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An Overview
of Recent
Developments
in Intelligent
e-Textbooks
and Reading
Analytics
BY DAVID BOULANGER
AND
VIVEKANANDAN KUMAR
Athabasca University | Canada
2
LITERATURE
13
9
1
Conference Journal Workshop
9 conferences
9 journals
1 workshop
23 References
3
8
5
6
1
2015 2016 2017 2018 2019
DATASETS
3
# of events Events /
reader
# of
readers
Reading
episode Domain Software Type of reading
2,812,727 939 2993 Semester (43 courses) BookLooper Lecture
567,193 7988 71 Semester
Human Computer
Interaction,
Information Retrieval
Reading Circle
Textbook,
research
publication, etc.
129,451 555 233 Semester (11 courses) CourseSmart Textbook
75,748 276 274 Semester Social Sciences,
Business, Education - Textbook
65,755 608 108 Semester (2 courses) BookRoll Lecture
10,994 - - ~1 year (110 different magazine
issues)
Viewerplus + APP-
BI Magazine
10,188 35 289 Semester Interactive Systems
Design
AnnotatED+,
Reading Circle Textbook
~7200 109 66 Semester Research Methods - Textbook
1370 80 17 1.5 hours Educational Technology DITeL Journal article
65 7 9 30 minutes Introductory Biology - Textbook
In-house readers except for CourseSmart
4 types of reading
The semester is the typical period during
which reading behavior is evaluated.
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
4
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Feature
SelectionSmart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
DOCUMENT
• Page
• Section
• Chapter
Raw Data
Interaction
Data
Self-Reported
Data
DOCUMENT FORMAT
• PDF
• EPUB
• Word documents
• Web documents
• Any reading resource
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
5
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Feature
SelectionSmart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
Raw Data
Interaction
Data
Self-Reported
Data
HARDWARE
• Augmented reality
• Eye tracker
• Mobile
• Remote
• Smartphones
• Tablets
• E-readers
• Computers
SOFTWARE
• BookLooper
• Reading Circle
• CourseSmart
• BookRoll
• Viewerplus
• AnnotatED+
• DITeL
• Adobe Reader
• Kindle
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
6
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Smart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
INTERACTION DATA
• Next page
• Previous page
• Jump to page
• Annotations
• Marking of
unknown words
• Comments
• Highlighting
• Underlining
• Changing marker
color
• Zooming in/out
• Searching
• Search jump
• Screen orientation
• Portrait
• Landscape
• Scrolling
• Bookmarks
• Opening, exiting,
minimizing e-textbooks
SELF-REPORTED DATA
• Survey
• Interview
• Questionnaires
Raw Data
Interaction
Data
Self-Reported
Data
• Online
• Offline
Feature
Selection
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
7
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Smart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
METRICS
• # of blinks
• Distances of eye
movements
• Coordinates of eye gazes
• Fixations
• Saccades
• Time of adoption
• Reading speed
• Engagement level
• Reading session
• Attention
• Reading/visit time
• Last pages read
• Pages previewedRaw Data
Interaction
Data
Self-Reported
Data
Feature
Selection
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
8
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Smart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
READING BEHAVIOR
• Progressive sequential
analysis
• Self-reported behavior
• Perception gap
Raw Data
Interaction
Data
Self-Reported
Data
ENGAGEMENT MODEL
• Blocked linear regression
• Clustering
• None
• Low
• Medium
• High
GAMING BEHAVIOR
• Classifier (gaming,
normal)
• Logistic regression
• KNN
• Naïve Bayes
• Decision tree
• SVM
Feature
Selection
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
9
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Smart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
TECHNIQUES
• Gradient tree boosting
• Survival analysis
• PCA
• Regression
• Simple linear
• Non-linear
• Mixed-model linear
• Deep learning
• Stochastic block model
• Descriptive statistics
• Classification
• Random forest
• KNN
• Support vector machine
Raw Data
Interaction
Data
Self-Reported
Data FEATURE SELECTION
• Random forest regressor
• F-regression
• K-means transformations
• t-test
ACCURACY
• Training/testing set
• Cross-validation
• AUC
• RMSE/MSE
Feature
Selection
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
10
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Smart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
SMART FEATURES
• Prediction of student
performance
• Identification of at-risk
students/drop-outs
• Real-time improvement
of learning materials
• Provision of teacher
annotations
• Measurement of
reader’s interest and
competence
• Assessment of a
concept’s difficulty level
• Automatic correction of
students’ answers
• Provision of formative
feedback
• Recommendation of
next topics to be learned
• Recommendation of
effective learning
strategies
• Real-time lecture
supporting system
• Many more …
Raw Data
Interaction
Data
Self-Reported
Data
Feature
Selection
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
11
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Smart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
INTERACTIVE COMPONENTS
• Multimedia/visuals
• Video
• Sketch
• Animation
• Diagram
• Problems to solve
• Collaborative
• Q&A
• Quizzes (multiple-
choice/open-ended
questions)
• Explanations of key
concepts
• Examples with different
parameters
Raw Data
Interaction
Data
Self-Reported
Data
Feature
Selection
CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS
12
E-Reader
Document
Document
Format
Hardware
Reading
Behavior
Software
Metrics
Engagement
Model
Gaming
BehaviorTechniques
Accuracy
Smart
Features
Interactive
Components
Adaptive
Engine
Instructional
Modeling
Student
Modeling
Content
Modeling
Student
ADAPTIVE ENGINE
• Student model
• Learning style
• Instructional model
• Knowledge level
• Content model
• Curriculum
Raw Data
Interaction
Data
Self-Reported
Data
STUDENT MODELING
• Transfer GPA
• Demographics
CONTENT MODELING
• Concept-based
hyperspace organization
• Automatic
• Bag-of-words
• LDA
• Manual
INSTRUCTIONAL MODELING
• Knowledge Tracing
• Hidden Markov model
• Behavior model
• Performance model
• Behavior-performance
model
Feature
Selection
FUTURE WORK
13
▪ Benchmark datasets on both reading activities and heterogeneous learning
activities
▪ Further experimentation of deep learning techniques (e.g., multimodal RNNs)
▪ Analysis of the reading process in the broader frame of the learning process
(e.g., how reading is used as a learning strategy)
▪ Impact of e-textbook’s smart features on reading and learning performance
▪ Discovery of optimal reading behaviors
▪ E-textbook’s interactive components implemented in virtual/augmented reality
environments
▪ Intelligent printed textbooks/documents through augmented reality
▪ Intelligent adaptivity to nurture self-regulatory traits
▪ Adaptivity (computer-driven) vs. adaptability (human-driven)
http://www.bbc.com/capital/story/20190127-humanics-a-way-to-robot-proof-your-career
LEARNING ANALYTICS, COGNIFICATION & HUMANICS
14
Measuring the impact of
learning analytics on
learning performance
• Accelerating learning time
• Increasing knowledge retention
• Formation of soft skills
• Greater transfer of learning Humanics-oriented life-long learning
Ensuring humans remain relevant
in the world of AI.
• Mastery of learning content:
• Traditional literacies
• Tech literacy
• Data literacy
• Human literacy
• Nurture and assessment of cognitive
capacities:
• Creativity
• Mental flexibility
• Critical thinking
• Systems thinking
• Leadership
Cognification of learning
The process of making learning
objects and environments
increasingly and ethically
smarter.
• Intelligent textbook
• Competence assessment
• Automated scoring
• Formative feedback
• Remedial interventions
“A generation ago, the half-life of a skill
was about 26 years. Today, it’s 4 and
half years and dropping.” – Indranil Roy
Learner Instructor
Organization Governance
Stakeholders
IoT Conversations
Data
Artisanship
Dashboard
Insights
Descriptive
Diagnostic
Predictive
Prescriptive
Dimensions
Causality
Observational
Randomized
Longitudinal
Meta-analysis
Meta-analysis
Meta-analysis
Data Mining &
Research Methods
Knowledge
Base
“An ethics-bound, semi-autonomous,
and trust-enabled human-AI fusion that
measures and advances knowledge
boundaries in human learning.”
A DEFINITION OF LEARNING ANALYTICS
15
Kumar, V., Boulanger, D., Fraser, S.: Inferring Causal Effects from Learning Analytics: Discovering the Nature of Bias (2019).
Instructional Model
Topic E
Topic D
Topic G
Topic H
Topic J
Assessment of Decision Making
Content Model
Expert Machine Learning
Topics ready
to be learned
Topic D
Topic E
Learner Model
Adaptive Engine
Learning Object
Repository
Trust Model
Talent
SRL
Learning
Style
Grit
Interest
Engage-
ment
Demo-
graphics
Learning
Strategy
Moti-
vation
Prior
Know-
ledge
Learner/Teacher/AI Decision Making
Pedagogical Strategy
2x
E
1x
D
3x
G
5x
H
2x
J
ADAPTIVITY
16

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An Overview of Recent Developments in Intelligent e-Textbooks and Reading Analytics

  • 1. An Overview of Recent Developments in Intelligent e-Textbooks and Reading Analytics BY DAVID BOULANGER AND VIVEKANANDAN KUMAR Athabasca University | Canada
  • 2. 2 LITERATURE 13 9 1 Conference Journal Workshop 9 conferences 9 journals 1 workshop 23 References 3 8 5 6 1 2015 2016 2017 2018 2019
  • 3. DATASETS 3 # of events Events / reader # of readers Reading episode Domain Software Type of reading 2,812,727 939 2993 Semester (43 courses) BookLooper Lecture 567,193 7988 71 Semester Human Computer Interaction, Information Retrieval Reading Circle Textbook, research publication, etc. 129,451 555 233 Semester (11 courses) CourseSmart Textbook 75,748 276 274 Semester Social Sciences, Business, Education - Textbook 65,755 608 108 Semester (2 courses) BookRoll Lecture 10,994 - - ~1 year (110 different magazine issues) Viewerplus + APP- BI Magazine 10,188 35 289 Semester Interactive Systems Design AnnotatED+, Reading Circle Textbook ~7200 109 66 Semester Research Methods - Textbook 1370 80 17 1.5 hours Educational Technology DITeL Journal article 65 7 9 30 minutes Introductory Biology - Textbook In-house readers except for CourseSmart 4 types of reading The semester is the typical period during which reading behavior is evaluated.
  • 4. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 4 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Feature SelectionSmart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student DOCUMENT • Page • Section • Chapter Raw Data Interaction Data Self-Reported Data DOCUMENT FORMAT • PDF • EPUB • Word documents • Web documents • Any reading resource
  • 5. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 5 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Feature SelectionSmart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student Raw Data Interaction Data Self-Reported Data HARDWARE • Augmented reality • Eye tracker • Mobile • Remote • Smartphones • Tablets • E-readers • Computers SOFTWARE • BookLooper • Reading Circle • CourseSmart • BookRoll • Viewerplus • AnnotatED+ • DITeL • Adobe Reader • Kindle
  • 6. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 6 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Smart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student INTERACTION DATA • Next page • Previous page • Jump to page • Annotations • Marking of unknown words • Comments • Highlighting • Underlining • Changing marker color • Zooming in/out • Searching • Search jump • Screen orientation • Portrait • Landscape • Scrolling • Bookmarks • Opening, exiting, minimizing e-textbooks SELF-REPORTED DATA • Survey • Interview • Questionnaires Raw Data Interaction Data Self-Reported Data • Online • Offline Feature Selection
  • 7. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 7 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Smart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student METRICS • # of blinks • Distances of eye movements • Coordinates of eye gazes • Fixations • Saccades • Time of adoption • Reading speed • Engagement level • Reading session • Attention • Reading/visit time • Last pages read • Pages previewedRaw Data Interaction Data Self-Reported Data Feature Selection
  • 8. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 8 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Smart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student READING BEHAVIOR • Progressive sequential analysis • Self-reported behavior • Perception gap Raw Data Interaction Data Self-Reported Data ENGAGEMENT MODEL • Blocked linear regression • Clustering • None • Low • Medium • High GAMING BEHAVIOR • Classifier (gaming, normal) • Logistic regression • KNN • Naïve Bayes • Decision tree • SVM Feature Selection
  • 9. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 9 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Smart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student TECHNIQUES • Gradient tree boosting • Survival analysis • PCA • Regression • Simple linear • Non-linear • Mixed-model linear • Deep learning • Stochastic block model • Descriptive statistics • Classification • Random forest • KNN • Support vector machine Raw Data Interaction Data Self-Reported Data FEATURE SELECTION • Random forest regressor • F-regression • K-means transformations • t-test ACCURACY • Training/testing set • Cross-validation • AUC • RMSE/MSE Feature Selection
  • 10. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 10 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Smart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student SMART FEATURES • Prediction of student performance • Identification of at-risk students/drop-outs • Real-time improvement of learning materials • Provision of teacher annotations • Measurement of reader’s interest and competence • Assessment of a concept’s difficulty level • Automatic correction of students’ answers • Provision of formative feedback • Recommendation of next topics to be learned • Recommendation of effective learning strategies • Real-time lecture supporting system • Many more … Raw Data Interaction Data Self-Reported Data Feature Selection
  • 11. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 11 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Smart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student INTERACTIVE COMPONENTS • Multimedia/visuals • Video • Sketch • Animation • Diagram • Problems to solve • Collaborative • Q&A • Quizzes (multiple- choice/open-ended questions) • Explanations of key concepts • Examples with different parameters Raw Data Interaction Data Self-Reported Data Feature Selection
  • 12. CONCEPT MAP OF READING ANALYTICS & INTELLIGENT E-TEXTBOOKS 12 E-Reader Document Document Format Hardware Reading Behavior Software Metrics Engagement Model Gaming BehaviorTechniques Accuracy Smart Features Interactive Components Adaptive Engine Instructional Modeling Student Modeling Content Modeling Student ADAPTIVE ENGINE • Student model • Learning style • Instructional model • Knowledge level • Content model • Curriculum Raw Data Interaction Data Self-Reported Data STUDENT MODELING • Transfer GPA • Demographics CONTENT MODELING • Concept-based hyperspace organization • Automatic • Bag-of-words • LDA • Manual INSTRUCTIONAL MODELING • Knowledge Tracing • Hidden Markov model • Behavior model • Performance model • Behavior-performance model Feature Selection
  • 13. FUTURE WORK 13 ▪ Benchmark datasets on both reading activities and heterogeneous learning activities ▪ Further experimentation of deep learning techniques (e.g., multimodal RNNs) ▪ Analysis of the reading process in the broader frame of the learning process (e.g., how reading is used as a learning strategy) ▪ Impact of e-textbook’s smart features on reading and learning performance ▪ Discovery of optimal reading behaviors ▪ E-textbook’s interactive components implemented in virtual/augmented reality environments ▪ Intelligent printed textbooks/documents through augmented reality ▪ Intelligent adaptivity to nurture self-regulatory traits ▪ Adaptivity (computer-driven) vs. adaptability (human-driven)
  • 14. http://www.bbc.com/capital/story/20190127-humanics-a-way-to-robot-proof-your-career LEARNING ANALYTICS, COGNIFICATION & HUMANICS 14 Measuring the impact of learning analytics on learning performance • Accelerating learning time • Increasing knowledge retention • Formation of soft skills • Greater transfer of learning Humanics-oriented life-long learning Ensuring humans remain relevant in the world of AI. • Mastery of learning content: • Traditional literacies • Tech literacy • Data literacy • Human literacy • Nurture and assessment of cognitive capacities: • Creativity • Mental flexibility • Critical thinking • Systems thinking • Leadership Cognification of learning The process of making learning objects and environments increasingly and ethically smarter. • Intelligent textbook • Competence assessment • Automated scoring • Formative feedback • Remedial interventions “A generation ago, the half-life of a skill was about 26 years. Today, it’s 4 and half years and dropping.” – Indranil Roy
  • 15. Learner Instructor Organization Governance Stakeholders IoT Conversations Data Artisanship Dashboard Insights Descriptive Diagnostic Predictive Prescriptive Dimensions Causality Observational Randomized Longitudinal Meta-analysis Meta-analysis Meta-analysis Data Mining & Research Methods Knowledge Base “An ethics-bound, semi-autonomous, and trust-enabled human-AI fusion that measures and advances knowledge boundaries in human learning.” A DEFINITION OF LEARNING ANALYTICS 15 Kumar, V., Boulanger, D., Fraser, S.: Inferring Causal Effects from Learning Analytics: Discovering the Nature of Bias (2019).
  • 16. Instructional Model Topic E Topic D Topic G Topic H Topic J Assessment of Decision Making Content Model Expert Machine Learning Topics ready to be learned Topic D Topic E Learner Model Adaptive Engine Learning Object Repository Trust Model Talent SRL Learning Style Grit Interest Engage- ment Demo- graphics Learning Strategy Moti- vation Prior Know- ledge Learner/Teacher/AI Decision Making Pedagogical Strategy 2x E 1x D 3x G 5x H 2x J ADAPTIVITY 16