A Pathway Towards STEM Integration: Embodiment, Mathematization, and Mechanistic Reasoning

Weinberg, Paul

doi:10.18260/1-2--34030

Cited by 1 publication

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Humans are frequently embedded in contexts in which machines learn from their everyday actions. However, constructing explanations about the underlying causal mechanisms of a machine learning system (such as predictive text when texting a friend, or image recognition in collections of photographs) requires individuals to examine an engineered system in ways that consider parts of the system and the relationships among multiple parts of that system [1], [2]. These reasoning abilities are germane to engineering disciplines.…”

Section: Introductionmentioning

confidence: 99%

“…In this line of research, more sophisticated mechanistic explanations explain how components of one part of a system affect other parts of the system, or the behaviors of the system as a whole [1], [3], [6], rather than descriptions of the whole that do not account for how those patterns arise. Mechanistic reasoning [2], [3], provides an analytical lens that can be used to categorize a learner's causal explanations of phenomena they have experienced. Studies of mechanistic reasoning in school settings have been concerned with ecological systems (e.g., [7]) kinetic toys [2], and computational models of invisible processes in physical systems (e.g., [8]).…”

Section: Introductionmentioning

confidence: 99%

“…Mechanistic reasoning [2], [3], provides an analytical lens that can be used to categorize a learner's causal explanations of phenomena they have experienced. Studies of mechanistic reasoning in school settings have been concerned with ecological systems (e.g., [7]) kinetic toys [2], and computational models of invisible processes in physical systems (e.g., [8]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preservice Teachers’ Mechanistic Reasoning about Machine Learning and Artificial Intelligence

Farris,

Kim

2022 Spring ASEE Middle Atlantic Section Conference Proceedings

View full text Add to dashboard Cite

Modern humans are frequently embedded in contexts in which machines learn from their everyday actions. Examples include encountering predictive text when texting a friend and facial recognition in personal digital photographs. However, explanations that account for the underlying causal mechanisms of machine learning systems require learners to consider parts of the system and the relationships among the parts of that system. While mechanistic reasoning [3] is a foundational body of abilities germane to engineering, children and many adults rarely conceptualize their interactions with machines in ways that are consistent with the complex and dynamic nature of machine learning systems.We investigated how undergraduate teacher candidates (TCs) explained an example of machine learning, Google Quick, Draw! [13] after playing the game and participating in a series of machine learning investigations. We found that even among an elite group of non-science major undergraduate students, initial explanations of how the computer recognized images rarely focused on how events that lead to a correct guess are linked to one another. In contrast, given opportunities to read and think together about the mechanisms of machine learning, TCs' descriptions of how Quick, Draw! works became more sophisticated in terms of the sense of mechanism and the chaining [3] of events that lead to a correct guess. For example, in students' final explanations at the end of our activities, 12 of 22 (54.5%) described the importance of the beginning "stroke" in their doodles or described patterns of key features of doodles as important to how image recognition is accomplished.Secondly, we explored how building basic understanding of machine learning can support engineering across disciplinary boundaries in K12 contexts. We asked the same preservice teachers to think about how machine learning could be relevant to the content and practices in their area of disciplinary specialization, and to create an initial lesson design that could be used with middle school students (U.S. Grades 4 -8). The participating preservice teachers' disciplinary specializations were Social Studies (n = 3), English Language Arts (n = 8), and Mathematics (n = 12). We found that all students portrayed that learning goals about artificial intelligence (in general) and machine learning (in particular) were relevant to their focal disciplinary areas and their understanding of literate participation in society. Additionally, some TCs focused on students' understandings of the social and ethical dimensions of artificial intelligence technologies. This included perceptions of the ethical dimensions of AI and the diverse cultural contexts in which machine learning operates. We report the connections they saw and discuss the relevance of machine learning as an example of reasoning about complex engineered systems for young students and for teachers.

show abstract

Section: Introductionmentioning

confidence: 99%