Scaffolded knowledge integration
- Scaffolded knowledge integration (SKI) or scaffolded knowledge integration framework is an instructional design model to enhance science teaching in school classrooms. It was developped by Marcia C. Linn (and collaborators) at University of California, Berkely.
“In the SKI framework, learners are viewed as adding to their repertoire of ideas and reorganising their knowledgeweb about science. Students sort out their ideas as a result of instruction, experience, observation, and reflection (Linn & Hsi, 2000). The framework is organised around four principles to promote knowledge integration: (a) making science accessible for students, (b) making thinking visible for students, (c) providing social supports for students, and (d) promoting lifelong science learning.” (Williams & Linn, 2002: 416).
“The Scaffolded Knowledge Integration framework offers guidelines to help designers create materials that promote integration. Scaffolded Knowledge Integration builds on results from related research (Linn and Hsi 2000, Hawkins and Pea 1987, Mokros and Tinker 1987, Bransford et al.1990, Hawkins 1991, Pea and Gomez 1992, Pea and Gomez 1993, Gordin et al.1994, Means et al. 1996, Means and Coleman, ).To promote knowledge integration, Scaffolded Knowledge Integration has four main tenets: making science accessible for all students; making thinking visible so students understand the process of knowledge integration; helping students to listen from each other; and promoting life long science learning.” (Linn, 2000: 784)
(1) Instruction should connect science to personally relevant problems and prior knowledge, i.e. to make a link between instructed and spontaneous concepts.
(2) Students and teachers are encouraged to “make their thinking visible, describing how they recognise new ideas, and reorganise and connect new and prior ideas. Students explore events and phenomena first hand and develop from those observations important concepts and ideas. Technological supports such as visualisations, films, models, and simulations can also make thinking visible.We ask students to make predictions, drawinferences, and construct generalisations” ((Williams & Linn, 2002: 417)
(3) Based on Vygotsky's concept zone of proximal development - a foundation of most socio-constructivist designs, the SKI “[...] emphasises that providing students with social supports in a science classroom can promote knowledge integration. Collaborative learning situations such as discussions and debates can be designed so students offer explanations, interpretations, and resolutions supported by a peer or a scientist.” (Williams & Linn, 2002: 418)
(4) Promote autonomy for lifelong science learning: “To prepare students to integrate the ideas they learn in science and revisit them once they have completed a science course, WISE software supports questioning, analysing, and reflecting. [...] Students are asked to identify weaknesses in arguments and question the validity of the scientific information presented. These activities allow students to link their real world experiences with scientific concepts taught in school and prompt students to make the links between spontaneous and instructed ideas. [...] In addition, the WISE software features "Amanda the Panda", an electronic guidance tool that supplies students with hints regarding salient aspects of Internet evidence and also reminds students of the purpose of a project activity. These forms of guidance make the computer a learning partner in the classroom, encouraging students to link their real world experiences with scientific concepts.” (Williams & Linn, 2002: 418)
- See the article about the WISE project
- Linn, M. C. (1995). Designing computer learning environments for engineering and computer science: The scaffolded knowledge integration framework. Journal of Science Education and Technology, 4(2), 103-126. Abstract/PDF (Access restricted)
- Bransford, J. D., Sherwood, R. D., Hasselbring, T. S., Kinzer, C. K. and Williams, M. (1990) Anchored instruction: why we need it and how technology can help. In D. Nix and R. Spiro, Cognition, Education and Multimedia (Hillsdale, NJ: Lawrence Erlbaum Associates), 115- 142.
- Gordin, D. N., Polman, J. L. And Pea, R. D. T. (1994) The Climate Visualizer: Sense-making through scientific visualization. Journal of Science Education and Technology, 3(4), 203-226.
- Hawkins, J. (1991) Technology-mediated communities for learning: designs and consequences. In V. M. Horner and L. G. Roberts, Electronic links for learning: The annals of the American Academy of Political and Social Science (Newbury Park: Sage), 514, 159-174.
- Hawkins, J. And Pea, R. D. (1987) Tools for bridging the cultures of everyday and scientific thinking. Journal for Research in Science Teaching, 24, 291- 307.
- Linn, M. C. and HSI, S. (2000) Computers, Teachers, and Peers: Science Learning Partners, (Hillsdale, NJ: Lawrence Erlbaum Associates)
- Means, B. And Coleman, E. (in press) Technology supports for student participation in science investigations. In M. J. Jacobson and R. B. Kozma, Learning the Sciences of the 21st Century: Theory, Research, and the Design of Advanced Technology Learning Environments (Hillside, NJ: Erlbaum).
- Note: This ref is wrong.
- Mokros, J. R. and Tinker, R. F. (1987) The impact of microcomputer-based labs on children’s ability to interpret graphs. Journal of Research in Science Teaching, 24(5), 369-383.
- Pea, R. And Gomez, L. (1992) Distributed multimedia learning environments: why and how? Interactive Learning Environments, 2, 73-109.
- Pea, R. D. And Gomez, L. (1993) Distributed multimedia learning environments: the collaborative visualization project. Communications of the ACM, 36(5), 60- 63.