examples: The effects of example variability and elicited self-explanations. Contemporary Educational Psychology, 23(1), 90-108.
Schauble, L., Klopfer, L. E., & Raghavan, K. (1991). Students' transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 28(9), 859-882.
Shneiderman, B., & Mayer, R. (1979). Syntactic/semantic interactions in programmer behavior: A model and experimental results. International Journal of Computer and Information Sciences, 8(3), 219-238. doi:10.1007/BF00977789
Siegler, R. S. (2002). Microgenetic studies of self-explanation. Microdevelopment: Transition Processes in Development and Learning, 31-58.
Stanford. (1987). Protégé. Retrieved from http://protege.stanford.edu/
Stern, L., Barnea, N., & Shauli, S. (2008). The effect of a computerized simulation on middle school students’ understanding of the kinetic molecular theory. Journal of Science Education and Technology, 17(4), 305-315.
Tsami, E. (2011). Innovation in teaching economics: the case of Greek post -secondary level. Journal of Administrative Sciences and Technology, 2, 1-11.
Van Joolingen, W. R., & De Jong, T. (1991). Supporting hypothesis generation by learners exploring an interactive computer simulation. Instructional Science, 20(5-6), 389-404.
VanLehn, K., & Jones, R. M. (1993). What mediates the self-explanation e ect? Knowledge gaps, schemas or analogies? Ann Arbor, 1001, 48109-42110.
VanLehn, K., Jones, R. M., & Chi, M. T. (1992). A model of the self-explanation effect. The Journal of the Learning Sciences, 2(1), 1-59.
VanLehn, K., Jordan, P. W., Rosé, C. P., Bhembe, D., Böttner, M., Gaydos, A., . . . Roque, A. (2002). The Architecture of Why2-Atlas: A coach for qualitative physics essay writing. Paper presented at the Intelligent tutoring systems.
Webb, N. M. (1989). Peer interaction and learning in small groups. International Journal of Educational Research, 13(1), 21-39.
Wellman, H. M., & Liu, D. (2007). Causal reasoning as informed by the early development of explanations. Causal learning: Psychology, Philosophy, and Computation, 261-279.
Wikipedia. (2013).
Williams, J. J., & Lombrozo, T. (2010). The role of explanation in discovery and generalization: evidence from category learning. Cognitive Science, 34(5), 776-806.
Williams, J. J., Lombrozo, T., & Rehder, B. (2010). Why does explaining help learning? Insight from an explanation impairment effect. Paper presented at the Proceedings of the 32nd Annual Conference of the Cognitive Science Society.
Wong, R. M., Lawson, M. J., & Keeves, J. (2002). The effects of self-explanation training on students' problem solving in high-school mathematics. Learning and Instruction, 12(2), 233-262.
Yeh, Y.-F., Chen, M.-C., Hung, P.-H., & Hwang, G.-J. (2010). Optimal self-explanation prompt design in dynamic multi-representational learning environments. Computers and Education, 54(4), 1089-1100.
Zacharia, Z. C. (2007). Comparing and combining real and virtual experimentation: an effort to enhance students' conceptual understanding of electric circuits. Journal of Computer Assisted Learning, 23(2), 120-132.
Zacharia, Z. C., & Constantinou, C. P. (2008). Comparing the influence of physical and virtual manipulatives in the context of the Physics by Inquiry curriculum: The case of undergraduate students’ conceptual understanding of heat and temperature. American Journal of Physics, 76, 425.
Zhang, H.-P., Yu, H.-K., Xiong, D.-Y., & Liu, Q. (2003). HHMM-based Chinese lexical analyzer ICTCLAS. Paper presented at the Proceedings of the second SIGHAN workshop on Chinese language processing-Volume 17.
Zhao, Z., Wu, N., & Song, P.-P. (2012). Sentence semantic similarity calculation based on multi—feature fusion. Computer Engineering, 1, 055.