Table Of ContentAdvanced Technologies and Societal Change
Ninni Singh
Vinit Kumar Gunjan
Jacek M. Zurada
Cognitive
Tutor
Custom-Tailored Pedagogical Approach
Advanced Technologies and Societal Change
Series Editors
Amit Kumar, Bioaxis DNA Research Centre (P) Ltd, Hyderabad, Telangana, India
Ponnuthurai Nagaratnam Suganthan, School of EEE, Nanyang Technological
University, Singapore, Singapore
Jan Haase, NORDAKADEMIE Hochschule der Wirtschaft, Elmshorn, Germany
Editorial Board
Sabrina Senatore, Department of Computer and Electrical Engineering and Applied
Mathematics, University of Salerno, Fisciano, Italy
Xiao-Zhi Gao , School of Computing, University of Eastern Finland, Kuopio,
Finland
Stefan Mozar, Glenwood, NSW, Australia
Pradeep Kumar Srivastava, Central Drug Research Institute, Lucknow, India
This series covers monographs, both authored and edited, conference proceedings
and novel engineering literature related to technology enabled solutions in the area
of Humanitarian and Philanthropic empowerment. The series includes sustainable
humanitarian research outcomes, engineering innovations, material related to
sustainable and lasting impact on health related challenges, technology enabled
solutions to fight disasters, improve quality of life and underserved community
solutions broadly. Impactful solutions fit to be scaled, research socially fit to be
adopted and focused communities with rehabilitation related technological outcomes
get a place in this series. The series also publishes proceedings from reputed
engineering and technology conferences related to solar, water, electricity, green
energy, social technological implications and agricultural solutions apart from
humanitarian technology and human centric community based solutions.
Major areas of submission/contribution into this series include, but not limited
to: Humanitarian solutions enabled by green technologies, medical technology,
photonics technology, artificial intelligence and machine learning approaches, IOT
based solutions, smart manufacturing solutions, smart industrial electronics, smart
hospitals, robotics enabled engineering solutions, spectroscopy based solutions and
sensor technology, smart villages, smart agriculture, any other technology fulfilling
Humanitarian cause and low cost solutions to improve quality of life.
· ·
Ninni Singh Vinit Kumar Gunjan
Jacek M. Zurada
Cognitive Tutor
Custom-Tailored Pedagogical Approach
Ninni Singh Vinit Kumar Gunjan
Department of Computer Science Department of Computer Science
and Engineering and Engineering
CMR Institute of Technology CMR Institute of Technology
Hyderabad, Telangana, India Hyderabad, Telangana, India
Jacek M. Zurada
Department of Electrical and Computer
Engineering
University of Louisville
Louisville, KY, USA
ISSN 2191-6853 ISSN 2191-6861 (electronic)
Advanced Technologies and Societal Change
ISBN 978-981-19-5196-1 ISBN 978-981-19-5197-8 (eBook)
https://doi.org/10.1007/978-981-19-5197-8
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Preface
Artificial intelligence is an advanced field of research. It is particularly used in the
field of education to increase the effectiveness of teaching and learning techniques.
With the advancement of internet technology, it has been observed that there is a
rapid growth in distance learning modality through the web. This mode of learning
is better known as the e-learning system. These systems present low intelligence
because they offer a preidentified learning frame to their learners. The advantage
of these systems is to offer to learn anytime and anyplace without putting emphasis
on a learner’s needs, competency level, and previous knowledge. Every learner has
different grasping levels, previous knowledge, and preferred mode of learning, and
hence, the learning process of one individual may significantly vary from other
individuals.
Ongoing research and development initiatives have led us to the origin of intel-
ligent tutoring. It has gained immense popularity in current times specifically due
to the advancement of intelligent tutoring systems (ITS). Research in psychology,
education, and computer science (AI and machine learning) fueled the foundation
of the field of intelligent tutoring systems. Thus, ITS aims to cognize the learner’s
needs, and grasping levels, and offer the learning material that best suits the learner’s
requirements. ITS acts as a cognitive tutor that not only solves the learner’s issues
(hints and feedbacks) but also keeps an eye on the learner’s performance and activity
during earning and deduces the competency level of learner’s in the particular
subject domain. It seeks to determine the learner’s cognitive state of mind. Thus,
identification of the cognitive state of learners makes a computer-assisted learning
system an intelligent tutoring system. There is progressive growth of a computer-
assisted learning system to various forms of web-based learning systems and a further
advancement to form a personalized tutoring system.
This book illustrates the design, development, and evaluation of the personal-
ized intelligent tutoring system that emulates the human cognitive intelligence by
incorporating the artificial intelligence features. This book provides a complete refer-
ence for novice students, researchers, and industry practitioners interested in keeping
abreast of recent advancements in this field. This book is suitable for postgraduate
students, researchers, scholars, and developers willing to gain cognitive intelligence
v
vi Preface
knowledge. This book encompasses cognitive intelligence and artificial intelligence
which are very important for deriving a roadmap for future research on intelligent
systems.
Hyderabad, India Ninni Singh
Hyderabad, India Vinit Kumar Gunjan
Louisville, USA Jacek M. Zurada
Contents
1 Introduction ................................................... 1
1.1 Introduction ............................................... 1
1.2 Need of an Intelligent Tutoring System ........................ 3
1.3 A Growing Field Intelligent Tutoring System ................... 4
1.4 Effectiveness of ITS ........................................ 6
1.5 Intelligent Tutoring System Architecture ....................... 8
1.5.1 Learner Model ....................................... 10
1.5.2 Pedagogy Model ..................................... 17
1.5.3 Domain Model ...................................... 18
1.5.4 Expert Model ........................................ 20
1.5.5 Learner Interface ..................................... 21
1.6 Book Contributions ......................................... 21
1.6.1 Development of Adaptive Knowledge Base .............. 22
1.6.2 Learner-Centric Curriculum Recommendation ........... 22
1.6.3 Personalized Tutoring Strategy ......................... 23
1.6.4 Identification of Learner Understand-Ability ............. 23
1.6.5 Identification of Learner Emotional State ................ 23
1.7 Organization of Content ..................................... 24
References ..................................................... 25
2 Domain Modeling .............................................. 31
2.1 Introduction ............................................... 31
2.2 An Epistemological Outlook Related to Domain Knowledge ...... 32
2.3 Preliminary Research on Domain Model in IT .................. 34
2.3.1 The Black Box Models ............................... 34
2.3.2 The Glass Box Models ................................ 34
2.3.3 The Cognitive Models ................................ 35
2.4 Experiential (Tacit Domain Knowledge) ....................... 35
2.5 Experiential Knowledge Acquisition Approaches ............... 36
2.5.1 Cognitive Map ....................................... 37
2.5.2 Causal Map ......................................... 37
vii
viii Contents
2.5.3 Self-Q .............................................. 37
2.5.4 Semi-Structured ..................................... 37
2.6 Ontology Engineering ....................................... 38
2.7 Building Domain Ontologies from Texts ....................... 38
2.7.1 Concept Extraction ................................... 39
2.7.2 Attribute Extraction .................................. 40
2.7.3 Taxonomy Extraction ................................. 41
2.7.4 Conceptual Relationship Extraction ..................... 42
2.7.5 Instance Extraction ................................... 43
2.7.6 Axioms Extraction ................................... 44
2.8 Summary .................................................. 45
References ..................................................... 45
3 Pedagogy Modeling ............................................. 51
3.1 Introduction to Pedagogy Model .............................. 51
3.2 Preliminary Research on Pedagogy Model in ITS ............... 51
3.2.1 Open Education System ............................... 51
3.2.2 Massive Online Open Courses ......................... 52
3.3 Path Sequencing of Learning Material in Learning Systems ....... 54
3.4 Impact of Emotion Capturing in Learning System ............... 56
3.4.1 Emotion Recognition in Learning System ............... 56
3.5 Summary .................................................. 58
References ..................................................... 58
4 Building SeisTutor Intelligent Tutoring System
for Experimental Learning Domain .............................. 61
4.1 Introduction ............................................... 61
4.2 Seismic Data Interpretation: As Experiential Learning Domain .... 61
4.3 Development of Adaptive Domain Model ...................... 63
4.3.1 Phase 1: Tacit Knowledge Acquisition
and Characterization .................................. 64
4.3.2 Phase 2: Knowledge Representation: Multilevel
Hierarchical Model ................................... 70
4.4 Summary .................................................. 77
References ..................................................... 77
5 Pedagogy Modeling for Building SeisTutor Intelligent Tutoring
System ........................................................ 79
5.1 Introduction ............................................... 79
5.2 Workflow of SeisTutor ...................................... 79
5.2.1 Development of Custom-Tailored Curriculum ............ 81
5.2.2 Development of Tutoring Strategy Recommendation ...... 88
5.2.3 CNN-Based Emotion Recognition Model ................ 92
5.2.4 Development of Performance Analyzer Model ........... 95
5.3 Summary .................................................. 101
References ..................................................... 101
Contents ix
6 Execution of Developed Intelligent Tutoring System ............... 103
6.1 Implementation of a System ................................. 103
6.2 Learner Interface Model ..................................... 103
6.2.1 Learner Registration .................................. 103
6.3 Domain Model ............................................. 107
6.4 Learner Model ............................................. 108
6.5 Pedagogy Model ........................................... 110
6.5.1 Performance Analyzer Model .......................... 112
6.6 Learner Statistics ........................................... 113
6.7 Learner Feedback .......................................... 114
6.8 Summary .................................................. 115
References ..................................................... 116
7 Performance Metrics: Intelligent Tutoring System ................. 117
7.1 Overview .................................................. 117
7.2 Learner Statistics ........................................... 120
7.2.1 Data Preparation ..................................... 123
7.2.2 Min-Max Normalization .............................. 123
7.3 Learner Performance Metrics ................................ 123
7.3.1 Pretutoring and Post-Tutoring Performance .............. 124
7.3.2 Predictive Statistical Analysis of Degree
of Understanding Module ............................. 124
7.3.3 Kirkpatrick Four Stage Evaluation (Second Aspects
of Evaluation) ....................................... 124
7.4 SeisTutor: A Comparative Analysis with Teachable,
My-Moodle and Course-Builder Learning Management
System .................................................... 128
7.4.1 My-Moodle ......................................... 128
7.4.2 Course-Builder ...................................... 129
7.4.3 Teachable ........................................... 130
7.4.4 SeisTutor ........................................... 130
7.5 Summary .................................................. 137
References ..................................................... 137
8 Analysis of Performance Metrics ................................. 139
8.1 Critical Analysis of Performance Metrics ...................... 139
8.2 Statistical Analysis of Learner Engagement .................... 147
8.3 Pretutoring and Post-tutoring Performance ..................... 149
8.4 Learner Learning Analysis Using Evaluation Model
“Kirkpatrick” .............................................. 151
8.4.1 Kirkpatrick Phase 1: Evaluation of Reaction ............. 151
8.4.2 Kirkpatrick Phase 2: Evaluation of Learning ............. 152
8.4.3 Kirkpatrick Phase 3: Evaluation of Behavior: ............ 154
8.4.4 Kirkpatrick Phase 4: Evaluation of Results ............... 162
