Location of Repository

Schema Theory-based Computational Approach to Support Children's Conceptual Understanding

By Zukeri Ibrahim


Researchers acknowledge the difficulty faced by children in understanding new concepts. Explaining new concepts to children requires supporting their reasoning based on concrete objects and ideas. Human tutors normally use some dialogue to introduce new concepts and tailor the explanations to the prior knowledge of the children. There is a lack of interactive pedagogical agents that guide children's reasoning and adapt explanation to their cognitive state. The design of such agents can be based on learning theories that explain how children understand new concepts, as well as on studies of how human teachers support children's conceptual understanding.\ud \ud The goal of this research is to develop a computational framework to inform the design of a pedagogical agent capable of engaging in a dialogue that supports children's conceptual understanding, the thesis proposes an approach for Schema Activation and Interpersonal Communications (SAIC) to support cognitive tasks that occur when a child is learning new concepts through one-to-one interaction with a computer agent. The approach is based on schema theory that explains how meaning-making occurs and stresses the importance of prior knowledge, and on the results of an experimental study that identifies strategies human teachers use to support schema-based cognitive tasks. \ud \ud A novel architecture of a pedagogical agent whose behaviour is based on schema activation and modification is described. The architecture addresses three important issues: describing the process of activation and modification of relevant prior knowledge to be used in introducing new concepts; defining the reasoning and decision making of the agent to promote schema-based cognitive tasks; and providing adaptive explanations tailored to the child's relevant prior knowledge. The schematic knowledge of the SAIC agent is represented as frames, the dialogue is planned as a sequence of dialogue games, and the interaction language is implemented with linguistic templates extracted from a study with teachers. The applicability of the SAIC approach is demonstrated in a multimedia educational system 'Going to the Moon', as an integrated as an integrated component in a reading session. An experimental study with the multimedia system has validated the SAIC design approach and has examined the usefulness of the agent in supporting children's conceptual understanding in terms of improving their schematic knowledge. \ud \ud The thesis makes original contributions to the fields of Artificial Intelligence in Education by defining reasoning and decision making based on the principles of schema theory, and by designing a schema-based pedagogical agent to support children's conceptual understanding; Education by demonstrating the application of learning theories to inform the design of intelligent tutoring systems; and Knowledge-based systems by demonstrating the feasibility of frames as the representation formalism in Intelligent Tutoring Systems, and by proposing some original mechanism for using frames to design pedagogical agents

Publisher: School of Computing (Leeds)
Year: 2006
OAI identifier: oai:etheses.whiterose.ac.uk:1340

Suggested articles



  1. A (2003). MArCo: Building an Artificial Conflict Mediator to Support Group Planning Interactions.
  2. (2002). A 3-tier Planning Architecture for Managing Tutorial Dialogue, Intelligent Tutoring Systems
  3. (1999). A Classification of Evaluation Methods for Intelligent Tutoring Systems.
  4. (1998). A collaborative planning model of intentional structure.
  5. (1981). A Framework for Representing Knowledge.
  6. (1975). A framework for representing knowledge. The psychology of computer vision.
  7. (2005). A Generic Tool to Browse Tutor-Student Interactions: Time Will Tell!
  8. (1998). A knowledge-based teaching system for SQL. In T. Ottmann & I. Tomek (Eds.),
  9. (1997). A Multimedia Authoring System for Crafting Topic Hierarchy, Learning Strategies, and Intelligent Models.
  10. (1997). A programming by demonstration authoring tool for modeltracing tutors.
  11. (1988). A Schema-theoretic View of Basic Processes in Reading Comprehension.
  12. (1984). A schema-theoretic view of basic reading processes.
  13. (2005). A Semi-Automated Wizard of Oz Interface for Modelling Tutorial Strategies.
  14. (1998). Accessing prior knowledge to remember text: A comparison of advance organizers and maps.
  15. (1978). Accretion, tuning and restructuring: Three modes of learning.
  16. (2001). ActiveMath: A Generic and Adaptive WebBased Learning.
  17. (2001). Adaptation in Open Corpus Hypermedia.
  18. (1999). Adaptation of problem presentation and feedback in an intelligent mathematics tutor. In
  19. (2002). Adapting to prior knowledge of learners.
  20. (1996). Adaptive Hypermedia, an attempt to analyse and generalize.
  21. (2003). Adaptive web-based educational systems.
  22. (2001). Adding an Instructor Modelling Component to the Architecture of ITS Authoring Tools.
  23. (2002). An agent that helps children to author rhetorically-structured digital puppet presentations. Intelligent Tutoring Systems
  24. (2000). An approach to analyzing the role and structure of reflective dialogue.
  25. (1989). An Architecture for Theory-Driven Scientific Discovery.
  26. (2005). An architecture to combine meta-cognitive and cognitive tutoring: Pilot testing the Help Tutor,
  27. (2003). An Augmented TemplateBased Approach to Text Realization.
  28. (1999). An educational program for paper sculpture: a case study in the design of software to enhance children’s spatial reasoning.
  29. (2005). An Evaluation of a Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals.
  30. (1997). An Inquiry into the spontaneous transfer of Problem-solving skill.
  31. (2002). An Intelligent Tutoring System Incorporating a Model of an Experienced Human Tutor. Intelligent Tutoring System
  32. (1986). An investigation of poor readers' use of a thematic strategy to comprehend text.
  33. (2002). An iterative data collection approach for multimodal dialogue systems. In:
  34. (2004). An Open Learner Model for Children and Teachers: Inspecting Knowledge Level of Individuals and Peers.
  35. (1994). An update semantics for dialogue.
  36. (1981). Analogical processes in learning.
  37. (2003). Analysing student reflection in The Learning Game.
  38. (1999). Analyzing Educational Discourse: The DISCOUNT Scheme. Version 3,
  39. and Virtual Reality,
  40. (2000). Andes: An intelligent tutor for classical physics,
  41. (2001). Applying Interactive Open Learner Models to Learning Technical Terminology.
  42. (1993). Applying the Wizard of Oz Technique to the Study of Multimodal Systems. In:
  43. (2000). Applying wizard of oz method to learning interface agent. In:
  44. (1997). Approximate Reasoning Techniques for Intelligent Diagnostic Instruction.
  45. (1988). Architectures for intelligence. The Twenty-second Carnegie Mellon
  46. (1987). Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge.
  47. (1988). Artificial Intelligence Programming with Turbo Prolog.
  48. (1987). Assessing cognitive structure: verifying a method using pattern notes.
  49. (1992). Assessment of prior knowledge as a determinant for future learning.
  50. (2001). Astronomy the Study of the Universe. Snapping-Turtle Guide.
  51. (1999). Atlas: A Plan Manager for Mixed-Initiative, Multimodal Dialogue.
  52. (1997). Augmenting the user’s knowledge via comparison. User modeling:
  53. (1999). Authoring Intelligent Tutoring Systems: An Analysis of the State of the Art.
  54. (2004). AutoTutor: A tutor with dialogue in natural language.
  55. (2005). Be Bold and take a challenge”: Could motivational strategies improve help-seeking?
  56. (1990). Book of Space. Grisewood and Dempsey.
  57. (2001). Bootstrapping knowledge representations: from entailment meshes via semantic nets to learning webs.
  58. (2000). Broader Bandwidth in Student Modeling: What if ITS Were “Eye”TS? Intelligent Tutoring Systems,
  59. (1998). Building a knowledge base in reading (2nd ed.).
  60. (1997). Building a knowledge based in reading.
  61. (2003). Building intelligent tutoring systems.
  62. (1990). Bypassing the Intractable Problem of Student Modeling. Intelligent Tutoring Systems: at the Crossroads of Artificial Intelligence and Education.
  63. (2004). Can Automated Questions Scaffold Children’s Reading Comprehension? Intelligent Tutoring Systems
  64. (2003). Care – Making the Affective Leap: More Than a Concerned Interest in a Learner’s Cognitive Abilities.
  65. (2000). CAROL5: An Agent-Oriented Programming Language for Developing Social Learning Systems.
  66. (2001). CAST: Collaborative Agents for Simulating Teamwork,
  67. Chapteltown & Harehills Assisted Learning Computer School. Annual Performance Report/Results for Sponsors, Patrons & Parents.
  68. (2001). CIRCSIM-Tutor: An intelligent tutoring system using natural language dialogue.
  69. (2000). Clarissa: A Laboratory for the Modelling of Collaboration.
  70. (2002). CLARISSE: A machine Learning Tool to Initialize Student Models.
  71. (1994). Cognition in the wild.
  72. (2004). Cognitive and language development in children.
  73. (1988). Cognitive load during problem solving: Effects on learning,
  74. (1996). Cognitive schema theory in the constructivist debate.
  75. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring.
  76. (1998). Collagen: A collaboration manager for software interface agents.
  77. (1996). Commenting tools as design support - a Wizard-of-02 study.
  78. (2003). Comparing the learning effectiveness of REDEEM
  79. (1999). Constraint-Based Modeling: Representing Student Knowledge.
  80. (2000). Constructing collaborative pedagogical situations in classrooms: a scenario and role based approach, CSCL’2002,
  81. (1997). Cosmo: A Life-like Animated Pedagogical Agent with Deictic Believability.
  82. (1996). CREAM-TOOLS: An Authoring Environment for Curriculum and Course Building in an Intelligent Tutoring System.
  83. (1987). Cultural Models in Language and Thought. Cambridge:
  84. (1989). Decision-Making in an Embedded Reasoning System.
  85. (1999). Deploying Intelligent Tutors on the Web: An Architecture and an Example,
  86. (1977). Dialogue games: meta-communication structures for natural language interaction.
  87. (2000). DT Tutor: A Decision-Theoretic, Dynamic Approach for Optimal Selection of Tutorial Actions. Intelligent Tutoring Systems
  88. (1998). ECOLAB: Explorations in the Zone of Proximal Development.
  89. (1998). Ecolab: Exploring the construction of a Learning Assistant.
  90. (1999). Ecolab: The development and evaluation of a Vygotskian design framework.
  91. (1991). Educational psychology(3rd ed.).
  92. (2000). Elementary A Space Mission. Dorling Kindersley.
  93. (2001). Elementary schools where students succeed in reading. The Northeast and Islands Regional Educational Laboratory. The Education Alliance.
  94. (1994). Eliciting Selfexplanations Improves Understanding.
  95. (2001). ELM-ART: An Adaptive Versatile System for Web-based Instruction.
  96. (2004). Emotion Based Agent Architectures for Tutoring Systems The INES Architecture. Cybernetics and Systems.
  97. (1998). Encouraging Student Reflection and Articulation using a Learning Companion.
  98. (1980). Episodes as chunks in narrative memory.
  99. (2004). Evaluating a Probabilistic Model of Student Affect. Intelligent Tutoring Systems
  100. (1991). Evaluating software: A review of the options.
  101. (1993). Evaluation methodologies for intelligent tutoring systems,
  102. (2005). Evaluation methods for learning environment.
  103. (1999). Evaluation of a Constraint-based Tutor for a Database Language.
  104. (2003). Evolution of User Interaction: The Case of Agent Adele.
  105. (1998). EXEMPLARS: A Practical, Extensible Framework for Dynamic Text Generation.
  106. (2005). Experimental evaluation of polite interaction tactics for pedagogical agents.
  107. (2000). Explaining away ambiguity: Learning verb selectional preference with Bayesian networks.
  108. (1987). Explanations of reading comprehension: Schema theory and critical thinking theory.
  109. (2002). Feedback on children’s stories via multiple agents. Intelligent Tutoring Systems
  110. (1994). Formal approaches to student modelling.
  111. (1987). Foundations in Learning Research.
  112. (2001). Four easy pieces: development systems for knowledge-based generative instruction.
  113. (1973). From Childhood to Adolescence.
  114. (1993). From Discourse to Logic: Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic, and Discourse Representation Theory.
  115. (2004). From Modelling Teaching Strategies to Modelling Affordances.
  116. (2005). Generating Computer-based Advice in Web-based Distance Education Environments.
  117. (1996). Generating Facial Expressions for Speech.
  118. (2002). Getting to know me: helping learners understand their own learning needs through metacognitive scaffolding.
  119. (2000). Graphic organizers to the rescue! Teaching exceptional children,
  120. (2003). Guided reading implementation. Case study of implementation Pinellas County,
  121. (2003). Help seeking and help design in interactive learning environments,
  122. (2000). Helping children learn vocabulary during computer-assisted oral reading.
  123. (2002). Hierarchical Representation and Evaluation of the Students in an Intelligent Tutoring System.
  124. (2003). How to find trouble in communication.
  125. (1979). Human cognition: Learning, understanding, and remembering.
  126. (2000). Improving story choice in a Reading Tutor that listens. Intelligent Tutoring Systems
  127. (2000). Inspecting and Visualizing Distributed Bayesian Student Models.
  128. (1997). Intelligent tutoring goes to school in the big city.
  129. (2001). Interactive Conceptual Tutoring in Atlas-Andes.
  130. (2000). Investigation by Design: Developing Dialogue Models to Support Reasoning and Conceptual Change.
  131. (2000). Involving effectively teachers and students in the life cycle of an intelligent tutoring system.
  132. (2004). ITS Evaluation in Classroom: The Case of AMBRE-AWP. Intelligent Tutoring Systems
  133. (1987). Junior Illustrated Encyclopedia The Universe. Grisewood and Dempsey.
  134. (2004). Language Literacy Impacting on Mathematical Literacy.
  135. (2001). Learning from human tutoring.
  136. (1984). Learning how to learn. New York:
  137. (1967). Learning theory and classroom practice.
  138. (2000). Limitations of student control: do students know when they need help? Intelligent Tutoring Systems
  139. (1995). Logical foundations of object-oriented and frame-based languages,
  140. (2002). Macromedia Director 8.5 Shockwave Studio for 3D.
  141. (1976). Mapping cognitive structure: A comparison of methods.
  142. (1998). Maximum entropy models for natural language ambiguity resolution.
  143. (1978). Mind and society: The development of higher mental processes.
  144. (1993). Model of Utterance and Its Use in Cooperative Response Generation,
  145. (1988). Modeling the user in natural language systems.
  146. (2001). Modelling human teaching tactics and strategies for tutoring systems.
  147. (2004). Modelling Scenarios of Cooperation Promoting Human Learning:
  148. (2003). Modelling the Student to Individualise Tutoring in a Web-Based ICALL.
  149. (1995). Mr. Collins: A collaboratively constructed, inspectable student model for intelligent computer assisted language learning.
  150. (2001). Narrative Evolution: Learning From Students’ Talk About Species Variation.
  151. (1983). Natural language versus computer language. In
  152. (1993). New Directions in Reading Assessment.
  153. (1993). No distribution without individual’s cognition: a dynamic interactional view.
  154. (1977). Overlays: A theory of modeling for computer aided instruction.
  155. (1998). Pedagogical agents.
  156. (2001). Peer and parent assisted learning in reading, writing, spelling and thinking skills:
  157. (1990). Plans for Discourse. Intentions in Communication.
  158. (1988). Practical Planning: Extending the Classical AI Planning Paradigm, Morgan-Kaufmann:
  159. (2004). Preliminary English Test. Examination Report. University of Cambridge.
  160. (1993). Principles for evaluating intelligent tutoring systems.
  161. (1996). Principles for the design of cooperative spoken human-machine dialogue.
  162. (1997). Prolog Programming for Artificial Intelligence.
  163. (1992). Psychology the science of mind and behaviour.
  164. (1997). Quantifying qualitative analyses of verbal data: a practical guide.
  165. (2003). Rapid Assessment of Learners' Knowledge in Adaptive Learning Environments.
  166. (1982). Reading and Understanding.
  167. (1998). Reading instruction that works: The case for balanced teaching.
  168. (2005). Real vs. template-based natural language generation: a false opposition.
  169. (1999). Relating dialogue games to information state,
  170. (2000). Report of the National Reading Panel.
  171. (1997). Representing Conversation Acts in a Unified Semantic/Pragmatic Framework,
  172. (2003). Responding to and Recovering from Mistakes During Collaboration,
  173. (1984). Role of the reader's schema in comprehension, learning, and memory. Learning to Read in American Schools. Hillsdale, N.J.: Lawrence Erlbaum Associates.
  174. (2001). Scaffolding, contingent tutoring and computer-supported learning.
  175. (1985). Schema activation and schema acquisition.
  176. (2000). Schema Theory-based instructional design of asynchronous Webbased language courses. In
  177. (2003). Schema theory-based pre-reading tasks: A neglected essential in the ESL reading class. The reading matrix,
  178. (1992). Schemas and motivation. In
  179. (1995). Schemas in problem-solving. London:
  180. (1980). Schemata: The building blocks of cognition. In
  181. (1977). Scripts, Plans, Goals, and Understanding.
  182. (1996). Serving the Strategic Reader: Reader Response Theory and Its Implications for the Teaching of Writing.
  183. (1992). SHERLOCK: A coached practice environment for an electronics troubleshooting job.
  184. (1997). Spontaneous Peer Tutoring from Sharing Student Models
  185. (1989). Stereotypes and user modeling. In: Kobsa, A., Wahlster, W.: User models in dialog systems.
  186. (2000). Stereotypes, Student Models and Scrutability. Intelligent Tutoring Systems
  187. (2001). Strategies and thinking about number in children aged 9-11 years.
  188. (1998). Strategies for teaching students with learning and behavior problems (4 th ed.). Needham Heights,
  189. (1998). Student modeling and machine learning.
  190. (1994). Student Modeling: The Key to Individualized Knowledge-Based Instruction.
  191. Student Question-Asking Patterns in an Intelligent Algebra Tutor.
  192. (1989). Studies of diagnosis and remediation with high school algebra students.
  193. (2003). STyLE-OLM: Interactive open learner modelling.
  194. (2002). Supporting high quality interaction and motivation in the classroom using ICT: the social and emotional learning and engagement in the NIMIS project,
  195. (2002). Supporting learning by opening the student model.
  196. (2004). Supporting learning with open learner models.
  197. (2002). Supporting Young Children Learning to Write Stories Together in a 'Classroom of the Future'.
  198. (1994). Synthesis of research: Reading comprehension: What works.
  199. (2000). System intelligence in constructivist learning.
  200. (2002). Teaching students math problem-solving through graphic representations.
  201. (2000). Teaching tactics in AutoTutor. Proceedings of the workshop on modeling human teaching tactics and strategies at the Intelligent Tutoring Systems
  202. (1993). Techniques of Prolog programming, with implementation of logical negation and quantified goals.
  203. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring.
  204. (1990). The Adaptive Character of Thought. Lawrence Erlbaum.
  205. (1984). The Adult Learner: A Neglected Species (3rd Edition).
  206. (2005). The Andes Physics Tutoring System: Lessons Learned.
  207. (1983). The architecture of cognition.
  208. (1993). The children’s machinge: Rethinking school in the age of the computer.
  209. (1995). The cognitive tutors: lessons learned.
  210. (2000). The conceptual helper: An intelligent tutoring system for teaching fundamental physics concepts. In
  211. (1999). The defining characteristics of intelligent tutoring systems research: ITSs care, precisely.
  212. (1998). The Design of Everyday Things.
  213. (2000). The explanation agent. Intelligent Tutoring Systems
  214. (2000). The Fragmentation of Culture, Learning, Teaching and Technology: Implications for the Artificial Intelligence
  215. (1985). The geometry tutor.
  216. (2002). The Guided Reading Approach Theory and Research. Learning Media Limited, New Zealand. Available online: http://www.thebiddulphgroup.net.nz/Portals/0/GuidedReading.pdf
  217. (2005). The Large Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.
  218. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information.
  219. (2000). The Role of Different Media in Designing Learning Environments.
  220. (1994). The role of student models in learning environments.
  221. (2000). The roles of models in Artificial Intelligence and Education research: a prospective view.
  222. (2000). The social responsibility of computer science specialists for the creative potential of the young generation.
  223. (1994). The State of Student Modelling. Student Modeling: the Key to Individualized Knowledge-based Instruction.
  224. (2001). The tutor's role: An investigation of the power of exchange structure analysis to identify different roles in CMC seminars.
  225. (2001). the Tutoring Reasearch Group.
  226. (2004). the Tutoring Research Group
  227. (2000). The UNIX Consultant Project.
  228. (1996). to Improve the Tools for Educators
  229. (1997). TOBIE: An Implementation of a Domain- Independent ITSArchitecture in the Domain of Symbolic Integration,
  230. (1988). Toward a new approach to predicting text comprehensibility. In
  231. (1996). Toward a Theory of Instruction,
  232. (2000). Toward Computer-Based Support of MetaCognitive Skills: a Computational Framework to Coach Self-Explanation.
  233. (1998). Towards an Axiomatization of Dialogue Acts,
  234. (1997). Towards Flexible Teamwork,
  235. (2001). Towards tutorial dialog to support self-explanation: Adding natural language understanding to a cognitive tutor.
  236. (1990). Understanding and debugging novice programs.
  237. University of Cambridge Local Examinations Syndicate (UCLES) report on the Preliminary English Test project with Realschule pupils in Baden Württemberg.
  238. (1979). User Modeling via Stereotypes.
  239. (1997). Using A Simulated Student for Instructional Design.
  240. (2004). Using fuzzy techniques to model students in web-based learning environments.
  241. (2004). Using knowledge tracing to measure student reading proficiencies.
  242. (2005). Using Learner Focus of Attention to Detect Learner Motivation Factors. User Modelling,
  243. (2000). Using ontological engineering to overcome common AI-ED problems.
  244. (2005). Using Word-level Pitch Features to Better Predict Student Emotions during Spoken Tutoring Dialogues.
  245. (2001). Web delivery of adaptive and interactive Language Tutoring.
  246. (2004). Web-Based Evaluations Showing Differential Learning for Tutorial Strategies Employed by the Ms. Lindquist Tutor.
  247. (2000). What (and how) are we trying to learn? Tutoring Systems that Learn workshop.
  248. (2000). What role do cognitive architectures play in intelligent tutoring systems?
  249. (2002). What works for children with literacy difficulties? The effectiveness of intervention schemes. Research Report RR380. London: Department for Education & Skills. Available online: http://www.dfes.gov.uk/research/data/ uploadfiles/RR380.pdf
  250. (1999). Where is education heading and how about AI?
  251. (1994). Why Some Material is Difficult to Learn.
  252. (1993). Wizard of OZ studies - why and how.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.