Visualizing Collaboration Christopher Teplovs Computational Social Science Lab (CSSL) Copenhagen Business School

Visualizing (Opportunities for) Collaboration Christopher Teplovs Computational Social Science Lab (CSSL) Copenhagen Business School

Outline background stakeholders – students – teachers – researchers & observers Knowledge Forum Knowledge Space Visualizer KISSME

Background

Online Discourse product (writing, drawing, etc.) process (who has done what and when) focus on text-based analysis

Knowledge Forum

based on principles of Knowledge Building assessment via embedded tools information visualization

Embedded and Transformative Assessment Standards and benchmarks are objects of discourse in Knowledge Forum, to be annotated, built on, and risen above.

Participation & Collaboration Tools These tools provide information about the number and nature of artifacts created by participants at the individual and group level, as well as relationships between individuals.

Social Network Analysis Tools

Writing Analysis Tools These tools parse and quantify the contributions of participants in terms of vocabulary growth and basic writing measures (e.g. total and unique words, mean sentence length).

Writing Analysis Tools (small multiples)

Semantic Analysis Tools These tools deal with the meaning of the discourse by analyzing the semantic fields of contributions.

Semantic Analysis Tools

Visualization Tools These tools allow participants and observers to make sense of the emergent knowledge building discourse.

The Knowledge Space Visualizer (KSV) designed to allow researchers to use computer-assisted 2D visualizations of learner-generated contributions to online discourse can visualize explicit links between documents – replies, references, annotations

KSV: Visualizing Implicit Links previous example showed explicit links between documents can also show implicit links based on similarity of documents LSA gives us vector representations of documents cosine between vectors is a good indicator of the similarity between documents

KSV: Computer-assisted layout axes can be operationally defined to provide a variety of layout options chronology and authorship can be used to see when participants contribute

Of course we can overlay additional information, like the relationships between documents

The KSV: Document-level networks flexible thresholds networks of documents scalability issues: “hairballs” what can we learn about the learners?

Learner Models (Interaction-based) work with Eric Bruillard, Christophe Reffay and François-Marie Blondel at ENS Cachan modelling learner communities as the products of interactions among their members

Learner Models (Content-based) time-consuming and/or intrusive variety of ITS-based approaches ours is simpler:

You are what you write! our approach is to use LSA to create a model of the learner that is based on all of what they have contributed to a community’s discourse space we have a vector representation for each user we can then start looking at the relationships between user models

Latent Semantic User Models can determine similarity between any two user models (again, by calculating cosine) can start looking at hypotheses about the conditions under which we might expect to see productive interactions (e.g. Vygotsky’s Zone of Proximal Development)

KISSME Knowledge, Interaction, and Semantic Student Model Explorer

Social Network Adjacency Matrix

Semantic Network Adjacency Matrix (high values darkest)

Semantic Network Adjacency Matrix (intermediate values darkest)

Stakeholders students teachers researchers

Students “If you want to take a look at someone who writes stuff that’s really similar to your stuff, check out Charlotte’s contributions. For inspiration, check out Ilsa’s work.”

Teachers “Most of the activity in the class is centered on the topics of sex and drugs. New topics this week include rock and roll. There are three cliques in the class, each with seven students.”

Researchers visualizations that need some refinement

Software Demo & Questions