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. 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

    3.

  3. 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

    4.

  4. 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

    5.

  5. 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.). p. 195-200

    6.

  6. 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

    7.

  7. 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

    8.

  8. 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

    9.

    2023

  9. 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

    10.

  10. 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

    11.

  11. 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

    12.

  12. 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

    13.

  13. 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

    14.

  14. 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

    15.

  15. 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

    16.

  16. 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

    17.

    2022

  17. 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

    18.

  18. 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

    19.

  19. 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

    20.

  20. 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

    21.

  21. 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

    22.

  22. 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

    23.

  23. 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

    24.

    2021

  24. Encouraging Teacher-Sourcing of Social Recommendations Through Participatory Gamification Design

    Yacobson E., Toda A., Cristea A. I. & Alexandron G. (2021), Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings. Cristea A. I. & Troussas C. (eds.). p. 418-429

    25.

  25. 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

    26.

  26. 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

    27.

    2020

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

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

    28.

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

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

    29.

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

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

    30.

    2019

  30. 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

    31.

  31. 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)

    32.

  32. 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

    33.

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

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

    34.

    2018

  34. 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

    35.

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

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

    36.

  36. 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

    37.

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

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

    38.

  38. ביג דאטה בחינוך - פוטנציאל ואתגרים

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

    39.

  39. Predicting Reading Comprehension in Digital Platforms

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

    40.

  40. Privacy and Security in Educational Technology

    Alexandron G. (2018)

    41.

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

    Alexandron G. (2018)

    42.

    2017

  42. 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

    43.

    2016

  43. 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

    44.

  44. 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

    45.

  45. 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

    46.

    2015

  46. 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)

    47.

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

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

    48.

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

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

    49.

  49. 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

    50.

    2007

  50. 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

    51.

    2006

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

    Alexandron G. (2006), Galileo

    52.

    2005

  52. 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

    53.