Computational approaches in Science Education | Publications

    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

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  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

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  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

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  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

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  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

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  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

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    2023

  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. 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

    11.

  11. 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

    12.

  12. 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

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  13. 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

    14.

  14. 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

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  15. 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

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  16. 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

    17.

    2022

  17. 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

    18.

  18. 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

    19.

  19. 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

    20.

  20. 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

    21.

  21. 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

    22.

  22. 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

    23.

  23. 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

    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 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

    35.

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

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

    36.

  36. ביג דאטה בחינוך - פוטנציאל ואתגרים

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

    37.

  37. Predicting Reading Comprehension in Digital Platforms

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

    38.

  38. Privacy and Security in Educational Technology

    Alexandron G. (2018)

    39.

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

    Alexandron G. (2018)

    40.

  40. 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

    41.

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

    Nazaretsky T., Hershkovitz S. & 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. Evidence of MOOC Students Using Multiple Accounts to Harvest Correct Answers

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

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

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

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  48. 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

    49.

  49. 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)

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    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.