addictive links: adaptive navigation support in college-level courses
DESCRIPTION
Slides from presentation given by Prof. Peter Brusilovsky at Joensuu, Finland, September 9, 2013TRANSCRIPT
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Addictive Links: Adaptive Navigation Support for College-Level Courses
Peter Brusilovsky with: Sergey Sosnovsky, Michael Yudelson, Sharon Hsiao
School of Information Sciences, University of Pittsburgh
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User Model
Collects information about individual user
Provides adaptation effect
Adaptive System
User Modeling side
Adaptation side
User-Adaptive Systems
Classic loop user modeling - adaptation in adaptive systems
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Adaptive Software Systems
• Intelligent Tutoring Systems – adaptive course sequencing – adaptive . . .
• Adaptive Hypermedia Systems – adaptive presentation – adaptive navigation support
• Adaptive Help Systems • Adaptive . . .
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Adaptive Hypermedia
• Hypermedia systems = Pages + Links
• Adaptive presentation
– content adaptation
• Adaptive navigation support
– link adaptation
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• Direct guidance • Hiding, restricting, disabling • Generation • Ordering • Annotation • Map adaptation
Adaptive Navigation Support
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Adaptive Link Annotation
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Adaptive Link Annotation: InterBook
1. Concept role 2. Current concept state
3. Current section state 4. Linked sections state
4
3
2
1
√"
Metadata-based mechanism
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Personalized Information Access
Adaptive���Hypermedia
Adaptive���IR
Web���Recommenders
Navigation Search Recommendation
Metadata-based mechanism
Keyword-based mechanism
Community-based mechanism
Adaptation Mechanisms
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Adaptive Link Annotation: ScentTrails Con
tent
-bas
ed m
echa
nism
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Adaptive Link Annotation: CoWeb Soc
ial n
avig
atio
n m
echa
nism
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The Value of ANS
• Lower navigation overhead – Access the content at the right time – Find relevant information faster
• Better learning outcomes – Achieve the same level of knowledge faster – Better results with fixed time
• Encourages non-sequential navigation
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The Case of QuizPACK • QuizPACK: Quizzes for
Parameterized Assessment of C Knowledge
• Each question is a pattern of a simple C program. When it is delivered to a student the special parameter is dynamically instantiated by a random value within the pre-assigned borders.
• Used mostly as a self-assessment tool in two C-programming courses
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QuizPACK: Value and Problems
• Good news: – activity with QuizPACK significantly correlated with
student performance in classroom quizzes – Knowledge gain rose from 1.94 to 5.37
• But: – Low success rate - below 40% – The system is under-used (used less than it deserves)
• Less than 10 sessions at average • Average Course Coverage below 40%
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Adding Motivation (2003) • Students need some better motivation to work with non-
mandatory educational content… • Added classroom quizzes:
– Five randomly initialized questions out of 20-30 questions assigned each week
• Good results - activity, percentage of active questions, course coverage - all increased 2-3 times! But still not as much as we want. Could we do better?
• Maybe students bump into wrong questions? Too easy? Too complicated? Discouraging…
• Let’s try something that worked in the past
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Questions of the current quiz, served by QuizPACK
List of annotated links to all quizzes available for a student in the current course
Refresh and help icons
QuizGuide = QuizPACK+ANS
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Demo: QuizPack
• KT Portal - http://adapt2.sis.pitt.edu/cbum/
• Create your account or use test / test
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Topic-Based Adaptation
Concept A
Concept B
Concept C
n Each topic is associated with a number of educational activities to learn about this topic
n Each activity classified under 1 topic
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QuizGuide: Adaptive Annotations • Target-arrow abstraction:
– Number of arrows – level of knowledge for the specific topic (from 0 to 3). Individual, event-based adaptation.
– Color Intensity – learning goal (current, prerequisite for current, not-relevant, not-ready). Group, time-based adaptation.
n Topic–quiz organization:
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QuizGuide: Architecture
User Model
QuizGuide QuizPACK
Student's Browser
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QuizGuide: Motivation
• Adaptive navigation support increased student's activity and persistence of using the system
Average activity
050
100150200250300
2002 2003 2004
Average num. of sessions
0
5
10
15
20
2002 2003 2004
Average course coverage
0%10%20%30%40%50%60%
2002 2003 2004
Active students
0%
20%
40%
60%
80%
100%
2002 2003 2004
n Within the same class QuizGuide session were much longer than QuizPACK sessions: 24 vs. 14 question attempts at average.
n Average Knowledge Gain for the class rose from 5.1 to 6.5
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QuizGuide: Success Rate?
n Well, that worked too, but the scale is not comparable
n One-way ANOVA shows that mean success value for QuizGuide is significantly larger then the one for QuizPACK: F(1, 43) = 5.07 (p-value = 0.03).
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A new value of ANS?
• The scale of the effect is too large… May be just a good luck?
• New effect after 15 years of research? • Not quite new, rather ignored and
forgotten - ELM-ART data
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ELM-ART: Navigation Support
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Round 2: Let’s Try it Again…
• Maybe the effect could only be discovered in full-scale classroom studies – while past studies were lab-based?
• Another study with the same system – QuizGuide+QuizPACK vs. QuizPACK
• A study with another system using similar kinds of adaptive navigation support – NavEx+WebEx vs. WebEx
• NavEx - a value-added ANS front-end for WebEx - interactive example exploration system
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WebEx - Code Examples
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Concept-based student modeling
Example 2 Example M
Example 1
Problem 1
Problem 2 Problem K
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Examples
Problems
Concepts
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NavEx = WebEx + ANS
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Does it work?
• The increase of the amount of work for the course
Clicks - Overall
0
50
100
150
200
250
300
Non-adaptive Adaptive
Examples
Quizzes
Lectures - Overall
0
2
4
6
8
10
12
Non-adaptive Adaptive
Examples
Quizzes
Learning Objects - Overall
0
5
10
15
20
25
30
Non-adaptive Adaptive
Examples
Quizzes
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Is It Really Addictive?
• Are they coming more often? Mostly, but there is no stable effect
• But when they come, they stay… like with an addictive game
Clicks - Per Session
0
5
10
15
20
Non-adaptive Adaptive
Examples
Quizzes
Learning Objects - Per
Session
0
1
2
3
4
Non-adaptive Adaptive
Examples
Quizzes
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Why It Is Working?
• Progress-based annotation – Displays the progress achieved so far – Does it work as a reward mechanism? – Open Student Modeling
• State-based annotation – Not useful, ready, not ready – Access activities in the right time – Appropriate difficulty, keep motivation
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A Deeper Look
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The Diversity of Work
• C-Ratio: Measures the breadth of exploration • Goal distance: Measures the depth
Self-motivated Work - C-Ratio
(%)
0
0.2
0.4
0.6
Non-adaptive Adaptive
Quizzes
Examples
Self-motivated Work - Goal
Distance (LO's)
0
5
10
15
20
Non-adaptive Adaptive
Quizzes
Examples
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Round 3: Trying another domain…
• Is it something relevant to C programming or to simple kind of content?
• New changes: – SQL Programming instead of C – Programming problems (code writing) instead of
questions (code evaluation) – Comparison of concept-based and topic-based
mechanisms in the same domain and with the same kind of content
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• SQL-KnoT delivers online SQL problems, checks student’s answers and provides a corrective feedback
• Every problem is dynamically generated using a template and a set of databases
• All problems have been assigned to 1 of the course topics and indexed with concepts from the SQL ontology
SQL Knowledge Tester
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• To investigate possible influence of concept-based adaptation in the present of topic-based adaptation we developed two versions of QuizGuide:
Topic-based Topic-based+Concept-Based
Concept-based vs Topic-based ANS
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• Two Database Courses (Fall 2007): § Undergraduate (36 students) § Graduate (38 students)
• Each course divided into two groups: § Topic-based navigation § Topic-based + Concept-Based Navigation
• All students had access to the same set of SQL-KnoT problems available in adaptive (QuizGuide) and in non-adaptive mode (Portal)
Study Design
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• Total number of attempts made by all students: in adaptive mode (4081), in non-adaptive mode (1218)
• Students in general were much more willing to access the adaptive version of the system, explored more content with it and to stayed with it longer:
Questions
0255075100
Quizzes
0510152025 Topics
0123456
Sessions
012345 Session Length
0510152025
Adaptive Non-adaptive
It works again! Like magic…
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• Did concept-based adaptation increase the magnitude of the motivational effect? § No significant difference in the average numbers of attempts,
problems answered, topics explored § No significant difference in the session length
• Was there any other observable difference? § Average number of attempts per question
§ Resulting Knowledge Level (averaged across all concepts)
Attempts per question
0
24
6
Knowledge level
0
0.2
0.4
0.6
Combined Topic-based
Concept-based ANS: Added Value?
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• Question-based Patterns:
• Topic-based Patterns:
Repetition 0: incorrect previous
attempt
Repetition 1: correct previous
attempt
Sequence Repetition Go-Back Skipping
Next Topic Jump-Forward Jump-Backward Combined
Pattern Analysis
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Topic Pattern Distribution
0%10%20%30%40%50%60%70%80%
Next-Topic Jump-Fwrd Jump-Bkwd
Combined (undegraduate)Combined (graduate)Topic-based (undegraduate)Topic-based (graduate)
Pattern Analysis (2)
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Question Pattern Distribution
0%
10%
20%
30%
40%
50%
60%
Sequence Go-Back Skipping Repetition-0 Repetition-1
Combined (undegraduate)Combined (graduate)Topic-based (undegraduate)Topic-based (graduate)
Pattern Analysis (3)
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• Difference in the ratio of Repetition1 pattern explains: § difference in the average number of attempts per question § difference in the cumulative resulting knowledge level
§ Students repeat the same question again and again: § They “get addicted“ to the concept-based icons § Is it a good thing for us?
− YES – they react to the navigational cues, they work more − NO – we expect them to concentrate on those questions where they have
smaller progress instead of drilling in the same question
Discussion
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Round 4: The Issue of Complexity • Let’s now try it for Java… • What is the research goal? • Java is a more sophisticated domain than C
– OOP versus Procedural – Higher complexity
• Will it work for complex questions?
• Will it work similarly? 0% 20% 40% 60% 80% 100%
C
Java
language complexity
Easy
Moderate
Hard
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Meet QuizJET!
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Naviga&on Area Presenta&on Area
JavaGuide
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!! !! JavaGuide
(Fall 2008) QuizJET
(Spring 2008) !! parameters (n=22) (n=31)
Overall User Statistics
Attempts 125.50 41.71 Success Rate 58.31% 42.63% Distinct Topics 11.77 4.94 Distinct Questions 46.18 17.23
Average User Session Statistics
Attempts 30.34 21.50 Distinct Topics 2.85 2.55 Distinct Questions 11.16 8.88
Magic… Here We Go Again!
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• Significantly more Attempts on the easy questions in JavaGuide than in QuizJET
• F(1, 153) = 7.081, p = .009, partial η2 = .044
The Effect Depends on Complexity
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• Significant higher Success Rate
• F(1, 153) = 40.593, p <.001, partial η2 = .210 • On average 2.5 times
more likely to answer a quiz correctly with adaptive navigation support
68.73% 67.00%
39.32% 38.00% 28.23%
11.90% 0%
20%
40%
60%
80%
100%
Easy Moderate Hard Su
cces
s Rat
e
Complexity Level
Success Rate
JavaGuide
QuizJET
Different Pattern For Success Rate
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0.94
0.61
0.29
1.85
1.01
0.44
0
0.5
1
1.5
2
Easy Moderate Hard
Att
empt
s/Q
uest
ion
Complexity Level
Average Attempts per Question
68.73% 67.00%
39.32% 38.00% 28.23%
11.90% 0%
20%
40%
60%
80%
100%
Easy Moderate Hard
Succ
ess R
ate
Complexity Level
Success Rate
JavaGuide
QuizJET
Prerequisite-based guidance prepared students to attempt complex questions after exploring easier ones
Putting it Together…
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Round 5: Social Navigation
• Concept-based and topic-based navigation support work well to increase success and motivation
• Knowledge-based approaches require some knowledge engineering – concept/topic models, prerequisites, time schedule
• In our past work we learned that social navigation – guidance extracted from the work of a community of learners – might replace knowledge-based guidance
• Social wisdom vs. knowledge engineering
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Open Social Student Modeling
• Key ideas – Assume simple topic-based design – No prerequsites or concept modeling – Show topic- and content- level knowledge progress of
a student in contrast to the same progress of the class • Main challenge
– How to design the interface to show student and class progress over topics?
– We went through several attempts
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QuizMap
53
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Parallel Introspective Views
54
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0
40
80
120
160
QuizJET+IV QuizJET+Portal JavaGuide
Attempts
Attempts
Results: Progress
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Class vs. Peers
• Peer progress was important, students frequently accessed content using peer models
• The more the students compared to their peers, the higher post-quiz scores they received (r= 0.34 p=0.004)
• Parallel IV didn’t allow to recognized good peers before opening the model
• Progressor added clear peer progress
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F(1,32)= 11.303, p<.01
71.35%
42.63% 58.31%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
QuizJET+IV QuizJET+Portal JavaGuide
Success Rate
Success Rate
Results: Success
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Why It Is Important?
• Many systems demonstrated their educational effectiveness in a lab-like settings: once the students are pushed to use it - it benefits their learning
• However, once released to real classes, these systems are under-used - most of them offer additional non-mandatory learning opportunities
• “Students are only interested in points and grades” • Convert all tools into credit-bearing activities? • Or use alternative approaches to increase motivation
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Progressor
59
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The Value of Peers
205.73
113.05
80.81
125.5
0
50
100
150
200
250
Attempts
Progressor
QuizJET+IV
QuizJET+Portal
JavaGuide
68.39% 71.35%
42.63%
58.31%
0.00%
20.00%
40.00%
60.00%
80.00%
Success Rate
Progressor
QuizJET+IV
QuizJET+Portal
JavaGuide
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The Secret
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Take-home messages
• A combination of progress-based and state-based adaptive link annotation increases the amount and the diversity of student work with non-mandatory educational content
• The effect is stable and the scale of it is quite large
• Properly organized Social Navigation might be at least as successful as the knowledge-based
• Requires a long-term classroom study to observe
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What we are doing now?
• Exploring new generation of open social modeling tools in wide variety if classes and domains from US to Nigeria – Interested to be a pilot site?
• Exploring more advanced guidance and modeling approaches based on large volume of social data
• Applying open social modeling to motivate readings
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Acknowledgements
• Joint work with – Sergey Sosnovsky – Michael Yudelson – Sharon Hsiao
• Pitt “Innovation in Education” grant • NSF Grants
– EHR 0310576 – IIS 0426021 – CAREER 0447083
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Try It!
• http://adapt2.sis.pitt.edu/kt/
• Brusilovsky, P., Sosnovsky, S., and Yudelson, M. (2009) Addictive links: The motivational value of adaptive link annotation. New Review of Hypermedia and Multimedia 15 (1), 97-118.
• Hsiao, I.-H., Sosnovsky, S., and Brusilovsky, P. (2010) Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming. Journal of Computer Assisted Learning 26 (4), 270-283.
• Hsiao, I.-H., Bakalov, F., Brusilovsky, P., and König-Ries, B. (2013) Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia [PDF]
Read About It!