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Today I’ve participated in a wonderful full-day workshop about Epistemic Network Analysis (ENA), which I had been wishing to learn for a long time. David and Gol did a great job explaining its theoretical background, walking through a bunch of mathematical computation behind it, and doing some interesting demos with some real data from their research project. I had a great time playing with the sample data with my “teammate” Alisa, and will definitely apply it on some Knowledge Forum data in the near future. It’s also interesting that the system is based on R, and it makes me seriously thinking about the possibility of building KF analytics on R.

ENA

I am posting my notes below. For more information about the workshop, check conference website

Intro by David Shaffer: Measure collaborative thinking with ENA

Theoretical background of ENA

  • Its roots in research of epistemic games. Show video about the epistemic game for engineering education
  • Data mining –> Data geology (data mining is a problematic term; have to understand the underline structure before digging into the data)
  • the need for 21st century thinking
  • community of knowledge —| culture, knowledge, skills, values, identity; shared epistemology is important for a community
  • Shawn’s research on creative community; reflection-on-action (pretty inspiring for my work on promisingness)
  • ENA aims to understand the structure of skills, knowledge, identity, values and epistemology.
  • go beyond psychometrics work focusing on specific skills. this tool is generic, can be applied to anyplace where network (of coding categories / epistemic frames) is more important rather than individual components.

Explanation of the tool, focusing on mathematical stuff behind it

  • get chat discourse from the engineering epistemic game
  • code each utterance
  • validate (alpha scores)
  • coded data ready – matrix, binary (rather than proportional data)
  • define stanza – this concept is very important, because it will decide how co-occurrences occur
  • build adjacency matrix, based on collapsing coding results in a same stanza
  • build adjacency vector, converted from adjacency matrix
  • decide the unit of analysis - student, group of students, or else?
  • collapse stanzas for each unit of analysis, to get vectors for each unit
  • vectors represented by their directions, rather than positions
  • dimensional reduction (SVD) – similar to Principal Component Analysis
  • each unit represented in a 3D space
  • making sense of reduced dimensions – loading
  • this tool will not allow the analyzer to see a final position of each unit, but also its development trajectory.

My questions:

  • What assumptions are made in the stanza compression process?
  • What if frequency of epistemic frames are different? (also asked by another participant)

Thoughts

  • For the analysis of math discourse we are doing, counting appearance of different types of vocabs in students’ utterance will basically give coded data of students’ mathematical thinking. And then the tool can be applied. It will also be interesting to see the links between mathematical thinking and KB contribution.
  • Being able to see evolution of thinking is interesting.

Demos by Golnaz Arastoopour

Link to the ENA tool

  • The project started from Excel, and now its backend is based on R, and front-end visualizations are based on JavaScript
  • csv or R data; can be exported as R data to be further explored in RStudio

Questions and comments:

  • Given the larger number of plots to look at, how to find the most helpful plot?
    • David: the reduced dimensions are ordered already by the percentage of variance.
  • This whole system is backed by R. Interesting way to develop LA systems.

Presentation by Chandra Orrill, University of Massachusetts Dartmouth

General intro

  • work a lot on math education; focusing on teacher’s professional development; proportional reasoning
  • diSessa’s knowledge in pieces as hypothesis of her research
  • novice vs. expert teachers; experts focus on big ideas
  • importance of knowledge coherence for teachers

Study

  • interview data of teachers about proportional reasoning
  • 7 teachers
  • Data coding
    • Ratio concepts: iteration, ideas of growth, invariance, ratio-as-measure, ration calculation
    • Fraction concepts: …
    • Problem solving: problem interpretation, verify math is correct, attend to context
    • Representation: interpretation diagram, validate representation
    • Other math ideas: …
  • different patterns for different levels of teachers (stronger, middle, weaker)
  • adding more items (concepts?) into analysis will change results

Lessons learned

  • It helps to start small – know data better
  • Not to be over-generous when coding – be more strict when doing. “Do I see … in this unit?”
  • Deal with codes that confound.

Questions:

  • Where I can access the R code that is used in RStudio?
  • What is a proper stanza for typical interview data? (Her answer is – in her case, each teacher was asked to talk about a series of items. and discussion about one item was treated as a stanza. that makes sense!)

David presenting the process of going from raw data to ENA

  • Example of analyzing Common Core standards
  • Important to make data reproducible; can go back
  • Be thoughtful in defining metadata, which is very useful for reproducing or reconstructing data
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Bodong Chen


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Bodong Chen, University of Minnesota

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