Metacognition and Learning最新文献

Students often make incorrect predictions about their exam performance, with the lowest-performing students showing the greatest inaccuracies in their predictions. The reasons why low-performing students make inaccurate predictions are not fully understood. In two studies, we tested the hypothesis that low-performing students erroneously predict their exam performance in part because their past performance varies considerably, yielding unreliable data from which to make their predictions. In contrast, high-performing students tend to have consistently high past performance that they can rely on to make relatively accurate predictions of future test performance. Results showed that across different exams (Study 1) and different courses (Study 2), low-performing students had more variable past performance than high-performing students. Further, results from Study 2 showed that variability in past course performance (but not past exam performance) was associated with poor calibration. Results suggest that variability in past performance may be one factor that contributes to low-performing students' erroneous performance predictions.

Metamemory monitoring, study behavior, and memory are presumably causally connected. When people misjudge their memory, their study behavior should be biased accordingly. Remedying metamemory illusions should debias study behavior and improve memory. One metamemory illusion concerns source memory, a critical aspect of episodic memory. People predict better source memory for items that originated from an expected source (e.g., toothbrush in a bathroom) rather than an unexpected source (e.g., shampoo in a kitchen), whereas actual source memory shows the opposite: an inconsistency effect. This expectancy illusion biases restudy choices: Participants restudy more unexpected than expected source-item pairs. The authors tested the causal relationships between metamemory and source memory with a delay and a source-retrieval attempt between study and metamemory judgment to remedy the expectancy illusion and debias restudy choices. Debiased restudy choices should enhance source memory for expected items, thereby reducing the inconsistency effect. Two groups studied expected and unexpected source-item pairs. They made metamemory judgments and restudy choices immediately at study or after delay, restudied the selected pairs, and completed a source-monitoring test. After immediate judgments, participants predicted better source memory for expected pairs and selected more unexpected pairs for restudy. After delayed judgments, participants predicted a null effect of expectancy on source memory and selected equal numbers of expected and unexpected pairs. Thus, the expectancy illusion was partially remedied and restudy choices were debiased. Nevertheless, source memory was only weakly affected. The results challenge the presumed causal relationships between metamemory monitoring, study behavior, and source memory.

The Self-Regulation for Learning Online (SRL-O) questionnaire was developed to encompass the breadth of motivational beliefs and learning strategies that are often used in online and/or blended learning contexts. No current measure meets all these needs. This study used two non-duplicate samples to provide evidence of the psychometric properties of SRL-O using exploratory factor analyses (sample 1, n = 313), and confirmatory factor analyses, convergent and content validity and reliability (sample 2, n = 321). The SRL-O has a 10-factor structure, made up of (1) online self-efficacy, (2) online intrinsic motivation, (3) online extrinsic motivation, (4) online negative achievement emotion, (5) planning and time management, (6) metacognition, (7) study environment, (8) online effort regulation, (9) online social support, and (10) online task strategies. The SRL-O was also found to have two superordinate factors (motivational beliefs and learning strategies). The SRL-O was demonstrated to be a psychometrically sound measure of online SRL for learners studying in online and blended learning contexts. There is no other online self-regulated learning questionnaire that currently covers such a wide range of motivational beliefs and learning strategies.