Dr Steven Bradley, BA MSc PhD PGCAP SFHEA
(email at firstname.lastname@example.org)
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 Associate Professor (teaching) since 2017.
- Invited Expert to teach on International MSc Programme at Beijing Jiaotong University
Indicators of Esteem
- 2019: Best Paper Award Koli Calling 2019: For "Addressing Bias to Improve Reliability in Peer Review of Programming Coursework"
- 2018: Chair, UK and Ireland ACM Special Interest Group in Computer Science Education (SIGCSE):
The UK and Ireland ACM SIGCSE aims to provide a national forum for the examination and exchange of research and practice related to the learning and teaching of computing.
- 2017: Excellence in Learning & Teaching Award: Awarded by Durham University in June 2017
- Innovative Computing
Department of Sociology
- Children’s hospice service data mapping project 2011/12
- Mapping Unit
- Computer Science education
- Citizen science
- Knowledge representation and student learning
- Web-based data collection
- Real-time systems
- Software engineering
- Hsing, P.-Y., Bradley, S.P., Kent, V.T., Hill, R.A., Smith, G.C., Whittingham, M.J., Cokill, J., Crawley, D., MammalWeb Volunteers, & Stephens, P.A. (2018). Economical crowdsourcing for camera trap image classification. Remote Sensing in Ecology and Conservation 4(4): 361-374.
- Rees, S.W., Bruce, M. & Bradley, S. (2014). Utilising Data-driven Learning in Chemistry Teaching: a Shortcut to Improving Chemical Language Comprehension. New Directions 10(1): 12-19.
- Bradley, Steven (2020), Creative Assessment in Programming, Durham, England, ACM, 1.
- Bradley, Steven (2019), Addressing Bias to Improve Reliability in Peer Review of Programming Coursework, Koli Calling 2019. Finland, ACM, New York, 19.
- Gajbhiye, Amit, Jaf, Sardar, Al-Moubayed, Noura, McGough, A. Stephen & Bradley, Steven (2018), An Exploration of Dropout with RNNs for Natural Language Inference, in Kurková, V., Manolopoulos, Yannis, Hammer, Barbara, Iliadis, Lazaros S. & Maglogiannis, Ilias G. eds, Lecture Notes in Computer Science, 11141 ICANN 2018: 27th International Conference on Artificial Neural Networks. Rhodes, Springer, Cham, 157-167.
- Gajbhiye, Amit, Jaf, Sardar, Al-Moubayed, Noura, Bradley, Steven & McGough, A. Stephen (2018), CAM: A Combined Attention Model for Natural Language Inference, in Abe, Naoki Liu, Huan Pu, Calton Hu, Xiaohua Ahmed, Nesreen Qiao, Mu Song, Yang Kossmann, Donald Liu, Bing Lee, Kisung Tang, Jiliang He, Jingrui & Saltz, Jeffrey eds, IEEE International Conference on BIG DATA. Seattle, United States of America, IEEE, Piscataway, N.J., 1009-1014.
- Bradley, Steven & Church, Stephen (2018), Collaborative Creative Computing, London Computing Education Research Symposium. London.
- Bradley, Steven (2016), Managing Plagiarism in Programming Assignments with Blended Assessment and Randomisation, in Sheard, Judy & Suero Montero, Calkin eds, 16th Koli Calling Conference on Computing Education Research. Koli, Finland, Association for Computing Machinery (ACM), New York, NY, 21-30.
- 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, ICCB 27th International Congress for Conservation Biology. Montpellier, France, Montpellier.
- Steven Bradley & Alexandra Cristea (2019). Proceedings of the 3rd Conference on Computing Education Practice. Computing Education Practice, Durham, UK, ACM.
- 2018: Durham University Enhancing the Student Learning Experience Award: Transition as an Affective Process
- 2014: google CS4HS (Computer Science for High Schools) award $12k for work on Computer Science Into Schools
- 2013: Durham University Enhancing the Student Learning Experience (ESLE) award: FOCUS Diagnostics – the development of an online diagnostic and instructional toolkit to enhance student understanding of subject specific language