Epistemic complexity

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1 Definitions

The term epistemic complexity is used in several contexts. In various learning science, epistemic complexity often refers to the cognitive challenge of a task, in particular in constructivist whole task settings such as inquiry-based learning. (e.g. Lakkala, 2010). It is also used to describe the complexity of student productions: “Epistemic complexity indicates students' efforts to produce not only descriptions of the material world but also theoretical explanations and articulation of hidden mechanisms central to the nature of science” (Salmon 1984 cited by Zhang 2009 or more simply: “Epistemic complexity represents the level of complexity at which a student chooses to approach an issue.” (Zhang, 2009).

  • In Biology, “Biological evolution is a progressing process of knowledge acquisition (cognition) and, correspondingly, of growth of complexity. The acquired knowledge represents epistemic complexity.” (Kováč, 2007). Bailly and Longo (2003) provide a similar definition: “By this notion we mean the global functions of a system, the external description of it as given by the knowing subject (thus "epistemic").”.
  • In computer science and artificial intelligence, epistemic complexity could be defined in terms of the “complexity of the decision problem for epistemic logics” (Vardi, 1989) or computational incompressibility (Kolmogorov-Chaitin). A philosopher defines epistemic complexity as the “richness of the knowledge that is embedded in an artifact.” (Dasgupta)
  • In systems theory, “Rescher (1998) distinguished three 'modes', namely epistemic, ontological and functional complexity. Among these modes of complexity, the epistemic embraces three categories: descriptive, generative and computational complexity” (Schlindwein et al., 2004).
  • In constructivism, epistemic complexity could be related to epistemic fluency, i.e. be able to communicate across epistemic divides using different epistemic games. (Morrison and Collins, 2996). In a similar way, Bing and Redish (2011) argue that: “First, experts have larger and better-organized banks of knowledge. Second, experts are better in-the-moment navigators during the problem solving process”. In-the-moment navigation happens between various epistemological framings such as warrants, epistemological resources, and epistemological framing.

See also: Creativity

2 Measures and instruments

2.1 Hakkarainen, 2003

Participants’ postings to CSILE’s database were firstly segmented into ideas: e.g. questions, working theories, and (authoritative) scientific information. Each knowledge idea constructed by students to answer their research questions was classified using a 5-step scale starting from 1=separated pieces of facts to 5=explanation:

  • Level 1. Separated Pieces of Facts. A rating of 1 was assigned to CSILE students’

knowledge ideas representing either simple statements of facts or lists of facts with fewconnecting linkages

  • Level 2. Partially Organized Facts. A rating of 2 was given to ideas that represented loosely connected pieces of factual information.
  • Level 3. Well-Organized Facts. A rating of 3 was assigned to ideas in which factual

information was introduced in a well-organized way.

  • Level 4. Partial Explanation. A rating of 4 was assigned to ideas that represented some

characteristics of explanation but the content of the explanation was limited or only partially articulated.

  • Level 5. Explanation. A rating of 5 was assigned to ideas for which a relatively wellelaborated explanation was provided.

(Hakkarainen, 2003: 1076-1077).

2.2 Depth of understanding - Zhang et al.

Zhang et al. (2009) use human judges to evaluate student productions.

Complexity scale

  • A 4-point scale (1 = unelaborated facts, 2 = elaborated facts, 3 = unelaborated explanations, and 4 = elaborated explanations)

Scientific sophistication

The authors also measure scientific sophistication. It represents the level of success a student has achieved in processing an idea at a certain complexity level.

  • (1 = pre-scientific, 2 = hybrid, 3 = basically scientific, 4 = scientific).

Composite score: Depth of understanding

  • Epistemic complexity X Scientific sophistication

2.3 Lexical variation and density

(Sun, 2008, Nation, 2001).

  • Type/Token Ratio (TTR): the number of word types (unique words) in the analyzed text divided by the number of all tokens (total words) in the analyzed text.

2.4 Vocabulary

(Sun, 2008, Nation, 2001).

  • Percentage of "academic words" and "domain-specific words".

“The [Coxhead (1998)] Academic Word List consists of 570 word families that are not in the most frequent 2,000 word families of English, but occur at a reasonably high frequency in academic texts of different disciplines. These words are typical of academic discourse, which references other authors and findings (e.g., assume, establish, conclude), and works with data and ideas (e.g., analyze, assess, category).” (Sun et al. 2008)

3 Links


4 Bibliography

  • Bailly, Francis and Giuseppe Longo, (2003). Objective and Epistemic complexity in Biology, Invited lecture, International Conference on Theoretical Neurobiology, National Brain Research Centre, New Delhi, INDIA, February 2003. PDF
  • Bing, Thomas J. , Edward F. Redish (submitted, 2011). Epistemic Complexity and the Journeyman-Expert Transition, Physics Education arXiv:1103.3325v1
  • Carsetti, A., “Epistemic Complexity and Knowledge Construction”.
  • Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic reasoning in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86, 175–218.
  • Coxhead, A. (1998). An Academic Word List. Occasional Publication Number 18, LALS, Victoria University of Wellington, New Zealand.
  • Dasgupta, Subrata (1997). Technology and complexity, Philisophica 59, 113-139. PDF
  • Kováč L. Fundamental principles of cognitive biology. Evolution and Cognition. 2000;6:51–69.
  • Giovanni B. Moneta (1993), A model of scientists’ creative potential: The matching of cognitive structure and domain structure, Philosophical Psychology Volume 6, Issue 1. DOI 10.1080/09515089308573075
  • Hakkarainen, K. 2003. Progressive inquiry in a computer-supported biology class.. Journal of Research in Science Teaching , 40: 1072–1088. DOI: 10.1002/tea.10121
  • Kolmogorov, N., (1968) "Logical Basis for Information Theory and Probability Theory",IEEE Trans.IT 14,5,pp.662-4.
  • Lakkala, Minna (2010). How to design educational settings to promote collaborative inquiry: Pedagogical infrastructures for technology-enhanced progressive inquiry, Dissertation, Institute of Behavioural Sciences, University of Helsinki, Finland. PDF
  • Laufer, B., & Nation, P. (1995). Vocabulary size and use: Lexical richness in L2 written production. Applied Linguistics, 16, 307–322. DOI:10.1093/applin/16.3.307
  • Morrison, D. and Allan Collins (1996), Epistemic Fluency and Constructivist Learning Environments, in Brent Gayle Wilson (ed.) Constructivist learning environments: case studies in instructional design, out of print. Google books
  • Nation, I. S. P. (2001). Learning vocabulary in another language. Cambridge, UK: Cambridge University Press.
  • Schlindwein, Sandro Luis and Ison, Ray (2004). Human knowing and perceived complexity: implications for systems practice. Emergence: Complexity and Organization, 6(3), pp. 27–32. http://oro.open.ac.uk/58/
  • Rescher, N. (1998). Complexity A Philosophical Overview, New Brunswick: Transaction publishers.
  • Salmon, W. C. 1984. Scientific explanations and the causal structure of the world. , Princeton, NJ: Princeton University Press.
  • Sun, Yanqing; Jianwei Zhang & Marlene Scardamalia (2008). Knowledge building and vocabulary growth over two years, Grades 3 and 4. Instructional Science. DOI:10.1007/s11251-008-9082-5
  • Vardi, M.Y. (1989). On the complexity of epistemic reasoning, Fourth Annual Symposium on Logic in Computer Science, 1989. LICS '89, Proceedings, 243-252.
  • Waight Noemi, Fouad Abd-El-Khalick, From scientific practice to high school science classrooms: Transfer of scientific technologies and realizations of authentic inquiry, Journal of Research in Science Teaching, 2011, 48, 1. DOI:10.1002/tea.20393
  • Zhang Jianwei; Marlene Scardamalia, Richard Reeve, Richard Messina (2009). Designs for Collective Cognitive Responsibility in Knowledge-Building Communities, Journal of the Learning Sciences Vol. 18, Iss. 1, 2009