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

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.

Benefits & Rewards

•

For students and instructors

•

Research

• Multidisciplinary and collaborative

• Computing • Civil Engineering • Electrical Engineering • Social Science • Biology • Finance • and others •

Engineering Education

Constructivism

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

PbL/IbL RbL

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

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?

Research as an Iterative Process of Phased

Activities Centered around a Problem

•

Aims & Objectives:

•

Provided vs. Students’ Proposed.

•

Flexible & Considerate

•

Limited time/resources

Competencies

Scope of work

Significance

Contributions

etc.

Identify

•

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

Define

•

The Problem Formally:

•

Mathematically stating the problem; objective(s), constraint(s), etc.

•

Mapping to known (classes of) problems.

•

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.

•

Solutions and Techniques:

•

Design, select and/or implement techniques.

•

Tune parameters, execute, collect, and evaluate results.

•

Expect the unexpected.

•

Assessed interestingness and usefulness

Interpret

•

Results & Compile Findings:

•

Interpretation and drawing of conclusions.

•

Quantitative vs. Qalitative

•

Confirm or reject initial hypotheses.

Indicate new trends.

Deliverables

•

Written reports.

•

Project portfolio.

•

Presentation.

vs.

UW QU

Course

Title SYDE 422:

Machine Intelligence and Soft-computing

CMPT 563:

Data Mining

Term Winter 2008 Spring 2011

No. of Stds 34 (full-time) 9 (part-time)

Dept. Systems Design, Electrical & Computer

Engineering

Master in Computing, CSE

Teaching Style Lectures, Labs, & Invited Talks Lectures, & Semi-Labs

Main Topics • Knowledge Representation

• Expert Systems (ES)

• Uncertainty Management in ES

• Fuzzy Logic, Fuzzy ES

• Machine Learning techniques (ANN)

• Evolutionary Computation

• Hybrid Intelligent Systems

• Data Preprocessing

• (Dis)Similarity Measures

• Data Exploration and Visualization

• Classification Methods & Evaluation

• Clustering & Evaluation

• Outlier Analysis

• Association Analysis

Project UW QU

Areas civil, electrical, industrial, and computer engineering medicine, finance, information systems, social

science, manufacturing, and banking

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

All Proposed by Students with no Co-supervision:

• 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

Outcomes UW QU Published

Papers

6 conference papers & 1 journal

50% of projects (80% of them with co-supervision) 1 conference 10% of projects Students Feedback • Very challenging • Unusual • Motivating experience • Impactful • New • Valuable • Important • Eye-opener • Career Choice • Uneasy • Demanding

• Hard to estimate the required time

• Hard to meet deadlines

Assessment Tools

1 typical and 1 open-ended assignment

Peer-assessed Presentations No midterm

No final exams

3 typical and 1 open-ended assignment 1 open-book midterm

No final exams

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.

20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 Gr ade Assignment-1 Assignment-2 Assignment-3 Assignment-4 Midterm-Exam Project Final Mark

Future Plans

•

Apply RbL again this year

•

CMPS 453: Data Mining, Fall 2012

•

CMPT 5##: Intelligent Systems, Spring 2013

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.

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