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Learning Analytics: On the Way to Smart Education Dr. Griff Richards Athabasca University Moscow, 08 October 2012.

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Presentation on theme: "Learning Analytics: On the Way to Smart Education Dr. Griff Richards Athabasca University Moscow, 08 October 2012."— Presentation transcript:

1 Learning Analytics: On the Way to Smart Education Dr. Griff Richards Athabasca University Moscow, 08 October 2012

2 60 o N 49 o N Distance Learning In Canada - Is growing, especially in K-12 public schools -15% of 2008 high school graduates in BC took at least one on-line course -Increase in “Blended Learning”, online activities for F2F courses. -Increasing concern for course quality and student retention Athabasca, Alberta

3 Learning Engagement The more a learner interacts with the content and with their peers about the content, the more they are likely to internalize it, and remember it... (Richard Snow, 1980)

4 Learning engagement promotes student retention and academic success. (George Kuh,2001) Engagement is “action based on internal motivation” Can not directly measure engagement Must look for external traces, actions that indicate interest, and involvement (NSSE) Learning Engagement

5 CAUTION: Engagement Arum & Roska (2011) Students who studied alone had higher marks than students studying in groups. Engagement that is not task-focused is unlikely to improve learning.

6 Interaction Equivalency Theory (Garrison & Anderson,1995) The quality of the interaction is more important than the source Course Content Instructor Other learners

7 4 places to improve learning Course Content Instructor Other learners 1. Clearer designs 2. Collaborative Learning activities 3. Less lecturing, more mentoring 4. More study skills

8 Engagement Online Same Five Factors: 1. active and collaborative learning, 2. student-faculty interaction, 3. supportive campus environment, 4. enriching educational experiences, and 5. the level of academic challenge. on line

9 We have insufficient data about which instructional strategies actually work best.

10 Hybrid or Blended Learning? Face to Face On-Line The use of online technologies to augment face to face delivery. -> replaces some F2F “face time” (forums save classroom space) -> uses LMS to track assessments and assignments -> uses technology in class e.g. pop quizzes, collaboration tools

11 Blended Learning: the Fuzzy Definition Diaz & Brown (2010) Asked 268 Blended Learning Symposium attendees what blended learning was at their college… It varied widely by school, by dept., & by course.

12 Analytic Measures Engagement is inferred from activity. Student interaction with Online systems leaves an “exhaust trail” of their activity Learning Analytics is not a statistical sample, it is all the data for all learners Questions: What patterns to look for? How to interpret them?

13 LMS interaction data e.g. Moodle discussion Extract linked list Plot interactions in star map Example: Snapp

14 SNAPP Visualization

15 SNAPP shot of Conference Students “engaged”

16 SNAPP shot of Conference Individuals with lower engagement, 3 or less messages

17 Does Activity = Engagement ? Beer (2010) plotted total LMS interactions with academic grades. Does activity = engagement? Is Blackboard more engaging than Moodle? 

18 Limitations: Activity Outside LMS As learners become engaged, they move to more engaging channels: , Elluminate, Skype This activity is not tracked by LMS. No data available. LMS

19 Interpretation of Analytics Data patterns require investigation Quantitative data requires interpretation --> make and test hypotheses --> create useful models When we measure something we risk changing it. – e.g. If learners know we count hits they may make meaningless hits to fool the system.

20 Analytics for Learners! The same analytics should be able to provide easy to understand information dashboards for students.

21 Analytics for Learners SIGNALS Arnold (2010) inserted a traffic light on each student’s course page to provide guidance on course success. A/B C D/F

22 Dashboard for Faculty Arnold (2010) reported 14 % shift from D’s to C’s & B’s. Same number of withdrawals, but W’s occurred earlier before damaging student grade point averages. 12% increase in students requesting assistance. N=220

23 Mesmots a dashboard for learners (Richards & Sehboub, 2008) Data for my class My webquest data

24 How to start: Analytics for online & blended learning? Measuring something is the first step “Better to measure something than to measure nothing” (Scrivens) Need more data than just page hits. We also need to ask learners about their experience, what worked, what needs improvement.

25

26 Dynamic Evaluation Model (Richards & Devries,2011) » PreparationConductReflection Design Facilitation Learning Analytics at the activity level

27 Dynamic Evaluation Model » PreparationConductReflection Design Facilitation Learning Timely feedback enables quick fixes

28 If Analytics, Then What? If analytics show students are failing, is there a moral obligation to help them? If analytics show a course has weaknesses, is there a business obligation to fix it? If analytics reveal weak instruction, who is responsible to intervene? If analytics are inaccurate, who fixes them? What about privacy & ethics? Who owns the data? Who has the right to see it?

29 The Analytics Box? joannaparypinski.com

30 Learning Analytics: On the Way to Smart(er) Education Dr. Griff Richards Athabasca University Moscow, 08 October 2012


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