Promoting Epistemic Cognition in Knowledge Building: Discourse, Tools, and Analytics

Epistemic cognition is about people's thinking about what they know and how they come to know. As a constructivist approach with unique onto-epistemological underpinnings, Knowledge Building (KB; Scardamalia & Bereiter, 2014) is deeply invested in facilitating epistemic cognition of learners. For example, the inculturation of learners into KB's theory-building discourse involves the exploration and development of learners' epistemic views; KB's supporting technology---Knowledge Forum---is built with epistemic scaffolds to promote epistemic diversity in the theory-building discourse. In this talk, I will discuss a line of research that aims to design discourse practices, tools, metrics, and analytics to promote epistemic cognition in KB. In the first study, we designed a "Discourse Moves tool" that visualizes epistemic diversity in a KB community. With this tool, we engaged a second grade class in metadiscourse about their discourse moves and salient concepts. Results indicated second graders' capability in reflecting on their epistemic moves and taking actions to enrich the epistemic diversity of their community. In the second study, I introduce my recent work on applying Network Science techniques to develop network representations of discourse data and derive network-based metrics of epistemic cognition. In this work, I conceptualize theory-building discourse in KB as a dynamic, multidimensional network involving epistemic agents, epistemic moves, ideas, and concepts. Epistemic cognition---of either an individual and a collective---is reflected in "meta-paths" and structural patterns of the multidimensional network. I will introduce nascent network-based metrics of epistemic cognition in KB discourse and discuss plans of developing analytics tools based on these metrics to promote epistemic cognition.

Transcript

1. Promoting Epistemic Cognition in Knowledge Building: Discourse, Tools, and Analytics Bodong Chen 陳伯棟, Associate Professor University of Minnesota–Twin Cities PEL2019, November 30, 2019
2. Agenda • Background • Knowledge Building • Epistemic Cultures • Design Research in Classrooms • Learning Analytics • Q&A Photo Credit
3. Shifting Landscapes in the Knowledge Age Photo Credit
4. Knowledge societies; innovation-driven societies (Drucker, 1968, 1993; OECD, 2010) “Disparities in the productivity and growth of different countries have far less to do with their abundance (or lack) of natural resources than with the capacity to create new knowledge and ideas” (David & Foray, 2003) 21st Century skills, higher-order competencies (Binkley et al., 2012; Scardamalia, Bransford, et al., 2012)
5. Knowledge Building (KB) “is an attempt to refashion education in a fundamental way, so that it becomes a coherent effort to initiate students into a knowledge creating culture.” — Scardamalia & Bereiter, 2003
6. Expert Writers, Creative Expertise Novice -- knowledge telling vs. Expert -- knowledge transforming
7. Chen, B., & Hong, H.-Y. (2016). Schools as knowledge-building organizations: Thirty years of design research. Educational Psychologist, 51(2), 266–288. doi:10.1080/00461520.2016.1175306
8. Knowledge Forum
9. Knowledge Building Epistemic Views, Beliefs… (e.g., Bielaczyc & Ow, 2014; Chuy, Scardamalia, et al., 2010; Hong, Chai, & Tsai, 2015; Hong, Chen, & Chai, 2016; Lin & Chan, 2018)
10. Epistemic Views, Beliefs, Cognition, etc. Two main areas (e.g., Mason, Boldrin, & Ariasi, 2010; Schraw, 2013) • What knowledge is? ◦ Certainty of knowledge ◦ Simplicity of knowledge • How one comes to know? ◦ Source of knowledge ◦ Justification for knowing Socio-pragmatic views (Knight & Littleton, 2017; Markauskaite & Goodyear, 2016) • Pragmatic (functional) • Normative (macro/cultural) • Communicative (micro/social)
11. Epistemic Cultures (Knorr Cetina, 1999, 2009) “cultures of creating and warranting knowledge” in scientific fields and laboratories (Knorr Cetina, 1999, p. 1) • Social and temporal orderings • Discourse, collaboration, management • Tools and instruments • Academic authorship
12. Photo Credit Photo Credit High energy physics Molecular biology
13. Epistemic Cultures in KB Classrooms Machineries of knowing • Held by the collective • Reflected in practices • Embedded in interaction and communication High-level conjecture: By configuring social orders and cultivating epistemic practices, we can shift the epistemic culture of a community to foster knowledge building and epistemic change.
14. #1: Epistemic Moves & Metadiscourse, Grade 2 Chen, B., & Resendes, M. (2012, May). Inviting students to reflect: Meta-discourse tool in Knowledge Forum. Paper presented at The Canadian Society for the Study of Education Annual Conference, Waterloo, Canada. My theory I need to understand Important info + source
15. Resendes, M., Scardamalia, M., Bereiter, C., Chen, B., & Halewood, C. (2015). Group-level formative feedback and metadiscourse. International Journal of Computer-Supported Collaborative Learning, 10(3), 309–336. https://doi.org/10.1007/s11412-015-9219-x Two Grade 2 classes (n = 44) in a lab school in Toronto, studying “Salmons” and “Birds” Metadiscourse intervention • Are we answering our questions? • Are we bringing in information? • Are we stuck on a problem? • What can we do to get unstuck? • …… #1: Epistemic Moves & Metadiscourse, Grade 2
16. Resendes, M., Scardamalia, M., Bereiter, C., Chen, B., & Halewood, C. (2015). Group-level formative feedback and metadiscourse. International Journal of Computer-Supported Collaborative Learning, 10(3), 309–336. https://doi.org/10.1007/s11412-015-9219-x Repertoire of making epistemic moves
17. Knowledge advancement
18. Resendes, M., Scardamalia, M., Bereiter, C., Chen, B., & Halewood, C. (2015). Group-level formative feedback and metadiscourse. International Journal of Computer-Supported Collaborative Learning, 10(3), 309–336. https://doi.org/10.1007/s11412-015-9219-x Vocabulary knowledge, shared attention Baseline Experimental
19. Chen, B. (2017). Fostering scientific understanding and epistemic beliefs through judgments of promisingness. Educational Technology Research and Development, 65(2), 255–277. “Creative individuals internalize the field’s criteria of judgement to the extent that they have the ability to separate bad ideas from good ones, so that they don’t waste much time exploring blind alleys.” — Mihaly Csikszentmihalyi, 2009 Creativity: Flow and the psychology of discovery and invention #2: Identifying Promising Ideas, Grade 6
20. Chen, B., Scardamalia, M., & Bereiter, C. (2015). Advancing knowledge building discourse through judgments of promising ideas. International Journal of Computer-Supported Collaborative Learning, 10(4), 345–366. https://doi.org/10.1007/s11412-015-9225-z #2: Identifying Promising Ideas, Grade 6
21. Context • Grade 6 students (n = 26), studying “Population Growth” • A school in Bogotá, Colombia Pedagogical intervention 1. Reinforcing design-mode thinking 2. Exploring the promisingness concept 3. Collective practice of Review-Reflect-Refocus (3R) #2: Identifying Promising Ideas, Grade 6 Chen, B. (2017). Fostering scientific understanding and epistemic beliefs through judgments of promisingness. Educational Technology Research and Development, 65(2), 255–277.
22. T: What to do with questions posted in Knowledge Forum? S: To answer them… T: What is a good or bad idea? S: A good answer is precise and accurate. S: … depends on one’s personal opinion T: What makes an idea promising? S: … not the answer; it is the idea that leads you to discussion.
23. Students’ understanding of promisingness
24. • Source of knowledge • Justification for knowing The changes were correlated with conceptual growth measured by a knowledge test. * * Epistemic beliefs (Conley et al. 2004)
25. Chen, B., Ma, L., Peebles, B., & Barbaro, V. (2020, April). Data Expedition in a Knowledge Building Community. Paper to be presented at the 2020 AERA Annual Meeting, San Francisco, CA. #3: Computing with Open Data, Grades 6 and 9 Photo Credit
26. DataX and CODAP
27. Context • One Grade 6 class (n = 22), discussing “UN Sustainability Goals” • A lab school in Toronto Pedagogical intervention 1. Knowledge building discourse and opportunistic collaboration 2. Analyzing data about issues at hand 3. Social media campaigns #3: Computing with Open Data, Grade 6
28. Graph comprehension
29. Discourse practices
30. Context • Two Grade 9 science classes, studying “Energy” • An urban school in St Paul. Culturally and linguistically diverse. Pedagogical intervention 1. Introducing data and DataX 2. Computing and reasoning with data related to energy #3: Computing with Open Data, Grade 9
31. Analyze and “play” with data
32. Summary Interventions Epistemic changes Conducting metadiscourse Making richer epistemic moves Justifying claims with evidence Identifying promising ideas More sophisticated epistemic beliefs Design-mode thinking Computing with open data Using data and graphs as epistemic tools Achieving pragmatic goals w/ knowledge Embracing messiness and playfulness
33. Data Science Methods and Learning Analytics How can we capture the epistemic culture of a community using learning trace data?
34. Quick Answers • Analysis of collectives (besides individuals) • Macro-level analysis of practices • Micro-level analysis of interactions
35. Sequential orderings of discourse moves Chen, B., Resendes, M., Chai, C. S., & Hong, H.-Y. (2017). Two tales of time: Uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interactive Learning Environments, 25(2), 162–175. Productive vs. Improvable
36. Chen, B., Resendes, M., Chai, C. S., & Hong, H.-Y. (2017). Two tales of time: Uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interactive Learning Environments, 25(2), 162–175. Sequential contingencies SD Q SA WI OI T Sequential orderings of discourse moves Sequence Productive Improvable Diff <T,T,T,T> 0.84 0.42 0.42 <T,T,OI,T> 0.42 0.16 0.26 <T,T,T,WI> 0.32 0.11 0.22 <OI,SD,SD> 0.10 0.32 -0.22 “Interesting” frequent sequences
37. Networks of epistemic moves (Shaffer et al., 2016) Productive Improvable
38. Structural signatures in discourse data
41. Painting -- Word networks
42. • Minnesota has more than 10,000 lakes! • A call for examining epistemic cultures in knowledge-building communities ◦ Not all knowledge-building classrooms are created equal ◦ They have unique machineries of knowing ◦ We may change a classroom by reshaping its epistemic culture • We can use discourse practices (and social reconfigurations) as “levers” to shift the epistemic culture • Analysis of epistemic cultures needs to rely more on trace data, as well as techniques that help maintain the complexity Key Takeaways
43. Thank You! Bodong Chen Email: chenbd@umn.edu Twitter: @bod0ng Website: http://bodong.me The 1st Learning Analytics Workshop on Modelling Digital Learning Networks https://colig.github.io/lak20network/ Frankfurt, Germany, March 23-27, 2020 Photo Credit