Principal collaborators
Scott Crossley, Professor of Applied Linguistics, Georgia State University
Scott Crossley is a professor of applied linguistics at Georgia State University. Professor Crossley’s primary research focus is on natural language processing and the application of computational tools and machine learning algorithms in language learning, writing, and text comprehensibility. His main interest area is the development and use of natural language processing tools in assessing writing quality and text difficulty. He is also interested in the development of second language learner lexicons and the potential to examine lexical growth and lexical proficiency using computational algorithms.
Contact: [email protected]
Laura Allen, Assistant Professor, University of Minnesota
Laura Allen received her PhD from Arizona State University in 2017 and is currently an assistant professor at the University of Minnesota. She is a cognitive scientist with expertise in computational linguistics, discourse analyses, and dynamic systems modeling.
Contact: [email protected]
Mihai Dascalu, Associate Professor, Computer Science and Educational Sciences, University Politehnica of Bucharest, Romania
Mihai Dascalu is an associate professor at University Politehnica of Bucharest. Complementary to his competencies in NLP, technology-enhanced learning and discourse analysis, Mihai holds a multitude of professional certifications (e.g., PMP, PMI-RMP, PMI-ACP, CBAP, CISA, CEH and CISSP) and has extensive experience on strategic projects on non-refundable funds (EU, WB, USTDA). Mihai was a visiting scholar in Danielle McNamara’s lab in the spring of 2015 and has been collaborating with her on various projects since then.
Contact: [email protected]
Rod D. Roscoe, Assistant Professor, Human Systems Engineering,. Arizona State University
Roscoe studies the metacognitive, cognitive, and motivational process of learning, and how these processes can be effectively facilitated via educational technology and games, strategy instruction, and peer support. He has contributed to the research and design of several technologies (e.g., Writing Pal, Coh-Metrix, Betty’s Brain, and iSTART-ME) that address diverse topics of reading, writing, science, self-explanation, self-regulated learning, and causal reasoning. He is particularly interested in how users’ expectations, perceptions, and roles can be leveraged to improve engagement with and efficacy of educational technologies.
Contact: [email protected]
Christian Soto, Educational Psychologist, Institute for the Science of Teaching and Learning, Arizona State University
Christian Soto received a PhD in the linguistics (psycholinguistics field) at the University of Conception in Chile. He has a large experience as an educational consultant intervening in schools and generating studies about educational topics. He is especially interested in the theory and application of metacognition to reading comprehension and learning, considering specifically the regulation and monitoring process. Currently he is very interested in understanding how these specific processes happen while using educational technology to improve reading comprehension. He is collaborating with Science of Learning and Educational Technology (SoLET) Lab on the elaboration of the Spanish version of iSTART (iSTART-E) and on the development of preliminary studies on the system.
Contact: [email protected]
Katie McCarthy, Assistant Professor, Georgia State University
Katie McCarthy earned her PhD in cognitive Psychology from University of Illinois at Chicago in 2016. Katie is interested in how people learn from what they read and the higher order comprehension processes that occur during reading. She investigates how these processes are affected by textual features and as extra-textual features such as reader goals and prior knowledge. She works on the development of adaptive features within iSTART and on an ETS collaboration that explores how background knowledge affects text comprehension and learning in different domains.
Contact: [email protected]
Renu Balyan, Assistant Professor, State University of New York at Old Westbury
Renu Balyan received her PhD in machine translation (MT) evaluation from IIT Delhi in 2016. Her research primarily focuses on applying machine learning algorithms for various NLP tasks such as named entity recognition and sentiment analysis. She is also passionate about data aggregation, curation, preparation of data sets for predictive modeling and performing statistical analyses on data. She is currently working on aggregating both structured and unstructured data for texts as well as working towards designing an algorithm for text classification using linguistic indices.
Contact: [email protected]