1) The document discusses research on using automatic concept extraction techniques to build student models for adaptive textbooks. It evaluates techniques like noun phrase extraction, Wikipedia title filtering, and LDA topic modeling to extract concepts from textbook passages. 2) The research aims to enhance traditional student modeling frameworks, which focus only on practice activities and ignore reading behavior, by incorporating concepts extracted from readings and students' reading activities. 3) Preliminary results found that combining topic-based concepts with key phrase extraction and using them in a Bayesian knowledge tracing model with added reading parameters achieved the best prediction accuracy of student performance compared to models using concepts from single extraction techniques alone.

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StudentModelingusing
AutomaticConcept
Extraction
for
AdaptiveTextbooks
KhushbooThaker, Peter Brusilovsky, Daqing He

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OnlineTextbooks
Trust
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2
Optional / Mandatory
reading components
Flip Courses
Blended Courses
MOOCs

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ResearchonReadingactivities
Students who completed optional
readings did better
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Students indicate reading as
optimal

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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

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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

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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

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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

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AutomaticConceptExtraction
willtheyworkforAdaptiveTextbooks?
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Goal-
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ACETechniques
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 Noun Phrase Extraction
 WikipediaTitle based Filtering
 Topic models – LDA
 Title as a concept
Topic Based Concept ExtractionPhrase based Concept
Extraction

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ACETechniques
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 Noun Phrase Extraction

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

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ACETechniques
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 Latent DirichletAllocation
LDA

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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

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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

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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

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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

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