Over the past twenty years, education researchers have been exploring the potential of complex knowledge systems as a heuristic frame for modeling facets of knowing and learning (especially focused on topics in science and mathematics). Within educationally oriented circles, “Knowledge in Pieces” (diSessa, 1993) is the most prominent example of this line of work that has led to novel reformulations and insights into a diverse set of foundational issues ranging from:
The nature of individuals’ intuitive knowledge of the physical world (Clark, D. B., D’Angelo, C. & Schleigh, 2011; diSessa, 1993; diSessa, Gillespie & Esterly, 2004);
The nature and robustness of “misconceptions” (diSessa, Smith & Roschelle, 1993);
Continuities across the expert-novice spectrum (Smith, 1995; diSessa & Sherin, 1998);
Processes by which individuals learn to model physical systems with algebra and programming (Sherin, 2001; Izsák, 2000);
Processes of analogical reasoning, transfer and “abstraction” (Wagner, 2006; Kapon & diSessa, 2012);
Processes by which representational activity mediates conceptual change (Parnafes, 2007; 2012);
The nature of strategic and conceptual knowledge and how they co-develop during mathematical problem solving (Levin, 2018)
Ideology change and how teachers understand race and racism (Philip, 2007; 2011)
Knowledge Analysis: A methodology for studying real-time processes of knowing and learning
Knowledge Analysis, a methodology for studying human intellectual performance and its development, can be characterized by a number of particular interests and modes of thinking including (1) a strong interest in theoretical innovation as part of the whole research enterprise—prototypically developing views of both the form and content of knowledge simultaneously; (2) a strong concern for “high-resolution” analysis of data, including, often, a fine time grain-size and also especially nuanced descriptions of “ideas”; (3) a “developmental” concern for long-term, complex learning, and the use of development as one important dimension of triangulation on knowledge, even of experts; (4) a persistent focus on contexuality (situatedness) of knowledge; and finally, (5) a respect toward reductionist modeling of human intellectual performance, but also a judiciously slow approach to it.
Theory building:
diSessa, A. A., & Cobb, P. (2004). Ontological innovation and the role of theory in design experiments. Journal of the Learning Sciences, 13(1), 77-103.
Representations of knowledge and learning:
diSessa, A. A. (1991). If we want to get ahead, we should get some theories. In R. G. Underhill (Eds.), Proceedings of the Thirteenth Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. (Plenary lecture and reaction). Vol. 1. Blacksburg, VA: Virginia Tech, 220-239.
diSessa, A. A. (1991). Epistemological micromodels: The case of coordination and quantities. In J. Montanegro & A. Tryphon (Eds.) Psychologie génétique et sciences cognitives. (Volume from the Eleventh Advanced Course.) Geneva: Archives, Jean Piaget, 169-194.
diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–226.
diSessa, A. A. (1994). Speculations on the foundations of knowledge and intelligence. In D. Tirosh (Ed.) Implicit and Explicit Knowledge: An Educational Approach. Norwood, NJ: Ablex, 1-54.
diSessa A. A. & Sherin (1998). What changes in conceptual change? International Journal of Science Education, 20(10),1155-1191.
diSessa. A. A. (2002). Why "conceptual ecology" is a good idea. In M. Limón & L. Mason (Eds.) Reconsidering conceptual change: Issues in theory and practice (pp. 29-60). Dortrecht: Kluwer.
diSessa, A. A. (2004). How should we go about attributing knowledge to students. In E. Redish and M. Vicentini (Eds.) Proceedings of the International School of Physics "Enrico Fermi": Research on physics education (pp. 117-135). Amsterdam: ISO Press/Italian Physics Society.
diSessa, A. A. (2004). Contextuality and coordination in conceptual change. In E. Redish and M. Vicentini (Eds.), Proceedings of the International School of Physics "Enrico Fermi" Research on physics education (pp. 137-156). Amsterdam: ISO Press/Italian Physics Society.
diSessa A. A. & Wagner, J. (2005). What coordination has to say about transfer. In J. Mestre (Ed.), Transfer of learning: Research and perspectives. Greenwich, CT: Information Age Publishing.
Hammer, D. (2000). Student resources for learning introductory physics, American Journal of Physics., Physics Education Research Supplement. 68(7), S52- S59.
Levin, M. (2018). Conceptual and Procedural Knowledge During Strategy Construction: A Complex Knowledge Systems Perspective. Cognition and Instruction. doi.org/10.1080/07370008.2018.1464003
Louca, L., Elby, A., Hammer, D., & Kagey, T. (2004). Epistemological resources: Applying a new epistemological framework to science instruction.Educational Psychologist, 39 (1), 57-68.
Sherin, B. (2001). How students understand physics equations. Cognition and Instruction. 19(4), 479-541.
Wittmann, M.C. (2006). Using resource graphs to represent conceptual change. Physical Review Special Topics - Physics Education Research, 020105, 2006.