Topic- and learning-related predictors of deep-level learning strategies

Abstract

The aim of this study was to examine which topic- and learning-related knowledge and motivational beliefs predict the use of specific deep-level learning strategies during an independent learning task. Participants included 335 Estonian fourth- and sixth-grade students who were asked to read about light processes and seasonal changes. The study was completed electronically. Topic-related knowledge was assessed via an open question about seasonal changes, and learning-related knowledge was assessed via scenario-based tasks. Expectancies, interest, and utility values related to learning astronomy and using deep-level learning strategies were assessed via questions based on the Situated Expectancy-Value Theory. Deep-level learning strategies (using drawings in addition to reading and self-testing) were assessed while completing the reading task. Among topic-related variables, prior knowledge and utility value—but not interest or expectancy in learning astronomy—were related to using deep-level learning strategies. Among learning-related variables, interest and utility value of effective learning—but not metacognitive knowledge of learning strategies or expectancy in using deep-level learning strategies—were related to using deep-level learning strategies. This study confirms that it is not enough to examine students’ knowledge and skills in using learning strategies with general or hypothetical questions, instead, it is of crucial importance to study students in real learning situations.