Dr. Quigley's research studies students’ engagement with learning technologies in the classroom. He analyzes students’ use of tools with a mixed-methods approach, using analytics, surveys, observations and interviews to demonstrate the impact of improved tool design and the incorporation of real-time feedback based on novel analytics on the student learning experience. This leads to three questions that drive his research trajectory: 1) How can we use analytics to automatically characterize students’ engagement with science and engineering practices at scale? 2) How can we use digital learning tool design to promote successful use of science and engineering practices? 3) How do students’ use of these practices influence their engagement and learning? This mix of analytics, design, and educational research allows for the exploration of new frontiers in the space of designing adaptive learning technologies.
keywords
Learning Analytics, Human Computer Interaction, User Centered Design, Machine Learning, Learning Sciences, Personalized Learning, Digital Learning
CSCI 1300 - Computer Science 1: Starting Computing
Primary Instructor
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Fall 2020
Teaches techniques for writing computer programs in higher level programming languages to solve problems of interest in a range of application domains. Appropriate for students with little to no experience in computing or programming. Degree credit not granted for this course and CSCI 1310 and CSCI 1320 and ECEN 1310. Same as CSPB 1300.
CSCI 2830 - Special Topics in Computer Science
Primary Instructor
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Summer 2020
Covers topics of interest in computer science at the sophomore level. Content varies from semester to semester. Does not count as Computer Science credit for the Computer Science BA, BS or minor. May be repeated up to 9 total credit hours.
CSCI 3002 - Fundamentals of Human Computer Interaction
Primary Instructor
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Spring 2021 / Summer 2022 / Fall 2022 / Fall 2023
Introduces the practice and research of human-computer interaction, including its history, theories, the techniques of user-centered design, and the development of interactive technologies. Covers computing in society at large with respect to domains such as health, education, assistive technology, ethics, environment, and more.
CSCI 3022 - Introduction to Data Science with Probability and Statistics
Primary Instructor
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Fall 2020
Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
CSCI 3702 - Cognitive Science
Primary Instructor
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Spring 2019 / Fall 2019
Introduces cognitive science, drawing from psychology, philosophy, artificial intelligence, neuroscience, and linguistics. Studies the linguistic relativity hypothesis, consciousness, categorization, linguistic rules, the mind-body problem, nature versus nurture, conceptual structure and metaphor, logic/problem solving and judgment. Emphasizes the nature, implications and limitations of the computational model of mind. Recommended prerequisites: two of the following CSCI 1300 or CSCI 2275 or LING 2000 or PHIL 2440 or PSYC 2145. Same as LING 3005 and PHIL 3310 and PSYC 3005 and SLHS 3003 and CSPB 3702.
CSCI 4622 - Machine Learning
Primary Instructor
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Fall 2021 / Spring 2022 / Spring 2023 / Fall 2024
Introduces students to tools, methods, and theory to construct predictive and inferential models that learn from data. Focuses on supervised machine learning technique including practical and theoretical understanding of the most widely used algorithms (decision trees, support vector machines, ensemble methods, and neural networks). Emphasizes both efficient implementation of algorithms and understanding of mathematical foundations. Same as CSPB 4622.
CSCI 5622 - Machine Learning
Primary Instructor
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Spring 2019 / Spring 2020 / Fall 2021 / Spring 2022 / Fall 2022 / Spring 2023
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.
CSCI 5839 - User-Centered Design and Development 1
Primary Instructor
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Fall 2018 / Summer 2023 / Fall 2023 / Summer 2024 / Fall 2024
Develops the skills and practices necessary to apply user-centered approaches to software requirements analysis, and the design and evaluation of computer applications.
CSPB 3702 - Cognitive Science
Primary Instructor
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Summer 2019 / Fall 2019
Introduces cognitive science, drawing from psychology, philosophy, artificial intelligence, neuroscience, and linguistics. Studies the linguistic relativity hypothesis, consciousness, categorization, linguistic rules, the mind-body problem, nature versus nurture, conceptual structure and metaphor, logic/problem solving and judgment. Emphasizes the nature, implications and limitations of the computational model of mind. Recommended prerequisites: LING 2000 or PHIL 2440 or PSYC 2145. Same as LING 3005 and PHIL 3310 and PSYC 3005 and SLHS 3003 and CSCI 3702.
LING 3005 - Cognitive Science
Primary Instructor
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Fall 2019
Introduces cognitive science, drawing from psychology, philosophy, artificial intelligence, neuroscience, and linguistics. Studies the linguistic relativity hypothesis, consciousness, categorization, linguistic rules, the mind-body problem, nature versus nurture, conceptual structure and metaphor, logic/problem solving and judgment. Emphasizes the nature, implications and limitations of the computational model of mind. Recommended prerequisites: two of the following CSCI 1300 or LING 2000 or PHIL 2440 or PSYC 2145. Same as CSCI 3702 and PHIL 3310 and PSYC 3005 and SLHS 3003 and CSPB 3702.
PHIL 3310 - Cognitive Science
Primary Instructor
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Fall 2019
Introduces cognitive science, drawing from psychology, philosophy, artificial intelligence, neuroscience, and linguistics. Studies the linguistic relativity hypothesis, consciousness, categorization, linguistic rules, the mind-body problem, nature versus nurture, conceptual structure and metaphor, logic/problem solving and judgment. Emphasizes the nature, implications and limitations of the computational model of mind. Recommended prerequisites: two of the following CSCI 1300 or LING 2000 or PHIL 2440 or PSYC 2145. Same as LING 3005 and CSCI 3702 and PSYC 3005 and SLHS 3003 and CSPB 3702.
PSYC 3005 - Cognitive Science
Primary Instructor
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Fall 2019
Provides an introductory survey of influential models, theoretical approaches, and methods of cognitive science. Emphasizes and explains the convergence by work in multiple fields - including psychology and neuroscience, linguistics, computer science, and philosophy - on the idea that mental activity is a form of computation. Students from diverse backgrounds are introduced to a wide range of methods and approaches, including behavioral and neuroimaging experimental approaches, computational modeling and philosophical work. Department enforced prerequisites: two of the following CSCI 1300 or LING 2000 or PSYC 2145. Same as CSCI 3702 and LING 3005 and PHIL 3310 and SLHS 3003.
