Intelligent Tutorial System

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  • 1. INTELLIGENT TUTORIAL SYSTEM & ITS ENHANCEMENT USING EMOTIONAL FEEDBACK1 Soumya Bose 11EC65R09, VIPES
  • 2. SCOPE● Emergence of Intelligent Tutoring System (ITS)● Central framework of ITS.● Different Modules● New Generation ITS (NGITS)● Need of emotional feedback● Facial Emotion Analysis● Conclusions● Future Work 2
  • 3. EXISTING TUTORING SYSTEMS (CAI)● E-learning – Web Based Tutorials using Audio/Video. ● Advantages: Low Cost. Learn when you need. Not constrained by geographical location. ● Disadvantages: Based on Simple Computer Aided Instructions No student teacher feedback. Lack of understanding. Appears to be boring. 3
  • 4. INTELLIGENT TUTORIAL SYSTEM Outgrowth of Computer Aided Instructions with added intelligence. System where teacher-student communication strengthens learning process. Tracks student’s performance. Adaptive system where next state of instruction is dependant upon student’s performance. 4
  • 5. CENTRAL FRAMEWORK OF ITS Traditionally ITS has the following components: DOMAIN PEDAGOGICAL MODULE MODULE STUDENT TASK MODULE ENVIRONMENT Fig.1: ITS Framework 5
  • 6. DOMAIN MODULE Knowledge database. Depends upon the domain in which the ITS is intended to instruct. Domain module is prepared with a view of cognitive psychology of human skill acquisition. Knowledge DECLARATIVE or PROCEDURAL 6
  • 7. Example An ITS to tutor school mathematics. Q. How to evaluate an expression with different arithmetic operands? Declarative: Procedural: Priority1. ‘( operation )’ Evaluation: (3-4) x 18 / 9 + 5 - 8 Step1. A= (3-4) = -1 Priority2. ‘/’ Step2. B= 18 / 9 = 2 Priority3. ‘x’ Step3. C= A x B = -1 x 2 = -2 Priority4. ‘+’ Step4. D= C+5 = -2+5 = 3 Priority5. ‘-’ Step5. Ans.= 3-8 = -5 Combination of both provides effective learning process 7
  • 8. TASK ENVIRONMENT Interface for student - teacher communication GUI may serve the purpose Tutor displays instructions. Problem Statement Tutor Instructions Student gives input through atext editor. Output Window Students Worksheet A simulation response to thestudent forms a feedback to thestudent. Fig.2: Typical Task Environment Feedback helps student inreasoning. 8
  • 9. PEDAGOGICAL / TUTORING MODEL ● Structuring the instructions. At curriculum level it is sequence of information. At problem solving level it can intervene to advise students. ● Next instructions should be on the basis of present state (can be modeled as a tree data structure). ● Present state includes (Logical decision): Current stage of domain module. Knowledge and Emotional state of student. 9
  • 10. INSTRUCTION STRUCTURES1. Primitive linear structure C 1 C 2 C 3 C 4 C n C C 3 42. Branched structure C 1 C 2 C 3 C 7 C n C C 6 83. Multilevel structure C 1 C 2 C 5 C n C C 3 4 C C C C1 C 6 7 8 0 n Cn is quantum of domain C 9 knowledge / information in the nth stage C1 C1 C1 C1 C 10 1 2 3 4 n
  • 11. STUDENT MODULE Record of students knowledge state. Student module is dynamic: Knowledge state is changing (Modeling is complex). e.g: Identifying Operands Identifying Operators Doing operations Evaluating Expressions Solving Equations Statistical methods used for estimating student’s knowledge state (by psychophysicist Green and Swets, 1973) Intelligence of Domain Expert module assess student’s 11 performance
  • 12. ITS DESIGN FLOW TUTORING SYSTEM Modeler Predicted and Preferred behavior Relations and Update Model Student Prototypes Expert Simulator Knowledge Base Student Model Explanation Data Problem Solving Students Current state Situation Tutor Problem Information Advice &Explanation Data Request Problem Student 12 Problem Data
  • 13. A T YPICAL ITS Problem Statement Tutor Instructions 1.Evaluate the expression: Problempriority donest priority are YouSolved!nd operations Evaluate 2 1 ☺nd independent 1st have the operation. st Hint: Calculate 1operationoperation Wrong! Now prioritypriority an done. 2 Z= (3-4) x 18 / 9 + 5 – 8 dependant. the is alsostindependent Write downCalculate 1st expression Operation whichmodified priority Operation.now on the1modified operation Calculate operation expressionoperation. Calculate 1st operation of the first priority operation Output Window Students Worksheet 1. A = -3 5 - 8 = -3 2. B=2 18/9 = 2 3. C= -1 3 - 4 = -1 4. Expression -1 x 2 – 3 5. -2 Wrong -1– 3 =1-2 2 x2= 6. -5 -2 – 3 = -5 13
  • 14. PRACTICALLY IMPLEMENTED SYSTEMS1. The PUMP Algebra Tutor (PAT) (Anderson, Corbett, Koedinger and Pelletier, 1995): Used for tutoring introductory algebra in Pittsburg Schools. Fig.3.: The PAT GUI (Courtesy: Ref.4) 2. The SHERLOCK Project (Lesgold, Laioie, Bunzo and Eggan, 1992; Katz and Lesgold, 1993): It is a practice environment for electronics troubleshooting Fig.4.: The SHERLOCK interface commissioned by Air-Force. 14 (Courtesy: Lawrence Elbaum Associates)
  • 15. SHORTCOMING OF CURRENT SYSTEMS Q. What is the value of force acting on a body of mass 3Kg and moving with a retardation of 4m/s2? Ans. : -12N Confident Ans. : -12N Not-Sure Evaluation by Current systems (Based on knowledge state): In either of the above cases student will be assumed to have a knowledge of ‘FORCE’. 15
  • 16. NEW GENERATION ITS Aimed at developing more adaptive tutors Expert Module evaluates both: Knowledge state Emotional / Mental state Measuring mental state of learner by bio signals analysis: • Facial expressions • Signals from Brain • Electro-dermal signals • ECG signals, etc. 16
  • 17. FACIAL EMOTION ANALYSISChallenges: ● Involves lot of real time image processing. ● Time is the constraint. ● Should be processed in parallel with knowledge evaluation. Hardware / Software co-design approach is adopted for fast processing. 17
  • 18. BASIC STEPS IMAGE GRABBER PRE PROCESSING FEATURE GENETIC EXTRACTION ALGORITHM / SOBEL COARSE LAPLACIAN WAVELET REGION TRANSFORM EDGE SEGMENTATION EDGE DETECTION INTERPRETATION MATCHING 18
  • 19. Object Segmentation and Labeling ● Labeling : Smoothing small variations in intensity. ● Segmentation : Finding edges or sharp transitions. Smoothening with linear resistive network blurs edges of objects. Resistive Fuse networks are used to label and segmenting the image. 19
  • 20. Resistive Fuse Network The resistive fuse acts as a linear resistor for |Vdiff| < Voff Acts as an open circuit for |Vdiff| > Voff Fig.5.: Fuse Resistor The change in voltage at each node can be calculated from Kirchoff’s current law: Vout2,2(t+1) – Vout2,2(t) = v[ ∑ G(Vout i,j – Vout2,2) + σ(Vin2,2- Vout2,2) ] i,j∈N 2,2 Fig.6.: Segmentation Circuit 20
  • 21. The equation v[ ∑ G(Vout i,j – Vout2,2) + σ(Vin2,2- Vout2,2) ] i,j∈N 2,2can be realized in FPGA: Fig.7.: Raster Scan of the image (Courtesy: Ref.7) e = sample pixel a – i = neighbor pixel Smem = pixel data Vin is stored Dmem = Output data Vout is stored LUT1 performs σ evaluation LUT2 performs G (fuse value) per channel (RGB) 21 Fig.8.: FPGA implementation (Courtesy: Ref.7)
  • 22. Gabor Wavelet Transform (GWT)Wavelet function is multiplication of a harmonic function and gaussian function determines frequency and the direction GWT is performed over the segmented object for feature extraction 22 Fig.9.: GWTs of the face (Courtesy: Ref.7)
  • 23. Matching & Interpretation Edges of features (eye / lips / nose): Sobel / Gaussian operator Matching: Genetic Algorithm (GA) •GA is an iterative process . •In each step shapes are Fig.10: Matching eye with an ellipse (Courtesy: Ref.8) matched with known curves •Termination occurs when error is minimized. Fig.11.: Matching lips with an irregular ellipse (Courtesy: Ref.8) Final matched curve parameters (like major axis/ minor axis) are matched with known values to predict emotions: Happy Sad 23 Frustration, etc.
  • 24. CONCLUSIONS ITS has been proved efficient and stronger than simple CAI Involves student in sustained reasoning activity. Problem solving tutor helps conceptual understanding as well as solving real life problems related to a domain. High level GUI attracts students for learning The ability to read the mental state of the learner through facial emotional analysis: Increases Adaptability Repeated instructions can be delivered on the basis of mental satisfaction Helps student in sound understanding However the accuracy of the emotional analysis can be improved significantly adding voice information The main drawback is it is an one-to-one process 24
  • 25. FUTURE DIRECTIONS Different bio-signals processing: Assess mental state of the student more correctly. Electro dermal signal(GSC): Human skin is a weak conductor of electricity. Fig: Galvanic Skin Response (Courtesy: Springer Images) Brain Signals (EEG): Higher frequency beta waves (15-25Hz) and low theta waves implies seriousness ECG can be analyzed to detect stress, low confidence of the student. Real time processing several bio-signals will make the design complex. But even if half the ability of real human tutor is realized the payoff to the society will be substantial 25
  • 26. REFERENCES 1. Abdolhossein Sarrafzadeh, Hamid Gholam Hosseini, Chao Fan, Scott P. Overmyer; Facial Expression Analysis for Estimating Learner’s Emotional State in Intelligent Tutoring Systems; Proceedings of the The 3rd IEEE International Conference on Advanced Learning Technologies (ICALT’03); 2003 2. Morteza BIGLARI-ABHARI, Abbas BIGDELI; FPGA Implementation of Facial Expression Analysis For Intelligent Tutoring Systems; Proceedings of the II International Conference on Multimedia and Information & Communication Technologies in Education; ICTE ‘2003, Spain 3. Sunandan Chakraborty, Devshri Roy, Anupam Basu; Development of Knowledge Based Intelligent Tutoring System; Indian Institute of Technology, Kharagpur, India; 2001 4. M.Helander, T. K. Landauer, P. Prabhu (Eds), Elsevier Science B. V.; Intelligent Tutoring Systems, Handbook of Human-Computer Interaction, Second Edition; 1997 5. Arjen Hoekstra and Joris Janssen; Linking Bio-signals to Transfer of Knowledge Towards Mind-reading ECAs; Faculty of Electrical Engineering, Mathematics and Computer Science University of Twente, The Netherlands 6. Teppei NAKANO, Hiroshi ANDO, Hideaki ISHIZU, Takashi MORIE, Atsushi IWATA; Coarse Image Region Segmentation Using Resistive-fuse Networks Implemented in FPGA; Graduate School of Life Science and Systems Engineering,Kyushu Institute of Technology; Graduate School of Advanced Sciences of Matter, Hiroshima University; Hiroshima Prefecture Industrial Research Institute 7. T. Nakano, T. Morie and A. Iwata; A Face/Object Recognition System Using FPGA Implementation of Coarse Region Segmentation; SICE Annual Conference in Fukui, Fukui University, Japan; 2003 8. M. Karthigayan, M. Rizon, R. Nagarajan and Sazali Yaacob; Genetic Algorithm and Neural 26 Network for Face Emotion Recognition; School of Mechatronics Engineering, Universiti Malaysia Perlis (UNIMAP); 2006