2024

  1. 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

    2.

  2. 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

    3.

    2023

  3. 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

    4.

  4. 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

    5.

  5. 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

    6.

  6. 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

    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.

    2022

  8. Teachers' trust in AIpowered 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

    9.

  9. 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

    10.

  10. 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

    11.

  11. 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. (2022), Journal of Computer Assisted Learning

    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.

    2020

  13. Assessment that matters: Balancing reliability and learner-centered pedagogy in MOOC assessment

    Alexandron G., Wiltrout M. E., Berg A. & Ruipérez-Valiente J. A. (2020)

    14.

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

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

    15.

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

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

    16.

    2019

  16. 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

    17.

  17. 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)

    18.

  18. Using Machine Learning to Detect 'Multiple-Account' Cheating and Analyze the Influence of Student and Problem Features

    Ruiperez-Valiente J. A., Munoz-Merino P. J., Alexandron G. & Pritchard D. E. (2019), IEEE Transactions on Learning Technologies. 12, 1, p. 112-122

    19.

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

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

    20.

    2018

  20. 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

    21.

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

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

    22.

  22. 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

    23.

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

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

    24.

  24. ביג דאטה בחינוך - פוטנציאל ואתגרים

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

    25.

  25. Predicting Reading Comprehension in Digital Platforms

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

    26.

  26. Privacy and Security in Educational Technology

    Alexandron G. (2018)

    27.

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

    Alexandron G. (2018)

    28.

    2017

  28. Copying@Scale: Using Harvesting Accounts for Collecting Correct Answers in a MOOC

    Alexandron G., Ruipérez-Valiente J. A., Chen Z., Muñoz-Merino P. J. & Pritchard D. E. (2017), Computers & Education. 108, p. 96-114

    29.

    2016

  29. Researching for better instructional methods using AB experiments in MOOCs: results and challenges

    Chen Z., Chudzicki C., Palumbo D., Alexandron G., Choi Y. J., Zhou Q. & Pritchard D. E. (2016), Research and Practice in Technology Enhanced Learning. 11, 1, 9

    30.

  30. 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

    31.

  31. 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

    32.

    2015

  32. 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)

    33.

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

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

    34.

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

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

    35.

  35. 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

    36.

    2007

  36. 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

    37.

    2006

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

    Alexandron G. (2006), Galileo

    38.

    2005

  38. Kinetic and Dynamic Data Structures for Convex Hulls and Upper Envelopes

    Alexandron G., Kaplan H. & Sharir M. (2005), Workshop on Algorithms and Data Structures. p. 269-281

    39.