Higher Level Learning Processes
Cognitive Neuroscience
Social and Motivational Factors in Learning
Research to Reform School Practice
Learning Technology
Informal Learning
Learning Policy
Reading and Language
College & Pre-College Reading  & Learning
jquery photo gallery
TEDx logo

in the news

Lindsay Page’s TEDx talk "Proactively Supporting Students To and Through College."

2018 LRDC Undergraduate Research Interns group shot

in the news

Congratulations to the 2018 LRDC Undergraduate Research Interns

IWALS 2018 image

in the news

IWALS 2018: Registration Now Open

more news ►

TALKS

Aug

27

University of Pittsburgh

First Day of Fall Classes

Sep

21

Carnegie Mellon Forum on Biomedical Engineering

Carnegie Mellon University, 9:00 AM - 5:00 PM

The Forum will bring in national leaders to deliver keynote and plenary talks discussing the grand challenges and trends in biomedical engineering, and provide a platform to facilitate interdisciplinary research collaborations among engineers, scientists and clinicians. More information at: https://www.cmu.edu/bme/bmeforum/index.html

Sep

24

eBrain Journal Club

GSPH 5th Floor Conference room 5140, 12:00 PM - 1:00 PM

Join the eBrain group's fall journal club on the fourth Monday of each month. An email with details on the journal article for discussion at each session will be sent prior to each meeting. More information at: https://www.facebook.com/e.brain.pitt/

More Talks

New Publications

  • Simons, A., & Warren, T. (2018). A closer look at strengthened readings of scalars. Quarterly Journal of Experimental Psychology, 71, 272-279.
  • Libertus, M. E., Feigenson, L. & Halberda, J. (2018). Infants extract frequency distributions from variable approximate numerical information. Infancy, 23(1), 29-44.
  • Witherspoon, E., Schunn, C. D., Higashi, R., & Shoop, R. (2018). Attending to structural programming features predicts differences in learning and motivation. Journal of Computer Assisted Learning, 34(2).
  • Fan, Q., Nummenmaa, A., Wichtmann, B. Witzel, T., Mekkaoui, C., Schneider, W., Wald, L. L., & Huang, S. Y. (2018). Validation of diffusion MRI estimates of compartment size and volume fraction in a biomimetic brain phantom using a human MRI scanner with 300 mT/m maximum gradient strength. NeuroImage.
  • Tuninetti, A., & Tokowicz, N. (2018). The influence of a first language: Training nonnative listeners on voicing contrasts. Language, Cognition, and Neuroscience.
  • Litman, D. J., & Nguyen, H. V. (2018). Argument mining for improving the automated scoring of persuasive essays. Association for the Advancement of Artificial Intelligence.
  • Ben-Eliyahu, A., Moore, D., Dorph, R., & Schunn, C. D. (2018). Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement across science activities and contexts. Contemporary Educational Psychology.
  • Page, L. C., Iriti, J. E., Lowry, D. J., & Anthony, A. M. (2018). The promise of place-based investment in postsecondary access and success: Investigating the impact of the Pittsburgh Promise. Education Finance and Policy.
  • Matsumura, L. C., & Correnti, R. (2018). Student writing accepted as high-quality analytic responses to analytic text-base writing tasks. The Elementary School Journal.
  • Derringer, C., & Rottman, B. (2018). How people learn about causal influence when there are many possible causes: A model based on informative transitions. Cognitive Psychology.
  • Kinloch, V., & Dixon, K. (2018). Professional development as publicly engaged scholarship in urban schools: Implications for educational justice, equity, and humanization. English Education, 50(2), 147-171
  • Cho, B-Y., Han, H., & Kucan, L. (2018) An exploratory study of middle-school-learners' historical reading in an internet environment. Reading and Writing.
  • Morris, P., Connors, M., Friedman-Krauss, A., McCoy, D., Weiland, C., Feller, A., Page, L., Bloom, H., & Yoshikawa, H. (2018). New findings on impact variation from the Head Start Impact Study: Informing the scale-up of early childhood programs. AERA Open, 4(2), 1–16.
  • Soo, K. W., & Rottman, B. M. (2018). Causal strength induction from time series data. Journal of Experimental Psychology: General, 147(4), 485-513.
  • Soo, K. W., & Rottman, B. M. (2018). Switch rates do not influence weighting of rare events in decisions from experience, but optional stopping does. Journal of Behavioral Decision Making.
  • Tseng, A. M, Doppelt, M. C., & Tokowicz, N. (2018). The effects of transliterations, thematic organization, and working memory on adult L2 vocabulary learning. Journal of Second Language Studies, 1(1), 141-165.
  • Byrnes, J.P., Miller-Cotto, D., & Wang, A. (2018). Children as mediators of their own cognitive development: the case of learning science in kindergarten and first grade. Journal of Cognition and Development.
  • Mountz, J., Minhas, D., Laymon, C., Beers, S., Puccio, A., Edelman, K., Sharpless, J., Lopresti B., Puffer, R., Schneider, W., Mathis, C., & Okonkwo, D. (2018). Comparison of [18F]AV-1451 PET with HDFT in chronic TBI subjects. The Journal of Nuclear Medicine, 59(1643).
  • Li, H., & Xiong, Y. (2018). The relationship between test preparation and state test performance: Evidence from the measure of effective teaching (MET) project. Education Policy Analysis Archives, 26 (64).
  • Galla, B. M., Amemiya, J., & Wang, M.-T. (2018). Using expectancy-value theory to understand academic self-control. Learning and Instruction, 58, 22-33.
  • Wang, J., Libertus, M., & Feigenson, L. (2018) Hysteresis-induced changes in preverbal infants’ approximate number precision. Cognitive Development, 47, 107-116.
  • McKeown, M., Crosson, A.C., Moore, D. & Beck, I. (2018). Word knowledge and comprehension effects of an academic vocabulary intervention for middle school students. American Educational Research, 55(3), 572-616.
  • Lugini, L., Litman, D., Godley, A., & Olshefski, C. (2018). Annotating student talk in text-based classroom discussions. Association for Computational Linguistics, 110-116.
  • Afrin, T., & Litman, D. (2018). Annotation and classification of sentence-level revision improvement. Association for Computational Linguistics, 240-246.
  • Zhang, H., & Litman, D. (2018). Co-attention based neural network for source-dependent essay scoring. Association for Computational Linguistics, 399-409.

OUR

RESEARCH AREAS