• Why teach CS? 
  • Lewis, C. M. (2017). Good (and Bad) Reasons to Teach All Students Computer Science. In S. B. Fee, A. M. Holland-Minkley, & T. E. Lombardi (Eds.), New Directions for Computing Education (pp. 15–34). Springer International Publishing. https://doi.org/10.1007/978-3-319-54226-3_2
• History – Overview of the field:
  • Guzdial, M., & du Boulay, B. (2019). The History of Computing Education Research. In S. A. Fincher & A. V. E. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 11–39). Cambridge University Press. https://doi.org/10.1017/9781108654555.002
• Overview: Novice programmers overview: 
  • Robins, A. V. (2019). Novice Programmers and Introductory Programming. In S. A. Fincher & A. V. E. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 327–376). Cambridge University Press. https://doi.org/10.1017/9781108654555.013
• Cognitive Science:
  • Robins, A. V., Margulieux, L. E., & Morrison, B. B. (2019). Cognitive Sciences for Computing Education. In S. A. Fincher & A. V. E. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 231–275). Cambridge University Press. https://doi.org/10.1017/9781108654555.010
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• Plans/Patterns: 
  • Soloway, E. (1986). Learning to program = learning to construct mechanisms and explanations. Communications of the ACM, 29(9), 850–858. https://doi.org/10.1145/6592.6594
• Conversational Programmers:
  • Cunningham, K., Ericson, B. J., Agrawal Bejarano, R., & Guzdial, M. (2021). Avoiding the Turing Tarpit: Learning Conversational Programming by Starting from Code’s Purpose. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–15. https://doi.org/10.1145/3411764.3445571

• Transfer:
  • Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. https://doi.org/10.1037/0033-2909.128.4.612
• Computational Thinking:
  • Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5

• Conceptual Change: 
  • diSessa, A. A. (2014). A History of Conceptual Change Research: Threads and Fault Lines. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2nd ed., pp. 88–108). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.007
• Overview of Learning Sciences:
  • Margulieux, L. E., Dorn, B., & Searle, K. A. (2019). Learning Sciences for Computing Education. In S. A. Fincher & A. V. E. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 208–230). Cambridge University Press. https://doi.org/10.1017/9781108654555.009
• Embodied Cognition/Distributed Cognition: 
  • Abrahamson, D., & Lindgren, R. (2022). Embodiment and Embodied Design. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (3rd ed., pp. 301–320). Cambridge University Press. https://doi.org/10.1017/9781108888295.019
    • https://ccl.northwestern.edu/2022/Dor%20Embod.pdf 

• Constructionism:
  • 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. https://doi.org/10.1111/bjet.13072
• BPC – K-12 Overview: 
  • Margolis, J., Estrella, R., Goode, J., Jellison Holme, J., & Nao, K. (2008). Stuck in the shallow end: Education, race, and computing. MIT Press.

• BPC – Racial Climate: 
  • Harper, S. R., & Hurtado, S. (2007). Nine themes in campus racial climates and implications for institutional transformation. New Directions for Student Services, 2007(120), 7–24. https://doi.org/10.1002/ss.254
• Representations: 
  • Johnson‐Glauch, N., Choi, D. S., & Herman, G. (2020). How engineering students use domain knowledge when problem‐solving using different visual representations. Journal of Engineering Education, 109(3), 443–469. https://doi.org/10.1002/jee.20348

• Implicit Learning/Dual Process 
  • Kahneman, D. (2013). Thinking, fast and slow (1st pbk. ed). Farrar, Straus and Giroux. Chapter 1 
• Tutoring / self-explanation effect:
  • Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471–533. https://doi.org/10.1207/s15516709cog2504_1

• Concept Inventory:
  • Hestenes, D., Wells, M., & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30(3), 141–158. https://doi.org/10.1119/1.2343497
    • Skipping:
      • All Tables
      • subsections I.0 through I.5
      • Section II
      • bullets (1) through (8) in Section III
    • Link to the FCI itself:  https://www.talkphysics.org/wp-content/uploads/2015/07/fci-rv95_1.pdf 
• Desirable difficulties:
  • Rawson, K. A., Dunlosky, J., & Sciartelli, S. M. (2013). The Power of Successive Relearning: Improving Performance on Course Exams and Long-Term Retention. Educational Psychology Review, 25(4), 523–548. https://doi.org/10.1007/s10648-013-9240-4

• Student perception of learning: 
  • Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251–19257. https://doi.org/10.1073/pnas.1821936116
• Active Learning:
  • Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809
• Problem Based learning:
  • Barron, B., Schwartz, D. L., Vye, N., Moore, A. L., Petrosino, A. J., Zech, L. K., & Bransford, J. D. (1998). Doing with Understanding: Lessons from Research on Problem- and Project-Based Learning. The Journal of the Learning Sciences, 7, 271–311.
• Cognitive apprenticeship: 
  • Collins, A., & Kapur, M. (2014). Cognitive Apprenticeship. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2nd ed., pp. 109–127). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.008
• Collaboration: 
  • Nokes-Malach, T. J., Richey, J. E., & Gadgil, S. (2015). When Is It Better to Learn Together? Insights from Research on Collaborative Learning. Educational Psychology Review, 27(4), 645–656. https://doi.org/10.1007/s10648-015-9312-8
• Inquiry vs. Direct Instruction
  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
• Order of learning activities: 
  • Xie, B., Loksa, D., Nelson, G. L., Davidson, M. J., Dong, D., Kwik, H., Tan, A. H., Hwa, L., Li, M., & Ko, A. J. (2019). A theory of instruction for introductory programming skills. Computer Science Education, 29(2–3), 205–253. https://doi.org/10.1080/08993408.2019.1565235

• Double Bind – Women of Color:
  • Ong, M., Wright, C. A., Espinosa, L. L., & Orfield, G. (2011). Inside the Double Bind: A Synthesis of Empirical Research on Undergraduate and Graduate Women of Color in Science, Technology, Engineering, and Mathematics. Harvard Educational Review, 81, 172–209.
• Paradigms: 
  • Greeno, J. G., Collins, A. M., & Resnick, L. (1996). Cognition and learning. In Cognition and Learning (pp. 15–46).
  • Gutiérrez, R. (2013). The Sociopolitical Turn in Mathematics Education. Journal for Research in Mathematics Education, 44(1), 37–68. https://doi.org/10.5951/jresematheduc.44.1.0037

Note – Papers after this point are included in other classes, but not the quals class Fall 2023. 

• Quantitative Methods: 
  • Haden, P. (2019). Descriptive Statistics. In S. A. Fincher & A. V. E. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 102–132). Cambridge University Press. https://doi.org/10.1017/9781108654555.006
  • Haden, P. (2019). Inferential Statistics. In S. A. Fincher & A. V. E. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 133–172). Cambridge University Press. https://doi.org/10.1017/9781108654555.007
• Qualitative Methods: 
  • Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation (Fourth edition). John Wiley & Sons.
• Learner models:
  • Rosé, C. P., McLaughlin, E. A., Liu, R., & Koedinger, K. R. (2019). Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology, 50(6), 2943–2958. https://doi.org/10.1111/bjet.12858
• Databases data mining: 
  • Yang, S., Wei, Z., Herman, G. L., & Alawini, A. (2021). Analyzing Patterns in Student SQL Solutions via Levenshtein Edit Distance. Proceedings of the Eighth ACM Conference on Learning @ Scale, 323–326. https://doi.org/10.1145/3430895.3460979
• Expert-novice differences 
  • Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and Representation of Physics Problems by Experts and Novices*. Cognitive Science, 5(2), 121–152. https://doi.org/10.1207/s15516709cog0502_2
• Notional machines:
  • Fincher, S., Jeuring, J., Miller, C. S., Donaldson, P., Du Boulay, B., Hauswirth, M., Hellas, A., Hermans, F., Lewis, C., Mühling, A., Pearce, J. L., & Petersen, A. (2020). Notional Machines in Computing Education: The Education of Attention. Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education, 21–50. https://doi.org/10.1145/3437800.3439202
• Intelligent Tutors: 
  • Koedinger, K. R., & Corbett, A. (2005). Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (pp. 61–78). Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.006
• Learning trajectories in CS: 
  • Rich, K. M., Strickland, C., Binkowski, T. A., Moran, C., & Franklin, D. (2017). K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals. Proceedings of the 2017 ACM Conference on International Computing Education Research, 182–190. https://doi.org/10.1145/3105726.3106166
• Assessment/Testing:
  • Chen, B., Azad, S., Fowler, M., West, M., & Zilles, C. (2020). Learning to Cheat: Quantifying Changes in Score Advantage of Unproctored Assessments Over Time. Proceedings of the Seventh ACM Conference on Learning @ Scale, 197–206. https://doi.org/10.1145/3386527.3405925
• Frequent Assessments: 
  • Smith, D. H., Emeka, C., Fowler, M., West, M., & Zilles, C. (2023). Investigating the Effects of Testing Frequency on Programming Performance and Students’ Behavior. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 757–763. https://doi.org/10.1145/3545945.3569821
• Defensive climates: 
  • Barker, L. J., & Garvin-Doxas, K. (2004). Making Visible the Behaviors that Influence Learning Environment: A Qualitative Exploration of Computer Science Classrooms. Computer Science Education, 14(2), 119–145. https://doi.org/10.1080/08993400412331363853

• Belonging in CS: 
  • Lewis, C. M., Anderson, R. E., & Yasuhara, K. (2016). “I Don’t Code All Day”: Fitting in Computer Science When the Stereotypes Don’t Fit. Proceedings of the 2016 ACM Conference on International Computing Education Research, 23–32. https://doi.org/10.1145/2960310.2960332
• Spatial Reasoning:
  • Margulieux, L. E. (2019). Spatial Encoding Strategy Theory: The Relationship between Spatial Skill and STEM Achievement. Proceedings of the 2019 ACM Conference on International Computing Education Research, 81–90. https://doi.org/10.1145/3291279.3339414
• BPC – Overview: 
  • Lewis, C. M., Shah, N., & Falkner, K. (2019). Equity and Diversity. In S. A. Fincher & A. V. E. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 481–510). Cambridge University Press. https://doi.org/10.1017/9781108654555.017
• Motivated Reasoning: 
  • Epley, Nicholas, and Thomas Gilovich. 2016. “The Mechanics of Motivated Reasoning.” Journal of Economic Perspectives, 30 (3): 133-40. DOI: 10.1257/jep.30.3.133