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IES Grant

Title: PosTPIER: Post-doctoral Training Program in Interdisciplinary Education Research
Center: NCER Year: 2011
Principal Investigator: Klahr, David Awardee: Carnegie Mellon University
Program: Postdoctoral Research Training Program in the Education Sciences      [Program Details]
Award Period: 3 years Award Amount: $648,974
Type: Training Award Number: R305B110003

Co-Principal Investigators: Aleven, Vincent; Koedinger, Kenneth

The postdoctoral program trained four scientists to do rigorous research on learning conditions related to curriculum, instruction, and assessment for diverse K–12 student populations. Postdoctoral fellows were recruited from strong doctoral programs in cognitive and developmental psychology, computer science, and STEM education. Training focused on cognitive science foundations for developing and implementing evidence-based instructional methods that can improve teaching and learning in authentic educational settings. Content emphasis was closely related to the development and efficacy goals in IES programs in Cognition and Student Learning, Math and Science Education, and Educational Technology.

Fellows worked on one of five funded research projects and proposed and ran an extension study to one of those projects.  Fellows worked with the project team and mentors to carry out their research proposal. During their training, fellows were exposed and contributed to cutting edge theory, methods, and procedures that are advancing the learning sciences. Fellows acted as apprentices, learning through activities such as joint data analysis; design and assessing research protocols; and guidance on how to collaborate with school representatives and teachers. Faculty mentors set specific productivity benchmarks for the fellows and engaged them in activities appropriate to their specific strengths, weaknesses, and goals. Examples of such activities include career counseling; grant writing; journal reviewing; publications and presentations; opportunities to improve teaching and presentation skills; guidance on how to collaborate with researchers from diverse backgrounds; and how to collaborate with researchers from diverse disciplinary areas.

Postdoctoral Fellows

Chase, Cathy (ORCID)
Goldin, Ilya (ORCID)
Kittredge, Audrey
Liu, Ran (ORCID)

As of 2020, Dr. Chase was an assistant professor of human development at Teachers College, Columbia University, Dr. Goldin was a principal data scientist at Phenom People, Dr. Kittredge was an independent consultant for program research, and Dr. Liu was a lead machine learning scientist at Amira Learning.


Book chapter

Chase, C.C. (2013). Motivating Persistence in the Face of Failure: Equipping Novice Learners With the Motivational Tools of Experts. In J.J. Staszewski (Ed.), Expertise and Skill Acquisition: The Impact of William G. Chase (pp. 59–84). New York: Psychology Press.

Galyardt, A., and Goldin, I. (2015). Evaluating Simplicial Mixtures of Markov Chains for Modeling Student Metacognitive Strategies. In Quantitative Psychology Research (pp. 377–393). Springer, Cham.

Journal article, monograph, or newsletter

Chase, C. C., and Klahr, D. (2017). Invention Versus Direct Instruction: For Some Content, It's a Tie. Journal of Science Education and Technology, 26(6), 582–596.

Chi, M.T.H., Roscoe, R., Slotta, J., Roy, M., and Chase, C.C. (2012). Misconceived Causal Explanations for Emergent Processes. Cognitive Science, 36(1): 1–61. doi:10.1111/j.1551–6709.2011.01207.x

Galyardt, A., and Goldin, I. M. (2015). Move Your Lamp Post: Recent Data Reflects Learner Knowledge Better than Older Data. Journal of Educational Data Mining, 7(2): 83–108.

Goldin, I.M., and Ashley, K.D. (2012). Eliciting Formative Assessment in Peer Review. Journal of Writing Research, 4(2): 203–237.

Kittredge, A.K., and Dell, G.S. (2016). Learning to Speak by Listening: Transfer of Phonotactics From Perception to Production. Journal of Memory and Language, 89: 8–22. doi:10.1016/j.jml.2015.08.001

Liu, R., and Koedinger, K. (2017). Going Beyond Better Data Prediction to Create Explanatory Models of Educational Data. The Handbook of Learning Analytics, 69–76. Liu, R., and Koedinger, K. R. (2017). Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains. Journal of Educational Data Mining, 9(1), 25–41. Full text

Schwartz, D.L., Chase, C.C., and Bransford, J.D. (2012). Resisting Overzealous Transfer: Coordinating Previously Successful Routines With Needs for New Learning. Educational Psychologist, 47(3): 204–214. doi:10.1080/00461520.2012.696317

Weisberg, D. S., Hirsh-Pasek, K., Golinkoff, R. M., Kittredge, A. K., and Klahr, D. (2016). Guided Play: Principles and Practices. Current Directions in Psychological Science, 25(3): 177–182. doi:10.1177/0963721416645512

Weisberg, D.S., Kittredge, A.K., Hirsh-Pasek, K., Golinkoff, R.M., and Klahr, D. (2015). Making Play Work for Education. Phi Delta Kappan, 96(8): 8–13. doi:10.1177/0031721715583955


Almond, R.G., Goldin, I. M., Guo, Y., and Wang, N. (2014). Vertical and Stationary Scales for Progress Maps. In Proceedings of 7th International Conference on Educational Data Mining (pp. 169–176). London UK: International Educational Data Mining Society. Full text

Chase, C. (2012). The Interplay of Chance and Skill: Exploiting a Common Game Mechanic to Enhance Learning and Persistence. In Proceedings of the 2012 International Conference of the Learning Sciences. Sydney AU: International Conference of the Learning Sciences (ICLS).

Chase, C., Harpstead, E., and Aleven, V. (2017). Inciting Out-of-Game Transfer: Adapting Contrast-Based Instruction for Educational Games. Proceedings of the Games Learning Society Conference (pp. 29–38).

Christel, M. G., Stevens, S. M., Maher, B. S., Brice, S., Champer, M., Jayapalan, L., Chen, Q., Jin, J., Hausmann, D., Bastida, N., Zhang, X., Aleven, V., Koedinger, K., Chase, C., Harpstead, E., and Lomas D. (2012). RumbleBlocks: Teaching Science Concepts to Young Children through a Unity Game. In Proceedings 17th International Conference on Computer Games (CGAMES): AI, Animation, Mobile, Interactive Multimedia, Educational and Serious Games . Louisville, KY: (IEEE). doi:10.1109/CGames.2012.6314570

Goldin, I. M., Renken, M., Galyardt, A., and Litkowski, E. (2014). Individual Differences in Identifying Sources of Science Knowledge. In Open Learning and Teaching in Educational Communities: Proceedings of 9th European Conference on Technology Enhanced Learning (pp. 152–164). Graz, AU: Springer International Publishing. doi:10.1007/978–3–319–11200–8_12

Goldin, I.M., Koedinger, K.R., and Aleven, V. (2012). Learner Differences in Hint Processing. In  Proceedings of the 5th International Conference on Educational Data Mining (pp. 73–80). Chania, Greece: International Educational Data Mining Society. Full text

Liu, R., and Koedinger, K. R. (2015). Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities. International Educational Data Mining Society. Full text

Liu, R., and Koedinger, K. R. (2017). Towards Reliable and Valid Measurement of Individualized Student Parameters. In Proceedings of the 10th International Conference on Educational Data Mining (pp. 135–142).

Liu, R., Davenport, J., and Stamper, J. (2016). Beyond Log Files: Using Multi -Modal Data Streams Towards Data-Driven KC Model Improvement. In Proceedings of the 9th International Conference on Educational Data Mining (pp. 436–441). Raleigh, NC.

Liu, R., Koedinger, K.R., and McLaughlin, E. (2014). Interpreting Model Discovery and Testing Generalization to a new Dataset. In Proceedings of the 7th International Conference on Educational Data Mining (pp. 107–113). London: Educational Data Mining. Liu, R., Patel, R., and Koedinger, K. R. (2016). Modeling Common Misconceptions in Learning Process Data. In Proceedings of the 6th International Learning Analytics and Knowledge (LAK) Conference (pp. 369–377). Edinburgh UK: ACM. doi:10.1145/2883851.2883967

MacLellan, C. J., Liu, R., and Koedinger, K. R. (2015). Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning. International Educational Data Mining Society (pp 53–60). Full text

Patel, R., Liu, R., and Koedinger, K. R. (2016). When to Block Versus Interleave Practice? Evidence Against Teaching Fraction Addition Before Fraction Multiplication. In Proceedings of the 38th Annual Meeting of the Cognitive Science Society (pp. 2070–2074). Philadelphia PA: Cognitive Science Society.

Working paper

Ashley, K., and Goldin, I. (2012). Computer-Supported Peer Review in a Law School Context (Legal Studies Research Paper 2012–24 ). Pittsburgh: University of Pittsburgh Working Paper. doi:10.2139/ssrn.2145570