Research-based Learning (RbL) in Computing Courses for Senior Engineering Students

Transcription

1 Research-based Learning (RbL) in Computing Courses for Senior Engineering Students Khaled Bashir Shaban, and Mahmoud Abdulwahed Computer Science and Engineering Department; and CRU, Dean s Office Best Paper Award IEEE International Conference on Teaching, Assessment, and Learning for Engineering IEEE TALE 2012, Hong Kong, August 20-23, 2012.

2 Benefits & Rewards For students and instructors Research Multidisciplinary and collaborative Computing Civil Engineering Electrical Engineering Social Science Biology Finance and others Engineering Education Effective Research Innovation Collaboration Publication

3 Motivation: Change the Course Dynamics!

4 A bit of Jargon!: Pedagogical Terminology Constructivist pedagogy approaches: Focus on self-experience as a mean for constructing knowledge Teamwork and social learning is essential Emphasis on real-world/authentic applications Lecturers act as coordinators of knowledge construction more than being passive sources of knowledge delivery Project/Problem/Inquiry-based Learning (PbL): Utilization of projects/problems Open-ended in nature usually Better venue for students innovation This is basically what engineers are asked for! Research-based Learning (RbL)/Undergraduate Research: Solving problems/projects also, with strong focus on inquiry Clear objective of scholarly output requirement Development of students to become independent researchers UREP Part-time RAs RbL Constructivism PbL/IbL

5 More on RbL/UR International trends: Pioneering US leadership in this approach Early reports of developing UR forms goes back to 1969 in MIT Research Experience for Undergraduates (REU) been in operation in NSF since 1987, total estimated funding since then is 327 Million USD UR is widely utilized in Technical Universities (TUs) in Germany Increased interest in RbL/UR during the last few years in UK, Europe, Australia, New Zealand, and also in Qatar Specialized research journals and conferences: MIT Undergraduate Research Journal Journal of Undergraduate Research Opportunities Journal of Undergraduate Research and Scholarly Excellence The National Conferences on Undergraduate Research (USA) The Annual Undergraduate Research Conference on Applied Computing (UAE)

6 Activity: 7-10 Minutes In groups of 2-4 members, discuss if RbL can be implemented in one of your courses, or departmental program; Please identify/discuss some of the following issues (but not limited to): What is the potential course(s) to implement an RbL/UR approach? Are there potentials of collaboration with other department(s)/disciplines in an inter-/multi-disiciplinary RbL/UR? Other than courses, how RbL/UR can be introduced? What could be your students perception of introducing RbL/UR in the curriculum? In your opinion, what are: The potential benefits of introducing RbL/UR in engineering education? The potential constraints of introducing RbL/UR in engineering education?

7 Research as an Iterative Process of Phased Activities Centered around a Problem

8 Identify Aims & Objectives: Provided vs. Students Proposed. Flexible & Considerate Limited time/resources Competencies Scope of work Significance Contributions etc.

9 Review Relevant and Recent Literature: Several papers from (suggested/approved by the instructor) reputable sources: Summarize Critique Evaluate Get familiar with good (flow of logic, formalism, results visualization, drawing of conclusions, etc.) Clear guidance (Do s and Don ts list) Could be divided into two stages Consider reproducing the work

10 Define The Problem Formally: Mathematically stating the problem; objective(s), constraint(s), etc. Mapping to known (classes of) problems. Mathematically describe tasks and potential solutions techniques

11 Solve Problems after Collecting Data and Tools: Gather data sets (data integration, preprocessing, transformation, exploration, and visualization ). Find existing tools (toolboxes, source code, packages). Reproduce first. Crucial and time consuming.

12 Integrate Solutions and Techniques: Design, select and/or implement techniques. Tune parameters, execute, collect, and evaluate results. Expect the unexpected. Assessed interestingness and usefulness

13 Interpret Results & Compile Findings: Interpretation and drawing of conclusions. Quantitative vs. Qalitative Confirm or reject initial hypotheses. Indicate new trends. Assess contribution(s) significance.

14 Deliverables Written reports. Project portfolio. Presentation. Final reports that are ready to be published.

15 vs. UW Course Title SYDE 422: QU CMPT 563: Machine Intelligence and Soft-computing Data Mining Term Winter 2008 Spring 2011 No. of Stds 34 (full-time) 9 (part-time) Dept. Systems Design, Electrical & Computer Master in Computing, CSE Engineering Teaching Style Lectures, Labs, & Invited Talks Lectures, & Semi-Labs Main Topics Knowledge Representation Data Preprocessing Expert Systems (ES) (Dis)Similarity Measures Uncertainty Management in ES Data Exploration and Visualization Fuzzy Logic, Fuzzy ES Classification Methods & Evaluation Machine Learning techniques (ANN) Clustering & Evaluation Evolutionary Computation Outlier Analysis Hybrid Intelligent Systems Association Analysis Lab Tutorials Matlab ANN, and Fuzzy Logic Toolboxes Weka, RapidMiner, and R

16 vs. Project UW QU Areas civil, electrical, industrial, and computer engineering medicine, finance, information systems, social Topics Provided with Co-supervision: Preference Discovery in Layout Design Hybridized Evolutionary Algorithms for Dynamic Scheduling of Flexible Manufacturing Systems Path Planning Techniques in Dynamic and Static Mobile Robot Environments Estimating Transformer Oil Parameters Modeling and Estimating Traffic in Wireless Channels Estimating of Bridge Maintenance Costs Estimating of Home Construction Costs Proposed by the students: Gaze Tracking, Solar Radiation Forecasting Credit Default Swap Pricing Prediction Interest-Determining Web Browser Recommending Web Objects based on Social Annotation Data Developing Mobile Robot Wall-Following Algorithm Optical Character Recognition of Handwriting science, manufacturing, and banking All Proposed by Students with no Cosupervision: System for Diagnosing Diabetes Analysis of Liver Cancer Survival Rate Prediction of Qatar Inflation Rate Analysis and Profiling of Students Data Data Analysis of The 100 Most Influential Persons in History Equipment Failure Prediction in Manufacturing Customer Segmentation and VIP Mining in Middle East Banking Environments teams 3 students (max) Individually

17 vs. Students Feedback Assessment Tools Outcomes UW QU Published 6 conference papers & 1 journal 1 conference Papers 50% of projects (80% of them with co- 10% of projects supervision) Very challenging Unusual Motivating experience Impactful New Valuable Important 1 typical and 1 open-ended assignment Peer-assessed Presentations No midterm No final exams Eye-opener Career Choice Uneasy Demanding Hard to estimate the required time Hard to meet deadlines 3 typical and 1 open-ended assignment 1 open-book midterm No final exams

18 Grade Interesting; in QU course Correlation between midterm exams and final marks and between projects and final marks are 0.9. Correlation between final marks and assignments range between 0.2 and 0.4. Wise weights distribution. Relying on projects only for assessment is sufficient to reflect students performance Assignment-1 Assignment-2 Assignment-3 Assignment-4 Midterm-Exam Project Final Mark

19 Research as a Process (Ideally)

20 Research as a Process (In Reality!)

21 Future Plans Apply RbL again this year CMPS 453: Data Mining, Fall 2012 CMPT 5##: Intelligent Systems, Spring 2013 You are all invited!

22 Questions?

23 Extra Slides

24 CMPS 453: Data Mining, Fall 2012 Description Principles concepts of data mining techniques and their practical application in pattern recognition and knowledge discovery from large data sets. Fundamental strategies and methodologies of various classification, clustering, association rules extraction algorithms applied on tabular data sets. Hands-on experience with a variety of different data mining tools. Topics Introduction: What is data mining? Motivations and challenges, and data mining tasks. Data: Types of data, data preprocessing, (dis)similarity measures, and data exploration and visualization. Classification: Basic methods, decision trees, rules, regression, k-nearest neighbor, other techniques, and classifier evaluation. Clustering: K-means, agglomerative hierarchical clustering, density based methods, grid based methods, cluster evaluation, outlier analysis. Association Analysis: Frequent itemset methods, mining multi-level association rules, and mining multi-dimensional association rules. Selected Topics: Anomaly Detection, Mining Complex Data: streams, multimedia, time series, natural languages, etc.

25 CMPT 5##: Intelligent Systems, Spring 2012 Description Principles of intelligent systems techniques and building of these systems. Fundamentals of expert systems, knowledge representation, dealing with uncertainty, and building of rule-based expert systems. Comprehensive background on fuzzy set theory, and how to build fuzzy systems, as well as decision trees, artificial neural networks, genetic algorithms, and hybrid intelligent systems. Topics Knowledge-Based Intelligent Systems: Artificial intelligence from the Dark Ages to knowledge-based systems What is knowledge? Knowledge representation techniques Rules as a knowledge representation technique and Expert Systems Uncertainty Management in Expert Systems: Introduction to uncertainty Bayesian reasoning Certainty factors theory and evidential reasoning Fuzzy Expert Systems: Fuzzy sets and linguistic variables and hedges Fuzzy inference for building a fuzzy expert system Machine Learning: Introduction to learning Decision Trees Artificial Neural Networks Evolutionary Computation Hybrid intelligent systems: Fuzzy-Neural Systems Evolutionary-Neural Networks Selected Topics: Knowledge Engineering, Solving Real-World Complex Problems, Research Methodologies