Dr Usman Naeem

Profile

Dr Usman Naeem is a Reader in Computer Science Education within the School of Electronic Engineering and Computer Science at Queen Mary University of London. Usman is also a Queen Mary Academy Fellow in Learner Engagement Analytics.
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Usman received his PhD from Queen Mary University of London in 2009.
 
His research focus is on educational and assistive technologies, which include machine learning techniques, mobile computing, and ambient intelligent environments.
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Usman has taught on a variety of programmes, ranging from traditional programmes such as BSc Computer Science to degree apprenticeships programmes such as BSc Digital & Technology Solutions Professional.
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Usman is also a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).

Teaching

Usman has over 15 years of successful teaching experience in higher education, which includes teaching on a variety of undergraduate and postgraduate modules.​

Usman is a Senior Fellow of the Higher Education Academy (SFHEA). He also has Postgraduate Certificate in Learning and Teaching in Higher Education.

Undergraduate Teaching

Fundamentals of Web Technology

This is a module designed to offer students practical skills as well as understanding of underlying principles of programming the World Wide Web. Major topics of study include Internet and web server basics; client-side programming using HTML5; Cascading Style Sheets, and Javascript. Students will develop practical skills in server-side programming using PHP and gain an understanding and hands on experience in the practical issues involved when setting up a website.

Research

Research Interests:

Usman's current  research focus is on educational and assistive technologies, which includes:

Publications

20627

• Naeem U, Simmons M, Hathaway G et al. . Use of Learner Engagement Analytics to empower medical educators to make data-informed decisions.

  
  

  DOI: 10.47408/jldhe.vi32.1427

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/101178
• Allen M, Naeem U, Gill SS (2024). Q-Module-Bot: A Generative AI-Based Question and Answer Bot for Module Teaching Support.

  
  

  DOI: 10.1109/te.2024.3435427

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98901
• Mota D, Naeem U, Gill SS . Machine Learning-Based Emotional State Inference Using Mobile Sensing.

  
  

  DOI: 10.1201/9781003467199-23

  
  

  QMRO:
• Naeem U, Shu C, Ma L et al. (2024). Data-Driven Interventions for Capstone Projects. 2024 IEEE Global Engineering Education Conference (EDUCON)

  
  

  DOI: 10.1109/educon60312.2024.10578897

  
  

  QMRO:
• Styve A, Virkki OT, Naeem U (2024). Developing Critical Thinking Practices Interwoven with Generative AI Usage in an Introductory Programming Course. 2024 IEEE Global Engineering Education Conference (EDUCON)

  
  

  DOI: 10.1109/educon60312.2024.10578746

  
  

  QMRO:
• Naeem U, Bosman L, Revell C et al. (2024). mySkills – A Curriculum Integrated Employability Framework. 2024 IEEE Global Engineering Education Conference (EDUCON)

  
  

  DOI: 10.1109/educon60312.2024.10578854

  
  

  QMRO:
• Gill SS, Naeem U, Kaur R et al. (2024). Enhancing Student Learning, Engagement, and Employment Through Inclusive Curriculum Design.

  
  

  DOI: 10.4018/979-8-3693-6100-9.ch006

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/96947
• Baysan A, Naeem U . Inclusive education: pedagogical approach to online versus face-to-face teaching.

  
  

  DOI: 10.3389/feduc.2023.1148344

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/91156
• Naeem U, Bosman L (2023). Learner Engagement Analytics in a Hybrid Learning Environment. 2023 IEEE Global Engineering Education Conference (EDUCON)

  
  

  DOI: 10.1109/educon54358.2023.10125108

  
  

  QMRO:
• Naeem U, Bosman L, Revell C (2023). mySkills - A Reflective Framework for Employability Skills. 2023 IEEE Global Engineering Education Conference (EDUCON)

  
  

  DOI: 10.1109/educon54358.2023.10125192

  
  

  QMRO: