Robotti, O.P.; (2007) Transfer of learning in binary decision making problems. Doctoral thesis , University of London.

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Abstract

Transfer, the use of acquired knowledge, skills and abilities across tasks and contexts, is a key and elusive goal of learning. Most evidence available in literature is based on a limited number of tasks, predominantly open-ended problems, game-like problems and taught school subjects (e.g. maths, physics, algebra). It is not obvious that findings from this work can be extended to the domain of decision making problems. This thesis, which aims to broaden the understanding of enhancing and limiting factors of transfer, examines transfer of binary decision making problems (analogs of the Monty Hall problem) under various conditions of semantic distance between learning and target problems, contextual shifts, and delay between learning and target. Our results indicate that not all findings of the classic analogical transfer studies based on open problem solving tasks extend to binary decision making transfer. Specifically, analogical encoding (i.e. learning two analogs by comparing them) did not lead to higher transfer than summarization. Furthermore, in our experiments, transfer rates were never significantly higher for participants learning two analogs by comparison (thus presumably forming a schema) than for those learning one analog by summarization (thus presumably not forming a schema). This leads us to cautiously hypothesize that the role of schema in mediating transfer could be less relevant in binary decision making than it is in open-ended problem solving. Finally, context shifts up to medium level, even coupled with several days delay, did not significantly reduce transfer, although high context shifts did. On the other hand, semantic distance, quality of learning and explicit recognition, were confirmed to have a significant relationship with transfer.

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