Classroom Adoptions

  • University of Pittsburgh: 1) CS2001, 2) CS2610, 3) CS0401, 4) CS1635, 5) PHYS0174, 6) PHYS0175, 7) PSY0422.
  • Purdue University: 1) ENGR131, 2) ENGR132, 3) MSE330, 4) MATH16020, 5) MET230, 6) CHEM126, 7) ME354, 8) IET33520, 9) EAPS111.
  • Bogazici University: 1) IE256, 2) IE312.
  • Thiel College: 1) MATH125.
  • Ivy Tech Community College: 1) MATH043, 2) MATH123, 3) MATH137, 4) MATH201.
  • University of Florida: 1) CGS2531, 2) COP2271, 3) COP2274.

Publications

  1. Butt, A. A., Anwar, S., & Menekse, M. (2023). Work in progress: Uncovering engineering students’ sentiments from weekly reflections using natural language processing. In Proceedings of the 2023 ASEE Annual Conference & Exposition, Baltimore, MD, June 25-28, 2023. (link)
  2. Menekse, M. (2023). Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges. Journal of Engineering Education, 112: 578-582. https://doi.org/10.1002/jee.20539
  3. Butt, A., Anwar, S., & Menekse, M. (2023). How Do NLP-Supported Scaffolding Techniques Support Students’ Written Reflections?, In proceedings of the 2023 17th International Technology, Education and Development Conference, Valencia, Spain, p. 7450, 6-8 March, 2023. http://dx.doi.org/10.21125/inted.2023.2036
  4. Butt, A., & Menekse, M. (2023). The Impact of Reminder Nudge on STEM Students’ Application Engagement. In proceedings of the 2023 17th International Technology, Education and Development Conference, Valencia, Spain, p. 7992, 6-8 March, 2023. https://doi.org/10.21125/inted.2023.2170
  5. Magooda, A., Litman, D., Butt, A. A., & Menekse, M. (2022). Improving the Quality of Students’ Written Reflections using Natural Language Processing: Model Design and Classroom Evaluation. In Proceedings of the 23rd International Conference on Artificial Intelligence in Education, Durham, UK, pp. 519-525, July 2022. https://doi.org/10.1007/978-3-031-11644-5_43
  6. Anwar, S., Butt, A. A., and Menekse, M. (2022) Exploring Relationships Between Academic Engagement, Application Engagement, and Academic Performance in a First-Year Engineering Course. In Proceedings of the 2022 IEEE Frontiers in Education Conference (FIE), Uppsala, Sweden, 2022, pp. 1-5, doi: 10.1109/FIE56618.2022.9962530.
  7. Butt, A. A., Anwar, S., Magooda, A., & Menekse, M. (2022). Comparative analysis of the rule-based and machine learning approach for assessing student reflections. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), In Proceedings of the 16th International Conference of the Learning Sciences – ICLS 2022 (pp. 1577-1580). International Society of the Learning Sciences. (pdf)
  8. Butt, A. A., & Anwar, S., & Menekse, M. (2022, August). WIP: Role of digital nudging strategies on STEM students’ application engagement. In Proceedings of the 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/41039.
  9. Magooda, A., Litman, D. & Elaraby M. (2021). Exploring Multitask Learning for Low-Resource Abstractive Summarization. Findings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Punta Cana, Dominican Republic, November 2021. (link)
  10. Magooda, A. & Litman, D. (2021). Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization. Findings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Punta Cana, Dominican Republic, November 2021. (link)
  11. Magooda, A., & Litman, D. (2020). Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis. In Proceedings of the 33rd International FLAIRS Conference, North Miami Beach, Florida, May 2020. (pdf)
  12. Menekse, M. (2020). The reflection-informed learning and instruction to improve students’ academic success in undergraduate classrooms. The Journal of Experimental Education88(2), 183-199. https://doi.org/10.1080/00220973.2019.1620159
  13. Anwar, S., & Menekse, M. (2020). A systematic review of observation protocols used in postsecondary STEM classrooms. Review of Education9(1), 81-120. https://doi.org/10.1002/rev3.3235
  14. Luo, W., Liu, F., Liu, Z., & Litman, D. (2018). A Novel ILP Framework for Summarizing Content with High Lexical Variety. Natural Language Engineering, Volume 24, Issue 6, pp. 887-920. http://dx.doi.org/10.1017/S1351324918000323
  15. Heo, D., Anwar, S., & Menekse, M. (2018). The relationship between engineering students’ achievement goals, reflection behaviors, and learning outcomes. International Journal of Engineering Education, 34(5), 1634-1643 (pdf)
  16. Menekse, M., Anwar, S., & Purzer, S. (2018). Self-Efficacy and Mobile Learning Technologies: A Case Study of CourseMIRROR. In C. B. Hodges (Ed.), Self-Efficacy in Instructional Technology Contexts, Springer Nature Switzerland AG 2018. (pdf)
  17. Anwar, S., Menekse, M., Heo, D., & Kim, D. (2018). Work-in-Progress: Students’ reflection quality and effective team membership. In Proceedings of the 2018 ASEE Annual Conference, Salt Lake City, Utah. (pdf)
  18. Heo, D., Anwar, S., & Menekse, M. (2017). How do engineering students’ achievement goals influence their reflection behaviors and learning outcomes? In Proceedings of the 2017 ASEE Annual Conference, Columbus, Ohio. (pdf)
  19. Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2017). Scaling reflection prompts in large classrooms via mobile interfaces and natural language processing. In Proceedings of 22nd ACM Conference on Intelligent User Interfaces (IUI 2017), Limassol, Cyprus. (pdf)
  20. Luo, W., Liu, F., & Litman, D. (2016). An improved phrase-based approach to annotating and summarizing student course responses. In Proceedings of the 26th International Conference on Computational Linguistics (COLING), pp. 53-63, Osaka, Japan. (pdf)
  21. Luo, W., & Litman, D. J. (2016). Determining the quality of a student reflective response. In Proceedings 29th International FLAIRS Conference, pp. 226-231, Key Largo, FL. (Best Student Paper Award Nominee) (pdf)
  22. Luo, W., Liu, F., Liu, Z., & Litman, D. (2016). Automatic summarization of student course feedback. In Proceedings Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT), pp. 80-85, San Diego, CA. (short paper) (pdf)
  23. Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2015). CourseMIRROR: Enhancing large classroom instructor-student interactions via mobile interfaces and natural language processing. Works-In-Progress, In Proceedings of ACM Conference on Human Factors in Computing Systems (CHI 2015), 1473-1478, Seoul, Korea. (extended abstract) (pdf)
  24. Luo, W., Fan, X., Menekse, M., Wang, J., & Litman, D. J. (2015). Enhancing instructor-student and student-student interactions with mobile interfaces and summarization. In Proceedings NAACL HLT Companion, 16-20, Denver, CO. (demo) (pdf)
  25. Luo, W., & Litman, D. J. (2015). Summarizing student responses to reflection prompts. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP ) pp. 1955–1960, Lisbon, Portugal (short paper). (pdf)

Dissertations

  1. Butt, A. A. (2023). The Role of Digital Nudges in Engineering Students’ Engagement with an Educational Mobile Application. (Doctoral Dissertation). Purdue University.
  2. Magooda, A. (2022). Techniques to Enhance Abstractive Summarization Model Training for Low Resource Domains. (Doctoral Dissertation). University of Pittsburgh.
  3. Anwar, S. (2020). Role of Different Instructional Strategies on Engineering Students’ Academic Performance and Motivational Constructs. (Doctoral Dissertation). Purdue University.
  4. Fan, X. (2017). Scalable teaching and learning via intelligent user interfaces. (Doctoral Dissertation). University of Pittsburgh.
  5. Luo, W. (2017). Automatic summarization for student reflective responses. (Doctoral Dissertation). University of Pittsburgh.