personal analytics: getting off the deficit path - alasi... · video tutorial of surgery and...
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Personal Analytics: Getting off the
deficit path
Professor Gregor Kennedy The University of Melbourne
What are Personal Analytics?
http://birdsontheblog.co.uk/getting-fitter-and-healthier-with-the-fitbit/
What are Personal Analytics?
https://gigaom.com/2011/11/07/is-klout-crossing-the-line-when-it-comes-to-privacy/
Big Data = Analytics
https://www.linkedin.com/today/post/article/20140312180810-246665791-the-future-of-big-data-and-analytics
With Great Promise
• Detect potential “at risk” students
• Formative and summative feedback to students on their learning processes and outcomes
• Assist with evidence-based resource allocation
• Improve institutional decision-making and responsiveness to known challenges
• Promote a shared understanding of institutional successes and challenges
• Academic research and development
(Long & Siemens, 2011)
Defining Analytics
Society for Learning Analytics Research (2011)
Learning Analytics is the measurement, collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning, and the environments in which it occurs. Academic Analytics is the improvement of organizational processes, workflows, resource allocation, and institutional measurement through the use of learner, academic, and institutional data.
Managers, Administrators, Funders
Learners, Educators, Teachers
THIS BIT IS NOT NEW
Taxonomies of Interaction
Taxonomies and Classifications e.g. Thompson & Jorgenson (1989)
Reactive Interactive Proactive
Interactivity Research
Taxonomies and Classifications e.g. Schwier & Misanchuk (1993)
Reactive Proactive Mutual
Interactivity Research
Taxonomies of Interaction
Interactivity Research
Concerns about the past
• Often use fairly raw metrics, simple student measures and inputs (e.g. MCQs, simple access counts).
• Largely descriptive (useful) but often fails to complete the feedback loop to students and/or teachers.
Two Traditions
Intelligent Tutoring Systems
Student Model
Pedagogical Model
Domain Knowledge
Feedback
Two Traditions
Intelligent Tutoring Systems Flag the Error
Explain the Error
Give a Hint
Show a worked example
(Mike Timms)
Intelligent Tutoring Systems
• “ITS were recognised as narrow and brittle” (Cumming & McDougall, 2000) • … heavily reliant on educational programs and applications that
had defined or discrete stages and steps.
• They were often tied to a program and were not generalisable.
Concerns about the past
Interactivity Research
Student Smart
System
Assess
Diagnose
Recognise Personal
Adaptive
Intelligent Tutoring Systems
Two Traditions Combined
Two Traditions
Intelligent Tutoring Systems
Interactivity Research
Drill and Practice
Procedural Simulation
Conceptual Simulation
Drill and Practice
Student Path
A B C
Content Content Content Content Content
A B C
A B C
A B C
A B C
Feedback Content
X
Detect “At Risk” Students
for Retention
Teaching & Learning Research, Evaluation
& QA
Personalised or Adaptive Feedback
for Learning
How Today’s Analytics are Used
✓
✓
✓
• Purdue University’s “Signals”
• Used to predict students who are “at risk”
• Individual student risk is predicted using an algorithm based on data from four sources:
− Performance … “points earned in a course to date”
− Effort … interaction with the learning management
system as compared to peers
− Academic history … e.g. GPA, prior academic history
− Student characteristics … e.g. residency, age
(Arnold & Pistilli, 2012)
#1 … “At Risk” Analytics
• Post to students’ LMS • Email or text students • Refer them to an advisor • Call for a chat
(Arnold & Pistilli, 2012)
“At Risk” Analytics
Analytics = Diagnosing Deficit Path
Preferred Parameters or Pathway
Student Path
Feedback
Assess, diagnose and recognise “deficit”
The field of educational technology has always been interested in using students’ digital traces to assess and diagnose when they move away from preferred learning pathways.
X
This approach has useful pedagogical applications … but
Macro: Attrition
Micro: Drill & Practice
Deficit Pathways
The Promise of Learning Analytics
How can we …
• harness different data analysis techniques
• for the provision of more meaningful feedback
• to students on their learning processes
• in real time
• for genuinely personlised learning environments?
A core promise of learning analytics is to improving students’ micro learning processes in order to enhance their learning outcomes
Example 1: Surgical Skills Simulation
James Bailey Professor, Computing & Information Systems
Ioanna Ioannou Research Fellow, Otolaryngology
Stephen O'Leary Professor, Otolaryngology
Patorn Piromchai PhD Student, Otolaryngology
Sudathi Wijewickrema Research Fellow, Otolaryngology
Yun Zhou PhD Student, Computing & Information Systems
Example 1: Surgical Skills Simulation
O'Leary, S., et al. (2008). Validation of a networked virtual reality simulation of temporal bone surgery. The Laryngoscope, 118(6), 1040-1046.
Metrics from the Simulator
• Tool position, orientation and force metrics - e.g. current force applied by the drill • Burr metrics - e.g. radius of the current burr • Anatomical structure metrics - e.g. distance of the drill tip to the closest point of one of three key anatomical structures • Bone specimen metrics - e.g. rotation of the bone
----- 15 records of 48 metrics generated per second -----
• A sequence of points containing a continuous drilling motion
• The end of a stroke is reached - when drilling ceases; or - when there is an abrupt change in the direction of drilling
• Once a way of identifying strokes has been determined a
range of “stroke metrics” can be calculated from the data stream output by the simulator (e.g. stroke duration, stroke length, average stroke speed, minimum distance of stroke to structures, etc.)
A Key Metric: Stroke
(Hall, Rathod, et al., 2008)
Data Mining for Personal Feedback
• We needed to provide personalised feedback to trainees across multiple dimensions or features in an open, complex, procedural simulation.
• Not just deficit feedback about manifest error or procedural stage - “You hit the facial nerve” - “You should have completed X before Y”
• For example:
- force used - stroke length - stroke smoothness - distance to critical structures, etc.
• Prototype 1: Hidden Markov Models built to discriminate patterns of novice and expert behaviour on a single association rule.
• Prototype 2: A range of analysis techniques used to develop models to provide feedback on multiple features:
- A random forest model to determine expert/novice behaviour
- Nearest neighbour techniques along with a random forest model to generate feedback in the case of novice behaviour
- An independent feedback system (application) was built
Data Mining for Personal Feedback
Feedback Parser
Feedback Generator
Simulator Stroke Detector Simulator Metrics
Proximity Triggers
Feedback
Stroke Metrics
Stroke Metrics Technique Feedback
A Personal Feedback System
• 24 medical students - 12 were provided with automated feedback - 12 were not
• Knowledge of anatomy but not surgery;
video tutorial of surgery and simulator familiarisation.
• Two group comparison of students’ performance on a cortical mastoidectomy - Effectiveness of technique feedback - Accuracy of feedback - Usability of system
Feedback System Test
Effectiveness of Technique
% of Expert Stokes
With Feedback
Without Feedback
M (SD) M (SD) F p
61.59 (16.19) 38.86 (13.11) 14.29 <.001
• A surgeon undertook a post hoc analysis of the feedback provided by the system
- False Positives: feedback was provided when stroke technique was acceptable
- False Negatives: feedback was not provided when technique was unacceptable.
- Wrong Feedback: participants’ technique was accurately classified as “trainee” but the content of the feedback was inaccurate.
Accuracy of Feedback
# of Feedback Messages
Percentage (of Total)
False Positives 39 6.8%
False Negatives 69 11.4%
Wrong Feedback 52 9.0%
Total Feedback 576
Accuracy of Feedback
Overwhelmingly positive feedback from participants on the basis of post-test interviews “it reminded me to be gentle near structures”
“particularly helpful was changing burr size and whether or not to zoom in”
“it gave me the confidence to go faster”
Usability
Example 2: Cognition and Interaction
Barney Dalgarno Charles Sturt University
Sue Bennett University of Wollongong
Dalgarno, B., Kennedy, G., & Bennett, S. (in press). The impact of students’ exploration strategies on discovery learning using computer-based simulations.Educational Media International. Accepted Oct 2014.
Example 2: Cognition and Interaction
How does the design of interactive multimedia impact on students’ learning strategies and cognition?
Study
• Two learning conditions (observation & exploration)
• Two content areas (global warming & blood alcohol concentration)
• Each participant (n=158) completed:
– the observation condition in one content area – the exploration condition in the other – a pre and post-test of knowledge in each content area
• Students’ actions were logged.
Exploration Condition • Content screens providing background to the area and
some terminology explanation, but no explanation of key concepts.
• A series of screens allowing students to manipulate the simulation parameters and asking them to “predict, observe, explain”.
Observation Condition • The same series of background content screens. • A series of simulation output screens each showing the
effect of pre-set manipulations of input parameters.
Multimedia Design
• No effect of learning condition for Global Warming
• Modest main effect of learning condition for Blood Alcohol
Mean Post-Test
Observation
Mean Post-Test
Exploration
F (1,155) p
Global Warming 1.42 1.72 2.40 0.13
Blood Alcohol 3.42 3.93 5.52 0.02
*ANCOVA using pre-test as a covariate
Results: Observation v Exploration
• We noticed that the variance in post-test scores for exploration participants was quite high.
• Eyeballing the logs showed some students seemed more systematic in their exploration of the simulation than others.
End of Story?
Students’ learning behaviours, strategies and approaches were characterised using various
heuristics as well as cluster analysis.
Cluster Analysis [not shown here!]
• time spent on the background material before simulation
• total time spent on the simulation
• number of cycles in which exactly one variable was changed from the previous cycle
• number of cycles in which exactly one variable was changed from the provided base values
• number of cycles where at least one variable was changed from the previous cycle
• the sum of the number of variables changed per cycle across all cycles
Classifying Students Approaches
Systematic Exploration • Students who completed 4 or more simulation cycles with
only one variable changed from previous cycle; or
• Students who completed 4 or more simulation cycles with only one variable changed from pre-set values
Non-Systematic Exploration • All other “exploration” students
Observation
• Students who completed the Observation learning condition (no simulation)
Classifying Students Approaches
• Significant main effect of condition in both content domains
Post-Test Observation
Non-Systematic Post-Test
Exploration
Systematic Post-Test
Exploration
F
p
Global Warming 1.42 (1.29) 1.33 (1.52) 2.48 (2.20) 4.17 .017
Blood Alcohol 3.42 (1.31) 3.51 (1.30) 4.56 (1.33) 8.69 <.001
Observation = Non Systematic < Systematic
Outcome by Approach
Implications …
• Each study presents a simulation-based, digital learning environment: one procedural, one conceptual.
• Each employs analytic approaches which are very much framed by the learning design of these environments and the pedagogical intent of the task:
– what we wanted students to do and learn.
• But the approaches attempt to move away from a narrow “deficit” model of analytics based on
– assessing what students know; – how much they fail to participate; and/or – how much they are “at risk” of disengaging from the task (or dropping out of the course)
Implications …
• These studies show how learning analytics can be used to uncover complex patterns of students’ on-task learning behaviour which are:
– indicative of distinct approaches to learning; – correspond to adaptive (and maladaptive) learning or “thinking” processes; and – are associated with good (or poor) learning outcomes.
Analytics that are used to determine students’ adaptive learning patterns and processes with digital learning tasks, which can be used as the basis for individualised, personal feedback, to improve their learning processes and, ultimately, their learning outcomes.
What are Personal Learning Analytics?
PLA : Simply a Matter of Perspective?
http://www.marquette.edu/magazine/recent.php?subaction=showfull&id=1357063200
A Work in Progress
Deficit Analytics in T&L Personal Analytics in T&L
Assessing what you know Determining how you are coming to know
Determining how much you don’t participate and access
Determining the way in which you participate
Macro: often multidimensional Macro: does not speak to macro
Micro: based on simple knowledge assessments
Micro: based on approaches and responses to the learning context
Micro: profiles of simple access to learning resources and activities
Patterns of interactions with learning activities and tasks
What are Personal Analytics?
Is there a role for Fit Bit for students? • Tracking engagement with University
• “macro” level interactions with
learning activities and resources
Conclusion
• The holy grail … and the somewhat elusive promise of learning analytics is to create genuinely adaptive and personalised online environments to improve individual student’s learning.
• Identifying when students’ transgress and step off the defined
learning path gets us some of the way …
• … but understanding how we can use students’ adaptive patterns of engagement with specific learning tasks is an important next step …
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