研究生: |
黃昭銘 Chao-Ming Huang |
---|---|
論文名稱: |
國小學童認知結構發展分析:以探究式教學為例 The development of cognitive structures for elementary school students: An experimental study of inquiry-oriented instruction |
指導教授: |
蔡今中
Tsai, Chin-Chung 張俊彥 Chang, Chun-Yen |
學位類別: |
博士 Doctor |
系所名稱: |
地球科學系 Department of Earth Sciences |
論文出版年: | 2008 |
畢業學年度: | 96 |
語文別: | 英文 |
論文頁數: | 316 |
中文關鍵詞: | 認知結構 、後設認知 、訊息處理 、語意流程圖 |
英文關鍵詞: | cognitive structure, metacognition, information processing, flow map |
論文種類: | 學術論文 |
相關次數: | 點閱:181 下載:38 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
Understanding how people think, organize and develop knowledge is an important issue for educational researchers. Recently, relevant research revealed the connections between cognitive structures and information processing strategies. The research of metacognition also indicated that the metacognition was related to individual’s learning outcomes. Moreover, it revealed that the relationship between metacognition and information processing was significant.
This study tried to explore the development of cognitive structure, including the indicators of extent and richness. Besides, the application of metacognitive regulation strategies, including selecting, short-term maintaining, long-term maintaining, updating and rerouting, were explored. Additionally, the usage of information processing strategies, including defining, describing, comparing, conditional inferring and explaining, were also investigated. This study also investigated the effect of instructional modes in these indicators. Besides, this study also tried to construct a convenient questionnaire, Pupils MetaCognition Scale (PMCS), for understanding pupils’ metacognition.
This study interviewed 110 Taiwanese elementary students (the sixth graders), who came from four different classes. Two classes were categorized into an experimental group (inquiry instructional mode, n=54) and two classes were categorized into a control group (traditional instructional mode, n=56). According to students’ science achievement, each group of students was categorized into three different achiever groups, including the high achiever group, the middle achiever group and the low achiever group. The interaction between instructional modes and different achiever groups was presented. Data collection covered three different units, including bicycle unit, rust-proof and decay-proof unit, and energy and eco-conservation. The role of knowledge domain was also discussed in this study.
The result revealed that the PMCS outcomes were related to the cognitive structure, metacognitive regulation strategies and information processing strategies. The PMCS questionnaire provided a convenient tool in the exploration of metacognition. Besides, the finding also revealed that the flow map method could be extended its potential function and offered for further applications. Besides, this study revealed that the relationship between information processing and metacognitive regulation was significant.
This study suggested that the inquiry instructional mode could enhance students’ application of higher-rank strategies in learning, including updating strategy, rerouting strategy, conditional inferring strategy and explaining strategy. The finding indicated that different achiever groups used different strategies in learning. The high achievers outperformed in higher-order strategies, including richness, updating strategy, rerouting strategy, conditional inferring strategy and explaining strategy. The middle achievers used the strategies more frequently than the low achievers; for example, extent, selecting and long-term maintaining.
Moreover, it revealed that the interaction between instructional modes and achievement was significant in the indicator, including richness, short-term maintaining strategy and rerouting strategy, especially for the high achievers. This study also explored the role of knowledge domain. According to the findings, it revealed that the effect of knowledge domain was significant across all twelve indicators. The content of instructional activities would influence the connections between prior knowledge and scientific knowledge. The findings also highlighted the importance of prior knowledge. The interaction between knowledge domain and achievement was significant in higher-rank strategies, including richness, updating, rerouting, conditional inferring and explaining.
The findings suggested that the students’ learning outcomes mainly depended on students’ academic achievement, application of metacognitive regulation strategies and information processing strategies. It also revealed that different instructional modes, knowledge domain and content of instructional activities influenced students’ performance on their cognitive structures, the usage of metacognitive regulation strategies, and information processing strategies.
Understanding how people think, organize and develop knowledge is an important issue for educational researchers. Recently, relevant research revealed the connections between cognitive structures and information processing strategies. The research of metacognition also indicated that the metacognition was related to individual’s learning outcomes. Moreover, it revealed that the relationship between metacognition and information processing was significant.
This study tried to explore the development of cognitive structure, including the indicators of extent and richness. Besides, the application of metacognitive regulation strategies, including selecting, short-term maintaining, long-term maintaining, updating and rerouting, were explored. Additionally, the usage of information processing strategies, including defining, describing, comparing, conditional inferring and explaining, were also investigated. This study also investigated the effect of instructional modes in these indicators. Besides, this study also tried to construct a convenient questionnaire, Pupils MetaCognition Scale (PMCS), for understanding pupils’ metacognition.
This study interviewed 110 Taiwanese elementary students (the sixth graders), who came from four different classes. Two classes were categorized into an experimental group (inquiry instructional mode, n=54) and two classes were categorized into a control group (traditional instructional mode, n=56). According to students’ science achievement, each group of students was categorized into three different achiever groups, including the high achiever group, the middle achiever group and the low achiever group. The interaction between instructional modes and different achiever groups was presented. Data collection covered three different units, including bicycle unit, rust-proof and decay-proof unit, and energy and eco-conservation. The role of knowledge domain was also discussed in this study.
The result revealed that the PMCS outcomes were related to the cognitive structure, metacognitive regulation strategies and information processing strategies. The PMCS questionnaire provided a convenient tool in the exploration of metacognition. Besides, the finding also revealed that the flow map method could be extended its potential function and offered for further applications. Besides, this study revealed that the relationship between information processing and metacognitive regulation was significant.
This study suggested that the inquiry instructional mode could enhance students’ application of higher-rank strategies in learning, including updating strategy, rerouting strategy, conditional inferring strategy and explaining strategy. The finding indicated that different achiever groups used different strategies in learning. The high achievers outperformed in higher-order strategies, including richness, updating strategy, rerouting strategy, conditional inferring strategy and explaining strategy. The middle achievers used the strategies more frequently than the low achievers; for example, extent, selecting and long-term maintaining.
Moreover, it revealed that the interaction between instructional modes and achievement was significant in the indicator, including richness, short-term maintaining strategy and rerouting strategy, especially for the high achievers. This study also explored the role of knowledge domain. According to the findings, it revealed that the effect of knowledge domain was significant across all twelve indicators. The content of instructional activities would influence the connections between prior knowledge and scientific knowledge. The findings also highlighted the importance of prior knowledge. The interaction between knowledge domain and achievement was significant in higher-rank strategies, including richness, updating, rerouting, conditional inferring and explaining.
The findings suggested that the students’ learning outcomes mainly depended on students’ academic achievement, application of metacognitive regulation strategies and information processing strategies. It also revealed that different instructional modes, knowledge domain and content of instructional activities influenced students’ performance on their cognitive structures, the usage of metacognitive regulation strategies, and information processing strategies.
Ackerman, P. L., Sternberg, R. J., & Glaser, R. (1989). Learning and Individual Differences. New York: W.H. Freeman and Company.
Alaiyemola, F. F., Jegede, O. J., & Okebukola, P. A. O. (1990). The effect of a metacognitive strategy of instruction on the anxiety level of students in science classes. International Journal of Science Education, 12, 95-99.
Alloway, T. P., Gathercole, S. E., Willis, G., & Adams, A. (2004). A structural analysis of working memory and related cognitive skills in young children. Journal of Experimental Child Psychology, 87, 85-106.
Anderson, J. (1983). Retrieval of information from long-term memory. Science, 220, 25-30.
Anderson, O. R. (1997). A neurocognitive perspective on current learning theory and science instructional strategies. Science Education, 81, 96-89.
Anderson, O. R., & Demetrius, O. J. (1993). A flow-map method of representing cognitive structure based on respondents' narrative using science content. Journal of Research in Science Teaching, 30, 953-969.
Anderson, R. C., Reynolds, R. E., Schallert, D. L., & Goetz, E. T. (1977). Frameworks for comprehending discourse. American Educational Research Journal, 14, 376-381.
Anderson, T., Howe, C., Soden, R., Halliday, J., & Low, J. (2001). Peer interaction and the learning of critical thinking skills in further education students. Instructional Science, 29, 1-32.
Ausubel, D. P., Novak, J. D., & Hanesian, H. (1978). Educational Psychology: A cognitive view. New York: Holt, Rinehart & Winston.
Baddeley, A. (1986). Working Memory. Oxford: Clarendon Press.
Baddeley, A. D. (1992). Working memory. Science, 235, 556-559.
Baddeley, A. D. (2000). The episodic buffer? A new component of working memory? Trends in Cognitive Sciences, 7, 417-423.
Baddeley, A. D. (2003) Working memory and language: A overview. Journal of Communication Disorders, 36, 189-208.
Baddeley, A. D., & Hitch, G. J. (2000). Development of working memory: Should the Pascual-Leone and the Baddeley and Hitch models be merged? Journal of Experimental Child Psychology, 77, 128-137.
Baird, J. R., Fensham, P. J., Gunstone, R. F., & White, R. F. (1991). The importance of reflection in improving science teaching and learning. Journal of Research in Science Teaching, 28, 163-182.
Bischoff, B., & Anderson, O. R. (2001). Development of the knowledge frameworks and higher order cognitive operations among secondary school students who studies a unit on ecology. Journal of Biological Education, 35, 81-88.
Bischoff, P. J., & Anderson, O. R. (1998). A Case study analysis of the development of knowledge schema, ideational networks, and higher cognitive operations among high school students who studied ecology. School Science and Mathematics, 98, 228-237.
Bodner, G. M. (1986). Constructivism: A theory of knowledge. Journal of Chemical Education, 63, 873-878.
Bodner, G. M., & McMillen, T. L. B. (1986). Cognitive restructuring as an early stage in problem solving. Journal of Research in Science Teaching, 23, 727-737.
Braver, T. S., Cohen, J. D., Nystrom, L. E., Jonides, J., Smith, E. E., & Noll, D. C. (1997). A parametric study of prefrontal cortex involvement in human working memory. NeuroImage, 5, 49-62.
Carr, M., Kurtz, B. E., Schneider, W., Turner, L. A., & Borkowski, J. G. (1989). Strategy acquisition and transfer among American and German children: Environmental influences on metacognitive development. Developmental Psychology, 25, 765-771.
Champagne, A. B., Gunstone, R. F., & Klopfer, L. E. (1983). Naive knowledge and science learning. Research in Science and Technological Education, 1, 173-183.
Champagne, A. B., Klopfer, L. E., Desena, A. T., & Squires, D. A. (1981). Structural representations of students' knowledge before and after science instruction. Journal of Research in Science Teaching, 18, 97-111.
Chang, C.-Y., & Mao, S.-L. (1999). Comparison of Taiwan science students' outcomes with inquiry-group versus traditional instruction. The Journal of Educational Research, 92, 340-346.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55-81.
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1985). Categorization & representation of physics problem by expert and novices. Cognitive Science, 5, 121-152.
Clough, E. E., & Driver, R. (1986). A study of consistency in the use of students' conceptual frameworks across different task contexts. Science Education, 70, 473-496.
Dabbagh, N., & Kitsantas, A. (2005). Using web-based pedagogical tools as scaffolds for self-regulated learning. Instructional Science, 33, 513-540.
Darden, G. F. (1997). Demonstrating motor skills-rethinking that expert demonstration. Journal of Physical Education, Recreation & Dance, 68, 31-35.
de Jager, B., Jansen, M., & Reezigt, G. (2005). The development of metacognition in primary school learning environments. School Effectiveness and School Improvement, 16, 179-196.
de Jong, T., & Ferguson-Hessler, M. G. M. (1986). Cognitive structures of good and poor novice problem solvers in physics. Journal of Educational Psychology, 78, 279-288.
D'Esposito, M., Aguirre, G. K., Zaragbm E., Ballard, D., Shin, R. K., & Lease, J. (1998). Functional MRI studies of spatial and nonspatial working memory. Cognitive Brain Research, 7, 1-13.
Diekhoff, G. M., & Diekhoff, K. B. (1982). Cognitive maps as a tool in communicating structural knowledge. Educational Technology, 22, 28-30.
Driver, R. (1983). The Pupil as Scientist? Philadelphia, PA: Open University Press.
Driver, R., & Easley, J. (1978). Pupils and Paradigms: a Review of literature related to concept development in adolescent science students. Studies in Science Education, 5, 61-84.
Duschl, R. A. (1990). Restructuring science education: The importance of their development. New York: Teachers College Press.
Dykstra, D. I., Boyle, C. F., & Monarch, I. A. (1992). Studying conceptual change in learning physics. Science Education, 76, 615-652.
Eylon, B., & Linn, M. C. (1988). Learning and instruction: An examination of four research perspectives in science education. Review of Educational Research, 58, 251-301.
Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000). Executive attention and metacognitive regulation. Consciousness and Cognition, 9, 288-307.
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34, 906-911.
Georghiades, P. (2004). From the general to the situated: Three decades of metacognition. International Journal of Science Education, 26, 365-383.
Georghiades, P. (2006). The role of metacognitive activities in the contextual use of primary pupils' conceptions of science. Research in Science Education, 36, 29-49.
Hacking, M. W., & Lawrence, J. A. (1988). Expert and novice solutions of genetic pedigree problems. Journal of Research in Science Teaching, 25, 531-546.
Hadwin, A. F., Wozeny.L., & Pontin, O. (2005). Scaffolding the appropriation of self-regulatory activity: A socio-cultural analysis of change in teacher-student discourse about a graduate research portfolio. Instructional Science, 33, 413-450.
Hewson, M. G., & Hewson, P. W. (1983). Effect of instruction using students' prior knowledge and conceptual strategies on science learning. Journal of Research in Science Teaching, 20, 731-743.
Howard, R. W. (1988). Schemata: Implications for science teaching. The Australian Science Teachers Journal, 34, 29-34.
Huang, C.-M., Tsai, C.-C., & Chang, C.-Y. (2005). An investigation of Taiwanese early adolescents' views about the nature of science. Adolescence, 40, 645-654.
Jegede, O. J., Alaiyemola, F. F., & Okebukola, P. A. O. (1990). The effect of concept mapping on students' anxiety and achievement in biology. Journal of Research in Science Teaching, 27, 951-960.
Jonassen, D. H., Beissner, K., & Yacci, M. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hillsdale, NJ: Lawrence Erlbaum Associates.
Keri, S. (2003). The cognitive neuroscience of category learning. Brain Research Reviews, 43, 85-109.
Kozma, R. B., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34, 949-968.
Krynock, K., & Robb, L. (1999). Problem solved: How to coach cognition. Educational Leadership, 57, 29-31.
Kuhn, D. (2007). How to produce a high-achieving child. Phi Delta Kappa, 88, 757-763.
Kuhn, D., & Dean, D. (2004). Metacognition: A bridge between cognitive psychology and educational practice. Theory into Practice, 43, 268-273.
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.
Larkin, J. H., & Reif, F. (1979). Understanding and teaching problem-solving in physics. European Journal of Science Education, 1, 191-203.
Larkin, S. (2006). Collaborative group work and individual development of metacognition in the early years. Research in Science Education, 36, 7-27.
McCrudden, M. T., Schraw, G., & Lehman, S. (2007). The use of adjunct displays to facilitate comprehension of causal relationships in expository text. Instructional Science.
Mishkin, M., & Appenzeller, T. (1987). The anatomy of memory. Scientific American, 256, 80-89.
Moreno, J., & Saldana, D. (2005). Use of a computer-assisted program to improve metacognition in persons with severe intellectual disabilities. Research in Developmental Disabilities, 26, 341-357.
Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in Science Teaching, 27, 937-949.
Novak, J. D. (1998). Learning, creating, and using knowledge: Concept MapsTM as facilitative tools in schools and corporations. Hillsdale, NJ: Lawrence Erlbaum Associates.
Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. Cambridge, MA: Cambridge University Press.
Osaka, M., Osaka, N., Kondo, H., Morishita, M., Fukuyama, H., Aso, T., & Shibasaki, H. (2003) The neural basis of individual differences in working memory capacity: An fMRI study. NeuroImage, 18, 789-797.
Pines, A. L., & West, L. H. T. (1983). How "rational" is rationality? Science Education, 67, 37-39.
Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63, 167-199.
Pope, M., & Gilbert, J. (1983). Personal experience and the construction of knowledge in science. Science Education, 67, 193-203.
Posner, G. J., & Gertzvg, W. A. (1982). The clinical interview and the measurement of conceptual change. Science Education, 66, 195-209.
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66, 211-227.
Preece, P. F. W. (1976). Mapping cognitive structure: A comparison of methods. Journal of Education Psychology, 68, 1-8.
Preece, P. F. W. (1984). Intuitive science: Learned or triggered? European Journal of Science Education, 6, 7-10.
Resnick, L. B. (1983). Mathematics and science learning: A new conception. Science, 220, 477-478.
Robertson, W. C. (1990). Detection of cognitive structure with protocol date: Predicting performance on physics transfer problems. Cognitive Science, 14, 253-580.
Rozencwajg, P. (2003). Metacognitive factors in scientific problem-solving strategies. European Journal of Psychology of Education, 18, 281-294.
Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33, 569-600.
Sadler-Smith, E., & Riding, R. (1999). Cognitive style and instructional preferences. Instructional Science, 27, 355-371.
Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26, 113-125.
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.
Schraw, G., Flowerday, T., & Lehman, S. (2001). Increasing situational interest in the classroom. Educational Psychology Review, 13, 211-224.
Schraw, G., & Lehman, S. (2001). Situational interest: A review of the literature and directions for future research. Educational Psychology Review, 13, 23-52.
Schulz, R., & Mandzuk, D. (2005). Learning to teach, learning to inquiry: A 3-year study of teacher candidates' experiences. Teaching and Teacher Education, 21, 315-331.
Shavelson, R. J. (1972). Some aspects of the correspondence between content structure and cognitive structure in physics instruction. Journal of Educational Psychology, 63, 225-234.
Shavelson, R. J. (1974). Methods for examining representations of a subject-matter structure in a student's memory. Journal of Research in Science Teaching, 11, 231-249.
Shimamura, A. P. (2000). Toward a cognitive neuroscience of metacognition. Consciousness and Cognition, 9, 313-323.
Slack, S., & Stewart, J. (1989). Improving student problem solving in genetics. Journal of Biological Education, 23, 308-312.
Smith, E. E., & Jonides, J. (1997). Working memory: A review from neuroimaging. Cognitive Psychology, 33, 5-42.
Songer, N. B., & Linn, M. C. (1991). How do students' views of science influence knowledge integration? Journal of Research in Science Teaching, 28, 761-784.
Sperling, R. A., Howard, L. A., & Murphy, C. (2002). Measures of children's knowledge and regulation of cognition. Contemporary Educational Psychology, 27, 51-79.
Staver, J. R., & Jacks, T. (1988). The influence of cognitive reasoning lever, cognitive restructing ability, disembedding ability, working memory capacity, and prior knowledge on students' performance on balance equations by inspection. Journal of Research in Science Teaching, 25, 763-775.
Szpir, M. (1992). Accustomed to your face. American Scientist, 80, 537-539.
Taber, K. S. (2000). Multiple frameworks? Evidence of manifold conceptions in individual cognitive structure. International Journal of Science Education, 22, 399-417.
Tobin, K., & Gallagher, J. J. (1987). What happens in high school science classrooms? Journal of Curriculum Studies, 19, 549-560.
Tsai, C.-C. (1996). The " qualitative" differences in problem-solving procedures and thinking structures between science and nonscience majors. School Science and Mathematics, 96, 283-289.
Tsai, C.-C. (1998a). An analysis of Taiwanese eighth graders' science achievement, scientific epistemological beliefs and cognitive structure outcomes after learning basic atomic theory. International Journal of Science Education, 20, 413-425.
Tsai, C.-C. (1998b). An analysis of scientific epistemological beliefs and learning orientations of Taiwanese eighth graders. Science Education, 82, 473-489.
Tsai, C.-C. (2000a). The effects of STS-oriented instruction on female 10th graders' cognitive structure outcomes and the role of student scientific epistemological beliefs. International Journal of Science Education, 22, 1099-1115.
Tsai, C.-C. (2000b). Relationships between student scientific epistemological beliefs and perceptions of constructivist learning environments. Educational Research, 42, 193-205.
Tsai, C.-C. (2000c). Enhancing science instruction: the use of 'conflict maps'. International Journal of Science Education, 22, 285-302.
Tsai, C.-C. (2001a). Probing students' cognitive structures in science: The use of a flow map method coupled with a meta-listening technique. Studies in Educational Evaluation, 27, 257-268.
Tsai, C.-C. (2001b). A review and discussion of epistemological commitments, metacognition, and critical thinking with suggestions on their enhancement in internet-assisted chemistry classrooms. Journal of Chemical Education, 78, 970-974.
Tsai, C.-C. (2003). Using a conflict map as an instructional tool to change student alternative conceptions in simple series electric-circuits. International Journal of Science Education, 25, 307-327.
Tsai, C.-C. (2004). Beyond cognitive and metacognitive tools: The use of the internet as an 'epistemological' tool for instruction. British Journal of Educational Technology, 35, 525-536.
Tsai, C.-C., & Chang, C.-Y. (2005). Lasting effects of instruction guided by the conflict map: Experimental study of learning about the causes of the seasons. Journal of Research in Science Teaching, 42, 1089-1111.
Tsai, C.-C., & Huang, C.-M. (2001). Development of cognitive structures and information processing strategies of elementary school students learning about biological reproduction. Journal of Biological Education, 36, 21-26.
Tsai, C.-C., & Huang, C.-M. (2002). Exploring students' cognitive structures in learning science: Review of relevant methods. Journal of Biological Education, 36, 163-169.
Veenman, M. V. J., Elshout, J. J., & Meijer, J. (1997). The generality vs. domain-specificity of metacognitive skills in novice learning across domains. Learning and Instruction, 7, 187-209.
Veenman, M. V. J., & Verheij, J. (2003). Technical students' metacognitive skills: Relating general vs. specific metacognitive skills to study success. Learning and Individual Differences, 13, 259-272.
Wadsworth, B. J. (1989). Piaget's theory of cognitive and affective development (4 ed.). New York: Longman.
Wandersee, J. H. (1990). Concept mapping and the cartography of cognition. Journal of Research in Science Teaching, 27, 923-936.
Wandersee, J. H., Mintzes, J. J., & Novak, J. D. (1994). Research on alternative conceptions in science. In D. L. Gabel (Ed.), Handbook of Research on Science Teaching (pp. 177-210). New York: Macmillan.
White, R. T., & Gunstone, R. F. (1989). Metalearning and conceptual change. International Journal of Science Education, 11, 577-586.
Wilson, F. A., O'Scalaidhe, S., & Goldman-Rakic, P. (1993). Dissociation of object and spatial processing domains in primate cortex. Science, 260, 1955-1958.
Wu, Y.-T., & Tsai, C.-C. (2005). Development of elementary school students' cognitive structures and information processing strategies under long-term constructivist-oriented science instruction. Science Education, 89, 822-846.
Yang, F.-Y. (2004). Exploring high school students' use of theory and evidence in an everyday context: the role of scientific thinking in environmental science decision-making. International Journal of Science Education, 26, 1345-1364.
Zion, M., Michalsky, T., & Mevarech, Z. R. (2005). The effects of metacognitive instruction embedded within an asynchronous learning network on scientific inquiry skills. International Journal of Science Education, 27, 957-983.
Zion, M., & Slezak, M. (2005). It takes two to tango: In dynamic inquiry, the self-directed student acts in association with facilitating teacher. Teaching and Teacher Education, 21, 875-894.