Recent lab publications
Crossley, S. A., & Skalicky, S. (in press). Making sense of polysemy relations in first and second language speakers of English. International Journal of Bilingualism.
Skalicky, S., Crossley, S.A., McNamara, D.S., & Muldner, K. (in press). Identifying creativity during problem solving using linguistic features. Creativity Research Journal.
Berger, C. M., Crossley, S., & Kyle, K. (in press). Using native-speaker psycholinguistic response norms to predict lexical proficiency in second language learners. Applied Linguistics.
Kyle, K., Crossley, S. A., & Berger, C. (in press). The Tool for the automatic analysis of lexical sophistication version 2.0 Behavior Research Methods.
Berger, C. M., Crossley, S., & Kyle, K. (2017). Using novel word context measures to predict human ratings of lexical proficiency. Journal of Educational Technology & Society, 20(2), 201-212.
Crossley, S. A., Francus Rose, D., Danekes, C., Rose, C. W. & McNamara, D. S. (2017). That noun phrase may be beneficial and this may not be: Discourse cohesion and text processing. Reading and Writing, 30 (3), 569-589.
Crossley, S. A., Skalicky, S., Dascalu, M., McNamara, D., & Kyle, K. (2017). Predicting text comprehension, processing, and familiarity in adult readers: New approaches to readability formulas. Discourse Processes, 54(5-6), 340-359.
Skalicky, S., Crossley, S.A., McNamara, D.S., & Muldner, Kasia. (2017). Automatically identifying humorous and persuasive language produced during a creative problem-solving task. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. [Open Access].
Current grants and other projects:
ALRC: Text Readability for Adults
Summary: This project aims to improve the predictability of traditional readability formulas using richer linguistic features supported by behavioral data. Under the direction of Scott Crossley, members of the lab (Cynthia Berger, Ali Heidari, and Stephen Skalicky) will use online crowd-sourcing, natural language processing, eye-tracking techniques in order to obtain measures of text readability that improve upon traditional readability formulas such as Flesch-Kincaid.
Linguistic Analysis and a Hybrid Human-Automatic Coach for Improving Math Identity
Ocumpaugh, J., Baker, R., Crossley, S. A. (Co-PI), Kostyuk, V., & Mingle, L.
National Institute of Health
Karter, A. J., Schillinger, D. et al.