2024

  1. 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

    2.

  2. 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.). p. 344-358

    3.

  3. 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. (2024), International Journal of Artificial Intelligence in Education. 35, 3, p. 1288-1316

    4.

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

    Feldman-Maggor Y., Blonder R. & Alexandron G. (2024), Journal of Science Education and Technology. 34, 1, p. 1-12

    5.

    2023

  5. 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

    6.

  6. 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

    7.

  7. 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

    8.

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

    Gershon S. K., Anghel E. & Alexandron G. (2023), Education and Information Technologies. 29, 3, p. 2625-2643

    9.

  9. 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

    10.

  10. 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

    11.

    2022

  11. 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

    12.

  12. 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

    13.

  13. 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

    14.

  14. 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

    15.

  15. 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

    16.

    2021

  16. 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

    17.

  17. 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

    18.

    2020

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

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

    19.

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

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

    20.

    2019

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

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

    21.

  21. 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)

    22.

  22. 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

    23.

    2018

  23. Privacy and Security in Educational Technology

    Alexandron G. (2018)

    24.

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

    Alexandron G. (2018)

    25.

  25. 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

    26.

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

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

    27.

  27. 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

    28.

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

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

    29.

  29. ביג דאטה בחינוך - פוטנציאל ואתגרים

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

    30.

  30. Predicting Reading Comprehension in Digital Platforms

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

    31.

    2016

  31. 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

    32.

  32. 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

    33.

    2015

  33. 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

    34.

  34. 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)

    35.

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

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

    36.

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

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

    37.

    2007

  37. 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

    38.

    2006

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

    Alexandron G. (2006), Galileo

    39.