William Tarimo

William Tarimo's research explores how technology and agile methodologies can be used to improve academic outcomes through applications in the learning and teaching processes.

Taking inspiration from his dissertation, Computer-Supported Agile Teaching (CSAT), Tarimo sees the discovery of optimal learning and teaching outcomes as a dynamic and non-static process that has to be discovered through continuous transparency of goals and methods; continuous inspection of the progress and suitability of the methods in achieving the goals; and finally, continuous adaptation of the learning/teaching methods, resources and attitudes.

We take advantage of technology in making all these feasible and efficient, and thus part of his research involves the design and development of educational technologies (EdTech), such as the Discovery Teaching platform (discoveryteaching.com). It is a web-based application with a range of tools designed to support interactive teaching and learning in the classroom, - remote, hybrid, or in-person.

Under the umbrella of Technology-Supported Agile Pedagogy, Tarimo engages in two main areas of technology-supported pedagogy studies:

1. Researching, developing, and evaluating various technology-supported pedagogies using the Discovery Teaching platform.

Study the effectiveness of interactive and technology-supported teaching and learning practices through an iterative and continuous process of classroom technology design and development, application through suitable activities in the classroom, and assessment of the experience and impact on learning and teaching outcomes.

2. Studying the feasibility of using data from personal wearable devices for studies on understanding and improving learning.

The purpose of the study is to explore the use of data from wearable devices to understand student learning, its underlying mechanisms, its important attributes, recognize it, factors influencing it, and how to maximize it. For example, from electroencephalogram (EEG) data, we are studying whether it is possible and practical to predict cognitive and affective (emotional) states through statistical analysis and classification of EEG data collected from groups of students wearing the Muse headbands. Data would typically be from groups of individuals engaged in learning activities in a common space, such as a classroom.

Other wearable devices, such as Oura rings, monitor and record physiological signals reflecting brain and body activities that influence learning, including smart watches and rings. Data from such devices can be collected over longer periods and potentially inform us about illnesses, mental health issues, and reactions to pedagogy, all of which can be correlated with or influence learning outcomes. Our long-term goal is to see if physiological, cognitive, and affective feedback in the form of real-time signals collected using consumer-grade wearable devices can be used reliably in studies on learning, to provide insights on learning processes, and to offer tools for improving learning and teaching methods and outcomes.

Elsewhere, Tarimo participated in a range of research projects in the areas of Applied Robotics, researching robotics deployment, design, and control.

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., 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.