Teaching Pedagogy and Active Learning (TPAL) 

The Teaching Pedagogy and Active Learning scholarship working group was established in January 2023 with the aim to identify and analyse relevant factors and variables that fall under the term “teaching and learning”, with a key priority of enhancing the student’s learning experience.

The main objectives of this scholarship working group are:

• To explore innovative teaching methods and strategies to enhance student engagement and learning experience,
• To pursue opportunities for Generative Artificial Intelligence (AI) in teaching and learning,
• To implement and evaluate the Active Curriculum for Excellence (ACE) approach,
• To explore opportunities for digitalisation in teaching and learning and new forms of virtual meeting environments.

TPAL Projects for 2024/25​​

Personalised Learning, Active Learning, Student Engagement, Gamification​

• A Guided Personalised Learning Model for Large and Diverse Classes (Yue Chen)​
• Enhancing Student Engagement and Academic Performance (Nickos Paltalidis, Usman Naeem)​
• Analysing the Correlation Between Active Learning Approaches and Student Engagement (Habiba Akter) ​
• The Impact of Gamification on Student Engagement and Performance in Programming Modules (Usman Naeem)​
• An Evaluation of the TNE Teaching Delivery Mode of JEI/JP (Nickos Paltalidis)​

GenAI and Teaching​

• GenAI and Teaching (Vindya Wijeratne)​
• Using Virtual AI Mentor in Practical Assessment for Cross-CulturalEngineering Education (Farha Lakhani)​
• Developing Critical Thinking Practices Interwoven with Generative AI Usage (Usman Naeem)​

Training Programmes for Teaching Assistants and Students​

• Fresher to Triver Programme (Yue Chen)​
• BUPT Teaching Assistant Training Programme (Na Yao)​

Digitalisation and Teamwork Online​

• Evaluating an innovative approach to supporting teamwork online – co-created spaces in the metaverse (Marie-Luce Bourguet)

Group Leaders: Nickos Paltalidis (n.paltalidis@qmul.ac.uk), Usman Naeem (u.naeem@qmul.ac.uk)

Group Members: Aisha Abuelmaatti, Bing Han, Damon Owen, Farha Lakhani, Fatma Benkhelifa, Habiba Akter, Jonathan Loo, Laurissa Tokarchuk, Ling Ma, Marie-Luce Bourguet, Michael Chai, Michaela MacDonald, Na Yao, Paula Fonseca, Riasat Islam, Vindya Wijeratne, Yixuan Zou, Yan (Cindy) Sun, Yue Chen, Hasan Sagor

Curriculum Enhancement (CurrEnt) 

In the Curriculum Enhancement SWG, the aim is to take a holistic approach to enhance degree programmes by considering different aspects of curriculum design, deliver and review, including inclusion, employability, well-being, graduate attributes, and quality assurance.

The projects cover various aspects of curriculum enhancement, leveraging technologies like graph data science and AI, and contributing to both pedagogic theory and practice.

CurrEnt Projects for 2024/25

• Future Ready: Integrating Artificial Intelligence, Interdisciplinary Practices, and Entrepreneurial Skills for Enhanced Student Employability.

-Lead: Aisha Abuelmaatti

-Aim: Equip students with essential skills for the evolving job market by integrating AI, interdisciplinary practices, and entrepreneurial skills into their education.

-Expected Outcomes: Insight into how AI will change skills requirements across occupations; Develop Entrepreneurial Attributes to encourage entrepreneurial skills in students; Design an Interdisciplinary Project-based Module; Promote interdisciplinary collaboration to solve complex problems; Foster entrepreneurial thinking; Engage with projects addressing the SDGs

• Creating an open, co-created and co-guided toolkit to support staff integration of AI literacy and skills into the curricula.

-Co-Lead: Aisha Abuelmaatti (in collaboration with HSS, FMD)

-Aim: Bridging the gap in AI competencies.

-Expected Outcomes: Develop a bottom-up, open, co-created and co-guided toolkit for integrating AI into the curriculum at both module and programme levels

• Cultivating GenAI Competence for the Human-AI Workforce of Tomorrow.

-Lead: Yue Chen

-Aim: Equip students with essential GenAI skills for the future workforce by enhancing curriculum content and delivery.

-Expected Outcomes: Definition of GenAI competence and Identification of gaps in current curriculum; Identification of curricular areas (modules delivery, assessment, extra-curricular activities) where GenAI competence development can be integrated; Proposal for curriculum enhancements for GenAI competence development and impact evaluation.

• Embedding Inclusivity into Academic Practices.

-Lead: Atm Alam

-Aim: Enhance and finalise the Inclusive Curriculum Toolkit for a school-wide rollout in the academic year 2024/25.

-Expected Outcomes: A fully developed & effective Inclusive Curriculum Toolkit ready for adoption by all teaching staff.

• Beyond the Norm: Integrating Computer Scientists in TNE Curricula

-Lead: Atm Alam

-Aim: Diversify course materials by integrating contributions from underrepresented scientists and pioneers, enriching the curriculum, inspiring students from diverse backgrounds.

-Expected Outcomes: A repository of biographies for diverse scientists and pioneers in EE and CS, ready to be used in the curriculum.

• A Data-Driven Approach for Engineering Degree Programme Review Based on Graph Theory

-Lead: Chao Shu

-Aims: Design a data-driven method to analyse curriculum design and delivery in higher education using graph theory and module assessment data; Develop a programme review tool which is able to highlight strengths and identify areas where the curriculum design and delivery may not align, enabling targeted improvements to enhance the academic degree programme.

Group Leaders: Chao Shu (chao.shu@qmul.ac.uk), Aisha Abuelmaatti (a.abuelmaatti@qmul.ac.uk)

Group Members: Atm Alam, Michael Chai, Yue Chen, Claria Guo, Gokop Longinus Goteng, Chao Liu, Jonathan Loo, Ling Ma, Claire Revell, Dandan Sun, Andy Watson, Vindya Wijeratne, Na Yao

Assessment and Feedback 

Group Leader: Yasir Alfadhl (yasir.alfadhl@qmul.ac.uk)