Science of Learning and Educational Technology (SOLET)
The Science of Learning and Educational Technology (SoLET) Lab, is directed by cognitive and learning scientist Dr. Danielle S. McNamara.
The SoLET lab focuses on applying research from computer science, education, and psychology in educational environments. The research aims to further the understanding of cognitive processes and to use this theoretical foundation to improve educational methods.
Reading Comprehension. The majority of high school students lack adequate literacy skills, placing them below the proficiency level for their grade, and calling for improvements in reading comprehension training. Dr. McNamara discovered the importance of textual coherence and a reader’s prior knowledge in the reader’s abilities to form coherent mental representations of text. This led to the consideration of the importance inferencing skills have when readers try to comprehend complex text. This research led to the current exploration of self-explanation training as a means of improving reading comprehension. Self-explanation prompts students to connect the information they are reading to their prior knowledge as well as to information previously presented in the text.
Intelligent Tutoring Systems. Dr. McNamara developed two Intelligent Tutoring Systems, iSTART and Writing Pal, for reading comprehension and writing instruction and practice. These educational technologies enable researchers to better understand cognitive processes involved in comprehension, knowledge and skill acquisition, and writing. Research on these technologies has explored methods for improving student engagement via game-based practice, enhancing adaptability functions, and assessing the feasibility and usability of these systems in real world settings such as high school classrooms.
Natural Language Processing. Much of the research in the lab employs computational linguistics—such as Natural Language Processing (NLP) techniques—as a means of analyzing discourse. This research has led to the development and testing of multiple NLP tools that have been used in various research projects involving essay writing, reading comprehension, second language learning, and creativity. These tools have been applied to the intelligent tutoring systems—the Writing Pal and iSTART—in order to assess students’ written responses and provide automated feedback. The lab’s current research also explores how these tools can be applied to other learning environments such as computer supported collaborative learning environments and massive open online courses (MOOCs).
Lab Director and Principal Investigator:
Danielle S. McNamara, PhD, Professor of Psychology
Dr. McNamara develops educational technologies and conducts research to better understand cognitive processes of comprehension, learning, comprehension strategies, text coherence, and individual differences. She and her team have developed a number of educational technologies (e.g., iSTART, iSTART-ME, Coh-Metrix, and Writing-Pal). She is particularly interested in how the effects of such tools interact with individual differences and can be optimized for individual learners. Curriculum Vitae.
Jianmin Dai, PhD, Assistant Research Professor
Dr. Jianmin Dai received his PhD in System Engineering from the Huazhong University of Science and Technology in China in 2006, and served as a Postdoctoral Fellow on the Writing Pal project with Danielle McNamara from 2008-2011. Jianmin's primary interests are in R&D Intelligent Tutoring Systems (ITS) and game-based education technology. His research focus is on the application of Natural Language Processing and Machine Learning in ITS and game-based education system.
ASU Faculty Collaborator(s)
Rod D. Roscoe, PhD, Assistant Professor, Human Systems Engineering
Dr. 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.
Post-doctoral Research Fellow(s)
Michelle Banawan, PhD
Dr. Michele Banawan received her PhD in Computer Science from the Ateneo de Manila University in the Philippines in 2017. Her research interests include machine learning, educational data mining/learning analytics and artificial intelligence in education.
Reese Butterfuss, PhD
Dr. Reese Butterfuss earned his PhD in Educational Psychology from the University of Minnesota in 2020. His research interests include reading comprehension and knowledge revision.
Ying Fang, PhD
Dr. Ying Fang received her PhD in Experimental Psychology from the University of Memphis in 2019. Her research interests include artificial intelligence in educational systems, game-based assessments, and learning analytics.
Tong Li, PhD
Dr. Tong Li received his PhD in Learning, Design, and Technology from the University of Georgia. His research interests include game-based learning, E-learning design, design thinking, and creativity.
Tracy Arner, PhD
Dr. Tracy Arner received her PhD in Educational Psychology from Kent State University in 2020. Her research interests include reading comprehension, instructional design, technology-enhanced instruction, game-based learning, and misconceptions.
Cecile Perret, Graduate Student, Department of Psychology
Cecile Perret is a graduate student in the Department of Psychology at Arizona State University. She is interested in how Intelligent Tutoring Systems can help students develop and apply their critical thinking skills to various tasks.
Micah Watanabe, Graduate Student, Department of Psychology
Micah Watanabe is a graduate student at Arizona State University. He graduated with a degree in instructional technology from Drexel University in 2017. His research interests are in intelligent tutoring systems and development of reading and writing processes in children.
Linh Huynh, Graduate Student, Department of Psychology
Linh Huynh is a graduate student in the Department of Psychology at ASU. Her broad research interests include reading comprehension and promoting meaningful learning from texts. She is also interested in applying technology to enhance teaching and learning, as well as helping readers become more critical when reading texts on the internet.
Natalie Newton, Research Specialist
Natalie graduated from ASU with a Bachelor’s of Science in Biology and a Bachelor’s of Fine Art in Drawing in May 2019. She is interested in how cognitive psychology research informs educational practices. Her other research interests include cognition as it relates to neurological impairment.
Emily Goblirsch, Research Assistant
Emily Goblirsch is a third year undergraduate at Arizona State University, currently pursuing a degree in psychology and neuroscience. She wants to gain research experience related to linguistics and text comprehension before applying to graduate school for cognitive science.
Kathryn Fleddermann, Research Assistant
Kathryn Fleddermann received her B.A. from Macalester College in May 2020. She majored in Psychology and Political Science with a concentration in Human Rights and Humanitarianism. Her research interests are primarily in clinical psychology, particularly in treatment of mental illness in incarcerated populations. She hopes to become a forensic psychologist in the future.
Arner, T., McCarthy, K. S., & McNamara, D. S. (2021). iSTART StairStepper - Using comprehension strategy training to game the test. Computers, 10(4), 48.
Butterfuss, R., Arner, T., McNamara, D. S., & Allen, L. K. (2021). Social media spillover: Attitude-inconsistent tweets reduce memory for subsequent information. In T. Fitch, C. Lamm, H. Leder, & K. Tessmar (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society. Vienna Austria: Cognitive Science Society.
Dascalu, M.-D., Ruseti, S., Dascalu, M., McNamara, D. S., Carabas, M., Rebedea, T., & Trausan-Matu, S. (2021). Before and during COVID-19: A cohesion network analysis of students' online participation in moodle courses. Computers in Human Behavior, 121, 106780.
McCarthy, K. S., & McNamara, D. S. (2021). The multidimensional knowledge in text comprehension framework. Educational Psychologist. DOI: 10.1080/00461520.2021.1872379.
Wang, Z., O'Reilly, T., Sabatini, J., McCarthy, K. S., & McNamara, D. S. (2021). A tale of two tests: The role of topic and general academic knowledge in traditional versus contemporary scenario-based reading. Learning and Instruction, 73, 101462.
Allen, L. K., & McNamara, D. S. (2020). Defining deep reading comprehension for diverse readers. In P. Afflerbach, E. Birr Moje, P. Enciso & N. K. Lesaux (Eds.), Handbook of Reading Research, Volume V (pp. 261-276). New York: Routledge.
Balyan, R., McCarthy, K. S., & McNamara, D. S. (2020). Applying natural language processing and hierarchical machine learning approaches to text difficulty classification. International Journal of Artificial Intelligence in Education (IJAIED).
Crossley, S. A., Balyan, R., Liu, J., Karter, A., McNamara, D. S., & Schillinger, D. (2020). Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study. Journal of Communication in Healthcare: Strategies, Media and Engagement in Global Health.
Dascalu, M. D., Ruseti, S., Dascalu, M., McNamara, D. S., & Trausan-Matu, S. (2020). Multi-document cohesion network analysis: Visualizing intratextual and intertextual links. In R. Luckin, V. Cavalli-Sforza, I. Ibert Bittencourt, M. Cukorova, & K. Muldner (Eds.), Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED 2020). Ifrane, Morocco: Springer.
McCarthy, K. S., Soto, C. M., Gutierrez de Blume, A. P., Palma D., González, J. I., & McNamara, D. S. (2020). Improving reading comprehension in Spanish using iSTART-E: A pilot study. International Journal of Computer-Assisted Language Learning and Teaching, 10(4), 66-82.
McCarthy, K. S., Watanabe, M., & McNamara, D. S. (2020). The Design implementation framework: Guiding principles for the redesign of a reading comprehension intelligent tutoring system. In M. Schmidt, A. Tawfik, Y. Earnshaw, & I. Jahnke (Eds.) Learner and User Experience Research: An Introduction for the Field of Learning Design & Technology. EdTech Books.
Jung, J., Crossley, S. A., & McNamara, D. S. (2019). Predicting Second Language Writing Proficiency in Learner Texts Using Computational Tools. The Journal of Asia TEFL.
McCarthy, K. S., Likens, A. D., Johnson, A. M., Guerrero, T. A., & McNamara, D. S. (2018). Metacognitive overload!: Positive and negative effects of metacognitive prompts in an intelligent tutoring system. International Journal of Artificial Intelligence in Education.
Ruseti, S., Dascalu, M., Johnson, A. M., McNamara, D. S., Balyan, R., McCarthy, K. S., & Trausan-Matu, S. (2018). Scoring summaries using recurrent neural networks. In Nkambou, R., Azevedo, R., Vassileva, J. (Eds.), Proceedings of the 14th International Conference on Intelligent Tutoring Systems (ITS) in Montreal, Canada (pp. 191-201). London, UK: Springer.
Sirbu, D., Dascalu, M., Crossley, S. A., McNamara, D. S., Barnes, T., Lynch, C., & Trausan-Matu, S. (2018). Exploring online course sociograms using cohesion network analysis. In C. P. Rosé, R. Martinez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018), Part II (pp. 337-342). London, UK: Springer.
2018 and older
Crossley, S. A., Sirbu, M.-D., Dascalu, M., Barnes, T., Lynch, C. F., & McNamara, D. S. (2018). Modeling math success using Cohesion Network Analysis. In C. P. Rosé, R. Martínez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018) , Part II (pp. 63-67). London, UK: Springer.
Likens, A. D., McCarthy, K. S., Allen, L. K., & McNamara, D. S. (2018). Recurrence Quantification Analysis as a method for studying text comprehension dynamics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK’18). Sydney, Australia.
McCarthy, K. S., Soto, C., Malibran, C., Fonesca, L., Simian, M., & McNamara, D. S. (2018). iSTART-E: Reading comprehension strategy training for Spanish speakers. In C. P. Rosé, R. Martinez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018), Part II (pp. 215-219). London, UK: Springer.
Likens, A. D., Allen, L. K., & McNamara, D. S. (2017). Keystroke dynamics predict essay quality. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Meeting of the Cognitive Science Society (CogSci 2017), (pp.2573-2578). London, UK: Cognitive Science Society.
Johnson, A., McCarthy, K. S., Kopp, K., Perret, C. A., & McNamara, D. S. (2017). Adaptive reading and writing instruction in iSTART and W-Pal. In Z. Markov & V. Rus (Eds.), Proceedings of the 30th Annual Florida Artificial Intelligence Research Society International Conference (FLAIRS), (pp. 561-566). Marco Island, FL: AAAI Press.
Shum, S. B., Knight, S. McNamara, D. S., Allen, L. K., Bektik, D., & Crossley, S. A. (2016). Critical perspectives on writing analytics. In D. Gašević, G. Lynch, S. Dawson, H. Drachsler, & C. P. Rosé (Eds.), Workshop Proceedings of the 6th International Learning Analytics and Knowledge Conference (LAK’16), (pp. 481-483). New York, NY: ACM.
Snow, E. L., San Pedro, M. O., Jacovina, M. E., McNamara, D. S., & Baker. R. S. (2015). Achievement versus experience: Predicting students’ choices during gameplay. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, & M. Desmarais (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), (pp.564-565). Madrid, Spain: International Educational Data Mining Society.
Allen, L. K., & McNamara, D. S., (2015). Promoting self-regulated learning in an Intelligent Tutoring System for writing. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Doctoral Consortium within the Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), (pp. 827-830). Madrid, Spain.
In the News and More!
Professor Danielle McNamara honored by UC Merced with Distinguished Cognitive Scientist Award of 2015. Read More (posted 6/16/15).