Motivation: Contrast a theory of broad (general mental functions, or faculty) transfer with a theory of narrow (component) transfer by testing statistical models of each theory on naturally occurring data collected from authentic use of educational technologies.
Theories of Transfer: Transfer is the ability to apply what you’ve learned to solve novel problems. For example, if you learn how an apple is processed in the digestive system, you can then explain how bread is processed if you know the differences between apples and bread. This is a type of near transfer, meaning that the differences between the original learning context and the new problem context are not very different.
Farther transfer is more challenging. An example of farther transfer could be, how is an inedible object, such as a coin, processed in the digestive system? In this case, much of the information about the digestive system learned in the original context with the apple is no longer relevant, and the learner must figure out what is and is not important for the new context. In the grand scheme of transfer, though, this example wouldn’t be considered very far transfer. Far transfer can include applying math skills to physics, knowledge of chemistry to biology, and engineering skills learned in courses to engineering problems on the job. These are examples of far transfer, but they still fall under the component theory of transfer.
The component theory of transfer focuses on units of transfer, specific content knowledge or procedural skills, that are shared in both contexts. For example, solving a physics problem about force requires matching parts of the problem to parts of the formula and solving the formula using procedures. Both of these skills are also used to solve math problems, just in a different context. To transfer knowledge, the learner needs to recognize the similarities between contexts. For a good review of types of component transfer, see Barnett and Ceci (2002).
In contrast, the faculty theory of transfer focuses on developing mental functions generally to promote transfer much more broadly. Some examples of faculty transfer are improving strategic thinking through playing chess, improving rhetoric skills through learning Latin, and improving reasoning skills through learning programming. Though strengthening general faculties in this manner is not ineffectual, this theory has fallen out of favor because it cannot consistently predict learner performance.
Results: Koedinger et al. tested two types of models for each component and faculty theories of transfer against naturally occurring datasets. Both types of models were based on units called “knowledge components,” which were cognitive functions that the learner had acquired and demonstrated through task performance. The knowledge components were used to account for differences in task difficulty and transfer from one task to another. The first type of model, the strong model, accounted for both of these simultaneously. The second type of model, the weak model, accounted for only the differences in transfer.
When using the strong models, they found that the component theory was better able to predict outcomes and explain task difficulty and transfer than the faculty theory. When using the weak models, component theory was again better than faculty theory, but to a lesser degree.
Why this is important: This research uses data from students engages in authentic learning tasks to demonstrate the component theory of transfer can better explain the difficulty of learning tasks and transfer of learning between tasks. In addition, the research justifies the grain size of transfer to consider when quantifying these difficulties, the knowledge components. Their findings can contribute significantly to developing instructions and assessments that use transfer appropriately to challenge learners without leaving them floundering, creating a learning environment that supports efficient learning without unnecessary frustration.
Koedinger, K. R., Yudelson, M. V., & Pavlik Jr., P. I. (2016). Testing theories of transfer using error rate learning curves. Topics in Cognitive Science, 8, 589-609.
Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(6), 612-637.
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