3 Matching Annotations
  1. Jun 2023
    1. In fact, classsic empirical “theory theory” research showed that children develop more abstract, framework knowledge over and above their specific causal knowledge. For example, when they make judgments about objects, children often seem to understand broad causal principles before they understand specific details.

      Throughout reading the research, there is a focus on very young children (under 4 years). As a secondary teacher, I've been thinking about applications of this idea in my social studies and english classes, settling on inquiry and project based learning. This kind of student-centered activity provides scaffolding, but encourages students to review new information, "experiment," and adjust their conclusions based on the information they have.

    2. they construct and change intuitive theories

      Bearing in mind similarities and differences re: constructionism vs. constructivism. Constructivist: learning is the process of building new knowledge atop prior knowledge. Constructionist: learners must be engaged in the process of construction.

      Blocher, M. (2016). Chapter 1. In Digital Tools for knowledge construction in the secondary grades (pp. 3–14). essay, Rowman & Littlefield.

    3. Children develop a succession of different, increasingly accurate, conceptions of the world and it at least appears that they do this as a result of their experience. But how can the concrete particulars of experience become the abstract structures of knowledge?

      I was unfamiliar with Bayesian learning/Bayesian interference before reading this article. I looked it up and found a helpful tool here: https://seeing-theory.brown.edu/bayesian-inference/index.html. Much of the information I read to familiarize myself with the topic referred to it in the context of machine learning. I can see how the idea of "how one should update one’s beliefs upon observing data" can apply to student learning, especially for young kids. Kunin, D., Guo, J., Dae Devlin, T., & Ziang, D. (n.d.). Bayesian inference. Seeing Theory. https://seeing-theory.brown.edu/bayesian-inference/index.html