Description
Statistical concepts are abstract and difficult to understand, thus statistical misconceptions can occur frequently among students, researchers and even teachers (Bodemer, Ploetzner, Feuerlein, & Spada, 2004; Castro Sotos et al., 2007; Liu, 2010). These concepts have different sources and occur in different forms. Interpreting results and applying methods accurately are key skills in an academic career. Therefore, well-developed support in learning these concepts is essential for students, researchers and teachers. Furthermore, discovering and resolving statistical misconceptions is also important from a technological perspective. Chance et al. (2007) mention that technical applications explaining difficult statistical concepts are widespread. Programs take over difficult computing processes hence students must learn to understand why and how data are organized and how results should be interpreted. Several research studies (Batanero, Tauber, & Sanchéz, 2005; Belia, Fidler, Williams, & Cumming, 2005; Chance, delMas, & Garfield, 2004; Cumming, Williams, & Fidler, 2004; delMas, Garfield, & Chance, 1999; Finch, 1998; Haller & Krauss, 2002; Jazayeri, Fidler, & Cumming, 2010; Kalinowski, Fidler, & Cumming, 2008; Saldanha & Thompson, 2007; Sedlmeier, 1998; Shaughnessy, & Ciancetta, 2002; Well, Pollatsek, & Boyce, 1990) and reviews (Beyth-Marom, Fidler, & Cumming, 2008; Chance, Ben-Zvi, Garfield, & Medina, 2007; Castro Sotos et al., 2007) present issues around statistical literacy and misconceptions. Some of the empirical reports present positive results of learning intervention in the form of live or online simulation tasks that try to explain statistical concepts on a visual level (Chance et al., 2004; delMas et al., 1999; Lipson, 2002; Shaughnessy & Ciancetta, 2002; Sedlmeier, 1998; Well et al., 1990). However, these interventions only improved some of the most problematic statistical concepts such as distribution types and variation and probability and the p-value. To the author’s knowledge there are no studies that have tried to improve understanding of one of the most misunderstood concepts on a visual level: the p-value. As a result, this preliminary study developed and investigated an e-learning intervention that tries to improve statistical understanding and reduce misconceptions of students with prior knowledge in statistics on a visual level that will include interactive simulation tasks. The developmental process of this tool included working through e-learning approaches that could be applied to support cognitive processes while learning and understanding in interactive e-learning environment including simulations. It is important to note that the goal of this project is to develop a tutorial that is able to support students having prior basic knowledge in statistics.