Several scholars have pointed out that computing can be used successfully as a medium for teaching and learning other subjects and that this can facilitate learning in both the subject and computing domains. For example, Papert (1991) stated that programming is reflexive with other domains; that is, learning programming in concert with concepts from another domain (such as math and science) can be easier than learning them separately. Kay and Goldberg (1977) showed that object-oriented programming using SmallTalk is useful for learning math, science, and art. Emile, a scaffolded graphical programming interface designed and used to help students learn physics, represents another example of synergistic learning (Guzdial 1994). Redish and Wilson (1993), Soloway (1993), and Kafai et al. (1997) also demonstrated that reorganizing scientific and mathematical concepts around computational mechanisms lowered the learning threshold, especially in domains like physics and biology. More recently, some researchers have exploited the synergy between CT and science to develop CT-based science curricular units for K-12 classrooms (Sengupta et al., 2015; Basu, Kinnebrew & Biswas 2014; Allan et al. 2010; Repenning et al. 2010).
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In each of the environments discussed above, students learn through an iterative model building process. Previous studies have shown that middle school and elementary children can successfully use programming as a mode of inquiry to develop models of scientific phenomena, which in turn helps them develop a deep understanding of the relevant scientific concepts (diSessa et al. 1991; Sengupta & Farris 2012). CTSiM adopts this learning-by-design pedagogical approach (Kolodner et al. 2003), and students iteratively design, test, and revise computational models of physics and ecology.
At the top of the C-World interface (see Fig. 1), students can choose the agent and the particular agent behavior/procedure they want to model. Most agent behaviors in CTSiM units are specified in terms of a sense and act computational model. The list of visual primitives is provided on the left pane, and students drag and drop these available primitives onto the right pane, arranging and parameterizing them spatially to construct their models. The domain-general computational primitives regulate the flow of execution in the computational model (for example, conditionals, loops), while the domain-specific primitives generally represent agent actions (for example, moving, eating, reproducing) or sensing conditions (for example, vision, color, touch, toxicity).
The sequencing of curricular modules allowed students to tackle modeling and reasoning with a single agent in kinematics first and then build more complex computational models with multiple agents in ecology. This was an intentional design decision because studies in developmental psychology (for example, Lehrer et al. 2008) and agent-based modeling for education (for example, Goldstone & Wilensky 2008) show that individual agent-level reasoning occurs developmentally prior to understanding interactions among agents, and eventual aggregate-level reasoning with multiple agents and processes. Furthermore, within each unit, the sequencing of the activities implied increasing conceptual challenges that students would face in learning the relevant phenomena. For example, in the kinematics unit, when students modeled a single agent, the computational modeling tasks were presented in the order of increasing complexity, starting from constant shapes (squares to triangles to circles) to spirals of the same shapes (where speed became a function of the acceleration) to modeling real-world systems involving constant and variable speed segments.
Modeling and simulation challenges were associated with representing scientific concepts and processes as computational models and refining constructed models (partial or full) based on observed simulations. More specifically, these challenges included difficulties in identifying the relevant entities in the phenomenon being modeled; specifying how the entities interact; choosing correct preconditions and initial conditions, model parameters, and boundary conditions; understanding dependencies between different parts of the model and their effect on the overall behavior; and verifying model correctness by comparing its behavior with that of an expert model. Subcategories of these challenges could be classified as: (1) challenges in identifying relevant entities and their interactions; (2) challenges in choosing correct preconditions; (3) systematicity challenges; (4) challenges with specifying model parameters and component behaviors; and (5) model verification challenges).
Next, we looked at previously observed challenge categories which resurfaced and increased in activities 4 and 5. In activity 4, the only previously observed challenges which increased instead of going down with time were the programming challenge related to understanding the syntax and semantics of domain-specific primitives and the modeling challenge related to model validation. Facing challenges with respect to understanding domain-specific primitives seems understandable in the wake of new domain knowledge and related domain knowledge challenges. Also, activity 4 marked the first time the students had to perform model validation by comparing their model simulations against expert simulations and had to compare the two sets of animations and plots to assess the correctness of their models. Similarly, in activity 5, there were a few challenges previously observed in activity 4 which resurfaced and increased. For example, programming challenges related to the use of CT primitives increased, as did modeling challenges related to identifying relevant entities and their interactions, choosing correct preconditions, and specifying model parameters and component behaviors. A new domain, increase in domain complexity, and dealing with modeling multiple agents and multiple behaviors for each agent seem to have been the primary contributors. Further, the size (number of blocks contained) of the fish macro expert model was about thrice that of the expert rollercoaster model, increasing the probability of facing various difficulties in this activity (activity 5). Challenges with using CT constructs like conditionals resurfacing in the context of complex domain content emphasize the need for further practice and a more holistic understanding of the constructs. Unfortunately, we did not study computational learning gains using pre- and post-tests in this initial study, but they may have indicated that students needed repeated practice in different contexts to gain a deep understanding of the computational constructs. In other more recent studies with modified versions of CTSiM (modified based on challenges identified in this paper), we have shown synergistic learning of science and CT concepts (Basu et al. 2014; Basu et al. 2016). In Figs. 6, 7, 8, and 9, we investigate these issues further, by analyzing the data available from this study to study how the four primary categories of challenges individually varied across activities.
Pratim Sengupta is an Assistant Professor in the Department of Learning Sciences at University of Calgary and received his Ph.D. in Learning Sciences from Northwestern University. His research focuses on designing new forms of generative, computational representational systems including visual programming languages, tangible computation, narrative-based programming, multi-agent-based models, and participatory simulations.
Amanda Dickes is a doctoral student at Vanderbilt University in the Learning Sciences and Learning Environment Design program. Her research interests lie in developing new learning tools in both material and computational mediums to engage elementary students in learning biology. 2ff7e9595c
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