Megat Johari Megat Mohd Noor Professor, Malaysia Japan International Institute of Technology &

1. Workshop on Improving Education Deliverance and Attainment Standards Through Transforming Academic Institutions Towards OBE System Megat Johari Megat Mohd Noor Professor, Malaysia Japan International Institute of Technology & Assoc Director (International Affairs), Engineering Accreditation Department Karachi & Peshawar, Pakistan 27 - 30 October 2015

2. Programme

3. Outlines • Introduction • Taxonomy • Programme Outcomes • Knowledge Profile • Level of Problem Solving • Exemplars • Conclusion

4. Challenges • Paradigm Shift – Outcome & Quality • Maintain Fundamentals while Encourage Inclusion of Latest Technology Advancement in the Curriculum • Allow Academic Innovation and Creativity • Avoid Side-tracked • Variety of Modes of Delivery

5. Engineering & Technology Domain Engineers Career in Research & Design Career in Supervision & Maintenance Work Technologists Strong in Mathematics, Engineering Sciences, Professional courses (Theoretical) Appropriate Mathematics, Engineering Sciences, Professional courses (Practical) Education Engineering Breadth & Depth of Curricula Technology Breadth & Depth of Curricula

6. Expectations of Accreditation • Education content and level (depth) are maintained • Programme Continual Quality Improvement (CQI) • Outcome-based Education (OBE) Programme • Systematic (QMS)

7. CQI CRITERIA

8. Different Levels of Outcomes Programme Educational Objectives Few years after Graduation – 3 to 5 years Programme Outcomes Upon graduation Course/subject Outcomes Upon subject completion Weekly/Topic Outcomes Upon weekly/topic completion

9. Outcome-Based Assessment

10. Big Picture Assessment – Constructive Alignment Programme or Student Improvement ? PHILOSOPHY ? Design Selective Culminating Hybrid MODEL ? Attainment Taxonomy Level (Average, From, Up To)

11. Programme Objectives What is expected (3-5 years) upon graduation(What the programme is preparing graduates in their career and professional accomplishments)

12. Programme Outcomes • What the graduates are expected to know and able to perform or attain by the time of graduation (knowledge, skills/psychomotor, and affective/interpersonal/attitude) • There must be a clear linkage between Objectives and Outcomes Need to distribute the outcomes throughout the programme, and not one/two courses only addressing a particular outcome

13. PO Attainment Final Year Project Final Year Project Final Year Design Project Final Year Design Project Final Year Courses Final Year Courses Third Year Courses Third Year Courses Second Year Courses Second Year Courses First Year Courses First Year Courses

14. 2017 - 2019 Compliance to Washington Accord • Knowledge Profile • Level of Problem Solving • Graduate Attributes (Programme Outcomes)

15. PEOWHAT YOU WANT YOUR GRADUATES TO BE IN 3 - 4 YEARS 4 YEARS WA 1 ENGINEERING KNOWLEDGE WA 2 PROBLEM ANALYSIS UNIVERSITY EXPERIENCE WA3 DESIGN WA9 IND & TEAM EXTRA-CURRICULAR WA10 COMMUNICAT-ION WA5 MODERN TOOLS WA11 PROJ MGMT & FINANCE WA6 ENGR & SOC WA7 ENV & SUST WA8 ETHICS WA4 INVESTIGATION WA12 LIFE LONG

16. Course Outcomes • Statement … explain, calculate, derive, design, critique. • Statement … learn, know, understand, appreciate – not learning objectives but may qualify as outcomes (non-observable). • Understanding cannot be directly observed, student must do something observable to demonstrate his/her understanding.

17. lower order Intermediate Higher order

18. lower order Intermediate Higher order

19. lower order Intermediate Higher order

20. Bloom’s Taxonomy New Bloom’s Taxonomy • Knowledge (list) • Comprehension (explain) • Application (calculate, solve, determine) • Analysis (classify, predict, model,derived) • Synthesis (design, improve) • Evaluation (judge, select, critique)

22. Three components of a learning outcome Verb (V), Condition (C) & Standard (S) • describe the principles used in designing X.(V) • orallydescribe the principles used in designing X. (V&C) • orallydescribe the five principles used in designing X. (V&C&S) • design a beam. (V) • design a beam using Microsoft Excel design template . (V&C) • design a beam using Microsoft Excel design template based on BS 5950:Part 1. (V&C&S)

23. Learning outcomes by adding a condition and standard Poor • Students are able to design research. Better • Students are able to independently design and carry out experimental and correlational research. Best • Students are able to independently design and carry out experimental and correlational research that yields valid results. Source: Bergen, R. 2000. A Program Guideline for Outcomes Assessment at Geneva College

24. Learning Style Model • Perception SensingIntuitive • Input Modality Visual Verbal • Processing Active Reflective • Understanding Sequential Global

25. Problem Organised Project Workor POPBL (Project Oriented Problem Based Learning) Group Studies Lectures Literature Problem Analysis Problem Solving Report Tutorials Field Work Experiment

26. Depth of Knowledge Required Can be solved using limited theoretical knowledge, but normally requires extensive practical knowledge Requires in-depth knowledge that allows a fundamentals-based first principles analytical approach Requires knowledge of principles and applied procedures or methodologies

27. Washington Accord Graduate Attributes PROGRAMME OUTCOMES

28. PROGRAMME OUTCOME

29. PROGRAMME OUTCOME

30. PROGRAMME OUTCOME

31. PROGRAMME OUTCOME

32. PROGRAMME OUTCOME

33. PROGRAMME OUTCOME

34. PROGRAMME OUTCOME

35. PROGRAMME OUTCOME

36. PROGRAMME OUTCOME

37. PROGRAMME OUTCOME

38. PROGRAMME OUTCOME

39. PROGRAMME OUTCOME

40. Knowledge Profile (Curriculum)

41. Knowledge Profile

42. WK1 natural sciences WK5 engineering design Knowledge Profile WK2 mathematics, numerical analysis, statistics, computer and information science 4 YEARS WK6 engineering practice WK7 engineering in society WK3 engineering fundamentals WK8 research literature WK4 engineering specialist knowledge

43. WA1 ENGINEERING KNOWLEDGE WA2 PROBLEM ANALYSIS 4 YEARS WA3 DESIGN WA9 IND & TEAM WK5 engineering design WK1 natural sciences WK2 mathematics, numerical analysis, statistics, computer and information science WA5 MODERN TOOLS WK6 engineering practice WA10 COMMUNICAT-ION WK7 engineering in society WA6 ENGR & SOC WA7 ENV & SUST WA8 ETHICS WA11 PROJ MGMT & FINANCE WK3 engineering fundamentals WK8 research literature WA4 INVESTIGATION WA12 LIFE LONG WK4 engineering specialist knowledge

44. WA1 ENGINEERING KNOWLEDGE WA2 PROBLEM ANALYSIS 4 YEARS WA3 DESIGN WA9 IND & TEAM WK5 engineering design WK1 natural sciences WK2 mathematics, numerical analysis, statistics, computer and information science WA5 MODERN TOOLS WK6 engineering practice WA10 COMMUNICAT-ION WK7 engineering in society WA6 ENGR & SOC WA7 ENV & SUST WA8 ETHICS WA11 PROJ MGMT & FINANCE WK3 engineering fundamentals WK8 research literature WA4 INVESTIGATION WA12 LIFE LONG WK4 engineering specialist knowledge

45. Complex Problem Need to think broadly and systematically and see the big picture Change Uncertain Complex Problem Difficult Decision Strategy Confusing Idea Contentious Product Intractable

46. Difficulty & Uncertainty • Complexity – the problem contains a large number of diverse, dynamic and interdependent elements • Measurement – it is difficult or practically unfeasible to get good qualitative data • Novelty – there is a new solution evolving or an innovative design is needed

47. Characteristics Complex Problems Technical Problems Isolatable boundable problem Universally similar type Stable and/or predictable problem parameters Multiple low-risk experiments are possible Limited set of alternative solutions Involve few or homogeneous stakeholders Single optimal and testable solutions Single optimal solution can be clearly recognised • No definitive problem boundary • Relatively unique or unprecedented • Unstable and/or unpredictable problem parameters • Multiple experiments are not possible • No bounded set of alternative solutions • Multiple stakeholders with different views or interest • No single optimal and/or objectively testable solution • No clear stopping point

48. Scientific/Technical Problems can combine to form A Complex Problem

49. Complex Technical

50. Complex Engineering Problems have characteristic WP1 and some or all of WP2 to WP7, EP1 and EP2, that can be resolved with in-depth forefront knowledge Complex Problems (Need High Taxonomy Level)