Dr. Dmitry Abbakumov
Psychometrics and Data Science for EdTech
Welcome! My name is Dmitry Abbakumov. I am passionate about creating added value in EdTech through data-driven insights and psychometric inference. Since 2021, I am leading the learning analytics and evidence-based education at Practicum by Yandex as its Chief Psychometrician. I previously worked with prof. dr. Wim Van den Noortgate and prof. dr. Piet Desmet on Psychometrics of MOOCs at KU Leuven from where I have graduated with the degree of Doctor of Educational Sciences with specialisation in statistical modelling.
Recent Project
Currently, I am working on a psychometric machine for evidence-based learning. The engine consists of theoretically-grounded, data-driven, explainable solutions to show when and why learning happens and to explain how learning products work. I have presented the first results at the Yet another Conference on Education 2021. Please find the video (in Russian only, sorry) on the right.

Five Selected Papers

For the most recent overview of my publications, please refer to Google Scholar.

  1. Abbakumov, D., Kravchenko, D., Kuskin, W., & Urban, A. (2020). How rewatching video lectures impacts students' performance in assessments in MOOCs.
  2. Abbakumov, D., Desmet, P., & Van den Noortgate, W. (2020). Psychometrics of MOOCs: Measuring learners' proficiency. Psychologica Belgica, 60(1), 115-131.
  3. Abbakumov, D., Desmet, P., & Van den Noortgate, W. (2020). Rasch model extensions for enhanced formative assessments in MOOCs. Applied Measurement in Education, 33(2), 113-123.
  4. Abbakumov, D., Desmet, P., & Van den Noortgate, W. (2019). Measuring growth in students' proficiency in MOOCs: Two component dynamic extensions for the Rasch model. Behavior Research Methods, 51(1), 332-341.
  5. Abbakumov, D., Desmet, P., & Van den Noortgate, W. (2018). Measuring student's proficiency in MOOCs: Multiple attempts extensions for the Rasch model. Heliyon, 4(12), 1-15.

Coursera Partners Conference

April 2-4, 2019

Senate House, University of London, London, the UK

Five Selected Talks

For the full list of my international talks, please check my CV.

  1. Ensuring the validity of data and the validity of predictions in digital learning. Times Higher Education Virtual Digital Transformation Forum, Virtual, 2020
  2. Combining explanatory IRT and psychological networks for understanding and modeling online learners' difficulties. International Meeting of the Psychometric Society, Virtual, 2020
  3. Learners' activity in MOOCs from a psychometric perspective. Coursera Partners Conference, London, the UK, 2019
  4. Measuring student's proficiency in MOOCs: Multiple attempts extensions for the Rasch model. International Meeting of the Psychometric Society, New York, USA, 2018
  5. Interest and interestingness: The new perspective on students and content. Coursera Partners Conference, the Hague, the Netherlands, 2016
Public Doctoral Defense
Friday, September 13, 2019
Promotiezaal, KU Leuven, Leuven, Belgium
Doctoral Dissertation

Psychometrics of MOOCs:
How to Measure Proficiency?

Supervisor: Prof. Dr. Wim Van den Noortgate, Co-supervisor: Prof. Dr. Piet Desmet

The dissertation consists of five stand-alone chapters following the intruductory chapter – four empirical and one conceptual. In the four empirical chapters, we have proposed extensions for the most common IRT model, the Rasch model, aimed at solving the following problems. In Chapter 2, we have improved the proficiency measures by modeling the effect of attempts and by involving non-assessment data such as learners’ interaction with video lectures and practical tasks. In Chapter 3, we have modeled individual growth in proficiency through the MOOC as an effect of the cumulative sum of video lectures a learner has watched before responding on a summative assessment item. In Chapter 4, we have established a more nuanced insight on the role of proficiency on the learners’ performance by involving one extra latent effect, the effect of learners’ interest. In Chapter 5, we have proposed a way to measure learners’ activity (e.g., watching videos, reading texts) as influenced by a latent learner characteristic and a latent content characteristic. In the final, reflective and conceptual, chapter (Chapter 6), we have summarized a connection between psychometrics, as a scientific discipline, and MOOCs, as an industry, and have sketched the future development of psychometrics of MOOCs.

For the full text, please visit the Thesis Commons repository.
Dr. Dmitry Abbakumov
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