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E-raamat: Intelligent Support for Computer Science Education: Pedagogy Enhanced by Artificial Intelligence

(University of Salford, UK), ,
  • Formaat: 306 pages
  • Ilmumisaeg: 22-Sep-2021
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781351684859
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Intelligent Support for Computer Science Education: Pedagogy Enhanced by Artificial IntelligenceSuurem pilt
  • Formaat: 306 pages
  • Ilmumisaeg: 22-Sep-2021
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781351684859
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"Intelligent Support for Computer Science Education presents the authors' research journey into the effectiveness of human tutoring, with the goal of developing educational technology that can be used to improve introductory Computer Science education atthe undergraduate level. Nowadays, Computer Science education is central to the concerns of society, as attested by the penetration of information technology in all aspects of our lives; consequently, in the last few years interest in Computer Science atall levels of schooling, especially at the college level, has been flourishing. However, introductory concepts in Computer Science such as data structures and recursion are difficult for novices to grasp. This book will be of special interest to the Computer Science education community, specifically instructors of introductory courses at the college level, and Advanced Placement (AP) courses at the high school level. Additionally, all the authors' work is relevant to the Educational Technology community,especially to those working in Intelligent Tutoring Systems, their interfaces, and Educational Data Mining, in particular as applied to human-human pedagogical interactions and to user interaction with educational software"--

Intelligent Support for Computer Science Education presents the authors’ research journey into the e ectiveness of human tutoring, with the goal of developing educational technology that can be used to improve introductory Computer Science education at the undergraduate level. 



Intelligent Support for Computer Science Education presents the authors’ research journey into the e ectiveness of human tutoring, with the goal of developing educational technology that can be used to improve introductory Computer Science education at the undergraduate level. Nowadays, Computer Science education is central to the concerns of society, as attested by the penetration of information technology in all aspects of our lives; consequently, in the last few years interest in Computer Science at all levels of schooling, especially at the college level, has been flourishing. However, introductory concepts in Computer Science such as data structures and recursion are di cult for novices to grasp.

Key Features:

  • Includes a comprehensive and succinct overview of the Computer Science education landscape at all levels of education.
  • Provides in-depth analysis of one-on-one human tutoring dialogues in introductory Computer Science at college level.
  • Describes a scalable, plug-in based Intelligent Tutoring System architecture, portable to different topics and pedagogical strategies.
  • Presents systematic, controlled evaluation of different versions of the system in ecologically valid settings (18 actual classes and their laboratory sessions).
  • Provides a time-series analysis of student behavior when interacting with the system.

This book will be of special interest to the Computer Science education community, specifically instructors of introductory courses at the college level, and Advanced Placement (AP) courses at the high school level. Additionally, all the authors’ work is relevant to the Educational Technology community, especially to those working in Intelligent Tutoring Systems, their interfaces, and Educational Data Mining, in particular as applied to human-human pedagogical interactions and to user interaction with educational software.

About the Authors xiii
Contributors xv
Preface xvii
Acknowledgments xix
Section I Four Scientific Pillars
Chapter 1 Introduction
3(6)
1.1 An Interdisciplinary Perspective
3(3)
1.2 The Structure Of The Book
6(3)
Chapter 2 Related Work
9(42)
Stellan Ohlsson
2.1 Cognition And Multiple Modes Of Learning
11(8)
2.1.1 Background
11(2)
2.1.2 Nine Modes of Learning
13(1)
2.1.2.1 Discussion
15(4)
2.2 Pragmatics And Dialogue Processing
19(3)
2.3 Introductory Computer Science Education
22(8)
2.3.1 Elementary and Secondary Education
23(4)
2.3.2 From high school to college
27(1)
2.3.3 Post-Secondary Education for CS Majors
28(2)
2.4 Intelligent Tutoring Systems (ITSS)
30(10)
2.4.1 Natural Language Processing (NLP) for ITSs
33(4)
2.4.2 Modes of Learning and ITSs
37(1)
2.4.2.1 Positive and Negative feedback
37(1)
2.4.2.2 Worked-Out Examples
38(1)
2.4.2.3 Analogy
39(1)
2.5 ITSS For Computer Science And NLP
40(12)
2.5.1 ITSs for CS
40(2)
2.5.1.1 NLP in ITSs for CS
42(9)
Section II From Human Tutoring to ChiQat-Tutor
Chapter 3 Human Tutoring Dialogues and their Analysis
51(34)
Stellan Ohlsson
Mehrdad Alizadeh
Lin Chen
Rachel Harsley
3.1 Data Collection
52(5)
3.1.1 Learning Outcomes in Human Tutoring
54(1)
3.1.2 Measuring Learning Gains
54(1)
3.1.3 Learning Effects
55(2)
3.2 Transcription And Annotation
57(12)
3.2.1 Annotation
59(1)
3.2.1.1 Validating the Corpus Annotation
67(2)
3.3 Distributional Analysis
69(1)
3.3.1 Elementary Dialogue Acts
69(1)
3.3.2 Student Initiative
70(1)
3.3.3 Episodic Strategies
70(1)
3.4 Insights From The Corpus: Pedagogical Moves And Learning
70(12)
3.4.1 Individual Dialog Acts (Type 1 Models)
72(2)
3.4.2 Sequences of Dialogue Acts (Type 2 Models)
74(1)
3.4.2.1 Bigram Models
77(1)
3.4.2.2 Trigram Models
78(1)
3.4.3 Episodic Strategies (Type 3 Models)
79(1)
3.4.3.1 Worked-Out Examples
79(1)
3.4.3.2 Analogies
81(1)
3.5 Summary: Insights From Human Tutoring Analysis
82(3)
Chapter 4 ChiQat-Tutor and its Architecture
85(24)
Omar Alzoubi
Christopher Brown
4.1 The Domain Model
86(6)
4.1.1 Problem Definitions
86(2)
4.1.2 Solution Definitions
88(1)
4.1.3 Worked-Out Examples
88(1)
4.1.4 The Procedural Knowledge Model
89(3)
4.2 User Interface
92(1)
4.3 A Bird's Eye View Of Chiqat-Tutor In Action
92(3)
4.3.1 Solution Evaluator
93(2)
4.4 Tutor Module
95(10)
4.4.1 Code Feedback: Syntax and Executability
95(3)
4.4.2 Reactive & Proactive Feedback
98(1)
4.4.2.1 Reactive Procedural Feedback
101(1)
4.4.2.2 Proactive Procedural Feedback
101(4)
4.5 Training The PKM Graphs
105(4)
Chapter 5 Evaluation in the Classroom
109(34)
Rachel Harsley
Stellan Ohlsson
5.1 Evaluation Metrics
110(1)
5.2 Learning With Proactive And Reactive Feedback
110(8)
5.2.1 Insights on Learning from Student Behavior and Perceptions of ChiQat-Tutor-v1
112(1)
5.2.1.1 Student Behavior
112(1)
5.2.1.2 Student Satisfaction
115(2)
5.2.2 Chiqat-Tutor, Version 1: Summary of Findings
117(1)
5.3 Learning With Worked-Out Examples And Analogy
118(26)
5.3.1 WOE and Analogy Conditions
118(1)
5.3.1.1 Standard WOEs
119(1)
5.3.1.2 Length and Usage of WOEs
119(1)
5.3.1.3 Analogical Content in WOEs
121(1)
5.3.2 Learning Linked Lists among Non-Majors
122(4)
5.3.3 Learning Linked Lists Among Majors
126(1)
5.3.4 Learning and Initial Student Knowledge
127(1)
5.3.4.1 Mining the Logs: Predicting Initial Knowledge
129(9)
5.3.5 Chiqat-Tutor, Version 2: Summary of Findings
138(5)
Section III Extending ChiQat-Tutor
Chapter 6 Beyond Linked Lists: Binary Search Trees and Recursion
143(20)
Mehrdad Alizadeh
Omar Alzoubi
6.1 Binary Search Trees
144(3)
6.1.1 Pilot Evaluation
145(2)
6.2 Recursion
147(13)
6.2.1 Models for Teaching Recursion
148(1)
6.2.1.1 Conceptual Models
149(1)
6.2.1.2 Program Visualization
150(1)
6.2.2 A Hybrid Model for Teaching Recursion in ChiQat-Tutor
151(1)
6.2.3 Evaluation of the Recursion Module
152(1)
6.2.3.1 Experimental Protocol
153(1)
6.2.3.2 Experiments at CMU Qatar
154(1)
6.2.3.3 Experiments at UIC
154(2)
6.2.4 Analysis of Students' Interactions with the System
156(4)
6.3 Summary
160(3)
Chapter 7 A Practical Guide to Extending ChiQat-Tutor
163(10)
7.1 An Implementation Architecture
163(2)
7.2 Case Study: The Stack Tutor Plugin
165(8)
7.2.1 Stack Plugin Design
165(2)
7.2.2 Class Structure
167(2)
7.2.3 Setting up the Stage
169(1)
7.2.4 Graphical Interface
169(1)
7.2.5 Stack Problem Logic and Feedback
170(3)
Chapter 8 Conclusions
173(10)
8.1 Where We Are, And Lessons Learned
173(4)
8.2 Future Work
177(19)
8.2.1 Extending the Curriculum
177(1)
8.2.2 Enhancing Communication with the Student
178(1)
8.2.3 Mining the User Logs, and Deep Learning
179(4)
Appendix A A Primer On Data Structures 183(6)
A.1 Linked Lists (Lists)
184(1)
A.2 Stacks
185(1)
A.3 Binary Search Trees (BSTS)
185(4)
Appendix B Pre-/Post-Tests 189(6)
B.1 Pre-/Post-Test For Human Tutoring
189(3)
B.2 Pre-/Post-Test For Chiqat (Linked List Problems)
192(3)
Appendix C Annotation Manuals 195(26)
C.1 Dialogue Act Manual
196(15)
C.1.1 Direct Procedural Instruction: DPI
196(5)
C.1.2 Direct Declarative Instruction: DDI
201(4)
C.1.2.1 Not DDI (No!)
201(1)
C.1.2.2 DDI (Yes!)
202(3)
C.1.3 Prompt
205(3)
C.1.3.1 Types Of Prompts
205(3)
C.1.4 Feedback
208(4)
C.1.4.1 Positive Feedback
208(1)
C.1.4.2 Negative Feedback
208(1)
C.1.4.3 General Guidelines And Special Cases
208(3)
C.2 Student Initiative (SI)
211(1)
C.3 Worked-Out Examples
212(5)
C.3.1 Coding Categories
212(1)
C.3.2 Marking Worked-Out Examples
213(4)
C.3.2.1 Outline
213(1)
C.3.2.2 Examples
213(4)
C.4 Analogy Coding Manual
217(4)
C.4.1 Definition
217(1)
C.4.2 Analogous Terms
218(1)
C.4.3 Coding Category
218(1)
C.4.4 Marking Analogies
219(2)
C.4.4.1 Examples
219(2)
Appendix D Linked List Problem Set 221(6)
D.1 Problem 1
221(1)
D.2 Problem 2
222(1)
D.3 Problem 3
222(1)
D.4 Problem 4
223(1)
D.5 Problem 5
223(1)
D.6 Problem 6
224(1)
D.7 Problem 7
225(2)
Appendix E Stack Plugin Full Code 227(16)
E.1 Plugininstance.Java
227(2)
E.2 Stackview.Java
229(5)
E.3 Stackproblem.Java
234(5)
E.4 Stackproblemstep.Java
239(1)
E.5 Stackproblemfeedback.Java
239(1)
E.6 Stackstyle.CSS
240(3)
Bibliography 243
Barbara Di Eugenio is Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC), Chicago, IL, USA. There she leads the NLP laboratory (https://nlp.lab.uic.edu). Dr. Di Eugenio holds a Ph.D. in Computer Science from the University of Pennsylvania (1993); she joined UIC in 1999. Her interests focus on the theory and practice of Natural Language Processing, with applications to educational technology, health care, human robot interaction, and social media. Dr. Di Eugenio is an NSF CAREER awardee (2002), and a UIC University Scholar (2018-21). Her research has been supported by the National Science Foundation, the National Insti- tute of Health, the Office of Naval Research, Motorola, Yahoo!, Politecnico di Torino, and the Qatar Research Foundation. She has graduated 12 PhD students and 30 Master's students, and published more than one hundred refereed publications.

Davide Fossati is currently a Senior Lecturer in Computer Science at Emory University in Atlanta, GA, USA. Prior to joining the faculty at Emory in 2016, Dr. Fossati held positions at the Georgia Institute of Technology (2009-2010) and Carnegie Mellon University (2010-2015). He received his Ph.D. in Computer Science from the University of Illinois at Chicago in 2009. He also holds an M.Sc. degree in Computer Engineering from the Po- litecnico di Milano, Italy (2004), and an M.Sc. in Computer Science from the University of Illinois at Chicago (2003). Dr. Fossati's primary scholarly focus is Technology Enhanced Learning, with particular interest in the development of Artificial Intelligence systems to support Computer Science education.

Nick Green is a technology professional with 20 years of research and development experience in academia and industry. Dr. Green received his Ph.D. in Computer Science from the University of Illinois at Chicago in 2017, where he focused on educational technology, natural language processing, and software engineering. Outside of academia, he has worked for companies such as Sony Interactive Entertainment and Facebook. He has a passion for the startup scene where he is also a serial entrepreneur having founded companies in fields such as security and precision agriculture.
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