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Type-2 Fuzzy Logic Advisor for Evaluating Students’ Cooperative Training. 3rd UK Workshop on AI in Education. Owais Ahmed Malik King Fahd University of Petroleum & Minerals (KFUPM/HBCC) Saudi Arabia. Overview. Introduction Cooperative Training Assessment

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Type-2 Fuzzy Logic Advisor for Evaluating Students’ Cooperative Training


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    1. Type-2 Fuzzy Logic Advisor for Evaluating Students’ Cooperative Training 3rd UK Workshop on AI in Education Owais Ahmed Malik King Fahd University of Petroleum & Minerals (KFUPM/HBCC) Saudi Arabia mowais@kfupm.edu.sa

    2. Overview • Introduction • Cooperative Training Assessment • Motivation for the Perception-based Assessment • Fuzzy Logic and Fuzzy Logic System • Proposed Model for Cooperative Training Assessment • Experiments and Discussion • Conclusions and Future Directions mowais@kfupm.edu.sa

    3. Introduction • Students’ learning performance is measured by some evaluation means. • Students’ Evaluation • Process of collecting students’ work • Making decision based on collected information • Methods of Evaluation • Objective • Subjective mowais@kfupm.edu.sa

    4. Introduction • How to evaluate a student? mowais@kfupm.edu.sa

    5. Cooperative Training Assessment • Cooperative Training/Internship • An important tool to develop student skills • Some real work experience in industry • A typical assessment for Coop training: • Progress reports • Final report • Presenting the work • External supervisor remarks • Onsite visit by the internal supervisor mowais@kfupm.edu.sa

    6. Cooperative Training Assessment mowais@kfupm.edu.sa

    7. Coop Training Assessment Example Rubric for Presentation Assessment: mowais@kfupm.edu.sa

    8. Motivation for Perception-based Assessment • Assessment of different components of Coop training is subjective. • Communication skills during presentation • Organization of presentation/report • Literary quality of report • Quality of subject matter • Student’s attitude towards work • Enthusiasm and interest in work • Difficult to apply the objective methods to evaluate these student activities mowais@kfupm.edu.sa

    9. Motivation for Perception-based Assessment • Assessment mostly based on perception of an evaluator • Judgment in terms of words (Excellent, Very Good, and Good etc.) • Conventional assessment methods usually do not consider the uncertainties in usage of words • Motivation for type-2 fuzzy set be used to model a word mowais@kfupm.edu.sa

    10. Fuzzy Logic (FL) • Mathematical and Statistical techniques are often unsatisfactory in decision making. • Experts make decisions with imprecise data in an uncertain world. • They work with knowledge that is rarely defined mathematically or algorithmically but uses vague terminology with words. • FL designed to handle imprecision and uncertainty in the measurement process • Methodology of computing with words (CW) • Mimics the perception-based decision making done by humans mowais@kfupm.edu.sa

    11. Fuzzy Logic • Linguistic Variable • Example : Age of a person • Term Set: Young, Middle-aged, Old etc. • Each linguistic term is associated with a fuzzy set • Each term has a defined membership function (MF): • A fuzzy set A in X can be expressed as: or mowais@kfupm.edu.sa

    12. Fuzzy Logic • Example Fuzzy Set for Age: mowais@kfupm.edu.sa

    13. Fuzzy Logic • Example Fuzzy Set for Literary Quality of a Report: mowais@kfupm.edu.sa

    14. Type-2 Fuzzy Set • Imprecise perception-based data can be modelled by using type-2 fuzzy logic • Type-2 fuzzy set is 3-dimensional representation • Type-2 fuzzy sets help us to deal with the uncertainty • Footprint of Uncertainty (FOU): • Bounded region in the primary membership function of a type-2 fuzzy set • 2-Dimensional depiction of type-2 fuzzy sets • Upper and Lower Membership Functions For more details:Mendel J. M., Uncertain Rule-Based Fuzzy Logic Systems, Prentice-Hall, Upper Saddle River, NJ 07458, (2001) mowais@kfupm.edu.sa

    15. FOUs, Upper and Lower MFs mowais@kfupm.edu.sa

    16. Type-2 Fuzzy Logic System mowais@kfupm.edu.sa

    17. Proposed Model for Cooperative Training Assessment • Based on knowledge mining (knowledge engineering) methodology • Information extracted in the form of IF-THEN rules from evaluators (experts) • Rules are modelled using fuzzy logic system • Used as Fuzzy Logic Advisor (FLA) • Two-stage FLA based on interval type-2 fuzzy logic • Each assessment component is evaluated using an independent FLA • Results of these FLAs are combined to calculate the final grade mowais@kfupm.edu.sa

    18. Structure of Proposed Model mowais@kfupm.edu.sa

    19. Input/Output Fuzzy Sets for Proposed Model • Input (criteria of assessment) and output (evaluation) attributes divided into four fuzzy sets • Type-2 fuzzy sets: Excellent, Good, Fair and Poor • Survey results for labels of fuzzy sets mowais@kfupm.edu.sa

    20. Membership Functions for Proposed Model • FOUs forLiterary Quality of a Report: mowais@kfupm.edu.sa

    21. Rules Formulation • All possible combinations of antecedent fuzzy sets are employed • Consequents of rules are provided by the evaluators (experts) • Each rule has a histogram of responses • Number of rules depends on the number of inputs and fuzzy sets associated with them • Example rule for Coop Evaluation FLA Rl: IF Final Report is Excellent AND Progress Report is Good AND Final Presentation is Fair AND External Evaluation is Excellent THEN GRADE is (VERY GOOD) mowais@kfupm.edu.sa

    22. Type-1 FLA (Individual FLA) mowais@kfupm.edu.sa

    23. Partial Histogram of Survey Responses for Final Report Evaluation mowais@kfupm.edu.sa

    24. Experiments and Discussion Comparison for Individual and Type-1 Consensus FLAs mowais@kfupm.edu.sa

    25. Experiments and Discussion Comparison for Individual and Type-2 Consensus FLAs (50% uncertainty) mowais@kfupm.edu.sa

    26. Experiments and Discussion Comparison for Individual and Type-2 Consensus FLAs (100% uncertainty) mowais@kfupm.edu.sa

    27. Conclusions • Type-2 fuzzy sets model the perception-based evaluation • Proposed model has the potential to capture the uncertainties in subjective evaluation • Successful testing for small group of students • Provides more accurate evaluation of a student as compared to existing method mowais@kfupm.edu.sa

    28. Future Directions • Testing of the system for large number of students • Investigating the use of the system for other courses/situations e.g. assessing group projects etc. • Type-2 fuzzy sets to be tested for representing final grades • Deciding the optimal number of linguistic input/output variables for assessment components • Working with non-singleton input from evaluators mowais@kfupm.edu.sa

    29. Thank You mowais@kfupm.edu.sa

    30. Question/Answers mowais@kfupm.edu.sa