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. [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]

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. Proceedings of Educational Data Mining 2019. (22% acceptance rate) [Link]
  13. Alexandron, G., Wiltrout, M. E., Berg, A., 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), ACM.  [Preprint] [Link]

 

Refereed Abstracts in 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. Nazaretsky, T., Hershkovitz, S., and Alexandron, G. (2020). Identifying relations between items in an online learning tutor using educational data mining. Accepted to The Annual Conference of the Israeli Psychometric Association. 
  8. Yacobson, E., Bar-Yosef, A., Hen, E., 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.

Posters

  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. Accepted to EDM'20. [Preprint]

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]