2026

  1. Benchmarking AI on Standard Chemistry Exams: LLMs Still Underperform Compared to High School Students

    Yacobson E., Schleifer Y., Bar-Dov Z., Rap S., Blonder R. & Alexandron G. (2026), Journal of Science Education and Technology

    2.

  2. Preparing in-service science teachers for the AI era: insights from an 'AI competence development' academic course

    Alexandron G., Aroch I., Cukurova M. & Blonder R. (2026), International Journal of Science Education

    3.

    2025

  3. Perspectives of Generative AI in Chemistry Education Within the TPACK Framework

    Feldman-Maggor Y., Blonder R. & Alexandron G. (2025), Journal of Science Education and Technology. 34, 1, p. 1-12

    4.

  4. The Impact of Explainable AI on Teachers Trust and Acceptance of AI EdTech Recommendations: The Power of Domain-specific Explanations

    Feldman-Maggor Y., Cukurova M., Kent C. & Alexandron G. (2025), International Journal of Artificial Intelligence in Education. 35, 5, p. 2889-2922

    5.

  5. A review of main issues regarding validity, reliability, generalizability, transferability, and applicability of log-based measurement of computer-assisted learning

    Hershkovitz A. & Alexandron G. (2025), Research and Practice in Technology Enhanced Learning. 20, 20

    6.

  6. Reflective prompt engineering: a new strategy for automated short answer scoring in biology

    Ariely M., Salman A., Yarden A. & Alexandron G. (2025), International Journal of Science Education

    7.

  7. What Explains Teachers Trust in AI in Education Across Six Countries?

    Viberg O., Cukurova M., Feldman-Maggor Y., Alexandron G., Shirai S., Kanemune S., Wasson B., Tømte C., Spikol D., Milrad M., Coelho R. & Kizilcec R. F. (2025), International Journal of Artificial Intelligence in Education. 35, 3, p. 1288-1316

    8.

  8. Applying IRT to Distinguish Between Human and Generative AI Responses to Multiple-Choice Assessments

    Strugatski A. & Alexandron G. (2025), 15th International Conference on Learning Analytics and Knowledge, LAK 2025. p. 817-823

    9.

  9. Uncovering Measurement Biases in LLM Embedding Spaces: The Anna Karenina Principle and Its Implications for Automated Feedback

    Gurin Schleifer A., Beigman Klebanov B. & Alexandron G. (2025), International Journal of Artificial Intelligence in Education. 35, 5, p. 2821-2855

    10.

  10. Digital Cognitive Apprenticeship: Scaling Rigorous Machine Learning Education in High Schools

    Perach S. & Alexandron G. (2025), L@S 2025 - Proceedings of the 12th ACM Conference on Learning @ Scale. p. 286-290

    11.

    2024

  11. Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features

    Yacobson E., Toda A. M., Cristea A. I. & Alexandron G. (2024), Computers and Education. 210, 104960

    12.

  12. An evaluation of assessment stability in a massive open online course using item response theory

    Gershon S. K., Anghel E. & Alexandron G. (2024), Education and Information Technologies. 29, 3, p. 2625-2643

    13.

  13. Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers

    Feldman-Maggor Y., Nazaretsky T. & Alexandron G. (2024), Proceedings of the 16th International Conference on Computer Supported Education, CSEDU 2024. Ortega-Arranz A., McLaren B., Chounta I-A, Jovanovic J., Poquet O. & Viberg O. (eds.). p. 436-444

    14.

  14. Mind the Gap: Confronting the Vast Divide Between CS Teaching and Machine Learning Pedagogy

    Perach S. & Alexandron G. (2024), Technology Enhanced Learning for Inclusive and Equitable Quality Education. Pishtari G., Jivet I., Ruipérez Valiente J. A., Rummel N. & Ferreira Mello R. (eds.). Vol. 15159. p. 344-358

    15.

  15. Causal-mechanical explanations in biology: Applying automated assessment for personalized learning in the science classroom

    Ariely M., Nazaretsky T. & Alexandron G. (2024), Journal of Research in Science Teaching. 61, 8, p. 1858-1889

    16.

  16. Recommending Is Reflecting: A Surprising Benefit of Social Recommender Systems for Teachers

    Yacobson E. & Alexandron G. (2024), Technology Enhanced Learning for Inclusive and Equitable Quality Education. Pishtari G., Jivet I., Ruipérez Valiente J. A., Rummel N. & Ferreira Mello R. (eds.). Vol. 15160. p. 195-200

    17.

  17. The digital fingerprint of learner behavior: Empirical evidence for individuality in learning using deep learning

    Salman A. & Alexandron G. (2024), Computers and Education: Artificial Intelligence. 7, 100322

    18.

    2023

  18. Using participatory design to design gamified interventions in educational environments

    Toda A., Yacobson E., Alexandron G., Palomino P. T., Souza M., Santos E., Corrêa A., Lisboa R., Cordeiro T. D. & Cristea A. I. (2023), Gamification Design for Educational Contexts. p. 85-96

    19.

  19. How Do Teachers Search for Learning Resources? A Mixed Method Field Study

    Yacobson E. & Alexandron G. (2023), Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings. Jivet I., Perifanou M., Viberg O., Muñoz-Merino P. J. & Papathoma T. (eds.). p. 489-503

    20.

  20. Transformer-based Hebrew NLP models for Short Answer Scoring in Biology

    Schleifer A. G., Klebanov B. B., Ariely M. & Alexandron G. (2023), BEA 2023 - 18th Workshop on Innovative Use of NLP for Building Educational Applications, Proceedings of the Workshop. Tack A., Horbach A., Kochmar E., Burstein J., Madnani N., Laarmann-Quante R., Zesch T., Yaneva V. & Yuan Z. (eds.). p. 550-555

    21.

  21. Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology

    Ariely M., Nazaretsky T. & Alexandron G. (2023), International Journal of Artificial Intelligence in Education. 33, 1, p. 1-34

    22.

  22. Automated Identification and Validation of the Optimal Number of Knowledge Profiles in Student Response Data

    Din B., Nazaretsky T., Feldman-Maggor Y. & Alexandron G. (2023), Proceedings of the 16th International Conference on Educational Data Mining, EDM 2023. Feng M., Talukdar P. & Käser T. (eds.). p. 458-465

    23.

  23. A General Purpose Anomaly-Based Method for Detecting Cheaters in Online Courses

    Alexandron G., Berg A. & Ruiperez-Valiente J. A. (2023), IEEE Transactions on Learning Technologies. 17, p. 1-11

    24.

  24. Towards Automated Assessment of Scientific Explanations in Turkish using Language Transfer

    Nazaretsky T., Yolcu H. H., Ariely M. & Alexandron G. (2023), Proceedings of the 16th International Conference on Educational Data Mining, EDM 2023. Feng M., Talukdar P. & Käser T. (eds.). p. 453-457

    25.

  25. Simulated Learners in Educational Technology: A Systematic Literature Review and a Turing-like Test

    Kaser T. & Alexandron G. (2023), International Journal of Artificial Intelligence in Education. 34, 2, p. 545-585

    26.

  26. The effects of assessment design on academic dishonesty, learner engagement, and certification rates in MOOCs

    Alexandron G., Wiltrout M. E., Berg A., Gershon S. K. & Ruiperez-Valiente J. A. (2023), Journal of Computer Assisted Learning. 39, 1, p. 141-153

    27.

    2022

  27. Assisting Teachers in Finding Online Learning Resources: The Value of Social Recommendations

    Yacobson E., Toda A. M., Cristea A. I. & Alexandron G. (2022), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners and Doctoral Consortium - 23rd International Conference, AIED 2022, Proceedings. Cristea A. I., Rodrigo M. M., Matsuda N. & Dimitrova V. (eds.). p. 391-395

    28.

  28. Empowering Teachers with AI: Co-Designing a Learning Analytics Tool for Personalized Instruction in the Science Classroom

    Nazaretsky T., Bar C., Walter M. & Alexandron G. (2022), LAK 2022 - Conference Proceedings. p. 1-12

    29.

  29. Evaluating a learning analytics dashboard to detect dishonest behaviours: A case study in small private online courses with academic recognition

    Jaramillo-Morillo D., Ruipérez-Valiente J. A., Burbano Astaiza C. P., Solarte M., Ramirez-Gonzalez G. & Alexandron G. (2022), Journal of Computer Assisted Learning. 38, 6, p. 1574-1588

    30.

  30. Teachers' trust in AI-powered educational technology and a professional development program to improve it

    Nazaretsky T., Ariely M., Cukurova M. & Alexandron G. (2022), British Journal of Educational Technology. 53, 4, p. 914-931

    31.

  31. A Blended-Learning Program for Implementing a Rigorous Machine-Learning Curriculum in High-Schools

    Perach S. & Alexandron G. (2022), L@S 2022 - Proceedings of the 9th ACM Conference on Learning @ Scale. p. 267-270

    32.

  32. An Instrument for Measuring Teachers' Trust in AI-Based Educational Technology

    Nazaretsky T., Cukurova M. & Alexandron G. (2022), LAK 2022 -12th International Learning Analytics and Knowledge Conference. p. 56-66
    Submitted Version

    33.

  33. Trustworthy remote assessments: A typology of pedagogical and technological strategies

    Hilliger I., RuipérezValiente J. A., Alexandron G. & Gašević D. (2022), Journal of Computer Assisted Learning. 38, 6, p. 1507-1520

    34.

    2021

  34. De-identification is insufficient to protect student privacy, orWhat can a field trip reveal?

    Yacobson E., Fuhrman O., Hershkowitz S. & Alexandron G. (2021), Journal of Learning Analytics. 8, 2, p. 83-92

    35.

  35. Defining and measuring completion and assessment biases with respect to English language and development status: not all MOOCs are equal

    Gershon S. K., Ruipérez-Valiente J. A. & Alexandron G. (2021), International Journal of Educational Technology in Higher Education. 18, 1, 41

    36.

    2020

  36. Identifying relations between items in an online learning tutor using educational data mining

    Nazaretsky T., Hershkovitz S. & Alexandron G. (2020)

    37.

  37. Teacher-sourcing semantic information in a Physics blended-learning environment

    Yacobson E., Bar-Yosef A., Hen E. & Alexandron G. (2020)

    38.

    2019

  38. Towards a General Purpose Anomaly Detection Method to Identify Cheaters in Massive Open Online Courses

    Alexandron G., Ruiperez-Valiente J. A. & Pritchard D. E. (2019)

    39.

  39. Understanding the potential and challenges of Big Data in schools and education

    Hershkovitz A. & Alexandron G. (2019), Tendencias Pedagógicas. 35, p. 7-17

    40.

  40. Are MOOC Learning Analytics Results Trustworthy? With Fake Learners, They Might Not Be!

    Alexandron G., Yoo L. Y., Ruiperez-Valiente J. A., Lee S. & Pritchard D. E. (2019), International Journal of Artificial Intelligence in Education. 29, 4, p. 484-506
    Submitted Version

    41.

    2018

  41. A New Method for Measuring Similarity Between Educational Items from Response Data

    Nazaretsky T., Hershkovitz S. & Alexandron G. (2018)

    42.

  42. Analytics we can trust: On the importance of Verication in the design of Big Data educational technologies

    Alexandron G. (2018)

    43.

  43. Privacy and Security in Educational Technology

    Alexandron G. (2018)

    44.

  44. ביג דאטה בחינוך - פוטנציאל ואתגרים

    Alexandron G. & Hershkovitz A. (2018), Kriyat Beinayim. 31, p. 8-12

    45.

  45. Predicting Reading Comprehension in Digital Platforms

    Alexandron G., Fuhrman O. & Hershkovitz A. (2018)

    46.

  46. Kappa Learning: A New Method for Measuring Similarity Between Educational Items Using Performance Data

    Nazaretsky T., Hershkovitz S. & Alexandron G. (2018), arXiv
    Submitted Version

    47.

  47. Evaluating the Robustness of Learning Analytics Results Against Fake Learners

    Alexandron G., Ruipérez-Valiente J. A., Lee S. & Pritchard D. E. (2018), Lifelong Technology-Enhanced Learning - 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Proceedings. Drachsler H., Perez-Sanagustin M., Scheffel M., Elferink R. & Pammer-Schindler V. (eds.). p. 74-87
    Submitted Version

    48.

  48. Evaluating the Effectiveness of Animated Cartoons in an Intelligent Math Tutoring System Using Educational Data Mining

    Alexandron G., Keinan G., Levy B. & Hershkovitz S. (2018), Proceedings of EdMedia + Innovate Learning 2018. Fulford C., Weippl E., Sorensen E. K., Sointu E., Marks G., Davidson-Shivers G. V., Knezek G., Viteli J., Braak J. V., Voogt J., Kreijns K., DePryck K., Cantoni L., Castro M., Brown M., Ebner M., Fominykh M., Zawacki-Richter O., Weber P., Christensen R., Hatzipanagos S. & Bastiaens T. (eds.). Amsterdam, Netherlands p. 719-730

    49.

    2016

  49. Detecting cheaters in MOOCs using item response theory and learning analytics

    Alexandron G., Lee S., Chen Z. & Pritchard D. E. (2016), CEUR Workshop Proceedings. 1618, p. 53-56
    Submitted Version

    50.

  50. Using Multiple Accounts for Harvesting Solutions in MOOCs

    Ruiperez-Valiente J. A., Alexandron G., Chen Z. & Pritchard D. E. (2016), Proceedings of the Third ACM Conference on Learning @ Scale (L@S 2016). p. 63-70
    Submitted Version

    51.

    2015

  51. Learning Experiments using AB Testing at Scale in a Physics MOOC

    Chudzicki C., Chen Z., Choi Y., Zhou Q., Alexandron G. & Pritchard D. E. (2015)

    52.

  52. Discovering the Pedagogical Resources that Assist Students to Answer Questions Correctly A Machine Learning Approach

    Alexandron G., Zhou Q. & Pritchard D. E. (2015), Proceedings of the 8th International Conference on Educational Data Mining. p. 520-523
    Submitted Version

    53.

  53. Evidence of MOOC Students Using Multiple Accounts to Harvest Correct Answers

    Alexandron G., Ruiperez-Valientea J. A. & Pritchard D. E. (2015)

    54.

  54. Using Prediction Models to Analyze the Effectiveness of the Instructional Resources

    Alexandron G., Chen Z., Chudzicki C. & Pritchard D. E. (2015)

    55.

    2007

  55. Kinetic and dynamic data structures for convex hulls and upper envelopes

    Alexandron G., Kaplan H. & Sharir M. (2007), Computational Geometry-Theory And Applications. 36, 2, p. 144-158

    56.

    2006

  56. איך לחתוך את הסנדוויץ' - על גאומטריה חישובית ויישומיה

    Alexandron G. (2006), Galileo

    57.