ectel2012 motivational social visualizations for personalized elearning.pptx
DESCRIPTION
A presentation at EC-TEL 2012 conference:TRANSCRIPT
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Mo#va#onal Social Visualiza#ons for Personalized Elearning
Sharon Hsiao & Peter Brusilovsky Sept. 2012
School of Information Sciences, University of Pittsburgh
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Agenda
• Introduc#on • Background • Progressor+: An Innova#ve Tabular Open Social Student Modeling Interface
• Evalua#on & Results • Summary
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INTRODUCTION
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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
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What We Did Before
QuizGuide (Brusilovsky, Sosnovsky, et al., 2004; Sosnovsky &
Brusilovsky, 2005)
Knowledge Sea II (Brusilovsky, Chavan, & Farzan, 2004)
Personalized Learning Social Learning
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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
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Model precision
Model complexity
Target area
Personalized approach Social-based approach
Integrated-approach (Hybrid)
Problems illustration
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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
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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
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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)
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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
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• 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
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QuizJET JavaGuide Progressor Progressor+
Topic Coverage
Quiz
Example
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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
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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
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The Mechanism of Social Guidance
stronger students left the traces for weaker ones to follow
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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
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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
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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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