This research applied Bandura's (1986) social cognitive theory to examine engagement with courseware in traditional and online remedial mathematics learning environments. The study investigated the relationship of courseware engagement to age, computer self-efficacy, computer playfulness, and selfefficacy for self-regulated mathematics learning. The study also analyzed mathematics achievement in terms of engagement, age, gender, mathematics grade self-efficacy, and self-efficacy for self regulated mathematics learning. Participants were 88 students in a traditional environment and 76 students in an online environment. The two groups differed significantly in age, mathematics grade self-efficacy, computer self-efficacy, computer playfulness, courseware engagement, and mathematics achievement. When controlled for age, all significant differences found between the two groups persisted. When controlled for mathematics self-efficacy, the groups no longer differed significantly in mathematics achievement, but all other significant differences remained. Computer self-efficacy and computer playfulness each had a significant association with courseware engagement in the online environment, but not in the traditional environment. Regression models revealed that course setting, age, and self-efficacy for self-regulation jointly predicted engagement with courseware. Both mathematics grade

Researchers and statistics educators consistently suggest that students will learn statistics more effectively by conducting projects through which they actively engage in a broad spectrum of tasks integral to statistical inquiry, in the authentic context of a real-world application. In keeping with these findings, we share an implementation of discovery projects for students in elementary statistics classes. We delineate the purpose and scope of two types of projects-one covering linear regression analysis and the other covering comparisons with basic t-tests (matched pairs or two independent samples). We describe a set of curriculum materials developed to help instructors facilitate such projects and share access to these materials. We give examples of how the curriculum materials guide each stage of project implementation. We detail the requirements and student activities during each phase of the student-directed projects: Students select their own research topic, define their own variables, and devise and carry out their own data collection plan before analyzing and interpreting their data. Students then articulate their results, both in a written report and in a brief formal presentation delivered to the class. We give examples of specific projects that students have conducted. Finally, we discuss the potential benefits of such projects, including possible factors mediating those benefits.

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A multi-year study investigated the impact of incorporating student-directed discovery projects into introductory statistics courses. Pilot instructors at institutions across the United States taught statistics implementing student-directed projects with the help of a common set of instructional materials designed to facilitate such projects. Researchers measured the impact of these projects on student learning and on students’ attitudes and beliefs about statistics. Results of the quantitative analyses are shared, with subsequent discussion of their implications. Findings suggest that inclusion of student-directed research projects in introductory statistics can lead to greater statistics self-efficacy and improved statistical knowledge in specific domains. Additional analyses suggest that these student benefits may improve as their instructors gain more experience facilitating such projects. First published May 2017 at Statistics Education Research Journal Archives

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