Interfaces for learning data visualisations – #ascilite.

Live blogging workshop from Prof Judy Kay. A computer scientist from the user modeling, AIED background, pervasive computing. A focus on personalisation. Putting people in control – personal data.

Interest in open learner models.

Learning analytics seen as a form of learner/user modeling – with interfaces.

How to create interfaces in LA?

  • User-centered approaches – start with where people are, hence need to understand mental models. – stakeholders – mental models – the problem
  • core tools and principles – starts to influence mental models
  • Case studies

It’s an exciting time as we can influence the shape of the core tools and principles.

Interfaces..visualisations

Why?

Fekete, Van Wijik, Stasko, North (2008) – The value of information visualisation.

We’re hardwired – preattentively processed task.

How? No simple rules

Some principles

  • Individual data takes on more meaning when comparisons are supported: others, temporal, contextual

Note: Application for MAV

Patina: Dynamic heatmaps for visualising application usage – Matekjka, Grossman, Fitzmaurice (2013)

  • Ability to show different footprints – allowing comparison

Case study

The problem – group work is hard and important, creates problems. Stakeholders – learner as individual, team leaders, facilitators. Students with capstone project – working in term for client. Also used by Masters Education students. Using trac.

Build a tool – Narcissus – Upton and Kay (2009).

Integrated into trak. – showing comparison of user participation. Different colours for different types. Click on the cell and see the details about participation from that user for that cell. Since using this, never had to fail a group. They see the data, it’s visible. Worked as a conversation starter. Students told the data would never be used for assessment.

Navigating the information space in an entirely new way based on what the people are doing.

Note: Potentially useful for BIM – self-regulation – comparisons

Sequence mining – identify individuals and what they are doing and group them into categories – managers, developers, loafers, other.

Current problems

  • Teacher – early identification of at-risk indviduals
  • Learner – decision suport: Am I doing well enough? Am I doing what is expected of me?
  • Insitution – effectiviness of learning and teaching.

General principles

Bull and Kay (2007) – Student models that invite the learner in: The SMILI:() open learner modelling framework

Using this work as the foundation/source of principles

OLM – any interface to data that a system keeps about the learner.

Note: this literature would have some general principles for Information in IRAC.

What is open? How is it presented? Who controls access?

Purposes

  • Improving accuracy
  • Promoting learner
  • Helping learners to plan and/or monitor learning
  • Facilitating collaboration and/or competition
  • Faciliting navigation of the learning system
  • Assessment

Scrutable user models and personalised systems. Systems are deterministic.

Note: links to IRAC

Interfaces to substantial learner models – analysis of an SPOOC.

Mental models

The set of user beliefs. Kay doesn’t see enough about mental models in the learning analytics literature.

The importance here is that mental models influences what a user can “see” and “hear”, how the interpret information. Clashes exists between user, programmer, expert MMs.

Pervasive technologies

Mention of orchestration as some of what drives this work.

Principles

  • Skill meters
  • Game elements
  • Good match to mental models

Summary

Some pointers to interesting research from the AI/HCI fields that could help inform learning analytics and prevent a lot of reinventing the wheel. But the observation made by the presenter that there are no principles, does mean that that promise of how to build these visualisations hasn’t been answered directly in the workshop.

Leave a Reply

Your email address will not be published. Required fields are marked *

css.php