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1. Using imagery support strategies to develop powerful imagistic models.

   Price, N; Stephens, A; Clement, J; Nunez-Oviedo, M (December 2017, Science scope)

   A central question for teachers is how to engage students in active reasoning while still aiming for substantial content goals. Asking students to generate and evaluate imagistic models can support both content learning and scientific thinking goals. Recent research indicates that imagery is a central component of scientific modeling (Schwartz and Heiser 2009). When discussing scientific models, teachers and students often lean heavily on words alone and overlook how modeling uses mental pictures and “mental movies.” Metaphorically, modeling processes can be thought of as occurring on a “sketch pad or video screen” of mental imagery in the student’s head. The set of strategies described here are intended to help teachers promote the kind of imagery that is used in scientific models. 

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2. Identifying Multiple Levels of Heuristic Reasoning Used in Scientific Model Construction: A Framework Grounded in Imagistic Processing

   https://doi.org/10.1007/978-3-031-94709-4_8  

   Clement, John J (August 2025, Springer Nature Switzerland)

   Ippoliti, E; Sterpetti, F (Ed.)

   This chapter first consolidates a set of important heuristic strategies used for constructing innovative scientific models from three books, including studies in the history of genetics and electromagnetism, and an expert think-aloud study in mechanics. Twenty-four strategies are identified, most of which are field-general. Patterns in their use suggest a partially organized hierarchy of interconnected strategies and substrategies, contrary to the view that heuristics are simply tried in random order. Strategies at four different size and time scale levels are described, including larger Modeling Cycle Phases of model generation, evaluation, and modification, each of which can utilize many smaller Tactical Heuristics as substrategies, e.g., analogy, or testing predictions from the model. These in turn can utilize Grounded Imagistic Processes, such as imagistic mental simulation, an important alternative to deduction for evaluating a model by running it. The framework links higher level, serially organized processes with lower level, imagery-based processes. Its intermediate degree of organization is neither anarchistic, nor fully algorithmic. Possible benefits of organization are narrowing the search space involved and balancing sources of model construction and criticism for productive creativity. Unorganized, spontaneous processes are also discussed, along with their possible benefits. 

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3. Blind Users Accessing Their Training Images in Teachable Object Recognizers

   https://doi.org/10.1145/3517428.3544824  

   Hong, Jonggi; Gandhi, Jaina; Essuah Mensah, Ernest; Zeraati, Farnaz Z.; Jarjue, Ebrima H.; Lee, K.; Kacorri, Hernisa (October 2022, ACM SIGACCESS Conference on Computers and Accessibility (ASSETS))

   Teachable object recognizers provide a solution for a very practical need for blind people – instance level object recognition. They assume one can visually inspect the photos they provide for training, a critical and inaccessible step for those who are blind. In this work, we engineer data descriptors that address this challenge. They indicate in real time whether the object in the photo is cropped or too small, a hand is included, the photos is blurred, and how much photos vary from each other. Our descriptors are built into open source testbed iOS app, called MYCam. In a remote user study in (N = 12) blind participants’ homes, we show how descriptors, even when error-prone, support experimentation and have a positive impact in the quality of training set that can translate to model performance though this gain is not uniform. Participants found the app simple to use indicating that they could effectively train it and that the descriptors were useful. However, many found the training being tedious, opening discussions around the need for balance between information, time, and cognitive load. 

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4. Examining how prior knowledge impacts students’ discussions and knowledge construction during computational model building

   https://doi.org/10.3102/IP.22.1891766  

   Snyder, C. (April 2022, Proceedings of the American Educational Research Association Annual Meeting)

   Student discussions have been shown to be beneficial to student learning (Chi & Wylie, 2014), however, the impact of prior knowledge on these discussions is not fully understood. In this research, we analyze students’ synchronous spoken discussions to study how prior knowledge impacted group discussions and knowledge construction while constructing computational models of 1D and 2D motion. We present a method for evaluating the impact of prior knowledge on student discussions and individual work. We illustrate this method through a case study analysis of two groups with students across a spectrum of prior knowledge. Our exploratory findings suggest that students with low prior knowledge greatly benefit from group discussions followed by individual model construction. 

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5. Reasoning Patterns in Galileo’s Analysis of Machines and in Expert Protocols: Roles for Analogy, Imagery, and Mental Simulation

   https://doi.org/10.1007/s11245-018-9545-5  

   Clement, John J (September 2020, Topoi)

   Abstract Reasoning patterns found in Galileo’s treatise on machines, On Mechanics, are compared with patterns identified in case studies of scientifically trained experts thinking aloud, and many similarities are found. At one level the primary patterns identified are ordered analogy sequences and special diagrammatic techniques to support them. At a deeper level I develop constructs to describe patterns that can support embodied, imagistic, mental simulations as a central underlying process. Additionally, a larger hypothesized pattern of ‘progressive imagistic generalization’—Galileo’s development of a model or mechanism that becomes more and more general with each machine while still being imagistically projectable into many machines—provides a way to think about his progress toward a modern explanatory model of torque. By unpacking his arguments, we gain an appreciation of his skillful ability to foster imagistic processes underlying scientific thinking. 

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