grand challenges for the educational data mining and learning sciences communities

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Grand Challenges for EDM & the Learning Sciences Alyssa Wise, Simon Fraser University, @alywise

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Grand Challenges for EDM & the Learning Sciences

Alyssa Wise, Simon Fraser University, @alywise

A Pessimistic Snapshot of What’s Wrong with Formal Education Today

Impoverished / problematic understandings about

learning and increasingly diverse learners

Recalcitrant educational structures, punitive evaluation systems

Unproductive classroom cultures

Dysfunctional engagement in

formal schooling

Sea of digital information and informal (learning)

interaction opportunities

Growth of informal learning communities (e.g. maker-spaces,) -> alternative venues for learning

and demonstration of expertise

Rise of large scale learning environments -> opportunities for personalization and customized collaboration

Developments in computational discourse

and other methods -> new possibilities for tailored feedback

and assessment mechanisms

Increased data generated digitally (from physical and virtual spaces) -> opportunities for increased analytical insight

Emerging Technologies, Possibilities & Tensions

What we can build

What is worth

building

What we can

measure

What is worth

measuring

4. Adapting to increasingly diverse learner populations online and face-to-face

Grand Challenges

Measuring Things

1. Supporting community and interaction in learning spaces in the face of the current emphasis on scaling up and personalization

2. Making formal education relevant in a world where information is everywhere

3. Assessing and facilitating learning trajectories (vs. momentary states)

5. Being responsive to contextual differences bet. learning environments

Building Things

Challenge 1 – Building community in learning spaces in the face of the current emphasis on scaling up and personalization

• Productive classroom cultures and informal communities of practice foster efficacious and engaged learners

• Time for interactive refinement of mental models and knowledge practices, relationships, learners’ voices, agency and ownership key

• Focus on efficiency, economy, individualization and scale threatens the time needed for individual and collective sense-making

• Need for “slow learning”?

Challenge 2 – Keeping formal education relevant in a world where information is everywhere

• Today’s students have greater access to information than ever before (though this alone doesn’t cultivate knowledge, wisdom, understanding)

• Increasing challenges to schools as the primary venue for learning and demonstration of expertise

• Need to cultivate connections that penetrate the classroom walls , how can formal and informal learning become synergistic?

Challenge 3 – Facilitating and assessing learning trajectories (not momentary states)

• Increase in data granularity and temporal analysis techniques create possibilities to transform our paradigms of assessment to look at growth

• Opportunities to thinking about learning pathways not ‘bite sized chunks’

• Important issues of data rights and privacy - what are possibilities + dangers for “electronic learning records”?

• Role for student ownership and agency as learning occurs across contexts, expanding repertoire of ways to demonstrate / document expertise

Challenge 4 – Adapting to increasingly diverse learner populations online and face-to-face

• Immigration and global mobility are making classrooms are increasingly multi-cultural

• Online environments offer learning experiences to students coming with widely different cultural backgrounds + expectations

• Need for more robust ways to measure these differences in order to take them into account (tailored models + interventions)

Bergner, Kerr & Pritchard (2015) EDM 2015

MOOC Discussion Viewing

2 kinds of learners (whose activity needs to be modelled differently): those whose viewing was consistent over

time and those whose viewing changed

Challenge 5 – Being responsive to contextual differences between learning environments

• Online (and f2f) learning environments differ greatly in goals, practices and use of tools

Image Credit: World Map Parchment by Guy Sie via Flickr (CC BY 2.0)

Ogan, Baker, Walker, Rodrigo, Soriano, Castro (2015) IJAIED

X

X

Brooks, Greer & Gutwin, (2014) Learning Analytics: Research to Practice

Online Discussion Social Network Diagrams

Whether a particular pattern is “good” or “bad” depends on what the purpose of using the discussion forum was

(e.g. community building, Q&A/help, knowledge building)

Challenge 5 – Being responsive to contextual differences between learning environments

• Online and f2f learning environments differ greatly in goals, practices and use of tools

• Need to identify critical features on which they are similar / different (e.g. subject matter, pedagogy..)

• Balance between desire to generalize and recognition of key distinctions that need to be attended to for models to be locally useful

DATA MINING

Image Credit: Scott Clark via Flickr (CC BY 2.0), adapted

DATA GEOLOGY

Image Credit: APS Museum via Flickr (CC BY 2.0), adapted

( S H A F F E R , 2 0 1 3 )

DATA ARCHEOLOGY

Image Credit: Pedro Szekely via Flickr (CC BY 2.0), adapted

( W I S E , 2 0 1 4 )

Image Credit: Modified from cc licensed ( BY ) flickr photo of isole di brissago shared by mbeo

Image Credit: Modified from cc licensed ( BY ) flickr photos of isole di brissago shared by mbeo and Forth Bridge at dusk shared by Hilts uk

How do we start to recognize the boundaries of what we know , identify where other needed expertise resides, and learn enough about others’ areas to converse productively?