Abstract
Educational stakeholders would be better informed if they could use their students’ formative assessments results and personal background attributes to predict the conditions for achieving favorable learning outcomes, and conversely, to gain awareness of the “at-risk” signals to prevent unfavorable or worst-case scenarios from happening. It remains, however, quite challenging to simulate predictive counterfactual scenarios and their outcomes, especially if the sample size is small, or if a baseline control group is unavailable. To overcome these constraints, the current paper proffers a Bayesian Networks approach to visualize the dynamics of the spread of “energy” within a pedagogical system, so that educational stakeholders, rather than computer scientists, can also harness entropy to work for them. The paper uses descriptive analytics to investigate “what has already happened?” in the collected data, followed by predictive analytics with controllable parameters to simulate outcomes of “what-if?” scenarios in the experimental Bayesian Network computational model to visualize how effects spread when interventions are applied. The conceptual framework and analytical procedures in this paper could be implemented using Bayesian Networks software, so that educational researchers and stakeholders would be able to use their own schools’ data and produce findings to inform and advance their practice.
Highlights
When efforts are exerted by a student to learn, or by a teacher to teach, it would not be unreasonable to assume that not all of that “energy” would be converted directly into the educational outcomes that they want
The current paper will explore the notion of entropy, the spread of “energy” in a pedagogical system will be simulated using a Bayesian Network (BN) model
In Bayesialab, these tools can be accessed in the “network performance” menu
Summary
When efforts are exerted by a student to learn, or by a teacher to teach, it would not be unreasonable to assume that not all of that “energy” would be converted directly into the educational outcomes that they want. Expounding upon the concept of entropy, in a pedagogical system, work done (for example, efforts exerted in a class intervention by the teacher, or by an after-school tutor) on students might not necessarily result in that “energy” being converted. From the perspective of entropy, that “energy” was not lost Rather, it was spread out into other parts of the system. The current paper will explore the notion of entropy, the spread of “energy” in a pedagogical system will be simulated using a Bayesian Network (BN) model. Educational stakeholders who might not be familiar with advanced mathematics would be able to independently analyze their school data, and to harness the concept of entropy to work for them to predict the conditions that might contribute to optimal educational outcomes
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