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Staff profile

Overview

Professor Steven Bradley

Professor

Affiliations
AffiliationTelephone
Professor in the Department of Computer Science+44 (0) 191 33 41754

Biography

After studying maths and then Computer Science, Steven joined Durham University in 1997 as a lecturer in Computer Science. From 2004-2013 he was a part-time teaching fellow, spending the rest of his time on web consultancy, mainly on research projects across the university. From 2013 he was a full-time teaching fellow in the department of Computer Science and has been an Professor (teaching) since 2020.

Other skills and qualifications

Steven likes playing musical instruments and singing, and holds many music qualifications including Grade 1 Violin (with merit).  He is qualified in Chainsaw Maintenance and Cross-cutting as well as Felling and Processing Trees up to 380mm. In sailing he holds the RYA Day Skipper Practical qualification and acts as personal sailing advisor to Guy Gordon, the Highland Vet, having previously held the rank of patrol leader of the Penguins in the 19th Tynemouth 9th Tyne Sea Scouts.

Research interests

  • Computer Science education
  • Citizen science
  • Knowledge representation and student learning
  • Natural Language Processing
  • Real-time systems

Esteem Indicators

Publications

Chapter in book

  • Computing Education Research in the UK & Ireland
    Becker, B. A., Bradley, S., Maguire, J., Black, M., Crick, T., Saqr, M., Sentance, S., & Quille, K. (2023). Computing Education Research in the UK & Ireland. In M. Apiola, S. López-Pernas, & M. Saqr (Eds.), Past, Present and Future of Computing Education Research (pp. 421-479). Springer Verlag. https://doi.org/10.1007/978-3-031-25336-2_19
  • ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference
    Gajbhiye, A., Al Moubayed, N., & Bradley, S. (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V (pp. 460-472). Springer Verlag. https://doi.org/10.1007/978-3-030-86383-8_37
  • Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models
    Gajbhiye, A., Winterbottom, T., Al Moubayed, N., & Bradley, S. (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020. (pp. 633-646). Springer Verlag. https://doi.org/10.1007/978-3-030-61609-0_50

Conference Paper

  • PRIMM and Proper: Authentic Investigation in HE Introductory Programming with PeerWise and GitHub
    Bradley, S., & Ramezani, A. (2024). PRIMM and Proper: Authentic Investigation in HE Introductory Programming with PeerWise and GitHub. In CEP ’24: Proceedings of the 8th Conference on Computing Education Practice (pp. 33-36). ACM. https://doi.org/10.1145/3633053.3633062
  • Modeling Women's Elective Choices in Computing
    Bradley, S., Parker, M. C., Altin, R., Barker, L., Hooshangi, S., Kunkeler, T., Lennon, R. G., McNeill, F., Minguillón, J., Parkinson, J., Peltsverger, S., & Sibia, N. (2023, December 22). Modeling Women’s Elective Choices in Computing. Presented at ITiCSE 2023: Innovation and Technology in Computer Science Education, Turku Finland. https://doi.org/10.1145/3623762.3633497
  • Narrowing and Stretching: Addressing the Challenge of Multi-track Programming
    Bradley, S., & Akrida, E. (2022). Narrowing and Stretching: Addressing the Challenge of Multi-track Programming. In Proceedings of the 6th Conference on Computing Education Practice CEP 2022 (pp. 1-4). ACM. https://doi.org/10.1145/3498343.3498344
  • Evidence for Teaching Practices that Broaden Participation for Women in Computing
    Morrison, B. B., Quinn, B. A., Bradley, S., Buffardi, K., Harrington, B., Hu, H. H., Kallia, M., McNeill, F., Ola, O., Parker, M., Rosato, J., & Waite, J. (2021). Evidence for Teaching Practices that Broaden Participation for Women in Computing. In ITiCSE-WGR ’21: Proceedings of the 2021 Working Group Reports on Innovation and Technology in Computer Science Education (pp. 57-131). ACM. https://doi.org/10.1145/3502870.3506568
  • Creative Assessment in Programming: Diversity and Divergence
    Bradley, S. (2020). Creative Assessment in Programming: Diversity and Divergence. In Proceedings of the 4th Conference on Computing Education Practice 2020. ACM. https://doi.org/10.1145/3372356.3372369
  • Addressing Bias to Improve Reliability in Peer Review of Programming Coursework
    Bradley, S. (2019). Addressing Bias to Improve Reliability in Peer Review of Programming Coursework. In Koli Calling ’19 : proceedings of the 19th Koli Calling International Conference on Computing Education Research. (pp. 1-19). ACM. https://doi.org/10.1145/3364510.3364523
  • Proceedings of the 3rd Conference on Computing Education Practice
    Bradley, S., & Cristea, A. (Eds.). (2019). Proceedings of the 3rd Conference on Computing Education Practice. In Proceedings of the 3rd Conference on Computing Education Practice. ACM.
  • CAM: A Combined Attention Model for Natural Language Inference
    Gajbhiye, A., Jaf, S., Al-Moubayed, N., Bradley, S., & McGough, A. S. (2018). CAM: A Combined Attention Model for Natural Language Inference. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), 2018 IEEE International Conference on Big Data (Big Data) ; proceedings. (pp. 1009-1014). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/bigdata.2018.8622057
  • An Exploration of Dropout with RNNs for Natural Language Inference
    Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018). An Exploration of Dropout with RNNs for Natural Language Inference. In V. Kurková, Y. Manolopoulos, B. Hammer, L. S. Iliadis, & I. G. Maglogiannis (Eds.), Artificial neural networks and machine learning - ICANN 2018 : 27th international Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings. Part III. (pp. 157-167). Springer Verlag. https://doi.org/10.1007/978-3-030-01424-7_16
  • Collaborative Creative Computing
    Bradley, S., & Church, S. (2018, June 11). Collaborative Creative Computing. Presented at London Computing Education Research Symposium (2018 LCERS)., London, England.
  • Managing Plagiarism in Programming Assignments with Blended Assessment and Randomisation
    Bradley, S. (2016). Managing Plagiarism in Programming Assignments with Blended Assessment and Randomisation. In J. Sheard & C. Suero Montero (Eds.), Koli Calling ’16 : Proceedings of the 16th Koli Calling Conference on Computing Education Research. (pp. 21-30). Association for Computing Machinery (ACM). https://doi.org/10.1145/2999541.2999560
  • Monitoring Wild Mammals in County Durham with a Citizen Science Web Platform
    Hsing, P.-Y., Bradley, S., Kent, V., Hill, R., Whittingham, M., & Stephens, P. (2015). Monitoring Wild Mammals in County Durham with a Citizen Science Web Platform. Presented at ICCB : 27th International Congress for Conservation Biology, Montpellier, France.
  • Software evolution in an interdisciplinary environment.
    Bennett, K., Bradley, S., Glover, G., & Barnes, D. (2003). Software evolution in an interdisciplinary environment. In L. O’Brien & N. Gold (Eds.), Eleventh Annual International Workshop on Software Technology and Engineering Practice: (STEP 2003): proceedings: Amsterdam, the Netherlands, September 9-21, 2003 (pp. 199-203). IEEE Computer Press. https://doi.org/10.1109/step.2003.30
  • Using Model Checking for Pre-Planning Analysis
    Fox, M., Long, D., Bradley, S., & McKinna, J. (2001). Using Model Checking for Pre-Planning Analysis. Presented at AAAI Symposium on Model-based Validation of Intelligence.

Journal Article