Operationalizing and tracing goal orientation and learning strategy

Recent years have witnessed a substantial body of research investigating motivational characteristics and their relationships with academic performance. In achievement situations, one variable that has received a great deal of attention is goal orientation. Following the observation of a gap in the way motivational constructs (e.g., achievement goal orientation) are operationally defined, I suggest alternative methods, called traces, which can make these implicit constructs visible and measurable. Using trace methods, learners’ use of cognitive tools in a multimedia software environment (gStudy) reflects their goal orientations on the fly. This fine-grained trace data was analyzed with data mining techniques to address several issues in research. First, this study provided evidence that students’ perceived goal orientation did not accord with the behavioral traces of goals they pursued. Nor did self-reports predict test performance as well as trace data. Further, students high in need for cognition demonstrated better academic performance than those low in need for cognition, yet need for cognition did not interact with goal orientations reflected by traces as expected in predicting achievement. When comparing high-achievers’ versus low-achievers’ learning activities, no differences were observed between the two groups in terms of self-reports except for need for cognition where high-achievers scoring higher. In contrast, high-achievers exhibited significantly more frequent use of tools, and studied longer. The learning patterns identified in this sample, as shown by trace data, proved the existence of situational fluctuations of goal orientation within a single study session. Contrary to theory, a rigid pattern of studying that reflects mastery-approach goals does not assure higher achievement. A studying pattern marked by shifting between mastery-approach and performance-approach goals yielded the most positive achievement profile. Future research can gain substantially by merging trace methodologies with other means for gathering data about motivation and learning.