Learning to Think: Cognitive Mechanisms of Knowledge Transfer

Abstract

Learning to think is about transfer. The scope of transfer is essentially a knowledge representation question. Experiences during learning can lead to alternative latent representations of the acquired knowledge, not all of which are equally useful. Productive learning facilitates a general representation that yields accurate behavior in a large variety of new situations, thus enabling transfer. This chapter explores two hypotheses. First, learning to think happens in pieces and these pieces, or knowledge components, are the basis of a mechanistic explanation of transfer. This hypothesis yields an instructional engineering prescription: that scientific methods of cognitive task analysis can be used to discover these knowledge components, and the resulting cognitive models can be used to redesign instruction so as to foster better transfer. The second hypothesis is that symbolic languages act as agents of transfer by focusing learning on abstract knowledge components that can enhance thinking across a wide variety of situations. The language of algebra is a prime example and we use it to illustrate (1) that cognitive task analysis can reveal knowledge components hidden to educators; (2) that such components may be acquired, like first language grammar rules, implicitly through practice; (3) that these components may be "big ideas" not in their complexity but in terms of their usefulness as they produce transfer across contexts; and (4) that domain-specific knowledge analysis is critical to effective application of domain-general instructional strategies.

Alleviating misconceptions about Earth's climate: evidence of behavioral learning in stock‐and‐flow simulations

“…The cognitive psychology literature provides certain reasons why simulation tools could help people improve their incorrect mental models. For example, simulation tools may allow users to set specific goals iteratively (Yang et al, 2016); these tools may permit users to experience gradual increases in a problem's complexity (Yasarcan, 2010;Ozgun and Barlas, 2013;; and they may provide analogies and alternative representations to improve learning (Holyoak and Koh, 1987;Gentner et al, 2003;Gonzalez and Wong, 2012;Koedinger and Roll, 2012;Smith and Gentner, 2012;Dutt and Gonzalez, 2013a;2013b).…”

“…Prior research in cognitive psychology has shown that experience gained through analogies helps people to learn about stock-and-flow relationships (Koedinger and Roll, 2012;Smith and Gentner, 2012). Thus the use of examples in simulation tools would promote the learning of difficult system dynamics concepts (Gentner et al, 2003).…”

“…Furthermore, the gradual increase in a problem's complexity due to the changing curve shapes in DCCS (Kumar and Dutt, 2018) would likely help people speed up their learning (Yasarcan, 2010;Ozgun and Barlas, 2013;. In addition, the use of analogies like pipes, liquid and bathtub as well as the repeated feedback in DCCS would likely help people learn the underlying system dynamics concepts (Holyoak and Koh, 1987;Gentner et al, 2003;Gonzalez and Wong, 2012;Koedinger and Roll, 2012;Smith and Gentner, 2012). We expect that exposure to the DCCS tool will likely help people to improve their mental models about climate dynamics and help reduce people's climate misconceptions.…”

Framing the constructive alignment of design within technology subjects in general education

“…Prior knowledge has been shown to greatly affect how learners interpret and learn from their class materials (e.g., Rittle-Johnson et al 2009), how well they can implement domain-general problem-solving strategies (see, e.g., Koedinger and Roll 2012;Zimmerman 2000, for reviews), how well they are able to generalize knowledge encoded from earning materials to novel cases or problems (e.g., Carpenter et al 2006) and how well they are prepared for future learning tasks (e.g., Bransford and Schwartz 1999). Further, the degree with which new concepts conflict with the students' prior knowledge (e.g., their naïve theories) is one of the primary determinants of how difficult a concept is to learn (Chi et al 2012;Jacobson et al 2011).…”

Facilitating Role of 3D Multimodal Visualization and Learning Rehearsal in Memory Recall

“…A learning barrier constantly facing college students lies in the implementation of classroom concepts to solve real-life problems ( Day & Goldstone, 2013 ). Cognitive disassociation between textbook knowledge and decision-making processes often results in educational failure, and thereby hinders career success ( Koedinger & Roll, 2012 ). As virtual reality training rapidly gains popularity in many academic and professional settings worldwide, it is important from both theoretical and practical perspectives to examine the potential eff ects that immersive three-dimensional (3D) virtual reality may have on learning retention and transfer ( Holzinger, Kickmeier-Rust, & Albert, 2009 ;Kalyuga, Renkl, & Paas, 2010 ;Meissner & Bogner, 2012 ;Day & Goldstone, 2013 ).…”