Technology has become an inseparable part of modern statistical practice (Gould, 2010), and, to a large extent, modern statistics courses. The literature on technology in statistics education has focused heavily on the role of technology for improving students’ understanding. However, limited research has examined the development of technological skills for “doing” statistics, e.g. using statistical packages. This paper proposes a distinction between these two roles of technology and how both benefit student learning. The paper then applies Kanfer and Ackerman’s (1989) integrative model of skill acquisition to explain the variability in students’ technological skill development. The ability to use statistical packages, arguably the most pervasive example of statistics technology, is used as an example to illustrate this model. The implications of the model are then discussed in the context of teaching technological skills in statistics courses. Future directions and challenges related to this area of are discussed

Technology has transformed the modern introductory statistics course, but little is known about how students develop the skills required to use this technology. This study compares two different training approaches for learning to operate statistical software packages. Guided training (GT) uses direct instruction and explicit guidance during training, whereas active-exploratory training types, such as error-management training (EMT), promote self-directed exploration. Previous studies in general software training suggest that EMT outperforms GT at promoting adaptive skill transfer. This study recruited a sample of 115 psychology students enrolled in introductory statistics courses that ran concurrently across two campuses. These students completed weekly, one-hour training sessions learning to use the statistical package SPSS. In the final week of the semester, students completed an SPSS certification task to measure adaptive skill transfer. The EMT and GT approach was implemented in Campus A and B respectively. Due to non-random allocation, the covariates of gender, personal access, statistical knowledge, and training progress were taken into account when modeling adaptive transfer between training approaches. After controlling for these covariates, no difference in adaptive transfer was found between training approaches. The results suggest that improving access to statistical packages may provide a more powerful way to improve the development of technological skills over using different training approaches.

Course projects have been argued to help develop students’ statistical thinking, but implementing authentic and realistic course projects still presents major challenges. This paper evaluated students’ experiences and perceptions of using an online simulated virtual environment, known as the Island, for implementing major course projects within an online masters level introductory statistics course. The use of the Island aimed to overcome significant practical and ethical constraints imposed on project-based work in online courses. The project required students to answer a self-posed research question by gathering and analysing data using methods covered in the course. The project was divided into two parts, a mid-semester proposal and an end of semester online presentation. Following completion of the projects, forty-two students responded to a questionnaire which rated their level of agreement to three aspects of using the Island: engagement, ease of use and contributes to understanding. Students were also asked to provide qualitative comments and five students participated in semi-structured interviews. Qualitative feedback was analysed to help explain the results from the quantitative questionnaire. In conclusion, perceptions of the use of the Island for project-based assessment were very positive. Qualitative feedback provided insight into how the Island-based projects may help to develop students’ statistical thinking.