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Dr. Dawit Hailu

1. Hailu, D. H. (2025). Implementation of finite state logic machines via the dynamics of atomic systems. Results in Optics, 18, 100789. https://doi.org/10.1016/j.rio.2025.100789

2. Dawit Hiluf (2025), The physics of social distancing (in preparation) 

3. Hailu, D. H. (2024). An energy perspective on Galileo’s kinematic paradox. European Journal of Physics, 45(6), 065003. https://doi.org/10.1088/1361-6404/ad7c9a

4. Hailu, D. (2024). Solution for Matrix Exponentiation Using Eigenvalues. IntechOpen. doi: 10.5772/intechopen.1006203 

5. Fresch B., Yan TM., Hiluf D., Collini E., Levine R.D., Remacle F. (2018) Computing by Non-linear Optical Molecular Response. In: Stepney S., Rasmussen S., Amos M. (eds) Computational Matter. Natural Computing Series. Springer, Cham

6. Dawit Hiluf and Yonatan Dubi (2018) Phonon as environmental disturbance in three-level system (https://arxiv.org/abs/1803.08327)

7. Dawit Hiluf (2018), All-optical programmable logic array (PLA), Journal of Physics: Conference Series, Volume 987, Conference 1. 

8. Dawit Hiluf, Fesseha Kassahun (2017), A degenerate three-level laser coupled to a squeezed vacuum reservoir https://arxiv.org/abs/1709.10047

9. Fresch, B., Bocquel, J., Hiluf, D., Rogge, S., Levine, R. D. and Remacle, F. (2017), Implementation of multivariable logic functions in parallel by electrical addressing a molecule of three dopants in Silicon. ChemPhysChem. doi:10.1002/cphc.201700222

10. Dawit Hiluf (2017) Implementation of CNOT gate using Dynamics of two-level system 

11. Dawit Hiluf (2016) Link between Alhassid-Levine and Hioe-Eberly formalism of SU(N) equations of motion arXiv eprints 1612.03278.  https://arxiv.org/abs/1612.03276

12. Dawit Hiluf and Yonatan Dubi (2017) All-Optical Flip-Flops and Shift Registers in Multi-level systems (https://arxiv.org/abs/1703.05656)

13. Dawit Hiluf (2016) Solution of two-level system using 3rd order Magnus expansion, arXiv eprints 1610.05951

14. Fresch B, Hiluf D, Collini E, Levine RD, Remacle F (2013) Molecular decision trees realized by ultrafast electronic spectroscopy. Proc Natl Acad Sci USA 110:17183–17188.

15. Dawit Hiluf. 2005. A DEGENERATE THREE-LEVEL LASER COUPLED TO A SQUEEZED VACUUM RESERVOIR. (M.Sc thesis)Addis Ababa University.

Dr. Zhongzhou Chen

1. Wan, T., & Chen, Z. (2024). Exploring generative AI assisted feedback writing for students’ written responses to a physics conceptual question with prompt engineering and few-shot learning. Physical Review Physics Education Research, 20(1). https://doi.org/10.1103/physrevphyseducres.20.010152 

2. Chen, Z., Zhang, T., & Taub, M. (2024). How does a data-informed deliberate change in learning design impact students’ self-regulated learning tactics? Journal of Learning Analytics. https://learning-analytics.info/index.php/JLA/article/view/8083 

3. Kaldaras, Leonora, et al. “Developing Valid Assessments in the Era of Generative Artificial Intelligence.” Frontiers in Education, vol. 9, 7 Aug. 2024, https://doi.org/10.3389/feduc.2024.1399377.

4. Chen, Z. (2024, September 5). Junk Food Summary of a Very Healthy Paper on GenAI and Assessment. Retrieved February 21, 2025, from The Learning Plumber website: https://zhongzhou.github.io/the-learning-plumber/2024/09/05/GAI-Assessment.html

Caitlin Lim and Aleksandra Ambroziak

1. Thille, C., & Smith, J. (2011). Cold rolled steel and knowledge: What can higher education learn about productivity?. Change: The Magazine of Higher Learning. 43. 21-27. https://doi.org/10.1080/00091383.2011.556988

2. Bier, N., Moore, S., & Van Velsen, M. (2019). Instrumenting courseware and leveraging data with the Open Learning Initiative (OLI). Companion Proceedings 9th International Learning Analytics & Knowledge Conference, Tempe, AZ.

3. Koedinger, K.R., Corbett, A.T. & Perfetti, C. (2012). The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Cognitive Science 36(5): 757-798 (2012).

4. R. Glassey & O. Bälter, "Put the Students to Work : Generating Questions with Constructive Feedback," in IEEE Frontiers in Education Conference (FIE 2020), 2020.

Chase Randall 

1. McCartin- Lim, Mark, (2019). “Tools for Tutoring Theoretical Computer Science Topics”. Doctoral Dissertation, Manning College of Information Computer Sciences, University of Massachusetts Amherst, Amherst, MA. [pdf: https://drive.google.com/file/d/1A3lfVFXIgYJQv58SKv-JwipkKvT91sk_/view?usp=drive_link]

2. Maclellan, Christopher J., (2017). “Computational models of human learning: Applications for tutor development, behavior prediction, and theory testing”. Doctoral Dissertation, Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburg, PA. [pdf: https://drive.google.com/file/d/1yXBoa93uUpDoAcSVohj5Lsaz-s2fKxmv/view?usp=drive_link] [Read chapters 1 and 2]

3. Staikopoulos, Athanasios; I. Okeeffe; B. Yousuf; O. Conlan; E. Walsh; and V. Wade, (2015). "Enhancing Student Engagement through Personalized Motivations," 2015 IEEE 15th International Conference on Advanced Learning Technologies (ICALT), Hualien, Taiwan, pp. 340-344. DOI: 10.1109/ICALT.2015.116 [pdf: https://drive.google.com/file/d/1KSE4aIYqeKgOb1I68eyzFDDQ9UcVmbY9/view?usp=drive_link]

4. Woolf, Beverly P.; H. C. Lane; V. K. Chaudhri; and J. L. Kolodner, (2013). “AI grand challenges for education”, AI Magazine, vol. 34, no. 4, pp. 66-84. https://doi.org/10.1609/aimag.v34i4.2490 [pdf: https://drive.google.com/file/d/1AF0lBf3tQ5rIoZ-uKZXaUSaYcButIo5c/view?usp=drive_link]

5. Arroyo, Ivon; B. P. Woolf; D. G. Cooper; W. Burleson; and K. Muldner, (2011). "The Impact of Animated Pedagogical Agents on Girls' and Boys' Emotions, Attitudes, Behaviors and Learning," 2011 IEEE 11th International Conference on Advanced Learning Technologies, Athens, GA, USA, pp. 506-510, DOI: 10.1109/ICALT.2011.157. [pdf: https://drive.google.com/file/d/1e6JX0A5p7WSkYch1poH2Vkuam_sYTy6c/view?usp=drive_link]

6. Park Woolf, Beverly; and M. Floryan, (2011). "Students that Benefit from Educational 3D Games," Advanced Learning Technologies, IEEE International Conference, Athens, Georgia USA, pp. 473-475. DOI: 10.1109/ICALT.2011.147 [pdf: https://drive.google.com/file/d/1rlwMIC-1tMfcSq9sTd5KuSGL89RJNVYB/view?usp=drive_link]

7. VanLEHN, Kurt, (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems, Educational Psychologist, 46:4, 197-221, DOI: 10.1080/00461520.2011.611369 [pdf: https://drive.google.com/file/d/1kYNJLQkcVb90JRiHWXdk_9vu4EDmJAm6/view?usp=drive_link]

8. Woolf, Beverly P.; I. Arroyo; K. Muldner; W. Burleson; D. G. Cooper; R. Dolan; and R. M. Christopherson, (2010). “The effect of motivational learning companions on low achieving students and students with disabilities” Intelligent Tutoring Systems - 10th International Conference, ITS 2010, Proceedings. PART 1. ed. 2010. pp. 327-337. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6094 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-13388-6_3

 [pdf: https://drive.google.com/file/d/1IvsZuC_yX_AItkFjoXfKpt9YKmxkf8mB/view?usp=drive_link]

9. Gutierrez, Oscar, (2003). "A Strategy for the Development of Reusable Learning Objects in the Boston Area Advanced Technological Education Connections Partnership," Issues in Information Systems (IIS), International Association for Computer Information Systems (IACIS), USA, Vol. 4, pp. 143 – 149, ISSN 1529-7314 [pdf: https://drive.google.com/file/d/1uCWDN90pML4jIi9Gona2dnQOnoOk7u0z/view?usp=drive_link]

10. Mitrovic, Antonija, (2003). “An Intelligent SQL Tutor on the Web” International Journal of Artificial Intelligence in Education, vol. 13, no. 2-401, pp 173–197 [pdf: https://drive.google.com/file/d/1-vic77a8irfreUbQ21Eum22NXGqb2G0u/view?usp=drive_link]

11. Park Woolf, Beverly; and W. Hall, (1995). "Multimedia pedagogues: interactive systems for teaching and learning," in Computer, vol. 28, no. 5, pp. 74-80, DOI: 10.1109/2.384121. [pdf: https://drive.google.com/file/d/1IW1eFxKdbb8vuDRRzYnONDRCRKIAXLoD/view?usp=drive_link]

12. Rice University, (1999- 2023). Rice University's OpenStax. OpenStax. Retrieved August 2, 2023, from https://openstax.org/

13. Carnegie Mellon University, (2016 – 2023). Cognitive Tutor Authoring Tools. Retrieved August 2, 2023, from https://github.com/CMUCTAT/CTAT/wiki

14. Carnegie Mellon University (2004 -2023) LearnLab. Retrieved August 2, 2023, from https://learnlab.org/

15. Wiley, David A., (2000). Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. The instructional use of learning objects, 2830(435), pp.1-35. [pdf: https://drive.google.com/file/d/1hBmQ1LTXdUBmWhv03Lrb4j2RpDv3xHrP/view?usp=drive_link]

16. Carnegie Mellon University (licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License) Open Learning Initiative. Retrieved on August 14, 2023, from https://oli.cmu.edu/educators/

What Does It All Mean? Making Meaningful Learning Measures in Interdisciplinary Data Science Education: 

John Lopez

1. Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What’s the difference. Future of learning group publication, 5(3), 438.

2. Ahn, J., Campos, F., Hays, M., & DiGiacomo, D. (2019). Designing in Context: Reaching beyond Usability in Learning Analytics Dashboard Design. Journal of Learning Analytics, 6(2), 70-85.

3. Aksan, N., Kısac, B., Aydın, M., & Demirbuken, S. (2009). Symbolic interaction theory. Procedia-Social and Behavioral Sciences, 1(1), 902-904.

4. Ames, M. G. (2018). Hackers, computers, and cooperation: A critical history of logo and constructionist learning. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1-19.

5. Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning and education. Educational researcher, 25(4), 5-11.

6. Anderson, T., & Kanuka, H. (1999). Using constructivism in technology-mediated learning: Constructing order out of the chaos in the literature.

7. Berland, Matthew, Ryan S. Baker, and Paulo Blikstein. "Educational data mining and learning analytics: Applications to constructionist research." Technology, Knowledge and Learning 19 (2014): 205-220.

8. Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. Journal of the Learning Sciences, 23(4), 561-599.

9. Bhargava, R., Brea, A., Palacin, V., Perovich, L., & Hinson, J. (2022). Data theatre as an entry point to data literacy. Educational Technology & Society, 25(4), 93-108.

10. Cheng, R., Dasgupta, S., & Hill, B. M. (2022, April). How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices’ Use of Data Structures. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-16).

11. Dangol, A., & Dasgupta, S. (2023, June). Constructionist approaches to critical data literacy: A review. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 112-123).

12. Dasgupta, S., & Hill, B. M. (2020). Designing for critical algorithmic literacies. arXiv preprint arXiv:2008.01719.

13. De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M., & Cannavacciuolo, L. (2011, February). Discourse-centric learning analytics. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 23-33).

14. Desai, J., Watson, D., Wang, V., Taddeo, M., & Floridi, L. (2022). The epistemological foundations of data science: a critical review. Synthese, 200(6), 469.

15. D'Ignazio, C. (2017). Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6-18.

16. Doroudi, S. (2020). The bias-variance tradeoff: How data science can inform educational debates. AERA open, 6(4), 2332858420977208.

17. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317.

18. Fields, D. A., Quirke, L., Amely, J., & Maughan, J. (2016, February). Combining big data and thick data analyses for understanding youth learning trajectories in a summer coding camp. In Proceedings of the 47th ACM technical symposium on computing science education (pp. 150-155).

19. Gee, J. P. (2010). A situated-sociocultural approach to literacy and technology. The new literacies: Multiple perspectives on research and practice, 165, 193.

20. Gebre, E. (2022). Conceptions and perspectives of data literacy in secondary education. British Journal of Educational Technology, 53(5), 1080-1095.

21. Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021). Learning analytics in higher education: a preponderance of analytics but very little learning?. International Journal of Educational Technology in Higher Education, 18, 1-19.

22. Hardy, L., Dixon, C., & Hsi, S. (2020). From data collectors to data producers: Shifting students’ relationship to data. Journal of the Learning Sciences, 29(1), 104-126.

23. Hung, D., & Nichani, M. (2001). Constructivism and e-learning: Balancing between the individual and social levels of cognition. educational technology, 41(2), 40-44.

24. Kjelvik, M. K., & Schultheis, E. H. (2019). Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacy. CBE—Life Sciences Education, 18(2), es2.

25. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge university press.

26. Lee, V. R., & Wilkerson, M. H. (2018). Data use by middle and secondary students in the digital age: A status report and future prospects.

27. Mike, K. (2020, August). Data science education: Curriculum and pedagogy. In Proceedings of the 2020 ACM Conference on International Computing Education Research (pp. 324-325).

28. Miller, T. R., Baird, T. D., Littlefield, C. M., Kofinas, G., Chapin, F. S., & Redman, C. L. (2008). Epistemological Pluralism: Reorganizing Interdisciplinary Research. Ecology and Society, 13(2). http://www.jstor.org/stable/26268006

29. Kalina, C., & Powell, K. C. (2009). Cognitive and social constructivism: Developing tools for an effective classroom. Education, 130(2), 241-250.

30. Kirshner, D., & Whitson, J. A. (Eds.). (1997). Situated cognition: Social, semiotic, and psychological perspectives. Psychology Press.

31. Knight, S., Shum, S. B., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23-47.

32. Lee, V. R., & Dubovi, I. (2020). At home with data: Family engagements with data involved in type 1 diabetes management. Journal of the learning sciences, 29(1), 11-31.

33. Papert, S. A. (2020). Mindstorms: Children, computers, and powerful ideas. Basic books.

34. Qin, S. J., & Chiang, L. H. (2019). Advances and opportunities in machine learning for process data analytics. Computers & Chemical Engineering, 126, 465-473.

35. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley interdisciplinary reviews: Data mining and knowledge discovery, 10(3), e1355.

36. Sexton, S. S. (2020). Meaningful Learning—David P. Ausubel. Science Education in Theory and Practice: An Introductory Guide to Learning Theory, 163-175.

37. Tissenbaum, M., Weintrop, D., Holbert, N., & Clegg, T. (2021). The case for alternative endpoints in computing education. British Journal of Educational Technology, 52(3), 1164-1177.

38. Turkle, S., & Papert, S. (1992). Epistemological pluralism and the revaluation of the concrete. Journal of Mathematical Behavior, 11(1), 3-33.

39. Walshe, G. (2020). Radical constructivism—von Glasersfeld. Science Education in Theory and Practice: An Introductory Guide to Learning Theory, 359-371.

40. Wang, L., & Dasgupta, S. (2022, April). Dataland: An Informed, Situated, and Critical Approach to Data Literacy. In General Proceedings of the 2nd Annual Meeting of the International Society of the Learning Sciences 2022.

41. Wilkerson, M. H., & Gravel, B. (2020). 20STORYTELLING AS A SUPPORT FOR COLLECTIVE CONSTRUCTIONIST ACTIVITY.

42. Wilkerson, M. H., & Polman, J. L. (2020). Situating data science: Exploring how relationships to data shape learning. Journal of the Learning Sciences, 29(1), 1-10.

43. Wise, A. F. (2022). Educating data scientists and data literate citizens for a new generation of data. In Situating Data Science (pp. 165-181). Routledge.