ectel2012 motivational social visualizations for personalized elearning.pptx
DESCRIPTION
A presentation at EC-TEL 2012 conference:TRANSCRIPT
Mo#va#onal Social Visualiza#ons for Personalized Elearning
Sharon Hsiao & Peter Brusilovsky Sept. 2012
School of Information Sciences, University of Pittsburgh
1
Agenda
• Introduc#on • Background • Progressor+: An Innova#ve Tabular Open Social Student Modeling Interface
• Evalua#on & Results • Summary
2
INTRODUCTION
3
Personalized vs. Social?
Personalized Learning
Social Learning
increase motivation better performance
development of high level thinking skills
higher satisfaction
higher self-esteem, attitude better retention
increase learning rate increase learning quality
current knowledge level relevant interesting content
reduce navigational overhead increase satisfaction
increase motivation
What We Did Before
QuizGuide (Brusilovsky, Sosnovsky, et al., 2004; Sosnovsky &
Brusilovsky, 2005)
Knowledge Sea II (Brusilovsky, Chavan, & Farzan, 2004)
Personalized Learning Social Learning
Why do We Want a Merge?
Increasing amount of educational resources – High costs of maintain the associations between
content and domain – Social technologies provide collective wisdom
that might replace knowledge engineering Motivation is important – Even a great guidance will not provide good
impact without motivation
Model precision
Model complexity
Target area
Personalized approach Social-based approach
Integrated-approach (Hybrid)
Problems illustration
7
Challenge
How do we introduce personalized guidance to social technologies and harness the benefits from both approaches? • Keep the benefits of personalized guidance • Increase user motivation
This work…
1. Personalized Guidance – Navigation support: topic-based & progress-based
adaptation 2. Social Visualization – easy-to-grasp and holistic view of student model &
content model 3. Integration of 1&2 above in Open Student
Modeling visualization
Navigation Support in Open Social Student Modeling Visualization
Research Questions
1: What are the design principles (key features) to implement personalized guidance in open social student modeling visualizations?
2: Will navigation support combined with open social student modeling visualization work in realistic content collections?
3: Will this approach guide students to the right content at the right time?
4: Will this approach increase students motivation & engagement?
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Research model
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BACKGROUND
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Supporting Theories • Self-regulated theory (Zimmerman, 1990)
– High jumpers (students who gained higher conceptual understandings): good at using effective strategies, creating sub-goals, monitoring emerging understanding, and planning their time and effort.
– Low jumpers: did no spend much time monitoring their learning, tend to engage help seeking behavior.
• Social comparison theory (Festinger, 1954; Dijkstra. et al., 2008) – lateral comparison: self-evaluation – downward comparison: self-enhancement – upward comparison: self-improvement
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Related work
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Adaptive navigation support in E-Learning – AHA! (De Bra & Calvi, 1998) – ELM-ART (Weber &
Brusilovsky, 2001) – KBS-Hyperbook (Henze &
Nejdl, 2001) – INSPIRE (Grigoriadou,
Papanikolaou, Kornilakis, & Magoulas, 2001)
– InterBook (Brusilovsky, 1998) – NavEx (Brusilovsky, et al.,
2009) – ISIS-Tutor (Brusilovsky,
1994) – QuizGuide (Brusilovsky,
Sosnovsky, et al., 2004)
Social navigation and visualization for E-Learning – EDUCO (Kurhila, Miettinen,
Nokelainen, & Tirri, 2006) – KnowledgeSea II (Brusilovsky,
et al., 2009) – AnnotatEd (Farzan &
Brusilovsky, 2008) – Comtella (Vassileva & Sun,
2007) – OLMlets (Bull & Britland,
2007) – CourseVis (Mazza & Dimitrova,
2007)
THE WAY TO PROGRESSOR+
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QuizMap (EC-‐TEL 2011)
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Parallel Introspec#ve Views
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Progressor
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PROGRESSOR+
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Progressor+ design rationale
• Navigating and comparing segments of pie graphs in a huge dataset takes longer time for comprehension (Gillan & Callahan, 2000)
• Interacting and visualizing large data in Table Lens (Rao & Card, 1994)
• Small multiples principle (Tufte, 1990)
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Progressor+
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1. Sequence
• provides the direction for the students to progress through the course
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2. Identity
• simple rows & columns table representation, fragments can be easily cohesively shown
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3. Interactivity • Direct accessing content, sorting, comparing,
collapse-and-expand
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4. Comparison
• macro- and micro- comparisons
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5. Transparency
• Holistic view of all the models
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EVALUATION & RESULTS
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Students spent more time in Progressor+
Quiz =: 5 hours Example : 5 hours 20 mins
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60.04
150.19
224.7
296.9
69.52
121.23 110.66
321.1
0
50
100
150
200
250
300
350
400
QuizJET JavaGuide Progressor Progressor+
Total time spent (minutes)
Quiz
Example
• the more diverse of the questions the students tried, the higher success rate they obtained (r=0.707, p<.01)
• the more diverse of the example the students studied, the higher success rate they obtained (r=0.538, p<.01)
More diversity helped increase problem solving success
33.37
46.18 52.7
61.84
0
20
40
60
80
QuizJET JavaGuide Progressor Progressor+
distinct questions
10.86
17.3
25.125 27.37
0
10
20
30
QuizJET JavaGuide Progressor Progressor+
distinct examples
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7.81
11.77 11.47
12.92
8.48 9.15
12.28 12.2
0
2
4
6
8
10
12
14
QuizJET JavaGuide Progressor Progressor+
Topic Coverage
Quiz
Example
Students achieved higher Success Rate
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42.63%
58.31%
68.39% 71.20%
0.00%
20.00%
40.00%
60.00%
80.00%
QuizJET JavaGuide Progressor Progressor+
Success Rate
p<.01
Impact on Learning – cont.
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• The more time the students spent on the content (quizzes and examples), the higher the level of knowledge gain they obtained (r=0.563, p<.01; r=0.448, p<.01)
• The more the students studied (more lines), the higher level of knowledge they gained (r=0.492, p<.01)
-200.00
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
1400.00
1600.00
1800.00
time spent sorted by knowledge gain
example
quiz
Linear (example) Linear (quiz)
Time
Knowledge Gain 0 0 0 0 0 0 0 0 3 4 5 5 5 6 7 7 7 8 8 8 8 10 11 11 11 11 12 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 16 16 18 20
The Mechanism of Social Guidance
stronger students left the traces for weaker ones to follow
32 Time
Topics
uu uu uu uu uu
uu uu
uu
uu uu uu uu uu
uu uu
uu
uu uu uu uu
uu uu uu
uu
Strong students lead ahead in Progressor+
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Mixed collection
Quizzes Examples
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Non-adaptive adaptive
Social, adaptive, single content Progressor+
Students worked with the systems during exam prepara#on, especially in final exam period
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Non-adaptive adaptive
Progressor Progressor+
17.15
13.39
83.59
9.17
19.63
84.3
0
20
40
60
80
100
Easy Moderate Complex
Strong students were hours ahead of weak ones
Progressor
Progressor+
Strong students worked earlier than weaker ones
Subjective Evaluation
• Usefulness • Ease of Use • Ease of Learning • Satisfaction • Privacy & Data Sharing
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Students’ opinions
• Praised Progressor+
• “…it’s a great tool, should be used in other classes…”
• “… I find the examples and quizzes helped. I did recommend other students use it…”
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CONCLUSION
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Results Summary • Engaged longer with Progressor+
• Attempted more self-assessment quizzes • Explored more annotated examples & lines • Obtained higher knowledge gain • Achieved higher Success Rate • Stronger students left the traces for weaker ones to follow • Effectively led students to work at the right level of
questions among mixed collections of educational content • Both strong and weak student had consistent performance
across all different questions’ complexities
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THANK YOU J
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