A PEDAGOGICAL FRAMEWORK FOR INTEGRATING INDIVIDUAL LEARNING STYLE INTO AN INTELLIGENT TUTORING SYSTEM

A PEDAGOGICAL FRAMEWORK FOR INTEGRATING INDIVIDUAL LEARNING STYLE INTO AN INTELLIGENT TUTORING SYSTEM BY SHAHIDA M PARVEZ PRESENTED TO THE GRADUATE AND RESEARCH COMMITTEE OF LEHIGH UNIVERSITY IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN COMPUTER SCIENCE LEHIGH UNIVERSITY DECEMBER 2007 Approved and recommended for acceptance as a dissertation in partial fulfillment of the requirements for the degree of Doctor of Philosophy _ Date _ Accepted Date _ Professor Glenn D Blank Dissertation Advisor Committee Chair Computer Science and Engineering, Lehigh University Committee Members: _ Professor Hector Munoz-Avila Computer Science and Engineering, Lehigh University _ Professor Jeff Heflin Computer Science and Engineering, Lehigh University _ Professor Alec Bodzin College of Education, Lehigh University ii TABLE OF CONTENTS ACKNOWLEDGEMENT .iv LIST OF TABLES v LIST OF FIGURES vi ABSTRACT INTRODUCTION 3.1 Learning Styles 3.2 Adapting feedback to Learning Style in ITS 10 3.3 Hypothesis 14 14 3.4 Research Questions 15 3.5 Contributions 17 RELATED WORK 19 4.1 Learning Style theories 19 4.2 Felder-Silverman learning style model 27 4.3 Application of learning styles in adaptive educational systems .36 4.4 Intelligent Tutoring systems and feedback mechanisms 41 4.5 Pedagogical Modules in ITS 52 PEDAGOGICAL ADVISOR IN DESIGNFIRST-ITS 57 5.1 Feedback 61 LEARNING STYLE BASED PEDAGOGICAL FRAMEWORK 66 6.1 Feedback architecture 66 6.2 Feedback Generation Process 75 PEDAGOGICAL FRAMEWORK PORTABILITY .94 FEEDBACK MAINTENANCE TOOL 104 EVALUATION 113 9.1 Feedback evaluation .113 9.2 Learning style feedback effectiveness evaluation 117 9.3 Object Oriented Design Tutorial Evaluation 126 9.4 Feedback maintenance tool evaluation 127 10 CONCLUSIONS .130 11 FUTURE WORK 134 12 BIBLIOGRAPHY .135 iii ACKNOWLEDGEMENT I wish to express my sincere gratitude to my advisor, Dr Glenn D Blank, for giving me the opportunity to work on this research project and for the guidance and encouragement he has given me throughout my research I would also like to thank my Ph.D committee members: Dr Hector Munoz-Avila, Dr Alec Bodzin and especially Dr Jeff Heflin for his guidance and help during my time at Lehigh I am grateful to many individuals at Lehigh University who helped me during my studies at Lehigh I am grateful to Fang Wei, Sharon Kalafut and especially Sally H Moritz for helping me in my research and participating in my evaluation studies I am also grateful to many of my fellow graduate students for their encouragement and support when things did not go well Finally I would like to thank my parents, my daughters and especially my husband for their unconditional love and support I dedicate this dissertation to my late father for being my inspiration, to my mother for her tireless prayers and to my husband for his consistent encouragement, support and love This research was supported by National Science Foundation (NSF) and the Pennsylvania Infrastructure Technology Alliance (PITA) iv LIST OF TABLES Table – Characteristics of typical learners in Felder-Silverman learning style model .29 Table – Felder-Silverman model dimensions / learning preferences 67 Table – Learning style dimension and feedback component mapping 74 Table – Concept/related concept 98 Table – Concept/action/explanation phrases 98 Table – Error codes/concept/explanation phrases 98 Table – Student action record .99 Table – Feedback evaluation .115 Table – No-feedback group data 119 Table 10 –Textual-feedback group data 120 Table 11 – Learning-style-feedback group data .121 Table 12 – Summary statistics .122 Table 13 – Pedagogical advisor evaluation 125 Table 14 – Tutorial evaluation .126 Table 15 – Feedback maintenance tool evaluation 128 v LIST OF FIGURES Figure 3-1 DesignFirst-ITS Architecture 58 Figure 4-2 Feedback component attributes 73 Figure 4-3 Definition Component 74 Figure 4-4 Picture component 74 Figure 4-5 Datatypes 75 Figure 4-6 Attributes 76 Figure 4-7 Feedback generation process 76 Figure 4-8 Feedback message 82 Figure 4-9 Substitution process 83 Figure 4-10 Visual feedback examples 86 Figure 4-11 Visual/sequential 87 Figure 4-12 Visual/global 87 Figure 4-13 Visual/global 88 Figure 4-14 Visual/sequential 88 Figure 4-15 Verbal/sequential 91 Figure 4-16 Verbal/global .91 Figure 4-17 Visual/sensor 92 Figure 4-18 Visual/active 93 Figure 5-19 Feedback components 101 Figure 6-20 Feedback Maintenance Interface 105 Figure 6-21 Input Advice feedback-1 106 Figure 6-22 Input Advice feedback-2 106 Figure 6-23 Input Advice feedback-3 107 Figure 6-24 Input Advice feedback-4 108 Figure 6-25 Input Advice Feedback-5 109 Figure 6-26 Input New Concept .110 Figure 6-27 View/modify/delete tutorial feedback-1 .110 Figure 6-28 View/modify/delete tutorial fdbck-2 111 Figure 6-29 View/delete concept/related concept-1 .112 Figure 7-30 Feedback evaluation 116 Figure 7-31 Learning Gain – No-feedback group 119 Figure 7-32 Learning gains – Textual-Feedback group 120 Figure 7-33 Learning gains – learning-style-feedback group .121 Figure 7-34 Learning gains for all three groups 122 Figure 7-35 Pedagogical advisor survey 125 Figure 7-36 Tutorial evaluation - Questions 1-5 127 Figure 7-37 Feedback maintenance tool evaluation 129 vi ABSTRACT An intelligent tutoring system (ITS) provides individualized help based on an individual student profile maintained by the system An ITS maintains a student model which includes each student’s problem solving history and uses this student model to individualize the tutoring content and process ITSs adapt to individual students by identifying gaps in their knowledge and presenting them with content to fill in these gaps Even though these systems are very good at identifying gaps and selecting content to fill them; however, most of them not address one important aspect of the learning process: the learning style of a student Learning style theory states that different people acquire knowledge and learn differently Some students are visual learners; some are auditory learners; others learn best through hands-on activity (tactile or kinesthetic learning) The focus of this research is to integrate the results of learning style research into the pedagogical module of an ITS by creating a learning style based pedagogical framework that would generate feedback that is specific to the learner This integration of individual learning styles will help an ITS become more adapted to the learner by presenting information in the form best suited to his or her needs This framework has been implemented in the pedagogical module of DesignFirst-ITS, which help students learn object-oriented design This pedagogical module assists the students in two modes: the advice/hint mode, which provides real time feedback in the forms of scaffolds as the student works on his/her design solution, and the lesson/tutorial mode, which tutors students about specific concepts 2 INTRODUCTION Intelligent tutoring systems (ITS) are valuable tools in helping students learn instructional material both inside and outside of the classroom setting These systems augment classroom learning by providing an individualized learning environment that identifies gaps and misconceptions in the student’s knowledge to provide him/her with appropriate information to correct these misconceptions and fill in the gaps A typical ITS contains three main components: the expert module, the student model, and the pedagogical module The expert module contains the domain knowledge and methods to solve problems; the student model keeps track of the student knowledge; and the pedagogical module contains instructional strategies that it uses to help the student learn The purpose of the ITS is to replicate human tutoring behavior and provide individualized help to each learner A human tutor is able to observe various student problem solving behaviors, identifies deficiencies in student’s knowledge, and helps the student in overcoming these deficiencies Likewise, ITSs adapt to individual students by identifying gaps in their knowledge bases in terms of their problem solving behavior and then presenting them with appropriate content to bridge the gaps Different ITSs use different methodologies, such as comparing student solutions to a predefined expert solution[s], or by the errors in the student solutions to determine how well the student knows the domain concepts Once the system knows what the student needs help with, it can provide guidance by way of specific feedback Even though these systems are very good at identifying gaps and selecting content to fill in these vacancies, they only address one dimension of adaptability the knowledge level of the student This being the case, students with similar knowledge gaps are presented the same information content in the same format The individual characteristics and preferences of the student that impact his/her learning are not taken into account while individualizing the tutoring content and process These individual characteristics and preferences of the students are dubbed individual learning styles 3.1 Learning Styles The term learning style refers to individual skills and preferences that affect how a student perceives, gathers, and processes information (Jonassen & Grabowski, 1993) Each individual has his/her unique way of learning material For instance, some students prefer verbal input in the form of written text or spoken words, while others prefer visual input in the form of items such as maps, pictures, charts, etc Likewise, some students think in terms of facts and procedures while, others think in terms of ideas and concepts (Felder, 1996) Researchers have identified individual learning styles as a very important factor in effective learning Jonassen and Grabowski (1993) describe learning as a complex process that depends on many factors, one of which is the learning style of the student Learning style research became very active in the 1970’s and has resulted in over 71 different models and theories Some of the most cited theories are Myers-Briggs Type Indicator (Myers, 1976), Kolb’s learning style theory (Kolb, 1984), Gardner’s Multiple Intelligences Theory, (Gardner, 1983) and Felder-Silverman Learning Style Theory (Felder & Silverman, 1988; Felder, 1993) Even though there are so many different Figure 7-36 Tutorial evaluation - Questions 1-5 According to the data, the OO tutorial received the average rating of (highest) for its appeal, and ease of use The tutorial layout received an average rating of 4.5 because the content index for the verbal/sequential did not present concepts in the order of hierarchy The concept clarity received an average rating of 4.5 because the visual illustration for one of the OO concepts did not provide a complete definition The overall rating for the tutorial received an average of 4.5 The evaluation results demonstrate that the OO tutorial is useful in helping students learn object oriented concepts One thing to bear in mind is that object oriented design concepts are not trivial concepts and one cannot expect a student to learn and comprehend these concepts just by going through the tutorial The intent of this tutorial is to be used as a tool like the DesignFirst-ITS system along with a classroom instruction 9.4 Feedback maintenance tool evaluation Ms Moritz and Prof Kalafut also evaluated the feedback maintenance tool that is designed to allow an instructor to add/delete/modify feedback in the system They were provided with the following: 127 Instructions on how to access the feedback maintenance tool Instructions on what to evaluate They were asked to evaluate a The online user guide that provides an introduction to the pedagogical framework and its various components b Different options provided by the tool Their own login IDs to log into the system A questionnaire to fill out while they evaluated the tool The questionnaire contained questions that pertained to different aspects of the tools Some of these questions were yes/no questions while others required using a scale of - (1 = easy, 5=difficult) The results of the evaluation are summarized below Quest # Question FMT documentation helpful FMT interface easy to use All FMT options work Level of difficulty for adding advice mode feedback Level of difficulty for adding tutorial mode feedback Difficulty level for adding new concepts Able to add new concepts Able to view newly added advice Average Rating / answer yes yes yes 1.5 1.5 1.5 yes yes Table 15 – Feedback maintenance tool evaluation 128 Figure 7-37 Feedback maintenance tool evaluation The evaluation results show that the feedback maintenance tool documentation was helpful to instructors in understanding the pedagogical framework and the feedback components The instructors also found the FMT interface easy to use and were able to easily view and add new feedback The average rating for level of difficulty for adding new concept / new feedback was 1.5 The evaluation results demonstrate that the feedback maintenance tool is easy to use for adding/modifying feedback for the advice and tutorial modes The results also show that the tool works well The accompanying documentation is helpful in understanding the pedagogical framework The evaluation results suggest that the pedagogical framework and the accompanying tools are well designed and are effective in integrating learning styles into an ITS The results also suggest that students realize learning gains when they are presented with information in the form that matches their learning styles These evaluation studies set a stage for more comprehensive studies involving bigger sample size and evaluating the system at more diverse level 129 10 CONCLUSIONS Each individual has a preference in which he/she prefers to receive and process information (Jonassen & Grabowski, 1993) Learning style is a term that is used to describe these preferences Further research has shown that accommodating an individuals learning style helps the learning process (Felder, 1996) As a result, learning style research has produced many different types of theories/models, some of which have been applied in various settings, such as academia and industry to provide learning support The goal of this dissertation was to use learning style research to enhance the adaptability of an intelligent tutoring system Current intelligent tutoring systems not take an individuals’ learning style into account while providing learning support However, there are educational systems, such as adaptive hypermedia systems, that provide learning style adaptability, but there is no standard methodology for one to follow to integrate learning styles into a system This dissertation has contributed towards providing a standard methodology to incorporate learning styles into an intelligent tutoring system by creating a domain independent pedagogical framework based on Felder-Silverman learning style model The following section will describe the methodology that was used to create this pedagogical framework using the research questions posed in chapter one How can learning style based feedback architecture be created using a learning style model? This question was answered by using the various dimensions of the FelderSilverman learning style model to create different types of feedback components 130 The Felder-Silverman learning style model has four dimensions: sensing/intuitive, visual/verbal, active/reflective, and sequential/global Each component type contains information in the form that matches the style encompassed by each dimension of the model For example, the picture component contains information in visual form, such as pictures, diagram, etc This component is useful for a visual type learner while the definition component contains information in the form of text, which is preferred by a verbal learner The various types of components are picture, definition, application, scaffold, relation, question, exercise, and example Each of these components has certain attributes that identify each component and its content How can this feedback architecture be used to create learning style based feedback? A feedback generation process (FGP) was developed, which uses the feedback components to generate feedback that matches an individuals learning style The FGP creates feedback by using inputs, component attributes, and individual learning styles to automatically generate feedback from these components How can this feedback architecture be generalized to make it domain independent? This feedback architecture is generalized because it uses feedback components that are based on a learning style model and not on a particular domain The component attributes make it possible to create these components in any domain 131 How can this feedback architecture be made extendible, such that the instructor can easily add/update the feedback without requiring any help from the ITS developer? This feedback architecture was made extendible by developing a feedback maintenance tool (FMT) that allows an instructor to update, modify, and/or add new feedback to the architecture FMT is a web-based, easy to use tool that can be used to view, update, and/or add feedback in the architecture How can this feedback architecture be used to incorporate multiple pedagogical strategies into an ITS? The two strategies that were considered for this dissertation were to learn-by-doing and learn-by-example The learning-by-doing strategy was incorporated to some extent by providing the student hints about erroneous action and not an answer Since learners are not given answers at any level of feedback, they are forced to think for themselves The learning-by-example strategy was implemented by using examples in explaining and illustrating domain concepts How effective is this learning style ITS in helping students understand the domain knowledge? This question was answered by implementing this pedagogical framework in DesignFirst-ITS, an ITS that provides learning support to novices learning object oriented design The system was evaluated with high school students that were divided into three groups, no-feedback group, textual-feedback group, and learning-style-group All three groups of students used the system and received different types of feedback: no feedback, feedback in the form of text only, and 132 learning style feedback that matched the individuals learning style The students were given a pre-test before using the system and a pos-test after using the system The evaluation results showed no significant learning gains for no-feedback group and textual-feedback group but statistical significant learning gains for learningstyle-feedback group The following are the contributions made by this dissertation It provides a novel domain-independent pedagogical framework to integrate learning styles into an intelligent tutoring system This framework provides a standard methodology for ITS developers to adapt the feedback to the needs of individual learners This research contributed towards creating a pedagogical advisor in Design FirstITS, an ITS for teaching object-oriented design and programming The DesignFirst-ITS was used to provide learning support to high school students, who are novices to object oriented design and programming It was found to be effective in helping them realize learning gains This research provides a novel, graphic, user interface for extending the feedback network This interface is very important because it makes it easy to update and add feedback to the system making it more flexible The object-oriented design tutorial can be used by an instructor as a resource to reinforce object-oriented concepts to introductory class students 133 11 FUTURE WORK To a large extent, one of the goals of this dissertation was to facilitate future work by developing a standard methodology that would make it easier to integrate learning styles into an intelligent tutoring system The domain independent pedagogical framework that is the focus of this dissertation makes it possible for ITS designers to integrate learning styles into an intelligent tutoring system without starting from scratch As part of this dissertation, this framework was implemented in DesignFirst-ITS, in object oriented design, and programming domain It was found to be effective in helping students learn domain concepts The next logical step would be to implement this framework in an intelligent tutoring system (ITS) in another domain and evaluate the effectiveness of learning style based feedback The feedback maintenance tool that is part of this pedagogical framework can be used to create and/or update feedback components for the new ITS As with any tool, this feedback tool can be extended to create and maintain domain knowledge information that is used by other components of the system 134 12 BIBLIOGRAPHY Baghaei, N., Mitrovic, A.(2005) COLLECT-UML: Supporting individual and collaborative learning of UML class diagrams in a constraint-based tutor In: Rajiv Khosla, Robert J Howlett, Lakhmi C Jain (eds) Proc KES 2005, Springer-Verlag, LCNS 3684, pp 458-464 Bajraktarevic, N., Hall, W., Fullick, P (2003) Incorporating learning styles in hypermedia environment: Empirical evaluation, Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems, 41-53 Blank, G D., Moritz, S H., DeMarco, D W (2005) Objects or Design First? Nineteeth European Conference on Object-Oriented Programming (ECOOP 2005), Workshop on Pedagogies and Tools for the Teaching and Learning of Object Oriented Concepts, Glasgow, Scotland Bloom B S (1968) Learning for mastery In Evaluation Comment, 1(2), Los Angeles: University of California at Los Angeles, Center for the Study of Evaluation of Instructional Programs, 1-5 Brown, E.J, Brailsford, T (2004) Integration of learning style theory in an adaptive educational hypermedia (AEH) system Short paper presented at ALT-C 2004, Exeter, 1416 Brusilovsky, P (1999) Adaptive and Intelligent Technologies for Web-based Education In C Rollinger and C Peylo (eds.), Special Issue on Intelligent Systems and Teleteaching, Künstliche Intelligenz, 4, 19-25 Brusilovsky, P (2001) Adaptive Hypermedia User Modeling and User-Adapted nstruction, 11(1-2), 87-110 Brusilovsky, P (1996) Methods and techniques of adaptive hypermedia User Modeling and User-Adapted Interaction, (2-3), pp 87-129 Burns, H L, C G Capps (1988) Foundations of Intelligent Tutoring Systems, Lawrence Erlbaum Associates, Hillsdale, NJ Carver, C A., Howard, R A., Lane, W D (1999) Enhancing Student Learning through Hypermedia Courseware and Incorporation of Learning Styles IEEE Transactions on Education, 42(1), 22-38 135 Claxton, D S., Murrell, P (1987) Learning styles: Implications for improving educational practices (Report No 4) Washington: Association for the Study of Higher Education Corbett, A., Koedinger, K., Anderson, J (1992).LISP Intelligent Tutoring System: Research in Skill Acquistion In J.H Larkin and R.W Chabay, eds Computer-assisted Instruction and Intelligent Tutoring Systems: Shared Goals and Complementary Approaches Hillsdale, NJ: Erlbaum, pp 73 – 109 Corbett, A T., Anderson, J R (2001) Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes Proceedings of CHI 2002, Human Factors in Computing Systems (March 31 - April 5, 2001, Seattle, WA, USA), ACM, 2001 245-252 Cornett, C E (1983) What you should know about teaching and learning styles Bloomington, IN: Phi Delta Kappa (ERIC Document Reproduction Service No ED228235) Curry, L (1983) An organization of learning style theory and constructs ERIC Document, 235, 185 Curry, L (1987) Integrating concepts of cognitive or learning style: A review with attention to psychometric standards Ottawa, Ontario, Canada: Canadian College of Health Service Executives Dempsey, J.V and Sales, G.C (1994) Interactive Instruction and Feedback Englewood Cliffs, NJ: Educational Technology Publications Dunn, R., Dunn, K (1978) Teaching students through their individual learning styles: A practical approach Reston, VA: Reston Publishing Dunn R., Dunn, K., Price, G E (1979) Learning Style Inventory Lawrence, KS: Price Systems Dunn R., Dunn, K., Price, G E (1989a) Learning Style Inventory Lawrence, KS: Price Systems Dunn R., Dunn, K., Price, G E (1982) Productivity, Environmental Preference Survey Lawrence, KS: Price Systems Dunn R., Dunn, K., Price, G E (1989b) Productivity, Environmental Preference Survey Lawrence, KS: Price Systems Felder, R M., Silverman L K., (1988) Learning and Teaching Styles Engineering Education, 674-681, April 1988 136 Felder, R M., (1996) Matters of Style, ASEE Prism, 6(4), 18-23 Felder, R M., Felder G M., Dietz E J (1998) A longitudinal study of engineering student performance and retention V Comparisons with traditionally-taught students Journal of Engineering Education, 469-480, Oct 1998 Felder, R., (1993) Reaching the second tier: Learning and teaching styles in college science education Journal of College Science Teaching, 23(5), 286-290 Felder, R.M., Brent, R (2005) ‘Understanding Student Differences’ Journal of Engineering Education, Vol 94, No 1, pp 57–72 Felder, R M., Solomon, B A (2001) Learning styles and strategies [WWW document] URL http://www.ncsu.edu/effective_teaching/ILSdir/styles.htm North Carolina State University Felder R.M., Spurlin J.E (2005) "Applications, Reliability, and Validity of the Index of Learning Styles," Intl J Engr Education, 21(1), 103-112 Ford, N., Chen, S Y (2000) Individual differences, hypermedia navigation and learning: An empirical study Journal of Educational Multimedia and Hypermedia, 9(4), 281-312 Ford, N., Chen, S Y (2001) Matching/mismatching revisited: an empirical study of learning and teaching styles British Journal of Educational Technology, 32(1), 5-22 Gardner, H (1983) Frames of Mind New York: Basic Books Gardner, H (1993) Multiple Intelligences: The theory in practice New York: Basic Books Garsha, A (1972) “Observations on Relating Teaching Goals to Student Response Style and Classroom Methods.” American Psychologist 27:244-47 Gertner, A., VanLehn, K.(2000) Andes: A Coached Problem Solving Environment for Physics In G Gauthier, C Frasson and K VanLehn (Eds), Intelligent Tutoring Systems: 5th International Conference Berlin: Springer (Lecture Notes in Computer Science, Vol 1839), pp 133-142 Gilbert, J E., Han, C Y (1999) Adapting Instruction in search of ‘a significant difference’ Journal of Network and Computer Applications, 22(3), 149-160 Gilbert, J E., Han, C Y (1999a) Arthur: Adapting Instruction to Accommodate Learning Style Paper presented at the World Conference of the WWW and Internet, WebNet'99, Honolulu, USA, 433-438 137 Gilbert, J E., Han, C Y (2002) Arthur: A Personalized Instructional System Journal of Computing in Higher Education, 14(1), 113-129 Gilman, D A (1969) Comparison of several feedback methods for correcting errors by computer-assisted instruction Journal of Educational Psychology, 60(6), 503 — 508 Goodman, B., Soller, A., Linton, F., and Gaimari, R (1997) Encouraging Student Reflection and Articulation using a Learning Companion Proceedings of the AI-ED 97 World Conference on Artificial Intelligence in Education, Kobe, Japan, 151-158 Graesser, A., VanLehn, K., Rose, C., Jordan, P and Harter, D (2001) Intelligent tutoring systems with conversational dialogue, AI Mag., vol 22, pp 39 51 Graesser, A C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreus, R., and the Tutoring Research Group (1999) AutoTutor: A Simulation of a Human Tutor Journal of Cognitive Systems Research 1(1):35-51 Haukoos, G., and Satterfield, R (1986) “Learning Style of Minority Students (Native Americans) and Their Application in Developing Culturally Sensitive Science Classroom.” Community/Junior College Quarterly 10: 193-201 Jonassen, D H., Grabowski, B L (1993) Handbook of Individual Differences, Learning and Instruction Lawrence Erlbaum Associates Keefe, J W (1979) Student learning styles: Diagnosing and prescribing programs Reston, VA: National Association of Secondary School Principals Kelly, D., and Tangney, B (2002) Incorporating Learning Characteristics into an Intelligent Tutor Paper presented at the Sixth International Conference on Intelligent Tutoring Systems, ITS'02., Biarritz, France, 729-738 Koedinger, K (2001) Cognitive Tutors as Modeling Tools and Instructional Models in Smart Machines in Education Forbus, Kenneth and Feltovich, Paul, Eds AAAI Press/MIT Press, Cambridge, MA pp 145-167 Koedinger, K., Anderson, J., Hadley, W., Mark, M.(1997) Intelligent Tutoring Goes to School in the Big City International Journal of Artificial Intelligence in Education, 8(1), pp 30-43 Kolb, D A (1984) Experiential learning: Experience as the source of learning and development Englewood Cliffs, NJ: Prentice-Hall 138 Kulhavy, R W., Stock, W A (1989) Feedback in written instruction: The place of response certitude Educational Psychology Review, 1(4), 279 — 308 Kulik, J A., Kulik, C C (1988) Timing of feedback and verbal learning Review of Educational Research, 58(1), 79 — 97 Lester, J C., Converse, S A., Stone, B A., Kahler, S E., Barlow, S T (1997) Animated pedagogical agents and problem-solving effectiveness: A large-scale empirical evaluation In Proceedings of the Eighth World Conference on Artificial Intelligence in Education, 23-30 IOS Press Lester, J., Callaway, C., Grégoire, J., Stelling, G., Towns, S., Zettlemoyer, L (2001) Animated Pedagogical Agents in Knowledge-Based Learning Environments In Smart Machines in Education: The Coming Revolution in Educational Technology, Forbus, K., & Feltovich, P (Eds.), pp 269-298, AAAI/MIT Press, Menlo Park Lewis, M.W., Anderson, J R (1985) Discrimination of operator schemata in problem solving: Procedural learning from examples Cognitive Psychology, 17, 26-65 Litzinger T.A., Lee S.H , Wise J.C , Felder R.M (2005).A Study of the Reliability and Validity of the Felder-Soloman Index of Learning Styles, Proceedings, 2005 ASEE Annual Conference, American Society for Engineering Education Livesay, G., Dee, K., Felder, R M., Hites, L., Nauman, E., O’Neal, E (2002) Statistical evaluation of the index of learning styles Proceedings of the 2002 American Society for Engineering Education Annual Conference and Exposition, Montreal, Quebec, Canada Merril D C., Reiser B J., Ranney M., Trafton J.G (1992) Effective tutoring techniques: A comparison of human tutors and intelligent tutoring systems The Journal of the Learning Sciences, 3(2):277 305 Messick, S and Associates (Ed.) (1976) Individuality in Learning (San Fransisco, Jossey-Bass) Mitrovic, A., Ohlsson, S (1999) Evaluation of a constraint-based tutor for a database language, Int J Artificial Intelligence in Education Moritz, S., Blank, G (2005) A Design-First Curriculum for Teaching Java in a CS1 Course, SIGCSE Bulletin (inroads), June Mory E H (1996) Feedback Research In D H Jonassen (Ed.), Handbook of research for educational communications and technology New York: Simon and Schuster Macmillan 139 Myers, I B (1976) Introduction to Type Gainsville, Fla.:Center for the Application of Psychological Type Ohlsson, S (1996) Learning from Performance Errors Psychological Review 103(2) 241-262 Paredes, P., Rodriguez, P (2002) Considering Learning Styles in Adaptive Web-based Education Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics en Orlando, Florida, 481-485 Person, N K., Graesser, A C., Kreuz, R J., Pomeroy, V and the Tutoring Research Group (2001) Simulating human tutor dialgue moves in AutoTutor International Journal of Artificial Intelligence in Education, 12:23 39 Reichmann, S., Grasha, A (1974) “A Rational Approach to Developing and Assessing the Construct Validity of a Student Learning Style Scale Instrument.” Journal of Psychology 87: 213-23 Roper, W J (1977) Feedback in computer assisted instruction Programmed Learning and Educational Technology, 14(1), 43 — 49 Rosati, P (1999) Specific differences and similarities in the learning preferences of engineering students Proceedings of the 29th ASEE/IEEE Frontiers in Education Conference, (Session 12c1), San Juan, Puerto Rico Sims, R R., Sims, S J (1995) The Importance of Learning Styles: Understanding the Implications for Learning, Course Design, and Education Westport, CT: Greenwood Press Smith, N G., Bridge, J., Clarke, E (2002) An evaluation of students’ performance based on their preferred learning styles In Pudlowski, Z J (Ed.), Proceedings of the 3rd UNESCO International Center for Engineering Education (UICEE) Global Congress on Engineering Education, 284-287 Glasgow, Scotland Specht, M., Oppermann, R (1998) ACE: Adaptive CourseWare Environment New Review of HyperMedia and MultiMedia, 4, 141-161 Stone, B., Lester, J., (1996) Dynamically sequencing an animated pedagogical agent In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 424431 Suraweera, P., and Mitrovi c, A (2002) Kermit: a constraint-based tutor for database modeling In Proc 6th Int Conf on Intelligent Tutoring Systems ITS 140 Thomas, L., Ratcliffe, M., Woodbury, J., Jarman, E (2002) Learning styles and performance in the introductory programming sequence, Proceedings of the 33rd SIGCSE technical symposium on Computer science education (pp 33-37) Cincinnati, Kentucky: ACM Press Triantafillou, E., Pomportsis, A., Demetriadis, S (2003) The design and the formative evaluation of an adaptive educational system based on cognitive styles Computers and Education, 41, 87-103 VanLehn, K (1996) Conceptual and meta learning during coached problem solving In Frasson, C.; Gauthier, G.; and Lesgold, A., eds., Proceedings of the 3rd International Conference on Intelligent Tutoring Systems ITS ’96 Springer 29–47 Van Zwanenberg, N., Wilkinson, L J., Anderson, A (2000) Felder and Silverman’s Index of Learning Styles and Honey and Mumford’s Learning Styles Questionnaire: How they compare and they predict academic performance? Educational Psychology,Vol 20 (3), pp 365-381 Wager, W and Wager, S (1985) Presenting questions, processing responses, and providing feedback in CAI Journal of Instructional Development, 8(4),2-8 Weber, G., and Brusilovsky, P (2001) ELM-ART: An Adaptive Versatile System for Web-based Instruction In International Journal of Artificial Intelligence in Education 12, pp 351-384 Weber, G., and Möllenberg, A (1995) ELM programming environment: A tutoring system for LISP beginners In K F Wender, F Schmalhofer, & H.-D Böcker (Eds.), Cognition and computer programming Norwood, NJ: Ablex Publishing Corporation Weber, G., Schult, T (1998) CBR for Tutoring and Help Systems In Case-Based Reasoning Technology: From Foundations to Applications Lecture Notes in Artificial Intelligence 1400, Springer Verlag Wei, F., Moritz, S., Parvez, S., and Blank, G D (2005) A Student Model for ObjectOriented Design and Programming The Tenth Annual Consortium for Computing Sciences in Colleges Northeastern Conference, Providence, RI Witkin, H A (1954) Personality through perception: An experimental and clinical study New York: Harper Zywno, M.S (2003) “A Contribution of Validation of Score Meaning for FelderSoloman’s Index of Learning Styles.” Proceedings of the 2003 Annual ASEE Conference Washington, DC: ASEE 141 ... research was to create a pedagogical framework based on the Felder-Silverman learning style model that can serve as the basis for creating a pedagogical system that supports individual learning styles... integrate learning style into an adaptive educational system The rationale behind creating such a pedagogical framework is to provide a framework that developers can use to create learning style- based... are unstable and relatively easy to change European and Australian researchers have regarded observed learning behaviors as indicative of underlying psychological characteristics that are stable