William Tarimo

Specializations

• Educational Technology (EdTech): Computer-supported pedagogy & Agile teaching
• Affective Computing: Utilizing AI, Machine Learning, and wearable data
• Applied Robotics: Design, deployment, and control systems
• Software Development: Web/Mobile development & IT Entrepreneurship

Research Summary:

My research investigates how technology and agile methodologies can enhance academic outcomes by optimizing the processes of learning and teaching.

Inspired by my doctoral work on Computer-Supported Agile Teaching (CSAT), I view the pursuit of optimal educational outcomes as a dynamic, non-static process. This evolution is driven by three core pillars:

1. Transparency: Continuous clarity of teaching and learning objectives and methods.
2. Inspection: Regular evaluation of the suitability of specific methods in achieving those goals.
3. Adaptation: The agile adjustment of resources, personnel, and attitudes to meet evolving needs.

To make these endeavors feasible at scale, a significant portion of my research involves the design and development of EdTech tools, such as the Discovery Teaching platform (discoveryteaching.com). This web-based application supports interactive pedagogy across remote, hybrid, and in-person modalities.

Primary Research Areas:

Under the umbrella of Technology-Supported Agile Pedagogy, my work focuses on two distinct yet intersecting domains:

1. Iterative Pedagogy & Tool Development: I research, develop, and evaluate technology-supported pedagogies using the Discovery Teaching platform. This involves an iterative cycle of designing classroom technologies, applying them through specific activities, and assessing their impact on learning outcomes.

2. Wearable Data & Affective Computing in Education: I explore the feasibility of using data from consumer-grade wearable devices (such as Muse headbands, Oura rings, and smartwatches) to understand the underlying mechanisms of learning.

• Cognitive & Affective States: Using electroencephalogram (EEG) data, my team studies whether it is practical to predict emotional and cognitive states through statistical analysis and machine learning.
• Long-term Physiological Insights: By monitoring signals related to sleep, stress, and mental health over extended periods, we aim to correlate physiological well-being with pedagogical reactions.
• Long-term Goal: To determine if real-time feedback from wearables can reliably provide insights into the learning process and offer actionable tools for improving both teaching methods and student outcomes.

Applied Robotics:

Beyond EdTech, I have contributed to various research projects in the field of Applied Robotics, specifically focusing on the design, control, and deployment of robotic systems in real-world environments.

Courses regularly taught at Connecticut College include:

1. Introduction to Computer Science and Problem Solving (COM110, in Python)
2. Data Structures (COM212, in Java)
3. Mobile App Development (COM350, including iOS and Android)
4. Web Technologies & Development (COM214).

Recent Journal & Conference Publications:

• Tarimo, W. T., Abdullah, M., Mental Health and Equity in Agile Pedagogy: A Look at Student Experience. The 7th International Conference on Modern Educational Technology (ICMET). Fukuoka, Japan, Dec 2025.
• Tarimo, W. T., Flathers, E., Woods, N., Equity Pedagogy and the 21st Century Classroom: A Scoping Review. Proceedings of the 18th International Conference on Learning Technologies and Learning Environments (LTLE 2025). Kitakyushu, Japan, July 2025.
• Tarimo, W., Abdullah, M., & Premkumar, A. (2025). Student Mental Health and Well-Being vs. Academic Success: A Literature Survey. Proceedings of The World Conference on Teaching and Education,(WORLDCTE) 4(1), 34–52. https://doi.org/10.33422/worldcte.v4i1.1217
• Tarimo, W., Mim, K., Yoezer, K., & Marinis, M. (2025). GROUP-BASED PREDICTION OF LEARNING ACTIVITIES FROM EEG DATA. Proceedings of the 19th International Technology, Education and Development Conference (INTED2025) (pp. 7433-7441). IATED.
• Tarimo, W., Sabra, M. M., & Hendre, S. (2020, December). Real-time Deep Learning-based Object Detection Framework. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1829-1836). IEEE.
• Tarimo, W., Tran, D., & Yoezer, K. (2020). Uncovering the Nature and Impact of Affective Feedback in Teaching and Learning. In ICERI2020 Proceedings (pp. 7492-7499). IATED.
• Tarimo, W. (2019). ASSESSING THE IMPACT OF PEER GRADING DURING FORMATIVE ASSESSMENT ACTIVITIES. In ICERI2019 Proceedings (pp. 10807-10813). IATED.
• Tarimo, W. (2019). DISCOVERY TEACHING-A CLASSROOM APPLICATION FOR INTERACTIVE AND AGILE PEDAGOGY. In ICERI2019 Proceedings (pp. 7374-7383). IATED.
• Tarimo, W. T., & Hickey, T. J. (2017). Groupwork: Learning during Collaborative Assessment Activities. Philadelphia, PA: International Society of the Learning Sciences.
• Conference Demo. Peer-Reviewed Short Paper.
  Tarimo, W. T., & Hickey, T. J. "Tool Demo: Discovery Teaching: Computer-supported Real-time Peer Review". A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019
• Tarimo, W. T., & Hickey, T. J. (2016, October). Fully integrating remote students into a traditional classroom using live-streaming and TeachBack. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). IEEE.
• Tarimo, W. T., Deeb, F. A., & Hickey, T. J. (2016). Early detection of at-risk students in CS1 using TeachBack/Spinoza. Journal of Computing Sciences in Colleges, 31(6), 105-111.
• Tarimo, W. T., Deeb, F. A., & Hickey, T. J. (2016). A flipped classroom with and without computers. In Computer Supported Education: 7th International Conference, CSEDU 2015, Lisbon, Portugal, May 23-25, 2015, Revised Selected Papers 7 (pp. 333-347). Springer International Publishing.
• Tarimo, W. T., Deeb, F. A., & Hickey, T. J. (2015, May). Computers in the CS1 Classroom. In International Conference on Computer Supported Education (Vol. 2, pp. 67-74). SCITEPRESS.
• Tarimo, W. T., & Hickey, T. J. "Adopting a ‘Flipped’ Interactive Pedagogy using TeachBack: Tutorial Presentation." Journal of Computing Sciences in Colleges 30.6 (2015): 135-137.
• Hickey, T. J., & Tarimo, W. T. (2014). The Affective Tutor. Journal of Computing Sciences in Colleges, 29(6), 50-56.
• Parker, G. B., & Tarimo, W. T. (2011, October). Using cyclic genetic algorithms to learn gaits for an actual quadruped robot. In 2011 IEEE International Conference on Systems, Man, and Cybernetics (pp. 1938-1943). IEEE.
• Parker, G. B., Tarimo, W. T., & Cantor, M. (2011, June). Quadruped gait learning using cyclic genetic algorithms. In 2011 IEEE Congress of Evolutionary Computation (CEC) (pp. 1529-1534). IEEE.
• Parker, G. B., & Tarimo, W. T. (2011, June). The effects of using a greedy factor in hexapod gait learning. In 2011 IEEE Congress of Evolutionary Computation (CEC) (pp. 1509-1514). IEEE.

Doctoral Dissertation:

• Tarimo, W. T. (2016). Computer-Supported Agile Teaching. Brandeis University.

Book Chapters:

• Tarimo, W. T., Deeb, F. A., & Hickey, T. J. "A Flipped Classroom with and Without Computers." Computer Supported Education: 7th International Conference, CSEDU 2015, Lisbon, Portugal, May 23-25, 2015, Revised Selected Papers. Vol. 583. Springer, 2016.

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Major or minor in computer science.