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Journal Articles
Indexed Conference Proceedings

  1. Cukurova, M., Khan-Galaria, M., Millán, E., & Luckin, R. (2022). A Learning Analytics Approach to Monitoring the Quality of Online One-to-one Tutoring. Journal of Learning Analytics. https://doi.org/10.35542/osf.io/qfh7z 

  2. Kawamura, R., Shirai, S., Takemura, N., Alizadeh, M., Cukurova, M., Takemura, H., & Nagahara, H. (2021). Detecting Drowsy Learners at the Wheel of e-Learning Platforms With Multimodal Learning Analytics. IEEE Access, 9, 115165-115174.

  3. Cukurova, M., Kent, C., Luckin, R. (2019) Artificial Intelligence and Multimodal Data in the Service of Human Decision- making: A Case Study in Debate Tutoring, British Journal of Educational Technology, 50(6), pp. 3032-3046. DOI:10.1111/bjet.12829

  4. Mavrikis, M., Cukurova, M., Di Mitri, D., Schneider, J., & Drachsler, H. (2021). A short history, emerging challenges and co-operation structures for Artificial Intelligence in education. Bildung und Erziehung, 74(3), 249-263.

  5. Cukurova, M., Luckin, R., & Kent, C. (2020). Impact of an Artificial Intelligence research frame on the perceived credibility of educational research evidence, International Journal of Artificial Intelligence in Education, xx1-xx22, DOI: 10.1007/s40593-019-00188-w

  6. Cukurova, M., Luckin, R., & Clark‐Wilson, A. (2019). Creating the golden triangle of evidence‐informed education technology with EDUCATE. British Journal of Educational Technology, 50(2), 490-504. https://doi.org/10.1111/bjet.12727

  7. Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning. Journal of Computer Assisted Learning, 34(4), 366-377.

  8. Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences‐driven approach. British Journal of Educational Technology, 50(6), 2824-2838.

  9. Weatherby, K., Clark-Wilson, A., Cukurova, M., Luckin, R. (2022). The Importance of Boundary Objects in Industry- Academia Collaborations to Support Evidencing the Efficacy of Educational Technology, TechTrends, DOI: 10.1007/s11528-022-00705-0

  10. Torres, P.E., Ulrich, P.I.N., Cucuiat, V., Cukurova, M., Fercovic De la Presa, M.C., Luckin, R., Carr, A., Dylan, T., Durrant, A., Vines, J., Lawson, S. (2021). A systematic review of physical–digital play technology and developmentally relevant child behaviour, International Journal of Child-Computer Interaction, 30, DOI: 10.1016/j.ijcci.2021.100323

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  1. Suraworachet, W., Villa-Torrano, C., Zhou, Q., Asensio-Pérez, J. I., Dimitriadis, Y., & Cukurova, M. (2021). Examining the relationship between reflective writing behaviour and self-regulated learning competence: A time-series analysis. In European Conference on Technology Enhanced Learning (pp. 163-177). Springer, Cham.

  2. Zhou, Q., Suraworachet, W., Pozdniakov, S., Martinez-Maldonado, R., Bartindale, T., Chen, P., ... & Cukurova, M. (2021). Investigating students’ experiences with collaboration analytics for remote group meetings. In International Conference on Artificial Intelligence in Education (pp. 472-485). Springer, Cham.

  3. Nazaretsky, T., Cukurova, M., & Alexandron, G. (2022). An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology. In Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK’22).

  4. Alwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2021). The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review. The Handbook of Learning Analytics.

  5. Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021). Confirmation Bias and Trust: Human Factors that Influence Teachers’ Attitudes towards AI-based Educational Technology. AI for Blended-Learning: Empowering Teachers in Real Classrooms: The 16-Th European Conference on Technology Enhanced Learning (EC- TEL’21).

  6. Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Cukurova, M., Bartindale, T., Chen, P., Marshall, H., Richardson, D., Gasevic, D. (2022). The Question-driven Dashboard: How Can We Design Analytics Interfaces Aligned to Teachers' Inquiry? ACM International Conference Proceeding Series, pp. 175-185.

  7. Zhou, Q., Suraworachet, W., & Cukurova, M. (2021). Different modality, different design, different results: Exploring self-regulated learner clusters' engagement behaviours at individual, group and cohort activities. In CEUR Workshop Proceedings (Vol. 2902, pp. 28-40). CEUR.

  8. Khan-Galaria, M., Cukurova, M., & Luckin, R. (2020). A framework for exploring the impact of tutor practices on learner self-regulation in online environments. In International Conference on Artificial Intelligence in Education (pp. 135-139). Springer, Cham.

  9. Cukurova, M., Zhou, Q., Spikol, D., & Landolfi, L. (2020, March). Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 270-275).

  10. Kasparova, A., Celiktutan, O., & Cukurova, M. (2020, October). Inferring student engagement in collaborative problem solving from visual cues. International Conference on Multimodal Interaction (pp. 177-181).

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This page only has the most recent 10 journal and conference publications, please check team member pages for older papers.

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