FCI: Bibliography

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FCI: Bibliography
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Please note that many of these references focus on STEM topics. We are working to expand this database to include more articles with examples in the social sciences and humanities disciplines.

Policy and Cross-Cutting Discussion

Catalyzing transformative strategies to infuse STEM courses with proven, high impact teaching methods is a national priority. President Obama urged academic institutions to implement so-called “evidence-based reform” to improve retention in STEM majors and thereby produce the surplus of professionals needed to retain our country’s advantage in science and technology (Olson & Riordan, 2012). Two landmark publications from the National Research Council build upon this call by exploring the essential contribution of discipline-based education research (DBER) (Singer et al., 2012) and describing evidence-based instructional strategies in STEM (Kober, 2015). Other seminal studies demonstrate that evidence-based strategies lead to increases in academic performance relative to traditional approaches such as lecturing (Prince, 2004; Freeman et al., 2014).


Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. DOI: 10.1073/pnas.1319030111

Kober, N. (2015). Reaching students: What research says about effective instruction in undergraduate science and engineering. Washington, DC: National Academies Press. DOI: 10.17226/18687

Olson, S., & Riordan, D. G. (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Report to the President. Executive Office of the President. URL: https://eric.ed.gov/?id=ED541511

Prince, M. (2004). Does active learning work? A review of the research. Journal of engineering education, 93(3), 223-231. DOI: 10.1002/j.2168-9830.2004.tb00809.x

Singer, S. R., Nielsen, N. R., & Schweingruber, H. A. (Eds.). (2012). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering. Washington, DC: National Academies Press. DOI: 10.17226/13362

Learning Goals

This selection of publications progresses from theory to practice, explaining why learning goals are an essential element of foundational courses and how instructional teams can successfully collaborate to develop them. Learning goals are not only important to motivate self-efficacy in learning, but are also critical as a basis for assessment of learning gains toward understanding, synthesis, and application of essential course content and skills.

Studies in learning theory, educational psychology, and educational research elucidate the role of goal setting in directing students’ motivation and behavior, and demonstrate how different kinds of goals influence students’ self-efficacy and academic outcomes (Ashwin and Leake 1995, Schunk 1990, and Wirthwein et al. 2013). A survey of students and faculty in computer science, microbiology, and immunology courses demonstrates the perceived value of learning goals (Simon and Taylor 2009). Bloom’s well-known taxonomy of educational objectives (1956) and a widely recognized revision of this framework (Krathwohl 2002) provide conceptual models for structuring learning objectives based on the level of complexity. Finally, education researchers affiliated with the University of Colorado at Boulder’s Science Education Initiative offer practical guidance to departments aspiring to develop learning goals in the context of course reform (Smith and Perkins 2010, Pepper et al. 2011).


Leake, D. B., & Ram, A. (1995). Learning, goals, and learning goals: a perspective on goal-driven learning. Artificial Intelligence Review, 9(6), 387-422. DOI: 10.1007/BF00849065

Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational psychologist, 25(1), 71-86. DOI: 10.1207/s15326985ep2501_6

Wirthwein, L., Sparfeldt, J. R., Pinquart, M., Wegerer, J., & Steinmayr, R. (2013). Achievement goals and academic achievement: A closer look at moderating factors. Educational Research Review, 10, 66-89.DOI: 10.1016/j.edurev.2013.07.001

Simon, B., & Taylor, J. (2009). What is the value of course-specific learning goals. Journal of College Science Teaching, 39(2), 52-57. URL: http://proxy.lib.umich.edu/login?url=

Bloom, B. S. (1956). Taxonomy of educational objectives. Vol. 1: Cognitive domain. New York: McKay. ISBN-13: 978-0582280106

Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into practice, 41(4), 212-218. DOI: 10.1207/s15430421tip4104_2

Smith, M. K., & Perkins, K. K. (2010). “At the end of my course, students should be able to…”: The benefits of creating and using effective learning goals. Microbiology, 35. URL: https://www.researchgate.net/profile/Chris_Burke2/publication/235638013_Peer_review_of_teaching_to_promote_learning_outcomes/links/56b7b9ed08ae5ad3605dd80a.pdf#page=36

Pepper, R. E., Chasteen, S. V., Pollock, S. J., Perkins, K. K., Rebello, N. S., Engelhardt, P. V., & Singh, C. (2012, February). Facilitating faculty conversations: Development of consensus learning goals. In AIP Conference Proceedings-American Institute of Physics (Vol. 1413, No. 1, p. 291). URL: http://scitation.aip.org/content/aip/proceeding/aipcp/1413

Evidence-Based Teaching Methods

Teaching methods are empirically supported by quantitative, qualitative, and mixed methods research in fields such as the scholarship of teaching and learning, educational psychology, cognitive science, and discipline-based educational research (DBER). For example, studies in this section demonstrate the learning impact of authentic research in the classroom, strategies for eliciting student cooperation in learning, flipped classrooms, gameful learning environments, and engaging students in writing exercises as a strategy to learn scientific concepts. Other examples, such as techniques for correcting students’ misconceptions about hydrogen bonding, are discipline-specific.

Authentic Research in the Classroom

Corwin, L. A., Graham, M. J., & Dolan, E. L. (2015). Modeling course-based undergraduate research experiences: an agenda for future research and evaluation. CBE-Life Sciences Education, 14(1), es1. DOI: 10.1187/cbe.14-10-0167

Domin, D. S. (1999). A review of laboratory instruction styles. J. Chem. Educ, 76(4), 543. DOI: 10.1021/ed076p543

Hofstein, A., & Mamlok-Naaman, R. (2007). The laboratory in science education: the state of the art. Chemistry education research and practice, 8(2), 105-107. DOI: 10.1021/ed076p543

Shapiro, C., Moberg-Parker, J., Toma, S., Ayon, C., Zimmerman, H., Roth-Johnson, E. A. & Sanders, E. R. (2015). Comparing the impact of course-based and apprentice-based research experiences in a life science laboratory curriculum. Journal of microbiology & biology education, 16(2), 186. DOI: 10.1128/jmbe.v16i2.1045

Cooperative Learning

Other areas of the CRLT website provide an extensive list of references on cooperative learning, including articles related to principles and techniques, forming groups, group dynamics, grading issues, and use of small groups in large classes such as foundational courses (see http://www.crlt.umich.edu/publinks/clgt_bestpractices). In addition, the following articles have informed course reforms implemented by REBUILD faculty.

Aronson, E. (2010). Cooperation in the classroom: The jigsaw method. Pinter & Martin Limited. ISBN: 978-1905177226

Finelli, C. J., Bergom, I., & Mesa, V. (2011). Student teams in the engineering classroom and beyond: Setting up students for success. CRLT Occasional Papers, 29. URL: https://www.naefoee.org/File.aspx?id=12834

Smith, M. K., Wood, W. B., Adams, W. K., Wieman, C., Knight, J. K., Guild, N., & Su, T. T. (2009). Why peer discussion improves student performance on in-class concept questions. Science, 323(5910), 122-124. DOI: 10.1126/science.1165919

Springer, L., Stanne, M. E., & Donovan, S. S. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. Review of educational research, 69(1), 21-51.DOI: 10.3102/00346543069001021

Flipped Classrooms

Berrett, D. (2012). How ‘flipping’ the classroom can improve the traditional lecture. The chronicle of higher education, 12, 1-14. URL: http://chronicle.com.proxy.lib.umich.edu

Bishop, Jacob Lowell, and Matthew A. Verleger. “The flipped classroom: A survey of the research.” ASEE National Conference Proceedings, Atlanta, GA. Vol. 30. No. 9. 2013. URL: https://www.asee.org/public/conferences/20/papers/6219/view

Gross, D., Pietri, E. S., Anderson, G., Moyano-Camihort, K., & Graham, M. J. (2015). Increased preclass preparation underlies student outcome improvement in the flipped classroom. CBE-Life Sciences Education, 14(4), ar36. DOI: 10.1187/cbe.15-02-0040

Herreid, C. F., & Schiller, N. A. (2013). Case studies and the flipped classroom. Journal of College Science Teaching, 42(5), 62-66. Stable URL: http://www.jstor.org/stable/43631584

Lage, M. J., Platt, G. J., & Treglia, M. (2000). Inverting the classroom: A gateway to creating an inclusive learning environment. The Journal of Economic Education, 31(1), 30-43. DOI: 10.1080/00220480009596759

Strayer, J. F. (2012). How learning in an inverted classroom influences cooperation, innovation and task orientation. Learning Environments Research, 15(2), 171-193. DOI: 10.1007/s10984-012-9108-4

Gameful Learning Environments

Aguilar, S. J., Holman, C., & Fishman, B. J. (2015). Game-Inspired Design Empirical Evidence in Support of Gameful Learning Environments. Games and Culture, 1555412-15600305. DOI: 10.1177/1555412015600305

DomíNguez, A., Saenz-De-Navarrete, J., De-Marcos, L., FernáNdez-Sanz, L., PagéS, C., & MartíNez-HerráIz, J. J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63, 380-392. DOI: 10.1016/j.compedu.2012.12.020

Hakulinen, L., & Auvinen, T. (2014, April). The effect of gamification on students with different achievement goal orientations. In Teaching and Learning in Computing and Engineering (LaTiCE), 2014 International Conference on (pp. 9-16). IEEE. DOI: 10.1109/LaTiCE.2014.10

Hamari, J., Koivisto, J., & Sarsa, H. (2014, January). Does gamification work?–a literature review of empirical studies on gamification. In 2014 47th Hawaii International Conference on System Sciences (pp. 3025-3034). IEEE. DOI: 10.1109/HICSS.2014.377

Holman, C., Aguilar, S., & Fishman, B. (2013, April). GradeCraft: what can we learn from a game-inspired learning management system?. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 260-264). ACM. DOI: 10.1145/2460296.2460350

Student Difference

The large, introductory courses at U-M serve students with especially diverse learning needs shaped by different socioeconomic backgrounds, levels of preparedness, racial and ethnic backgrounds, learning styles, career goals, levels of motivation, attitudes about learning, and other characteristics of our identities as learners. As Felder and Brent (2005) explain, in order to design instruction that benefits all students, teachers must understand how these characteristics interact with “the different attitudes students have toward learning, the different ways they approach it, and how instructors can influence both their attitudes and approaches.” Instructional strategies that disregard important differences may produce achievement gaps.

Articles in the first section below explore instruments for assessing learning environments and psycho-social interventions that help students overcome achievement gaps. Subsequent sections explore the experience of different groups in terms of achievement, retention, career choice, and other factors. We include articles on gender, race and ethnicity, first generation college students, intersections of identity, and identification with LGBTQ. Finally, the last section is dedicated to articles about understanding and combating stereotype threat, “a psychological predicament in which individuals are inhibited from performing to their potential by the recognition that possible failure could confirm a negative stereotype that applies to their in-group and, by extension, to themselves” (Schmader, 2002).


Church, M. A., Elliot, A. J., & Gable, S. L. (2001). Perceptions of classroom environment, achievement goals, and achievement outcomes. Journal of educational psychology, 93(1), 43. DOI: 10.1037/0022-0663.93.1.43

Felder, R. M., & Brent, R. (2005). Understanding student differences. Journal of engineering education, 94(1), 57-72. DOI: 10.1002/j.2168-9830.2005.tb00829.x

Classroom Climate Assessment, Research, and Intervention

Fraser, B. J., & Walberg, H. J. (1991). Educational environments: Evaluation, antecedents and consequences. Pergamon Press. ISBN-13: 978-0080374161

Fraser, B. J. (1998). Classroom environment instruments: Development, validity and applications. Learning environments research, 1(1), 7-34. DOI: 10.1023/A:1009932514731

Kenthirarajah, D. T., & Walton, G. M. (2015). How Brief Social‐Psychological Interventions Can Cause Enduring Effects. Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. DOI: 10.1002/9781118900772.etrds0026

Kost-Smith, L. E., Pollock, S. J., Finkelstein, N. D., Cohen, G. L., Ito, T. A., Miyake, A., … & Singh, C. (2012, February). Replicating a self-affirmation intervention to address gender differences: Successes and challenges. In AIP Conference Proceedings-American Institute of Physics (Vol. 1413, No. 1, p. 231). DOI: 10.1063/1.3680037

Miyake, A., Kost-Smith, L. E., Finkelstein, N. D., Pollock, S. J., Cohen, G. L., & Ito, T. A. (2010). Reducing the gender achievement gap in college science: A classroom study of values affirmation. Science, 330(6008), 1234-1237. DOI: 10.1126/science.1195996

Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education They’re not magic. Review of educational Research, 81(2), 267-301. DOI: 10.3102/0034654311405999


Adamo, S. A. (2013). Attrition of women in the biological sciences: Workload, motherhood, and other explanations revisited. BioScience, 63(1), 43-48. DOI: 10.1525/bio.2013.63.1.9

Emerson, T. L., McGoldrick, K., & Mumford, K. J. (2012). Women and the choice to study economics. The Journal of Economic Education, 43(4), 349-362. DOI: 10.1080/00220485.2012.714306

Glass, J. L., Sassler, S., Levitte, Y., & Michelmore, K. M. (2013). What’s so special about STEM? A comparison of women’s retention in STEM and professional occupations. Social forces, 92(2), 723-756. DOI: 10.1093/sf/sot092

Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M. C. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice: A gender study. Journal of Research in Science Teaching, 47(8), 978-1003. DOI: 10.1002/tea.20363

Ho, D. E., & Kelman, M. G. (2014). Does class size affect the gender gap? a natural experiment in law. The Journal of Legal Studies, 43(2), 291-321. DOI: 10.1086/676953

Kost, L. E., Pollock, S. J., & Finkelstein, N. D. (2009). Characterizing the gender gap in introductory physics. Physical Review Special Topics-Physics Education Research, 5(1). DOI: 10.1103/PhysRevSTPER.5.010101

Kost-Smith, L. E., Pollock, S. J., & Finkelstein, N. D. (2010). Gender disparities in second-semester college physics: The incremental effects of a “smog of bias”. Physical Review Special Topics-Physics Education Research, 6(2), 020112. DOI: 10.1103/PhysRevSTPER.6.020112

Lorenzo, M., Crouch, C. H., & Mazur, E. (2006). Reducing the gender gap in the physics classroom. American Journal of Physics, 74(2), 118-122. DOI: 10.1119/1.2162549

Miller, E. (2002). The gender gap in cosmology: Results from a small case study of undergraduates. Astronomy Education Review, 1(2), 35-45. DOI: 10.3847/AER2002004

Pollock, S. J., Finkelstein, N. D., & Kost, L. E. (2007). Reducing the gender gap in the physics classroom: How sufficient is interactive engagement?. Physical Review Special Topics-Physics Education Research, 3(1), 010107. DOI: 10.1103/PhysRevSTPER.3.010107

Miyake, A., Kost-Smith, L. E., Finkelstein, N. D., Pollock, S. J., Cohen, G. L., & Ito, T. A. (2010). Reducing the gender achievement gap in college science: A classroom study of values affirmation. Science, 330(6008), 1234-1237. DOI: 10.1126/science.1195996

Riegle-Crumb, C., King, B., Grodsky, E., & Muller, C. (2012). The more things change, the more they stay the same? Prior achievement fails to explain gender inequality in entry into STEM college majors over time. American Educational Research Journal, 49(6), 1048-1073. DOI: 10.3102/0002831211435229

Sherman, D. K., Hartson, K. A., Binning, K. R., Purdie-Vaughns, V., Garcia, J., Taborsky-Barba, S., … & Cohen, G. L. (2013). Deflecting the trajectory and changing the narrative: how self-affirmation affects academic performance and motivation under identity threat. Journal of Personality and Social Psychology, 104(4), 591. DOI: 10.1037/a0031495

Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447-1451. DOI: 10.1126/science.1198364

Walton, G. M., Logel, C., Peach, J. M., Spencer, S. J., & Zanna, M. P. (2015). Two brief interventions to mitigate a “chilly climate” transform women’s experience, relationships, and achievement in engineering. Journal of Educational Psychology, 107(2), 468. DOI: 10.1037/a0037461

Race and Ethnicity

Egalite, A. J., Kisida, B., & Winters, M. A. (2015). Representation in the classroom: The effect of own-race teachers on student achievement. Economics of Education Review, 45, 44-52. DOI: 10.1016/j.econedurev.2015.01.007

Lord, S. M., Camacho, M. M., Layton, R. A., Long, R. A., Ohland, M. W., & Wasburn, M. H. (2009). Who’s persisting in engineering? A comparative analysis of female and male Asian, black, Hispanic, Native American, and white students. Journal of Women and Minorities in Science and Engineering, 15(2). DOI: 10.1615/JWomenMinorScienEng.v15.i2.40

Lusher, L., Campbell, D., & Carrell, S. (2015). TAs Like Me: Racial Interactions between Graduate Teaching Assistants and Undergraduates (No. w21568). National Bureau of Economic Research. DOI: 10.3386/w21568

Posselt, J. R., Jaquette, O., Bielby, R., & Bastedo, M. N. (2012). Access without equity longitudinal analyses of institutional stratification by race and ethnicity, 1972–2004. American Educational Research Journal, 49(6), 1074-1111. DOI: 10.3102/0002831212439456

Roksa, J., & Arum, R. (2015). Inequality in skill development on college campuses. Research in Social Stratification and Mobility, 39, 18-31. DOI: 10.1016/j.rssm.2014.09.001

First Generation College Students

Dika, S. L., & D’Amico, M. M. (2016). Early experiences and integration in the persistence of first‐generation college students in STEM and non‐STEM majors. Journal of Research in Science Teaching, 53(3), 368-383. DOI: 10.1002/tea.21301

Stephens, N. M., Hamedani, M., & Destin, M. (2014). Navigating the social class divide: A diversity education intervention improves first-generation students’ academic performance and all students’ college transition. Psychological Science, 25(4), 943-953. 10.1177/0956797613518349

Intersections and Cross-Cutting

Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of research in science teaching, 44(8), 1187-1218. DOI: 10.1002/tea.20237

Griffith, A. L. (2010). Persistence of women and minorities in STEM field majors: Is it the school that matters?. Economics of Education Review, 29(6), 911-922. DOI: 10.1016/j.econedurev.2010.06.010

Johnson, A. C. (2007). Unintended consequences: How science professors discourage women of color. Science Education, 91(5), 805-821. DOI: 10.1002/sce.20208

Maltby, J. L., Brooks, C., Horton, M., & Morgan, H. (2016). Long Term Benefits for Women in a Science, Technology, Engineering, and Mathematics Living-Learning Community. Learning Communities Research and Practice, 4(1), 2. Available at: http://washingtoncenter.evergreen.edu/lcrpjournal/vol4/iss1/2

Ohland, M. W., Brawner, C. E., Camacho, M. M., Layton, R. A., Long, R. A., Lord, S. M., & Wasburn, M. H. (2011). Race, gender, and measures of success in engineering education. Journal of Engineering Education, 100(2), 225. DOI: 10.1002/j.2168-9830.2011.tb00012.x

Tanner, K. D. (2013). Structure matters: twenty-one teaching strategies to promote student engagement and cultivate classroom equity. CBE-Life Sciences Education, 12(3), 322-331. DOI: 10.1187/cbe.13-06-0115


American Physical Society. (2016). LGBT climate in physics: Building an inclusive community. College Park, MD: Atherton, T.J., Barthelemy, R.S., Deconinck, W., Falk, M.L., Garmon, S., Long, E., Plisch, M., Simmons, E.H., & Reeves, K. URL: https://www.aps.org/programs/lgbt/upload/LGBTClimateinPhysicsReport.pdf

Stereotype Threat

Aronson, J., Lustina, M. J., Good, C., Keough, K., Steele, C. M., & Brown, J. (1999). When white men can’t do math: Necessary and sufficient factors in stereotype threat. Journal of experimental social psychology, 35(1), 29-46. DOI: doi:10.1006/jesp.1998.1371

Betz, D. E., Ramsey, L. R., & Sekaquaptewa, D. (2013). Gender stereotype threat among women and girls. The SAGE handbook of gender and psychology, 428-450.

Good, C., Aronson, J., & Harder, J. A. (2008). Problems in the pipeline: Stereotype threat and women’s achievement in high-level math courses. Journal of Applied Developmental Psychology, 19, 17–28. DOI:10.1016/j.appdev.2007.10.004

Johns, M., Schmader, T., & Martens, A. (2005). Knowing is half the battle: Teaching stereotype threat as a means of improving women’s math performance. Psychological Science, 16, 175– 179. DOI:10.1111/j.0956-7976.2005.00799.x

Kiefer, A. K., & Sekaquaptewa, D. (2007). Implicit stereotypes, gender identification, and math-related outcomes a prospective study of female college students. Psychological Science, 18(1), 13-18. DOI: 10.1111/j.1467-9280.2007.01841.x

Kiefer, A. K., & Sekaquaptewa, D. (2007). Implicit stereotypes and women’s math performance: How implicit gender-math stereotypes influence women’s susceptibility to stereotype threat. Journal of Experimental Social Psychology, 43(5), 825-832. DOI: 10.1016/j.jesp.2006.08.004

Martens, A., Johns, M., Greenberg, J., & Schimel, J. (2006). Combating stereotype threat: The effect of self-affirmation on women’s intellectual performance. Journal of Experimental Social Psychology, 42, 236–243. DOI:10.1016/j.jesp.2005.04.010

Miyake, A., Kost-Smith, L. E., Finkelstein, N. D., Pollock, S. J., Cohen, G. L., & Ito, T. A. (2010). Reducing the gender achievement gap in college science: A classroom study of values affirmation. Science, 330, 1234–1237. DOI:10.1126/science.1195996

Ramsey, L. R., & Sekaquaptewa, D. (2011). Changing stereotypes, changing grades: A longitudinal study of stereotyping during a college math course. Social Psychology of Education, 14(3), 377-387. DOI: 10.1007/s11218-010-9150-y

Schmader, T. (2002). Gender identification moderates stereotype threat effects on women’s math performance. Journal of Experimental Social Psychology, 38, 194–201. DOI:10.1006/jesp.2001.1500

Shapiro, J. R., & Williams, A. M. (2012). The role of stereotype threats in undermining girls’ and women’s performance and interest in STEM fields. Sex Roles, 66(3-4), 175-183. DOI: 10.1007/s11199-011-0051-0

Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women’s math performance. Journal of Experimental Social Psychology, 35, 4–28. DOI:10.1006/jesp.1998.1373

Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of personality and social psychology, 69(5), 797. DOI: 10.1037/0022-3514.69.5.797

Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100, 255– 270. DOI:10.1037/a0021385

Digital Tools

In this information age, with higher education focused on digital technologies that support engaged and personalized learning at scale, the University is uniquely positioned to improve the design and delivery of the next generation of courses that deliver the “foundations of learning” across campus. However, faculty simply do not have time to develop expertise in the plethora of digital education tools. Teaching teams require collaborative, oftentimes disciplinary-specific assistance from professionals in digital education to identify and implement proven tools that serve course-specific learning goals. Below we include articles on a few examples such as online discussion boards (Shultz et al., 2014) and E2Coach (McKay et al, 2012; Huberth et al., 2015).


Bayne, S. (2015). Teacherbot: interventions in automated teaching. Teaching in Higher Education, 20(4), 455-467. DOI: 10.1080/13562517.2015.1020783

Basu, S., Wu, A., Hou, B., & DeNero, J. (2015, March). Problems before solutions: Automated problem clarification at scale. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 205-213). ACM. DOI: 10.1145/2724660.2724679

Beggrow, E. P., Ha, M., Nehm, R. H., Pearl, D., & Boone, W. J. (2014). Assessing scientific practices using machine-learning methods: How closely do they match clinical interview performance? Journal of Science Education and Technology, 23(1), 160-182. DOI: 10.1007/s10956-013-9461-9

Castleman, B. L., & Page, L. C. (2015). Summer nudging: Can personalized text messages and peer mentor outreach increase college going among low-income high school graduates?. Journal of Economic Behavior & Organization, 115, 144-160. DOI: http://dx.doi.org/10.1016/j.jebo.2014.12.008

Caughran, J. A., & Morrison, R. W. (2015). Returning Written Assignments Electronically: Adapting Off-the-Shelf Technology To Preserve Privacy and Exam Integrity. Journal of Chemical Education, 92(7), 1254-1255. DOI: 10.1021/ed500577x

Haudek, K. C., Kaplan, J. J., Knight, J., Long, T., Merrill, J., Munn, A., Nehm, R., Smith, M., & Urban-Lurain, M. (2011). Harnessing technology to improve formative assessment of student conceptions in STEM: forging a national network. CBE-Life Sciences Education, 10(2), 149-155. DOI: 10.1187/cbe.11-03-0019

Shultz, G. V., Winschel, G. A., Inglehart, R. C., & Coppola, B. P. (2014). Eliciting student explanations of experimental results using an online discussion board. Journal of Chemical Education, 91(5), 684-686. 10.1021/ed4007265


McKay, T., Miller, K., & Tritz, J. (2012, April). What to do with actionable intelligence: E2Coach as an intervention engine. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 88-91). ACM. DOI: 10.1145/2330601.2330627

Huberth, M., Chen, P., Tritz, J., & McKay, T. A. (2015). Computer-Tailored Student Support in Introductory Physics. PloS one, 10(9), e0137001. DOI: 10.1371/journal.pone.0137001

Collaborative Teaching Teams

Lead instructors of foundational courses at Michigan often rotate within or between terms to accommodate academic and personal leaves of absence, responsibilities, and opportunities. Collaborative, multi-generational teaching teams lend stability to course pedagogy and bring multiple perspectives to course development, implementation, and assessment. Krockover et al. discuss the benefits of a “collaborative action-based” team model in a reform effort at Purdue University (2002).


Krockover, G. H., Shepardson, D. P., Adams, P. E., Eichinger, D., & Nakhleh, M. (2002). Reforming and assessing undergraduate science instruction using collaborative action‐based research teams. School Science and Mathematics, 102(6), 266-284. DOI: 10.1111/j.1949-8594.2002.tb17885.x

Collaboration Between Academic and Student Support Programs

The Foundational Course Initiative recognizes that collaboration between instructional teams and student support programs will create a seamless learning environment that enhances learning outcomes. At Michigan, key student support units include the Comprehensive Studies Program, Science Learning Center, Counseling and Psychological Services, Sweetland Writing Center, and Residential Learning Programs. The articles below discuss models and outcomes for collaboration between academic and student support programs.


Banta, T. W., & Kuh, G. D. (1998). A missing link in assessment: Collaboration between academic and student affairs professionals. Change: The Magazine of Higher Learning, 30(2), 40-46. DOI: 10.1080/00091389809602606

Bourassa, D. M., & Kruger, K. (2001). The national dialogue on academic and student affairs collaboration. New Directions for Higher Education, 2001(116), 9-38. DOI: 10.1002/he.31

Kezar, A. (2003). Enhancing innovative partnerships: Creating a change model for academic and student affairs collaboration. Innovative higher education, 28(2), 137-156. DOI: 10.1023/B:IHIE.0000006289.31227.25

Kezar, A. J., Hirsch, D., & Burack, C. (2002). Understanding the role of academic and student affairs collaboration in creating a successful learning environment. Jossey-Bass. ISBN-13: 978-0787957841

Nesheim, B. E., Guentzel, M. J., Kellogg, A. H., McDonald, W. M., Wells, C. A., & Whitt, E. J. (2007). Outcomes for students of student affairs-academic affairs partnership programs. Journal of College Student Development, 48(4), 435-454. DOI: 10.1353/csd.2007.0041

Tinto, V. (1999). Taking retention seriously: Rethinking the first year of college. NACADA journal, 19(2), 5-9. DOI: 10.12930/0271-9517-19.2.5

Student Data and Learning Analytics

Ferguson et al. (2014) define learning analytics as the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” The Foundational Course Initiative envisions a program of collaborative course design in which specialists in assessment (including learning analytics) work closely with multi-generational instructional teams to transform the large, introductory courses at Michigan.


Dynarski, S. M., Hemelt, S. W., & Hyman, J. M. (2013). The missing manual: Using National Student Clearinghouse data to track postsecondary outcomes (No. w19552). National Bureau of Economic Research. URL: http://www.nber.org/papers/w19552

Ferguson, R., Clow, D., Macfadyen, L., Essa, A., Dawson, S., & Alexander, S. (2014, March). Setting learning analytics in context: overcoming the barriers to large-scale adoption. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 251-253). ACM. DOI: 10.1145/2567574.2567592

Feild, J. (2015). Improving Student Performance Using Nudge Analytics. International Educational Data Mining Society. URL: http://files.eric.ed.gov/fulltext/ED560905.pdf

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. DOI: 10.1007/s11528-014-0822-x

Wright, M. C., McKay, T., Hershock, C., Miller, K., & Tritz, J. (2014). Better than expected: Using learning analytics to promote student success in gateway science. Change: The Magazine of Higher Learning, 46(1), 28-34. DOI: 10.1080/00091383.2014.867209

Assessment of Student Gains Toward Learning Goals

As described in Koester et al. (2016), despite generations of gradual progress, women and minorities remain underrepresented in the leadership of all STEM disciplines. The causes of this disparity are various, but one important factor is the existence of group performance differences (GPDs) in introductory STEM courses. These GPDs persist even when accounting for various measures of prior performance, including high school GPA, standardized tests, and prior college performance. We have uncovered a consistent pattern in GPDs: while they are ubiquitous and substantial in lecture courses evaluated by timed examinations, they are absent in lab courses evaluated through more authentic means. The pattern observed at Michigan has now been confirmed in data from other R1 universities. This pattern suggests that evaluative style might be responsible for substantial gendered performance differences, rather than subject matter or intrinsic ability. We hypothesize that stereotype threat (ST) plays a central role. When an individual is placed in an evaluative environment in which they know others might expect them to confirm a negative stereotype, they expend some cognitive resources on this concern, modestly reducing their ability to perform.

The Foundational Course Initiative envisions a program of collaborative course design in which specialists in assessment help instructional teams design evaluative schemes that optimize learning for all students, combating these gendered performance differences.


Durm, M. W. (1993, September). An A is not an A is not an A: A history of grading. In The educational forum (Vol. 57, No. 3, pp. 294-297). Taylor & Francis Group. DOI: 10.1080/00131729309335429

Knapp, C. (2007). Assessing grading. Public Affairs Quarterly, 21(3), 275-294. Stable URL: http://www.jstor.org/stable/40441463

Koester, B., Grom, G., & McKay, T. (2016) Patterns of Gendered Performance Differences in Introductory STEM Courses. Submitted to PLoS One.

Meyer, M. (1908). The grading of students. Science, 243-250. DOI: 10.1126/science.28.712.243

Pattison, E., Grodsky, E., & Muller, C. (2013). Is the sky falling? Grade inflation and the signaling power of grades. Educational Researcher, 42(5), 259-265. DOI: 10.3102/0013189X13481382

Robins, L. S., Fantone, J. C., Oh, M. S., Alexander, G. L., Shlafer, M., & Davis, W. K. (1995). The effect of pass/fail grading and weekly quizzes on first-year students’ performances and satisfaction. Academic Medicine, 70(4), 327-9. DOI: 10.1097/00001888-199504000-00019

Schinske, J., & Tanner, K. (2014). Teaching more by grading less (or differently). CBE-Life Sciences Education, 13(2), 159-166. DOI: 10.1187/cbe.CBE-14-03-0054

Schneider, J., & Hutt, E. (2014). Making the grade: a history of the A–F marking scheme. Journal of Curriculum Studies, 46(2), 201-224. DOI: 10.1080/00220272.2013.790480