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Ed Wakelam

Researcher at University of Hertfordshire

London, United Kingdom

53 Followers

Outstanding 40 year career in the computer industry, specialising in professional services, software development programmes and solutions development. Roles from programming and system design to project management, business development, solution management, operations and group management, Board Director, Vice President. Responsible for applications and system integration resources of over 1500 staff and their associated utilisation, growth and development. Reputation for fixing failing businesses and returning them to profitability and growth,and for fixing poorly functioning organisational structures and reducing costs. Now a University researcher focused on the application of artificial intelligence and data mining techniques to create organisational value. Lecturer in strategic IT management and IT in Organisations. Variety of guest lectures. Two peer reviewed journal papers and two peer reviewed conference papers published and presented at conference.

Available For: Consulting, Influencing, Speaking
Travels From: London UK

Ed WakelamPoints
Academic20
Author0
Influencer1
Speaker0
Entrepreneur0
Total21

Points based upon Thinkers360 patent-pending algorithm.

Thought Leader Profile

Portfolio Mix

Company Information

Company Type: Enterprise
Business Unit: School of Computer Science
Theatre: UK

Areas of Expertise

AI 32.68
Analytics 34.83
Big Data 32.33
Customer Experience
Innovation
Edtech 34.67
Predictive Analytics 32.29
Design Thinking 32.05
Emerging Technology 30.99

Industry Experience

Aerospace & Defense
Consumer Products
Federal & Public Sector
Financial Services & Banking
High Tech & Electronics
Higher Education & Research
Insurance
Media
Professional Services
Retail
Telecommunications
Travel & Transportation

Publications

2 Conference Publications
"The Mining and Analysis of Data with Mixed Attribute Types"
IMMM 2016: Sixth International Conference on Advances in Information Mining and Management
May 22, 2016
Abstract:
Mining and analysis of large data sets has become a major contributor to the exploitation of Artificial Intelligence in a wide range of real life challenges, including education, business intelligence and research. In the field of education, the mining, extraction and exploitation of useful information and patterns from student data provides lecturers, trainers and organisations with the potential to tailor learning paths and materials to maximize teaching efficiency and to predict and influence student success rates. Progress in this important area of student data analytics can provide useful techniques for exploitation in the development of adaptive learning systems. Student data often includes a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. In this paper, we summarise our progress in applying a combination of what we believe to be a novel technique to analyse nominal data by making a systematic comparison of data pairs, followed by numeric data analysis, providing the opportunity to focus on promising correlations for deeper analysis.

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Tags: Analytics, AI, Big Data

"The Potential for Using Artificial Intelligence Techniques to Improve e-Learning Systems"
European Conference on e-Learning (ECEL)
October 01, 2015
Abstract: There has been significant progress in the development of techniques to deliver more effective e-Learning systems in both education and commerce but our research has identified very few examples of comprehensive learning systems that exploit contemporary artificial intelligence (AI) techniques. We have surveyed existing intelligent learning/training systems and explored the contemporary AI techniques which appear to offer the most promising contributions to e-Learning. We have considered the non-technological challenges to be addressed and considered those factors which will allow step change progress. With the convergence of several of the required components for success increasingly in place we believe that the opportunity to make this progress is now much stronger. We present a description of the fundamental components of an adaptive learning system designed to fulfil the objectives of the teacher and to develop a close relationship with the learner, monitoring and adjusting the teaching based upon a wide variety of analyses of their knowledge and performance. This is an important area for future research with the opportunity to deliver significant value to both education and commerce. The development of improved learning systems in conjunction with trainers, teachers and subject matter experts will provide benefits to educational institutions and help commercial organisations to face critical challenges in the training, development and retention of the key skills required to address new, emerging technologies and business models.

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Tags: Analytics, AI, Big Data

2 Journal Publications
The potential for student performance prediction in small cohorts with minimal available attributes
British Journal of Educational Technology
June 25, 2019
The measurement of student performance during their progress through university
study provides academic leadership with critical information on each student’s
likelihood of success. Academics have traditionally used their interactions with
individual students through class activities and interim assessments to identify those “at
risk” of failure/withdrawal. However, modern university environments, offering easy
on-line availability of course material, may see reduced lecture/tutorial attendance,
making such identification more challenging. Modern data mining and machine
learning techniques provide increasingly accurate predictions of student examination
assessment marks, although these approaches have focussed upon large student
populations and wide ranges of data attributes per student. However, many university
modules comprise relatively small student cohorts, with institutional protocols limiting
the student attributes available for analysis. It appears that very little research attention
has been devoted to this area of analysis and prediction. We describe an experiment
conducted on a final-year university module student cohort of 23, where individual
student data are limited to lecture/tutorial attendance, virtual learning environment
accesses and intermediate assessments. We found potential for predicting individual
student interim and final assessment marks in small student cohorts with very limited
attributes and that these predictions could be useful to support module leaders in
identifying students potentially “at risk”.

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Tags: AI, Edtech, Predictive Analytics

"Developing an Agent-Based Simulation Model of Software Evolution"
Journal of Information and Software Technology
April 08, 2018
Abstract
Context: In attempt to simulate the factors that affect the software evolution behaviour and possibly predict it, several simulation models have been developed recently. The current system dynamic (SD) simulation model of software evolution process was built based on actor-network theory (ANT) of software evolution by using system dynamic environment, which is not a suitable environment to reflect the complexity of ANT theory. In addition the SD model has not been investigated for its ability to represent the real-world process of software evolution.
Objectives: This paper aims to re-implements the current SD model to an agent-based simulation environment ‘Repast’ and checks the behaviour of the new model compared to the existing SD model. It also aims to investigate the ability of the new Repast model to represent the real-world process of software evolution.
Methods: a new agent-based simulation model is developed based on the current SD model's specifications and then tests similar to the previous model tests are conducted in order to perform a comparative evaluation between of these two results. In addition an investigation is carried out through an interview with an expert in software development area to investigate the model's ability to represent real-world process of software evolution.
Results: The Repast model shows more stable behaviour compared with the SD model. Results also found that the evolution health of the software can be calibrated quantitatively and that the new Repast model does have the ability to represent real-world processes of software evolution.
Conclusion: It is concluded that by applying a more suitable simulation environment (agent-based) to represent ANT theory of software evolution, that this new simulation model will show more stable behaviour compared with the previous SD model; And it will also shows the ability to represent (at least quantitively) the real-world aspect of software evolution.

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Tags: Design Thinking, Emerging Technology

Blog

Opportunities

2 Speakers
"Understanding How Corporations Align Their Strategies and Objectives Ensures Career Development Ahe

Location: Anywhere    Date Available: March 23rd, 2018    Fees: For discussion

Submission Date: March 23rd, 2018    Service Type: Service Offered

How to succeed in your career. ("Understanding How Corporations Align Their Strategies and Objectives Ensures Career Development Ahead of People That Don't")

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"Strategy the Business and Me (well actually, you)"

Location: Anywhere    Date Available: March 23rd, 2018    Fees: For discussion

Submission Date: March 23rd, 2018    Service Type: Service Offered

Understanding how businesses and organisations work, top to bottom and how to maximise your own contribution and development.

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1 Technical
Creating business value and profitability with Artificial Intelligence and Data Mining techniques

Location: London, UK    Date Available: March 23rd, 2018    Fees: For discussion

Submission Date: March 23rd, 2018    Service Type: Service Offered

Creating business value and profitability with Artificial Intelligence and Data Mining techniques

Respond to this opportunity

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Ed Wakelam