As a researcher and educator, I work to build connections between research and practice in the areas of learning and motivation. My research has two purposes: first, to provide concrete recommendations that will help STEM educators and students improve learning and social outcomes, and second, to create better theoretical models of complex learning systems. To advance both of these purposes, I examine how cognitive and social factors of instructional systems impact learning. In educational technology environments, I track learning as it unfolds to better understand these interactions.
How can educational technology support self-regulation, motivation, and equitable learning outcomes?
My work on the PersonalizedLearning2 project is focused on building software to support equitable math outcomes through personalized mentoring and tutoring with artificial intelligence learning software. Through my work with Decimal Point, a digital learning game focused on decimal number operations, I apply principles from cognitive and social psychology to understand learning and motivational processes in digital games.
Hou, X., Nguyen, H. A., Richey, J. E., Harpstead, E., Hammer, J., & McLaren, B. M. (in press). Assessing the Effects of Open Models of Learning and Enjoyment in a Digital Learning Game. International Journal of Artificial Intelligence in Education, 1-31.
Huang, Y., Lobczowski, N., Richey, J. E., MacLaughlin, E., Asher, M., Harackiewicz, J., Aleven, V., & Koedinger, K. (2021). A general multi-method approach to data-driven redesign of tutoring systems. Proceedings of the International Conference on Learning Analytics and Knowledge, March 2021 (LAK’21). (pdf)
Richey, J. E., Lobczowski, N. G., Carvalho, P. F., & Koedinger, K. (2020, July). Comprehensive Views of Math Learners: A Case for Modeling and Supporting Non-math Factors in Adaptive Math Software. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-7_37 (pdf)
Hou X., Nguyen H.A., Richey J.E., McLaren B.M. (2020) Exploring How Gender and Enjoyment Impact Learning in a Digital Learning Game. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-7_21
Mogessie M., Richey, J. E., McLaren B.M., Andres-Bray J.M.L., Baker R.S. (2020) Confrustion and Gaming While Learning with Erroneous Examples in a Decimals Game. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham. https://doi.org/10.1007/978-3-030-52240-7_38
Richey, J. E., Andres-Bray, M., Mogessie, M., Scruggs, R., Andres, J. M. A. L., Star, J. R., Baker, R. S., & McLaren, B. (2019). More confusion and frustration, better learning: The impacts of erroneous examples. Computers & Education, 139, 173-190. doi:10.1016/j.compedu.2019.05.012 (pdf)
Richey, J. E., McLaren, B. M., Andres-Bray, J. M. L., Mogessie, M., Scruggs, R., Baker, R. S., & Star, J. R. (2019). Confrustion in learning from erroneous examples: Does type of prompted self-explanation make a difference? In: Isotani S., Millán E., Ogan A., Hastings P., McLaren B., Luckin R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science, vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_37 (pdf)
Harpstead, E., Richey, J. E., Nguyen, H., & McLaren, B. M. (2019). Exploring the subtleties of agency and indirect control in digital learning games. In Proceedings of the International Conference on Learning Analytics and Knowledge, Tempe, Arizona, March 2019 (LAK’19). Nominated for best paper.
How and when do cognitive science-based instructional techniques improve learning?
Cognitive and learning scientists have investigated a variety of instructional techniques to support learning, but there are still many questions regarding the cognitive processes supported by each technique and the critical factors for successfully applying them across learning contexts. I am interested in the mechanisms underlying instructional techniques such as worked examples, analogical comparison, and self-explanation, and the instructional features that promote learning. In my role as a Co-PI on the NSF-funded project Facilitating Teacher Learning with Video Clips of Instruction in Science, I am helping to test the role of contrasting cases and self-explanation in teacher learning through video-based teacher professional development.
Richey, J. E. & Nokes-Malach, T. J. (2015). Comparing four instructional techniques for promoting robust learning. Educational Psychology Review, 27, 181-218. doi: 10.1007/s10648-014-9268-0. (accepted manuscript pdf).
Nokes-Malach, T. J. & Richey, J. E. (2015). Knowledge transfer. In R. Scott and S. Kosslyn (Eds.), Emerging Trends in the Social and Behavioral Sciences. Hoboken, NJ: John Wiley and Sons.
Richey, J. E., Zepeda, C. D., & Nokes-Malach, T. J. (2015). Transfer effects of prompted and self-reported analogical comparison and self-explanation. In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P Maglio (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 1985-1990). Austin, TX: Cognitive Science Society.
Richey, J. E., Phillips, J. L., Schunn, C. D., & Schneider, W. (2014). Is the link from working memory to analogy causal? No improvement following working memory gains with training. PLOS ONE. 9(9): e106616. doi: 10.1371/journal.pone.0106616.
Richey, J. E. & Nokes-Malach, T. J. (2013). How much is too much? Learning and motivation effects of adding instructional explanations to worked examples. Learning and Instruction, 25, 104-124. doi: 10.1016/j.learninstruc.2012.11.006. (accepted manuscript pdf).
How does instruction affect motivation, metacognition, and belonging?
Selected publications from this space:
Richey, J. E., Bernacki, M. L., Belenky, D. M., & Nokes-Malach, T. J. (2018). Comparing class- and task-level measures of achievement goals. The Journal of Experimental Education, 86(4), 560-578. doi:10.1080/00220973.2017.1386155
Bernacki, M. L., Nokes-Malach, T. J., Richey, J. E., & Belenky, D. M., (2016). Science diaries: A brief writing intervention to improve motivation to learn science. Educational Psychology, 36(1), 26-46. doi: 10.1080/01443410.2014.895293
Richey, J. E., Nokes-Malach, T. J., & *Wallace, A. (2014). Achievement goals, observed behaviors, and performance: testing a mediation model in a college classroom. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1293-1298). Austin, TX: Cognitive Science Society.
Zepeda, C. D., Richey, J. E., Ronevich, P., & Nokes-Malach, T. J. (2015). Direct instruction of metacognition benefits adolescent science learning, transfer, and motivation: An in-vivo study. Journal of Educational Psychology, 107(4), 954-970. doi: 10.1037/edu0000022.
How does collaboration change learners' cognitive and motivational processes?
I am currently studying the impact of instructor-facilitated collaboration on students' feelings of belonging and motivation in large, introductory-level biology classes through my work on the project Assessing cognitive and social impacts of collaboration on student learning in introductory biology, funded by the Biology Leadership Community Catalytic Grant Program.
Selected publications from this space:
Nokes-Malach, T. J., Zepeda, C. D., Richey, J. E., & Gadgil, S. (2018). Collaborative learning: The benefits and costs. In J. Dunlosky and K. Rawson (Eds.), The Cambridge Handbook of Cognition and Education. Cambridge University Press.
Richey, J. E., Nokes-Malach, T. J., & Cohen, K. (2018). Collaboration facilitates abstract category learning. Memory and Cognition, 46(5), 685-698. doi:10.3758/s13421-018-0795-7.
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. doi: 10.1007/s10648-015-9312-8.