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Prior research by Hartwig and Dunlosky [(2012). Study strategies of college students: Are self-testing and scheduling related to achievement? Psychonomic Bulletin & Review, 19(1), 126-134] has demonstrated that beliefs about learning and study strategies endorsed by students are related to academic achievement: higher performing students tend to choose more effective study strategies and are more aware of the benefits of self-testing. We examined whether students' achievement goals, independent of academic achievement, predicted beliefs about learning and endorsement of study strategies. We administered Hartwig and Dunlosky's survey, along with the Achievement Goals Questionnaire [Elliot, A. J., & McGregor, H. A. (2001). A 2 × 2 achievement goal framework. Journal of Personality & Social Psychology, 80, 501-519] to a large undergraduate biology course. Similar to results by Hartwig and Dunlosky, we found that high-performing students (relative to low-performing students) were more likely to endorse self-testing, less likely to cram, and more likely to plan a study schedule ahead of time. Independent of achievement, however, achievement goals were stronger predictors of certain study behaviours. In particular, avoidance goals (e.g., fear of failure) coincided with increased use of cramming and the tendency to be driven by impending deadlines. Results suggest that individual differences in student achievement, as well as the underlying reasons for achievement, are important predictors of students' approaches to studying.

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Eye-tracking is widely used throughout the scientific community, from vision science and psycholinguistics to marketing and human-computer interaction. Surprisingly, there is little consistency and transparency in preprocessing steps, making replicability and reproducibility difficult. To increase replicability, reproducibility, and transparency, a package in R (a free and widely used statistical programming environment) called gazeR was created to read and preprocess two types of data: gaze position and pupil size. For gaze position data, gazeR has functions for reading in raw eye-tracking data, formatting it for analysis, converting from gaze coordinates to areas of interest, and binning and aggregating data. For data from pupillometry studies, the gazeR package has functions for reading in and merging multiple raw pupil data files, removing observations with too much missing data, eliminating artifacts, blink identification and interpolation, subtractive baseline correction, and binning and aggregating data. The package is open-source and freely available for download and installation: https://github.com/dmirman/gazer. We provide stepby-step analyses of data from two tasks exemplifying the package's capabilities.