Learning Strategies and Transfer in the Domain of Programming 论文

摘要

We report two studies involving an intelligent tutoring system for Lisp (the CMU Lisp Tutor). In Experiment 1, we develop a model, based on production system theories of transfer and analogical problem-solving, that accounts for effects of instructional examples, the transfer of cognitive skills across programming problems, and practice effects. In Experiment 2, we analyzed protocols collected from subjects as they processed instructional texts and examples before working with the Lisp Tutor and protocols collected after subjects solved each programming problem. The results suggest that the acquisition of cognitive skills is facilitated by high degrees of metacognition, which includes higher degrees of monitoring states of knowledge, more selfgenerated explanation goals and strategies, and greater attention to the instructional structure. Improvement in skill acquisition is also strongly related to the generation of explanations connecting the example material to the abstract terms introduced in the text, the generation of explanations that focus on the novel concepts, and spending more time in planning solutions to novel task components. We also found that self-explanation has diminishing returns. Finally, reflection on problem solutions that focus on understanding the abstractions underlying programs or that focus on understanding how programs work, seems to be related to improved learning.