Accepting Grad Students for the Fall 2018!

The Advanced Instructional Systems and Technologies research laboratory at the University of Utah is an interdisciplinary research group, housed at the Sorenson Arts and Education Complex. Its research draws from the fields of psychology, education, computer science, and domain-specific subject matters ranging from teacher professional development, natural sciences, and medical education.


The ASSIST lab was founded in 2014 by Eric Poitras.

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PROJECTS

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Medical Education

Supporting mental and physical health professionals in tasks varying from diagnostic reasoning to motivational interviewing. These training solutions are designed to deliver automated formative feedback through learner modeling and natural language processing.

The mental health strand of research is led by Dr. Zac Imel, Vivek Srikumar, and David Atkins from the University of Utah with funding provided by the National Science Foundation and the National Institute of Health. The medical diagnostic strand is led by Dr. Susanne Lajoie from McGill University, with funding provided by SSHRC.

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Teacher Professional Learning

Fostering self-regulated learning in preservice teachers to design lessons that implement technologies in the classroom with network-based tutors. This tutor authoring framework has led to the development of intelligent web browsing, online lesson plan authoring tools, and voice-activated digital assistants.

Funding for these projects was provided by the University of Utah, non-profit organizations, and SSHRC.

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STEM Education

Modeling critical thinking skills in an inquiry-based learning environment called Research Quest. This web-based learning environment was developed by the Natural History Museum of Utah to allow students to conduct authentic investigations in the classroom.

The projects are led by Dr. Kirsten Butcher in collaboration with Madlyn Runburg, director of the NHMU educational outreach program, with funding provided by the NHMU, SSHRC, and non-profit organizations.

PEOPLE

Eric Poitras

Eric G. Poitras, Ph.D.
Director

Laurel Udy

Laurel Udy, Ph.D. Student
Graduate Research Assistant

Kent Ellsworth

Kent Ellsworth, Ph.D. Student
Graduate Research Assistant

SERVICES

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Using technology as a means to scaffold learners' ability to monitor and control the cognitive, metacognitive, and affective/motivational processes that mediate learning and task performance.

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"Artificial Intelligence in Education. The Key to Personalized Learning Solutions."

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"Mining Trace Log Data. Discovering New Knowledge About Learning."

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Our research combines both quantitative and qualitative methods, leveraging contemporary techniques from educational data mining and learning analytics to discover knowledge from process data captured in several modalities.

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"Open-Ended Learning Environments. Design-Driven Process Grounded in Theories of Learning and Instruction."

We use technology as both a research and training tool to capture learner behaviors, build computational models, and prescribe the most suitable instructional content.

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PUBS

  • Doleck, T., Basnet, R. B., Poitras, E., & Lajoie, S. (2014). Exploring the Link Between Initial and Final Diagnosis in a Medical Intelligent Tutoring System. In Proceedings of the IEEE International Conference on MOOCs, Innovation and Technology in Education (IEEE MITE) (pp. 13-16). India : IEEE. Published, 12/31/2014.
    http://ieeemite2014.com/
  • Doleck, T., Basnet, R. B., Poitras, E., & Lajoie, S. (2014). BioWorldParser : A Suite of Parsers for Leveraging Educational Data Mining Techniques. In Proceedings of the IEEE International Conference on MOOCs, Innovation and Technology in Education (IEEE MITE) (pp. 32-35). India : IEEE. Published, 12/31/2014.
    http://ieeemite2014.com/
  • Poitras, E., Jarrell, A., Doleck, T., & Lajoie, S. (2014). Supporting Diagnostic Reasoning by Modeling Help-Seeking. In Proceedings of the 9th International Conference on Computer Science & Education (ICCSE 2014) (pp. 10-14). Vancouver, Canada. Published, 12/31/2014.
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp...
  • Poitras, E., Doleck, T., Lajoie, S. (2014). Mining Case Summaries in BioWorld. In Proceedings of the 9th International Conference on Computer Science & Education (ICCSE 2014) (pp. 6-9). Vancouver, Canada. Published, 12/31/2014.
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp...
  • Trevors, G., Muis, K. R., Pekrun, R., Sinatra, G., & Poitras, E. (2014). Epistemic beliefs and emotions predict the source of information recalled from multiple conflicting texts. In H. Tabbers, E. de Vries, N. Jacobson, B. de Koning, M. van Amelsvoort, J. van der Meij (Eds.), Proceedings of the Biennial Meeting of the EARLI Special Interest Group 2 Comprehension of Text and Graphics (p. 11). Rotterdam: Erasmus University. Published, 12/31/2014.
    http://www.eur.nl/fsw/psychologie/research/onderzo...
  • Goldberg, B., Sottilare, R., Roll, I., Lajoie, S., Poitras, E., Biswas, G., Segedy, J., Kinnebrew, J., Wiese, E., Long, Y., Aleven, V., Koedinger, K., Winne, P. (2014). Enhancing Self-Regulated Learning through Metacognitively-Aware Intelligent Tutoring Systems. In Proceedings of the 11th International Conference of the Learning Sciences (ICLS 2014). Colorado, USA. Published, 12/31/2014.
    http://www.isls.org/icls2014/downloads/ICLS%202014...
  • Doleck, T., Basnet, R. B., Poitras, E., & Lajoie, S. (2014). Augmenting Novice-Expert Overlay Model in an Intelligent Tutoring System : Using Confidence-Weighted Linear Classifiers. In Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (IEEE ICCIC) (pp. 87-90). Tamil Nadu, India. Published, 12/31/2014.
    http://itfrindia.org/2014ICCIC/
  • Lajoie, S., Hmelo-Silver, C., Wiseman, J., Chan, L., Lu, J., Khurana, C., Cruz-Panesso, I., Poitras, E., Kazemitabar, M. (2014). Using online digital tools and video to support international problem-based learning. The Interdisciplinary Journal of Problem-Based Learning, 8(2). DOI: 10.7771/1541-5015.1412. Published, 08/15/2014.
    http://docs.lib.purdue.edu/ijpbl/vol8/iss2/6/
  • Poitras, E., & Lajoie, S. (2014). Developing an agent-based adaptive system for scaffolding self-regulated inquiry learning in history education. Educational Technology Research & Development, 62(3), 335-366. DOI: 10.1007/s11423-014-9338-5. Published, 08/15/2014.
    http://link.springer.com/article/10.1007%2Fs11423-...
  • Lajoie, S. P., & Poitras, E. (2014). Macro and micro-strategies in for metacognition and co-regulation in the medical tutoring domain. In Sottilare, R., Graesser, A., Hu, X., & Goldberg, B. (Eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems: Adaptive Instructional Management (Volume 2). Orlando, FL: U.S. Army Research Laboratory. ISBN: 978-0-9893923-3-4. Published, 08/15/2014.
    https://gifttutoring.org/projects/gift/documents
  • Doleck, T., Basnet, R. B., Poitras, E., Lajoie, S. (2015). Mining learner-system interaction data: Implications for modeling learner behaviors an improving overlay models. Journal of Computers in Education, 2(4), 421-447. Published, 12/01/2015.
    http://link.springer.com/article/10.1007/s40692-01...
  • Lajoie, S., Lee, L., Poitras, E., Bassiri, M., Cruz-Panesso, I., Kazemitabar, M., Hmelo-Silver, C., Wiseman, J., Chan, L., Lu, J. (2015). The role of regulation in medical student learning in small groups: Regulating oneself and others' learning and emotions. Computers and Human Behavior, 52, 601-616. Published, 11/01/2015.
    http://www.sciencedirect.com/science/article/pii/S...
  • Lajoie, S. P., & Poitras, E. (2015). Problem- and task-centered approaches. In M. Spector (Ed.), Encyclopedia of Educational Technology. Thousand Oaks, CA: SAGE. Published, 01/15/2015.
    https://us.sagepub.com/en-us/nam/the-sage-encyclop...
  • Lajoie, S. P., Lee, L., Poitras, E., Hmelo-Silver, C., Hogaboam, P. (2015). Computer Supported Tools for Coregulation: Supporting Teachers and Learners in Problem Based Learning Activities. Regulated Learning in CSCL - Theoretical Progress for Learning Success. In O. Lindwall, P. Hakkinen, T. Koschmann, P. Tchounikine, & S. Ludvigsen (Eds.), Exploring the Material Conditions of Learning: The Computer Supported Collaborative Learning (CSCL) Conference 2015, Volume 1 (pp. 11-18). Gothenburg, Sweden: The International Society of the Learning Sciences. Published, 01/08/2015.
    https://www.isls.org/cscl2015/papers/CSCL2015Proce...
  • Doleck, T., Basnet, R. B., Poitras, E., & Lajoie, S. (2015). Towards examining learners' behaviors in a medical intelligent tutoring system: A hidden markov model approach. In Proceedings of the IEEE International Advance Computing Conference (IACC 2015). Banglore, India. Published, 01/01/2015.
    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
  • Doleck, T., Jarrell, A., Poitras, E., & Lajoie, S. (2015). Diagnosing Virtual Patient Cases: Gender Differences in Novice Physicians in a Computer Based Learning Environment. In S. Carliner, C. Fulford, & N. Ostashewski (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2015 (pp. 666-670). Association for the Advancement of Computing in Education (AACE). Montreal, Canada. Published, 01/01/2015.
    http://www.editlib.org/noaccess/151317/
  • Doleck, T., Jarrell, A., Poitras, E., & Lajoie, S. (2015). Towards investigating performance differences in clinical reasoning in a technology rich learning environment. Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED) (pp. 566-569). Madrid, Spain. Published, 01/01/2015.
    http://link.springer.com/chapter/10.1007%2F978-3-3...
  • Jarrell, A., Doleck, T., Poitras, E., & Lajoie, S. (2015). Learning to diagnose a virtual patient: An investigation of cognitive errors in medical problem solving. Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED) (pp. 176-184). Madrid, Spain. Published, 01/01/2015.
    http://link.springer.com/chapter/10.1007%2F978-3-3...
  • Lajoie, S. P., Poitras, E., Doleck, T., Jarrell, A. (2015). Modeling Metacognitive Activities in Medical Problem-Solving with BioWorld. In Pena-Ayala (Ed.), Metacognition: Fundaments, Applications, and Trends. Springer Series: Intelligent Systems Reference Library. Published, 01/01/2015.
    http://link.springer.com/chapter/10.1007%2F978-3-3...
  • Poitras, E. (2015). The MetaHistoReasoning tool: Studying Domain-Specific Metacognitive Activities in an Intelligent System for History. In Pena-Ayala (Ed.), Metacognition: Fundaments, Applications, and Trends. Springer Series: Intelligent Systems Reference Library. Published, 01/01/2015.
    http://link.springer.com/chapter/10.1007/978-3-319...
  • Doleck, T., Jarrell, A., Poitras, E., Chaouachi, M., & Lajoie, S. (2016). A tale of three cases: Examining accuracy, efficiency, and process differences in diagnosing virtual patient cases. Australasian Journal of Educational Technology, 32(5), 61-76. DOI: http://dx.doi.org/10.14742/ajet.2759. Published, 11/15/2016.
    http://www.ajet.org.au/...
  • Poitras, E., & Fazeli, N. (2016). Mining the Edublogosphere to Enhance Teacher Professional Development. In Shalin Hai-Jew (Ed.), Social Media Data Extraction and Content Analysis. IGI Global. DOI: 10.4018/978-1-5225-0648-5.ch002. Published, 08/01/2016.
    http://www.igi-global.com/chapter/mining-the-edubl...
  • Poitras, E., Harley, J., Compeau, T., Kee, K., Lajoie, S. (2016). Augmented Reality in Informal Learning Settings: Leveraging Technology for the Love of History. In R. Zheng & M. K. Gardner (Eds.), Handbook of Research on Serious Games for Educational Applications. IGI Global. DOI:10.4018/978-1-5225-0513-6.ch013. Published, 08/01/2016.
    http://www.igi-global.com/chapter/augmented-realit...
  • Doleck, T., Poitras, E. G., Naismith, L., & Lajoie, S. P. (2016). Detecting Dummy Learner Submitted Annotations in an Online Case Learning Environment. In Proceedings of the EdMedia: World Conference on Educational Media and Technology 2016. Vancouver, British Columbia, CA. Published, 06/28/2016.
    https://www.learntechlib.org/p/172994
  • Lee, L., Lajoie, S., Poitras, E., Nkangu, M., & Doleck, T. (2016). Co-regulation and Knowledge Construction in an Online Synchronous Problem Based Learning Setting. Education and Information Technologies. DOI: 10.1007/s10639-016-9509-6. Published, 06/11/2016.
    http://link.springer.com/article/10.1007%2Fs10639-...
  • Poitras, E., Naismith, L., Doleck, T., & Lajoie, S. P. (2016). Using learning analytics to identify medical student misconceptions in an online virtual patient environment. Online Learning, 20(2). Retrieved from http://olj.onlinelearningconsortium.org/index.php/olj/article/view/802/211. Published, 06/01/2016.
    http://olj.onlinelearningconsortium.org/index.php/...
  • Poitras, E. G., & Fazeli, N. (2016). Using an Intelligent Web Browser for Teacher Professional Development: Preliminary Findings from Simulated Learners. In Proceedings of Society for Information Technology and Teacher Education International Conference 2016 (pp. 3037-3041). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE). Published, 03/21/2016.
    https://www.learntechlib.org/p/172123/
  • Poitras, E., Lajoie, S. P., Jarrell, A., Doleck, T., & Naismith, L. (2016). Intelligent Tutoring Systems in the Medical Domain: Fostering Self-Regulatory Skills in Problem-Solving. In R. K. Atkinson (Ed.), Intelligent Tutoring Systems Structure, Applications, and Challenges. Nova Science Publishers Series: Education in a Competitive and Globalizing World. Published, 03/01/2016.
    https://www.novapublishers.com/catalog/product_inf...
  • Doleck, T., Jarrell, A., Poitras, E., Chaouachi, M., & Lajoie, S. P. (2016). Examining Diagnosis Paths: A Process Mining Approach. In Proceedings of the IEEE International Conference on Computational Intelligence & Communication Technology (IEEE ICICT), Los Alamitos, CA: Conference Publishing Services. IEEE. Published, 02/01/2016.
    http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumbe...
  • Harley, J. M., Poitras, E., Jarrell, A., Duffy, M. C., & Lajoie, S. P. (2016). Comparing virtual and location-based augmented reality mobile learning: Emotions and learning outcomes. Educational Technology Research & Development, 64(3), 359-388. DOI: 10.1007/s11423-015-9420-7. Published, 01/07/2016.
    http://link.springer.com/article/10.1007/s11423-01...
  • Ranellucci, J., Poitras, E., Bouchet, F., Lajoie, S. P., & Hall, N. C. (2016). Understanding emotional expressions in social media through educational data mining. Chapter submitted to S. Tettegah & R. E. Ferdig (Eds.). Emotions, Technology, and Social Media: Communication of Feelings for, with and through Digital Media. Waltham, MA: Elsevier. Published, 01/01/2016.
    http://www.sciencedirect.com/science/article/pii/B...
  • Poitras, E., Lajoie, S. P., Doleck, T., & Jarrell, A. (2016). Subgroup discovery with user interaction data: An empirically guided approach to improving intelligent tutoring systems. Educational Technology & Society, 19(2), 204-214. DOI: 10.1007/s11423-015-9420-7. Published, 01/01/2016.
    http://www.ifets.info/journals/19_2/15.pdf
  • Poitras, E., Doleck, T., Huang, F., Li, S., & Lajoie, S. (2017). Advancing teacher technology education using open-ended learning environments as research and training platforms. Australasian Journal of Educational Technology, 33(3), 32-45. DOI: https://doi.org/10.14742/ajet.3498.
    http://www.ajet.org.au/...
  • Jang, E., Lajoie, S., Wagner, M., Xu, Z., Poitras, E., & Naismith, L. (2017). Person-oriented approaches to profiling learners in technology-rich learning environments for ecological learner modeling. Journal of Educational Computing, 55(4), 552-597. DOI: https://doi.org/10.1177/0735633116678995. Published, 07/01/2017.
    http://journals.sagepub.com/doi/pdf/...
  • Lajoie, S., & Poitras, E. (2017). Crossing disciplinary boundaries to improve technology rich learning environments. Teachers College Record, 119(3), 1-30.
    http://www.tcrecord.org/library/...
  • Poitras, E. G., & Lajoie, S. (2017). The self-regulation of learning in the social sciences. In Dale H. Schunk & Jeffrey A. Greene (Eds.), Handbook of self-regulation of learning and performance. Routledge.
    https://www.routledge.com/Handbook-of-Self-Regulation...
  • Poitras, E. G., & Fazeli, N. (2017). Simulating preservice teachers' information-seeking behaviors while learning with an intelligent web browser. In 2017 Society for Information Technology and Teacher Educational Annual Conference. Austin, TX.
    https://www.learntechlib.org/p/...
Still in press... brewing more coffee.

CONTACT

The ASSIST Laboratory is an interdisciplinary group of creative thinkers that work on the design, development, and evaluation of adaptive learning technologies, improving the way they interact with learners.


Students come to the lab through several programs offered in the College of Education (Learning and Cognition, MSTAT, and IDET) as well as computer science, engineering, psychology, and the digital humanities. All graduate students should be:

  • committed and proficient in their own areas and committed to broadening their skills with an emphasis on contributing to their field of interest;
  • able to work using both qualitative and quantitative methodologies; and
  • proficient at computer programming, statistical analyses, data mining, and/or experimental methods with human subjects.

All graduate students are currently supported (tuition benefit, stipend, or GRA/TA appointments), and spend a majority of their time on research-related activities. Projects range from tools for learning, to innovate methods for learner modeling, and multi-modal data mining.



Information to All Applicants

The Learning and Cognition area grants Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in Educational Psychology. Students in this area acquire theoretical knowledge of psychological and/or educational principles and the methodological skills necessary to conduct original research on a variety of topics.

The Instructional Design and Educational Technology program grants Master of Science (M.S.) and Master of Education (M.Ed.) degrees, preparing students to analyze, design, develop, implement, and evaluate technology-based instruction for educational contexts. Students pursuing this degree will learn the theoretical issues associated with technology-supported instruction and the applications of technology to support best-teaching practices.

The Masters of Statistics program awards the professional MStat degree with concentration areas in Biostatistics, Econometrics, Educational Psychology, Mathematics, and Sociology. In the program you will have the opportunity to learn the latest in statistics and probability theory and learn about state-of-the-art programming and applications in your chosen area of concentration.

The Department of Educational Psychology and the University of Utah offers a range of internal and external funding opportunities for graduate research assistants, including but not limited to:

The Undergraduate Research Opportunities Program (UROP) and Work-Study program provides undergraduate students and faculty members the opportunity to work together on research or creative projects.

Advanced Instructional Systems and Technologies laboratory

College of Education, University of Utah

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For papers and external publications included on this website, please contact the author(s) or publisher(s) directly for licensing information.