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
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?

Grand challenges for the Educational Data Mining and Learning Sciences Communities

  • 1.
    Grand Challenges forEDM & the Learning Sciences Alyssa Wise, Simon Fraser University, @alywise
  • 2.
    A Pessimistic Snapshotof 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
  • 3.
    Growth of informallearning 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.
    4. Adapting toincreasingly 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
  • 5.
    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”?
  • 6.
    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?
  • 7.
    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
  • 8.
    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)
  • 9.
    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
  • 10.
    Challenge 5 –Being responsive to contextual differences between learning environments • Online (and f2f) learning environments differ greatly in goals, practices and use of tools
  • 11.
    Image Credit: WorldMap Parchment by Guy Sie via Flickr (CC BY 2.0) Ogan, Baker, Walker, Rodrigo, Soriano, Castro (2015) IJAIED
  • 12.
    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)
  • 13.
    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
  • 14.
    DATA MINING Image Credit:Scott Clark via Flickr (CC BY 2.0), adapted
  • 15.
    DATA GEOLOGY Image Credit:APS Museum via Flickr (CC BY 2.0), adapted ( S H A F F E R , 2 0 1 3 )
  • 16.
    DATA ARCHEOLOGY Image Credit:Pedro Szekely via Flickr (CC BY 2.0), adapted ( W I S E , 2 0 1 4 )
  • 17.
    Image Credit: Modifiedfrom cc licensed ( BY ) flickr photo of isole di brissago shared by mbeo
  • 18.
    Image Credit: Modifiedfrom 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?

Editor's Notes

  • #2 For the interactice part two things that might be useful to talk about (in addition to the what do you see as core challenges questions) are: "How can we build EDM models that account for contextual differences.... "How could we build sustainable bridges between EDM (community / researchers) and other communities / researchers to meet these challenges"
  • #3 Challenges aren’t (entirely) technical ones but we hope tech can help solve
  • #13 MOOC, social, Q&A, interactive discussion