臺灣博碩士論文加值系統

培養問題解決能力是科學教育中一個重要的基本能力，過去研究指出，學習者的問題解決表現與問題任務難易程度、學習者的後設認知行為以及其工作記憶廣度有關(Veenman,Prins,＆Elshout,2002；Shin,Jonassen,＆McGee,2003；Cho, Holyoak ,& Cannon, 2007)，然而有關學習者的後設認知行為表現是否會受到工作記憶廣度和問題解決任務難易度的影響，過去研究並未有較深入的探討，且任務多以類比推理為主，鮮少以問題解決為探討之情境。因此本研究目的有二：首先欲了解不同工作記憶廣度學習者之問題解決表現和後設認知行為是否受到問題難易程度影響，其次則探討後設認知能力在問題解決過程中所扮演的角色。
本研究採質量混合設計，以影響最大靜摩擦力的因素為主要概念，設計低、中、高三種不同難度之問題解決情境。研究對象為常態分班的國中二年級五個班級，全體學生先進行摩擦力概念測驗，篩選37位先備概念相當之學習者進行工作記憶廣度測驗及問題解決任務訪談，再將訪談資料進行後設認知能力編碼及問題解決能力評分，以分析問題解決表現是否與工作記憶廣度高、低有關?並探討高、低工作記憶廣度學習者於低、中、高難度之問題解決情境下，學習者的後設認知行為與問題解決表現之關聯，並於高、低工作記憶廣度組別中各選取2名個案，進行質性分析以了解在低、中、高三種不同難度下，學習者如何展現設認知行為進行問題解決。
結果顯示，學習者之問題解決表現受到問題難易度之影響，且於中難度的問題解決情境下，問題解決表現最佳，其中「分析題目的圖片和條件」及「關係圖的判斷」此兩個低層次之問題解決表現，隨難度增加並沒有太大變化；而「變因影響的關係判斷」及「統整及推論」兩個較高層次之問題解決表現於中難度任務下表現最佳。此外低、中、高三種不同難度問題解決情境下，問題解決表現皆為高廣度學習者優於中廣度學習者，中廣度學習者優於低廣度學習者，顯示學習者的工作記憶廣度大小的確會影響問題解決能力。質性的問題解決過程訪談分析則發現，高廣度學習者在面對難度較高之問題解決情境時，能監控推理過程以降低過程中的推理失誤，或於失誤時能及時覺察而回頭檢視並提出修正，顯示工作記憶廣度高之學習者有足夠的認知資源進行監控並調整學習策略進行問題解決。反之，低廣度學習者，僅於中難度之問題解決情境時，能監控推理過程以降低推理失誤發生，當問題難度提升至高難度時，則因認知負荷使過高而無法監控推理過程，甚至發生推理失誤而不自覺，降低問題解決成效。
本研究顯示，問題難易程度及後設認知行為對於問題解決表現有重要的影響。

Cultivate problem-solving ability is an important goal in science education. Previous studies indicated effects of task difficulty, learners’ metacognitive behaviors, and working memory span on problem-solving performance (Veenman, Prins, & Elshout, 2002; Shin, Jonassen, & McGee, 2003; Cho, & Cannon, 2007). Whetehr learenrs’ metacognitive behaviors are affected by their working memory span and the task difficulty remains unexplored. To address this gap, the present study has two aims, to understand whether students with different working memory span have different problem-solving performance and metacognitive behaviors, and how problem-solving performance and metacognitive behaviors vary accprding to task difficulty. This study also explore the role of metacognitive ability in problem- solving .
The study took a mixed-method approach. Problems with low, medium and high levels of difficulties were designed on the topic of friction. Participants were eighth-graders who have received related instructions. A test friction was implemented to select, 37 students who have similar base of prior knowledge on Friction. These students were interviewed while completing the problemsolving tasks. Their task performances were scored and the interviews were coded for cognitive and metacognitive habavoirs. Relationships between learners’ working memory span, metacognitive behaviors, and problems-solving performance in different levels of difficulties were then analyzed. Cases were also used , to understand learners’ metacognitive behaviors and problem-solving processs in different task difficulties.
The results show that learner's’ problem-solving performance varied depended on task difficulty. Performance on “analysis of task conditions”and “chart decomposion”did not vary across task diffulcities; however, learners exhibited optimal performance at the medium  
difficult level for performance on “reasoning causal relations”and “integrating and inferring”. tudents with large working memory span outperform their counter cohors on all diffiultiy levels. Qualitative findings also showed that learners with higher working memory span were abled to monitor problem-solving process to reduce reasoning flaws and/or made corrections with errors occurred, which in turn, yielded better performances. Learenrs with lower working memory span demonstrated the aforementioned behavioral pattern only on the task with midiun level of difficulty.problem-solving perofmrance was droped at the high difficulty level due to cognitive overload. In that situation, learenrs no longer monitored their reasoning process and were not aware of realsoing flaws. This study shows that task difficulty and emtacognitive behaviors have great influence on problem- solving process and performance.

摘要 i
英文摘要 ii
誌謝 iii
第一章 緒 論 1
第一節 研究背景與動機 1
第二節 名詞解釋 2
第三節 研究範圍與限制 3
第二章 文 獻 探 討 3
第一節 問題解決 3
第二節 後設認知 5
第三節 工作記憶廣度與複雜認知歷程的相關研究 7
第四節 工作記憶廣度、後設認知能力和問題解決表現之關聯 8
第三章 研 究 方 法 10
第一節 研究對象 10
第二節 研究設計與流程 10
第三節 研究工具 12
第四節 資料分析 19
第四章 研究結果與分析 20
第一節 不同難度之問題解決表現 20
第二節 不同難度下使用認知和後設認知行為之分析 22
第三節 不同難度下，工作記憶廣度、認知與後設認知策略與問題解決表現之相關性分析 25
第四節 工作記憶廣度分組在不同難度下之問題解決表現差異分析 27
第五節 不同難度下，工作記憶廣度分組之認知及後設認知行為差異分析 30
第六節 工作記憶廣度分組不同難度認知、後設認知行為及問題解決歷程表現分析 33
第五章 結論與建議 65
第一節結論 65
第二節 建議 68
參考文獻: 70
一、中文部份 70
一、 英文部份 70
附錄一自然科學常用詞彙熟悉度問卷 75
附錄二 摩擦力概念測驗 80
附錄三問題解決能力測驗卷 85
附錄四 問題解決能力評分表 90
附錄五 後設認知能力編修過程 94
附錄六 後設認知能力編碼表 98
附錄七 學習者問題解決歷程特徵分析表 103

一、中文部份
1. 林慧芳（2002）。國小六年級低閱讀能力學生工作記憶與推論能力之研究。國立彰化師範大學特殊教育碩士班。
2.林晏如(2011)。探討後設認知能力對國中生類比學習成果之影響-以比熱和熱平橫概念為例。國立交通大學教育研究所碩士班。
3.教育部(2000):國民中小學九年一貫課程站行綱要：自然與生活科技。台北：教育
部。
4.蔡春來(2003)。探討國中生對摩擦力的迷思概念。國立台灣師範大學科學教育研究所碩士班。
5.楊之明(2005)。國小中高年級學童摩擦力概念之研究。臺中師範學院自然科學教育學系碩士班。
6.吳明烈(2010):UNESCO、OECD與歐盟終身學習關鑑能力之比較研究.教育政策論壇，13(1)，45-75。
7.簡瑋成(2011)。大學生核心就業素養之探究。教育人力與專業發展雙月刊，28(4)，
75-88。
8.張珉甄(2011)。以創造性問題解決融入與「力」相關之科學遊戲的教學成效之研究。
國立台中教育大學科學應用與推廣學系科學教育碩士班。

一、 英文部份
1. Alexander, P. A. (2004). A model of domain learning: Reinterpreting expertise as a multidimensional, multistage process. In D. Y. Dai & R. J. Sternberg (Eds.), Motivation, emotion, and cognition: Integrative perspectives on
intellectual functioning and development (pp. 273–298). Mahwah, NJ: Erlbaum
2. Azevedo, R. (2005). Computer environment as metacognitive tools for enhancing learning. Educational Psychologist, 40(4), 193–197.
3. Bransford, J. D., Zech, L., & Schwartz, D. (1996). Fostering mathematical thinking in middle school students:Lessons from research. In R. J. Sternberg & T. Ben-Zeev (Eds.), The nature of mathematical thinking (pp. 203–218). Mahwah, NJ: Erlbaum.
3. Brown,A.(1987).Metacognition,executive control,self-regulation and other more mysterious mechanisms.In F.Weinter ＆R.Kluwe(Eds.),Metacognition,motivation,and understanding(pp.65-116).
4. Brown, A. L. (1990). Domain-specific principles affect learning and transfer in children. Cognitive Science, 14(1),107–133.
5. Bryce, D.＆ D. Whitebread (2012). The development of metacognitive skills: evidence from observational analysis of young children’s behavior during problem-solving. Metacognition and Learning ,7(3), 197-217.
6. Bilal, D. (2002). Children's use of the Yahooligans! Web search engine. III. Cognitive and physical behaviors on fully selfgenerated search tasks. Journal of the American Society for Information Science and Technology, 53(13), 1170-1183.
7. Bulu, S. T. ＆ S. Pedersen (2012). Supporting problem-solving performance in a hypermedia learning environment: The role of students’ prior knowledge and metacognitive skills.Computers in Human Behavior, 28(4), 1162-1169.
8. Carretti,B.＆ Borella,E.＆Cornoldi,C.＆Beni,R.D.(2009). Role of working memory in explaining the performance of individuals with specific reading comprehension difficulties: A meta-analysis. Learning and Individual Differences,19,246-251.
9. Chang, C.-Y. (2010). Does Problem Solving = Prior Knowledge plus Reasoning Skills in Earth Science? An Exploratory Study. Research in Science Education, 40, 103–116.
10. Chi, M.T.H., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5,121–152.
11. Cho, S., Holyoak, K. J., & Cannon, T. D. (2007). Analogical reasoning in working memory: Resources shared among relational integration, interference resolution, and maintenance. Memory & Cognition, 35, 1445–1455.
12. Clair-Thompson,H.L＆ Overton,T,＆Bugler,M.(2012).Metal capacity and working memory in chemistry：algorithmic versus open-ended problem solving.Chemistry Education Research and Practice,13,484-489.
13. Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences, 7, 547-552.
14. Copeland, D., & Radvansky, G. (2004). Working memory and syllogistic reasoning. The Quarterly Journal of Experimental Psychology A: Human Experimental Psychology, 57, 1437–1457.
15. Conway, A. R. A., et al. (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review ,12(5),769-786.
16. Engle, R. W. (2002). Working memory capacity as executive attention.Current Directions in Psychological Science, 11, 19-23.
17. Erbas, A. K. ＆ S. Okur (2010). Researching students’ strategies, episodes, and metacognitions in mathematical problem solving. Quality & Quantity ,46(1): 89-102.
18. Elshout, J. J. (1987). Problem solving and education. In E. De Corte, H. Lodewijks, R. Parmentier, & P. Span (Eds.), Learning and instruction (pp.259-273). Oxford, UK/Leuven, Belgium: Pergamon Books/University Press.
19. Eisenberg, M. B., & Berkowitz, R. E. (1990). Information problem solving: The Big Six skills approach to library & information skills instruction. Norwood, NJ: Ablex.
20. Flavell,J.H.(1979).Metacognition and cognitive monitoring：A new area of Cognitive-Developmental Inquiry.American Psychologist,34,906-991.
21. Gagne,R.M.＆ Medsker,K.L.(1996).The Conditions of Learning:Training Applicaions.Harcourtt Brace College Publishers.
22. Hacker,D.J.(1997).Comprehension monitoring of written discourse across early-to-middle adolescence.Reading and Writing,9,207-240.
23. Higgins, J. M. (1994). Creative problem solving techniques: The handbook of new ideas for business. Winter Park, FL: New Management.
24. Hwang, W. Y., Chen, N. S., Dung, J. J., & Yang, Y. L. (2007). Multiple representation skills and creativity effects on mathematical problem solving using a multimedia whiteboard system. Educational Technology & Society, 10(2), 191-212.
25. Hwang, G. J., & Kuo, F. R. (2011). An information-summarizing instruction strategy for improving web-based problem-solving abilities of students. Australasian Journal of Educational Technology, 27(2), 290-306.
26. Jacobse, A. E.＆ E. G. Harskamp (2012). Towards efficient measurement of metacognition in mathematical problem solving.Metacognition and Learning 7(2), 133-149.
27. Jonassen, D.H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology: Research and Development, 45,65–94.
28. Jonassen, D.H., Beissner, K., & Yacci, M. (1993). Structural knowledge. Hillsdale, NJ:Erlbaum.
29. Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne,T. W., & Engle, R. W. (2004). The generality of working memory capacity:A latent-variable approach to verbal and visuo-spatial memoryspan and reasoning. Journal of Experimental Psychology: General,133, 189-217.
30. Kazemi, F., et al. (2010). A Subtle View to Metacognitive Aspect of Mathematical Problems Solving.Procedia - Social and Behavioral Sciences, 8,420-426.
31. Kuo, F. R., Hwang, G. J., & Lee, C. C. (2012). A hybrid approach to promoting students' web-based problem-solving competence and learning attitude. Computers & Education, 58(1), 351-364.
32. McCabe, D. P. (2010). The influence of complex working memory span task
administration methods on prediction of higher-level cognition and
metacognitive-control of response times. Memory & Cognition,38(7),868-882.
33. Meijer, J., M.V.J. Veenman, and B.H.A.M. van Hout Wolters.(2006). Metacognitive activities in text-studying and problem solving: Development of a taxonomy. Educational Research and Evaluation ,12(3), 209–37.
34. Moos, D. C., & Azevedo, R. (2006). The role of goal structure in undergraduates’ use of self-regulatory variables in two hypermedia learning tasks. Journal of Educational Multimedia and Hypermedia, 12(2), 117–134.
35. Oloruntegbe, K., Ikpe, A., & Kukuru, J. (2010). Factors in students' ability to connect school science with community and realworld life. Educational Research and Reviews, 5(7), 372-379.
36. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 532-566). San Diego,77 CA: Academic Press.
37. Pressley, M., Wharton-McDonald, R., & Allington, R. (2001). A study of effective first grade literacy instruction.Scientific Studies of Reading, 15(1), 35–58.
38. Prins, F. J., Veenman, M. V. J., & Elshout, J. J. (2006). The impact of intellectual ability and metacognition on learning: New support for the threshold of problematicity theory. Learning and Instruction, 16(4), 374–387.
39. Schraw, G.& Moshman, D. (1995).Metacognitive theories. Educational Psychological Review, 7, 351-371.
40. Schraw, G., & Sinatra, G. M. (2004). Epistemological development and its impact on cognition in academic domains. Contemporary Educational Psychology, 29(2), 95–102.
41. Schraw, G., Crippen, K.J., & Hartley, K. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36, 111-139.
42. Schmiedek,F.＆Hildebrandt,A.＆Lovden,M.＆Wilhelm,O.＆Lindenberger,U.(2009).Complex Span Versus Updating Tasks of Working Memory：The Gap Is Not That Deep.Journal of Experimental Psychology:Learning Memory,and Cognition,35(4),1089-1096.
43. Scherer, R. ＆ Tiemann,R. (2012). Factors of problem-solving competency in
a virtual chemistry environment: The role of metacognitive
knowledge about strategies. Computers & Education ,59(4), 1199-1214.
44. Shin, N.,＆ Jonassen ,D.J.,＆McGe ,S.(2003). Predictors of well-structured and ill-structured problem solving in an astronomy simulation. Journal of Research in Science Teaching ,40(1),6-33.
45. She, H.C., et al. (2012). Web-based undergraduate chemistry problem-solving: The interplay of task performance, domain knowledge and web-searching strategies. Computers & Education, 59(2),750-761.
46. Stamovlasis, D. and G. Tsaparlis (2012). Applying catastrophe theory to an information-processing model of problem solving in science education. Science Education ,96(3),392-410.
47. Sternberg, R. J. (1988). A three-facet model of creativity. In R. J. Sternberg (Ed.), The natural of creativity: Contemporary psychological perspectives. New York, NY: Cambridge University Press.
48. Tabak, I. (2004). Synergy: A complement to emerging patterns in distibuted scaffolding. Journal of the Learning Sciences, 13(3), 305–335.
49. Tsai, C. W., & Shen, P. D. (2009). Applying web-enabled self-regulated learning and problem-based learning with initiation to involve low-achieving students in learning. Computers in Human Behavior, 25(6), 1189-1194.
50. Tsaparlis, G., & Angelopoulos, V. (2000). A model of problem solving: Its operation, validity and usefulness in the case of organic-synthesis problems. Science Education, 84, 131 – 153.
51. Tsaparlis, G. (2005). Non-algorithmic quantitative problem solving in university physical chemistry: A correlation study of the role of selective cognitive factors. Research in Science and Technological Education, 23, 125 – 148.
52. Veenman, M. V. J., Prins, F. J., & Elshout, J. J. (2002). Initial inductive learning in a complex computer simulated environment: the role of metacognitive skills and intellectual ability. Computers in Human Behavior, 18, 327-342.
53. Veenman, M. V. J. and M. A. Spaans (2005). Relation between intellectual and metacognitive skills: Age and task differences. Learning and Individual Differences ,15(2),159-176.
54. Veenman, M. V. J., et al. (2006). Metacognition and learning: conceptual and methodological considerations. Metacognition and Learning ,1(1),3-14.
55. Voss, J.F. (1988). Problem solving and reasoning in ill-structured domains. In Antaki, C.(Ed.), Analyzing everyday explanation: A casebook of methods (pp. 74–93). London: Sage.
56. Winne, P., & Perry, N. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R.
57. Whitebread,D.(1999).Interaction between children,s metacognitive abilites,working memory capacity,strategies and performance during problem-solving.European Journal of Psychology of Education,14(4),489-507.