1. Contoso
Pharmaceuticals
StudentModelingusing
AutomaticConcept
Extraction
for
AdaptiveTextbooks
KhushbooThaker, Peter Brusilovsky, Daqing He

2. Contoso
Pharmaceuticals
OnlineTextbooks
Trust
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suscipit ante eu
varius. Morbi a purus
dolor.
Cost
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ipsum varius finibus
justo viverra blandit.
2
Optional / Mandatory
reading components
Flip Courses
Blended Courses
MOOCs

3. Contoso
Pharmaceuticals
ResearchonReadingactivities
Students who completed optional
readings did better
• Ut congue quis tortor eget
sodales. Nulla a erat eget
nunc hendrerit ultrices eu nec
nulla.
• Donec viverra leo aliquet,
auctor quam id, convallis orci.
3
Students indicate reading as
optimal

4. Contoso
Pharmaceuticals
StudentModelingFramework
State-of-the-art student modeling frameworks
are based on traditional ITS
Main focus of ITS is to increase student
performance through practice activities
Basic architecture of an ITS
4

5. Contoso
Pharmaceuticals
TraditionalStudentModelingFramework
State-of-the-art student modeling frameworks
are based on traditional ITS
Main focus of ITS is to increase student
performance through practice activities
Ignore student reading behavior
Basic architecture of an ITS1
5

6. Contoso
Pharmaceuticals
Student models that considerreading
• Variant of Bayesian Knowledge Tracing
• Comprehension Factor Analysis
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Reading behavior
helps
in inferring
Student’s
knowledge state

7. Contoso
Pharmaceuticals
State of the art reading models for performance prediction
• Variant of Bayesian Knowledge Tracing
• Comprehension Factor Analysis
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Subject Experts
Knowledge Components/ SKILLS

8. Contoso
Pharmaceuticals
AutomaticConceptExtraction
willtheyworkforAdaptiveTextbooks?
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9. Contoso
Pharmaceuticals
Goal-
9

10. Contoso
Pharmaceuticals
ACETechniques
10
 Noun Phrase Extraction
 WikipediaTitle based Filtering
 Topic models – LDA
 Title as a concept
Topic Based Concept ExtractionPhrase based Concept
Extraction

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Pharmaceuticals
ACETechniques
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 Noun Phrase Extraction

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Pharmaceuticals
ACETechniques
12
 WikipediaTitle based KCs
Wikipedia
Title list
Textbook
N grams
Top N
Keyphrases

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Pharmaceuticals
ACETechniques
13
 Latent DirichletAllocation
LDA

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Pharmaceuticals
14
Reading
Activities
KCA
KCB
KCA
KCA
KCB
KCA
R = 1 R = 3
Reading
Opportunity
every page they spent
substantial amount of time
KC – Knowledge components

15. Contoso
Pharmaceuticals
log
𝑝𝑖𝑗
1 − 𝑝𝑖𝑗
= 𝛼𝑖 +
𝑘
𝛽 𝑘 𝑄 𝑘𝑗 +
𝑘
𝑄 𝑘𝑗 ( 𝜇 𝑘 𝑆𝑖𝑘 + 𝜌 𝑘 𝐹𝑖𝑘 + 𝛾 𝑘 𝑅𝑖𝑘
where,
𝑝𝑖𝑗 is probability of success of student 𝑖 on item 𝑗
𝑖 is a student, 𝑗 is a step and 𝑘 is a Skill
𝛼𝑖 is a coefficient associated with student 𝑖 represents the proficiency of student 𝑖.
𝑄 is a Qmatrix
𝑄 𝑘𝑗 is Qmatrix cell associated with item 𝑗 and Skill 𝑘
𝛽 𝑘, 𝜇 𝑘, 𝜌 𝑘 𝑎𝑛𝑑 𝛾 𝑘 are coefficients associated with skill 𝑘
𝑆𝑖𝑘 𝑎𝑛𝑑 𝐹𝑖𝑘 as number of success and failure attempts respectively of student 𝑖 on skill 𝑘
𝑅𝑖𝑘 is reading opportunities of student 𝑖 on skill 𝑘
Reading Activity
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Reading
behavior
Classic PFA
Reading
Opportunities
Skill connected
with reading
skill hardness
Performance
on quiz

16. Contoso
Pharmaceuticals
Student Performance on PredictionTask
ACE
METHOD
No of
Concepts
Accuracy
Title 25 48.43
LDA 200 56.34
Noun Phrase 619 60.32
Wikipedia 521 61.34
10 fold cross validation on CFM model
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Surprises:
No of concepts do not have a
substantial effect on student models
NP and Wikipedia extracted too many
concepts which break the assumption
of independence
Topics are under representative of
concepts
Key-phrases are over representative
and bring about too many dependent
concepts in paly

17. Contoso
Pharmaceuticals
Future work
page 17
• CombineTopic based concepts + Key phrase based concepts
• Compare the models with expert annotated concepts
• 30% of reading activities were skimming
• Understand how pre-requisites and concepts can be differentiated

18. Contoso
Pharmaceuticals
ThankYou
KhushbooThaker
k.thaker@pitt.edu
http://pitt.edu/~kmt81
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