Publications

Refereed Articles

Refereed Articles in Journals

  1. Alexandron, G., Kaplan, H., and Sharir, M. (2007). Kinetic and dynamic data structures for convex hulls and upper envelopes. Computational Geometry: Theory and applications, 36(2): 144-158. [Link]
  2. Alexandron, G., Armoni, M., Gordon, G., and Harel, D. (2014). Scenario-based programming, usability-oriented perception. ACM Transactions on Computing Education, 14(3), 21:1-23. [Link]
  3. Alexandron, G., Armoni, M., Gordon, M., & Harel, D. (2016). Teaching nondeterminism through programming. Informatics in Education 15(1), 1-2 [Link]
  4. Chen, Z., Chudzicki, C., Palumbo D., Alexandron G., Choi Y. J., Zhou Q., Pritchard D. E. (2016). Researching for better instructional methods using AB experiments in MOOCs: results and challenges. Research and Practice in Technology Enhanced Learning, 11(9): 1-20. [Link]
  5. Alexandron, G., Valiente, J. A. R., Chen, Z., Muñoz-Merino, P. J., and Pritchard, D. E. (2017). Copying@Scale: Using Harvesting Accounts for Collecting Correct Answers in a MOOC. Computers & Education, Volume 108, 96-114. [Link]
  6. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2017). Teaching Scenario-Based Programming: An Additional Paradigm for the High School Computer Science Curriculum, PART 1. Computing in Science & Engineering, Volume 19, 58-67. [Link]
  7. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2017). Teaching Scenario-Based Programming: An Additional Paradigm for the High School Computer Science Curriculum, PART 2. Computing in Science & Engineering, Volume 20. [Link]
  8. Valiente, J. A. R., Muñoz-Merino, P. J.,  Alexandron, G., and Pritchard, D. E. (2017). Using Machine Learning to Detect `Multiple-Account' Cheating and Analyze the Influence of Student and Problem Features. IEEE Transactions on Learning Technologies (TLT). [Link]
  9. Hershkovitz, A., and Alexandron, G. (2019). Understanding the potential and challenges of Big Data in schools and education. Tendencias PedagóGicas, 35, 7-17. [Link]
  10. Alexandron, G., Yoo, L. Y., Ruipérez-Valiente, J.A., Lee, S., and Pritchard, D. E. (2019). Are MOOC Learning Analytics Results Trustworthy? With Fake Learners, They Might Not Be!. The International Journal of Artificial Intelligence in Education (IJAIED) [Link] [Preprint]
  11. Yacobson, E., Fuhrman, O., Hershkovitz, S., and Alexandron, G. (2021). De-identification is Insufficient to Protect Student Privacy, or – What Can a Field Trip Reveal?. Journal of Learning Analytics, 8(2), 83-92. [Link]
  12. Gershon, S. K., Ruipérez-Valiente, J.A., and Alexandron, G. (2021). Defining and Measuring Completion and Assessment Biases with Respect to English Language and Development Status: Not All MOOCs are Equal. The International Journal of Educational Technology in Higher Education (ETHE), , 18(1), 1-21. [Preprint] [Link
  13. Jaramillo-Morillo, D., Ruipérez-Valiente, J. A., Burbano Astaiza, C. P., Solarte, M., Ramirez-Gonzalez, G., & Alexandron, G. (2022). Evaluating a learning analytics dashboard to detect dishonest behaviours: A case study in small private online courses with academic recognition. Journal of Computer Assisted Learning, 1– 15. [Link]
  14. Ariely, M., Nazaretsky, T., and Alexandron, G. (2022). Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology. The International Journal of Artificial Intelligence in Education (IJAIED). [Link]
  15. Nazaretsky, T., Ariely, M., Cukurova, M., and Alexandron, G. (2022). Teachers' trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53, 914– 931. [Link]
  16. Alexandron, G., Wiltrout, M. E., Berg, A., Gershon, S. K., and Ruipérez-Valiente, J.A. (2022). The Effects of Assessment Design on Academic Dishonesty, Learner Engagement, and Certification Rates in MOOCs. Journal of Computer Assisted Learning, 39(1), 141– 153. [Link]
  17. Scherz, Z., Salman, A., Alexandron, G., and Shwartz, Y. (2022.) WhatsApp Discourse Throughout COVID-19: Towards Computerized Evaluation of the Development of a STEM Teachers Professional Learning Community. The International Journal of Artificial Intelligence in Education, 1-25[Link]
  18. Hilliger, I.,  Ruipérez-Valiente, J.A, Alexandron, G., and Gašević, D. (2022). Trustworthy Remote Assessments: A Typology of Pedagogical and Technological Strategie. Journal of Computer Assisted Learning. [Link]
  19. Käser, T., and Alexandron, G. (2023). Simulated Learners in Educational Technology: A Systematic Literature Review and a Turing-like Test. The International Journal of Artificial Intelligence in Education. [Link]
  20. Gershon, S., Anghel, E., and Alexandron, G. (2023). An Evaluation of Assessment Stability in a Massive Open Online Course Using Item Response Theory. Education and Information Technologies. [Link]
  21. Alexandron, G., Berg, A., and Ruipérez-Valiente, J.A. (2023). A General Purpose Anomaly-based Method for Detecting Cheaters in Online Courses. IEEE Transactions on Learning Technologies. [Link]
  22. Yacobson, E., Toda, A. M., Cristea, A. I., and Alexandron, G. (2023). Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features. Computers & Education. [Link]
  23. Ariely, M., Nazaretsky, T., and Alexandron, G. (2024). Causal-mechanical explanations in biology: Towards automated assessment and personalized learning in the science classroom. Journal of Research in Science Teaching (JRST) [Link]

Refereed Articles in Conference Proceedings

  1. Alexandron, G., Kaplan, H., and Sharir, M. (2005). Kinetic and dynamic data structures for convex hulls and upper envelopes. In Proceedings of the 9th international conference on Algorithms and Data Structures, p. 269-281. [Link]
  2. Rich, A., Alexandron, G., and Naveh, R. (2009). An Explanation-based constraint debugger. In Proceedings of the 5th international Haifa verification conference on Hardware and software: verification and testing, p. 52-56. [Link]
  3. Alexandron, G., Armoni, M., and Harel, D. (2011). Programming with the User in Mind. In Proceedings of the 23rd Annual Conference of the Psychology of Programming Interest Group, p. 1-12. [Link]
  4. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2012). The effect of previous programming experience on the learning of scenario-based programming. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research, p. 151-159. [Link]
  5. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2013). On Teaching Programming with Nondeterminism. In Proceedings of the 8th Workshop in Primary and Secondary Computing Education, p. 71-74. [Link]
  6. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2014). Scenario-based programming: Reducing the cognitive load, fostering abstract thinking. In Proceedings of the 36th International Conference on Software Engineering, p. 311-320. (15% acceptance rate) [Link]
  7. Alexandron, G., Zhou, Q., and Pritchard, D. E. (2015). Discovering the Pedagogical Resources that Assist Students to Answer Questions Correctly – A Machine Learning Approach. In Proceedings of the 8th International Conference on Educational Data Mining, p. 520-523. [Link]
  8. Alexandron, G., Lee, S., Chen, Z., and Pritchard, D. E. (2016). Detecting Cheaters in MOOCs Using Item Response Theory and Learning Analytics. In Proceedings of the 6th International Workshop on Personalization Approaches in Learning Environments, p. 53-56. [Link]
  9. Ruipérez-Valiente, J.A., *Alexandron, G., Chen, Z., and Pritchard, D. (2016). Using Multiple Accounts for Harvesting Solutions in MOOCs. In Proceedings of the Third ACM Conference on Learning @ Scale, p. 63-70. (22% acceptance rate; honorable mention for best conference paper; *equal contribution) [Link]
  10. Alexandron, G., Keinan, G., Levy, B., and Hershkovitz, S. (2018). Evaluating the Effectiveness of Animated Cartoons in an Intelligent Math Tutoring System Using Educational Data Mining. In Proceedings of EdMedia: World Conference on Educational Media and Technology, p. 719-730. [Preprint] [Link]
  11. Alexandron, G., Ruipérez-Valiente, J.A., Lee, S., and Pritchard, D. E. (2018). Evaluating the Robustness of Learning Analytics Results Against Fake Learners. In Proceedings of the 13th European Conference on Technology Enhanced Learning (EC-TEL'18), p. 74-87. [Preprint] [Link]
  12. Nazaretsky, T., Hershkovitz, S., and Alexandron, G. (2019). Kappa Learning: A New Method for Measuring Similarity Between Educational Items Using Performance Data. In Proceedings of the 12th International Conferenceon Educational Data Mining
    (EDM'19), p. 129-138. (22% acceptance rate)  [PDF] [Link]
  13. Alexandron, G., Wiltrout, M. E., Berg, A., and Ruipérez-Valiente, J.A. (2020). Assessment that matters: Balancing reliability and learner-centered pedagogy in MOOC assessment. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (LAK ’20), p. 512-517, ACM.  [Preprint] [Link]
  14. Perach, S., and Alexandron, G. (2021). A MOOC-BASED COMPUTER SCIENCE PROGRAM FOR MIDDLE SCHOOL: RESULTS, CHALLENGES, AND THE COVID-19 EFFECT. In Proceedings of  The seventh European MOOCs Stakeholder Summit (EMOOCs'2021) [Preprint]
  15. Gershon, S. K., Ruipérez-Valiente, J.A., and Alexandron, G. (2021). MOOC MONETIZATION CHANGES AND COMPLETION RATES: ARE LEARNERS FROM COUNTRIES OF DIFFERENT DEVELOPMENT STATUS EQUALLY AFFECTED? In Proceedings of  The seventh European MOOCs Stakeholder Summit (EMOOCs'2021). [Preprint]
  16. Yacobson, E., Toda, A. M., Cristea, A. I., and Alexandron, G. (2021). Encouraging Teacher-sourcing of Social Recommendations Through Participatory Gamification Design. In Proceedings of The 17th International Conference on Intelligent Tutoring Systems (ITS'21), p. 418-429(25% acceptance rate) [Preprint] [Link]
  17. Nazaretsky, T., Bar, C., Walter, M., and Alexandron, G. (2022). Empowering Teachers with AI: Co-Designing a Learning Analytics Tool for Personalized Instruction in the Science Classroom.  In Proceedings of the 12th International Conference on Learning Analytics & Knowledge (LAK '22), p. 1-12[Preprint] [Link]
  18. Nazaretsky, T., Cukurova, M., and Alexandron, G. (2022). An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology. In Proceedings of the 12th International Conference on Learning Analytics & Knowledge (LAK '22), p. 56-66. [Preprint] [Link
  19. Ariely, M., Nazaretsky, T., and Alexandron, G. (2022).  Personalized Automated Formative Feedback Can Support Students in Generating Causal Explanations in Biology. In Proceedings of the 16th International Conference of the Learning Sciences (ICLS'22),  pp. 953-956. [Link]
  20. Perach, S., and Alexandron, G. (2022). A Blended-Learning Program for Implementing a Rigorous Machine-Learning Curriculum in High-Schools. In Proceedings of the Ninth ACM Conference on Learning@ Scale (pp. 267-270). [Link]
  21. Yacobson, E., and Alexandron, G. (2023). How Do Teachers Search for Learning Resources? a Mixed Method Field Study. In Procceedings of the 18th European Conference on Technology Enhanced Learning (EC-TEL'23), pp. 489-503. [Link]
  22. Feldman-Maggor, Y., Nazaretsky, T., and Alexandron, G. (Forthcoming). Explainable AI for unsupervised machine learning: A proposed scheme applied to a case study with science teachers. Accepted to The 16th International Conference on Computer Supported Education (CSEDU'24). [Preprint]

Book Chapters 

  1. Toda, A., Yacobson, E., Alexandron, G., Palomino, P. T., Souza, M., Santos, E., ... & Cristea, A. I. (2023). Using Participatory Design to Design Gamified Interventions in Educational Environments. In Gamification Design for Educational Contexts: Theoretical and Practical Contributions (pp. 85-96). Cham: Springer International Publishing.

Conferences w/o proceedings and Workshops 

  1. Alexandron, G., Lagoon, V., Naveh, R., and Rich, A. (2010). Gendebugger: An explanation-based constraint debugger. Workshop on Techniques for Implementing Constraint programming System[Link]
  2. Alexandron, G., Chen, Z., Chudzicki, C., and Pritchard, E. D. (2015). Using Prediction Models to Analyze the Effectiveness of the Instructional Resources in MOOCs. Presented in Learning with MOOCs II workshop, NY. 
  3. Alexandron, G., Ruipérez-Valiente, J.A., and Pritchard, E. D (2015). Evidence of MOOC Students Using Multiple Accounts to Harvest Correct Answers. Presented in Learning with MOOCs II workshop, NY. [Link]
  4. Alexandron, G., Fuhrman, O., and Hershkovitz A. (2018). Predicting Reading Comprehension in Digital Platforms. The 4th Learning Sciences Symposium, Tel-Aviv University.
  5. Alexandron, G. (2018). Privacy and Security in Educational Technology. The 5th Privacy, Cyber and Technology Workshop , Tel-Aviv University, May 2018. 
  6. Alexandron, G. (2018). Analytics we can trust: On the importance of Verication in the design of Big Data educational technologies. The 1st Workshop on the "Profession" in Technology-Enhanced Learning: Open Science, EC-TEL'18, Leeds, UK. [Abstract
  7. Yacobson, E., Bar-Yosef, A., Hen, E., and Alexandron, G. (2020). Teacher-sourcing semantic information in a Physics blended-learning environment. In Learning Analytic Services to Support Personalized Learning and Assessment at Scale Workshop at LAK'20.
  8. Ariely, M., Nazaretsky, T., and Alexandron, G. (2021) Towards automated formative assessment of students’ scientific explanations in Biology using Natural Language Processing. The 94th International Conference of the National Association for Research in Science Teaching (NARST’21).
  9. Yacobson, E., Toda, A. M., Cristea, A. I., and Alexandron, G. (2021). Participatory design of feedback mechanism in a physics blended-learning environment. Companion Proceedings of The 16th European Conference on Technology Enhanced Learning. [Link]
  10. Nazaretsky, T., Cukurova, M., Ariely, M., and Alexandron, G. (2021). Confirmation bias and trust: Human factors that influence teachers' attitudes towards AI-based educational technology. Companion Proceedings of The 16th European Conference on Technology Enhanced Learning. [Link]
  11. Gurin-Schleifer, A., Klebanov, B.B., Ariely, M., and Alexandron, G. (2023). Transformer-based Hebrew NLP models for Short Answer Scoring in Biology. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA'23), pp. 550-555) [Link]

Posters and Demos

  1. Chudzicki, C., Chen, Z., Choi, Y.-J., Zhou, Q., Alexandron, G. and Pritchard, D. E. (2015). Learning Experiments using AB Testing at Scale in a Physics MOOC. Poster presented at the Annual Meeting of The ACM Conference on Learning at Scale, Vancouver, British Columbia.
  2. Nazaretsky, T., Hershkovitz, S., and Alexandron, G., (2018). A New Method for Measuring Similarity Between Educational Items from Response Data. The Annual Conference of the Israeli Statistics Association, Weizmann Institute of Science, Israel. [PDF]
  3. Alexandron, G., Ruipérez-Valiente, J.A., and Pritchard, E. D (2019). Towards a General Purpose Anomaly Detection Method to Identify Cheaters in Massive Open Online Courses.  In Proceedings of the 12th International Conferenceon Educational Data Mining. 480–483. [preprint] [Proceedings]
  4. Yacobson, E., Fuhrman, O., Hershkovitz, S., and Alexandron, G. (2020). De-identification is not enough to guarantee student privacy: De-anonymizing personal information from basic logs. In Companion Proceedings 10th International Conference on Learning Analytics & Knowledge (LAK20). [Preprint]
  5. Ariely, M.,  Nazaretsky, T., and Alexandron, G. (2020). First Steps Towards NLP-based Formative Feedback to Improve Scientific Writing in Hebrew. Thirteenth International Conference on Educational Data Mining (EDM 2020). [Preprint]
  6. Yacobson, E., Toda, A. M., Cristea, A. I., and Alexandron, G. (2022). Assisting teachers in finding resources in online learning platforms: the value of social recommendations. AIED'2022. 
  7. Nazaretsky, T., Feldman-Maggor, Y., Alexandron, G. (2023). GrouPer: Group-based Personalization Application. Demo presented at the 13th International Conference on Learning Analytics & Knowledge (LAK '23).
  8. Din, B., Feldman-Maggor, Y., Nazaretsky, T., and Alexandron, G. (2023) Automated Identification and Validation of the Optimal Number of Knowledge Profiles in Student Response Data. The 16th International Conference on Educational Data Mining (EDM'2023). [Preprint]
  9. Nazaretsky, T., Ariely, M.,  Yolcuand, H. H., and  Alexandron, G. (2023) Towards Automated Assessment of Scientific Explanations in Turkish using Language Transfer. The 16th International Conference on Educational Data Mining (EDM'2023). [Link] (Best Poster Award)

Local Conferences (Hebrew)

  1. Nazaretsky, T., Hershkovitz, S., and Alexandron, G. (2020). Identifying relations between items in an online learning tutor using educational data mining. The Annual Conference of the Israeli Psychometric Association. 
  2. Gershon, S.,K., and Alexandron, G. (2021). מדידת הטיות בהישגי לומדים ממדינות מתפתחות בקורסים מקוונים המוניים פתוחים (Massive Open Online Courses) בעזרת תיאוריית התגובה לפריט. The Annual Conference of the Israeli Psychometric Association. 
  3. Ariely, M.,  Nazaretsky, T., and Alexandron, G. (2021). יישומים של ניתוח שפה טבעית להערכה אוטומטית של שאלות פתוחות במדעים. The Annual Conference of the Israeli Psychometric Association. 
  4. Yacobson, E., Olsher, S., and Alexandron, G. (2021). שימוש בלמידת מכונה לצורך סיווג משימות מתמטיות אינטראקטיביות בסביבת למידה מקוונת. The 9th Jerusalem Conference on Research in Mathematics Education. 

Other Publications

  1. Alexandron, G. (2006). How to cut a sandwich - on computational geometry and its applications. Galileo 91:40-46. (Hebrew, Popular science)  [Link]
  2. Alexandron, G., and Hershkovitz, A. (2018). Big Data in Education - Opportunities and Challenges. The journal of the Israeli middle-school STEM teachers. (Hebrew, Popular Science) [PDF]
  3. הערכה ומדידה בקורסים מקוונים:סקירת ספרות. (2021). אבי אללוף, סער קרפ גרשון, גיורא אלכסנדרון, וענת בן סימון. דו״ח מחקר, מרכז ארצי לבחינות ולהערכה. לינק