This chapter briefly introduces the field of statistics education, and provides a short synopsis for each of the seven proceeding chapters. In introducing the field, the importance of integrating technology into all aspects of the curriculum, and using a well-designed data driven methodology which takes an interdisciplinary approach, is argued to be central to the success of developing an online statistics course. By integrating technology and using the scientific method to align course materials and adhere to fundamental educational psychological findings, we argue that the primary objective of statistics education—to enhance statistics cognition—can be achieved.
...it is the job—we would argue—of statistical cognition to provide research-based guidance as to how statistical communication can be best accomplished. - Beyth-Marom, Fidler, & Cuming, 2008, pp. 23-24
Top1. Introduction
Much of the research reviewed throughout this book can be traced back to a single question. Namely, what is the nature of schooling? This question, dating as far back to the 1880s (Levin, 1991), would become a central focus of educational discussion as westward expansion continued through the 1930s.
What was at stake was deciding what schools would be designed to do, and there were two wings of education that took opposing stances. The first, led by the grandfather of behaviorism - Edward Thorndike - proposed that the purpose of schooling was to help students obtain a job. That by approaching education with the traditions and values of science and measurement, we could make learning highly effective and efficient (Thorndike, 1918). This mass-education wing was built around the notion that, by using standardized assessments, students learning could be quantified and adjusted to serve the economic needs of the time. Conversely, a progressive reformist named John Dewey advocated for what would become the child-centered wing of education, proposing that the purpose of schooling was to teach children how to become citizens who thought and acted critically for the future we wanted to see.
Ultimately, Thorndike's mass-education wing won this debate in the minds of 20th century education reformists (Lewin, 1991; Tomlinson, 1997). And while the pendulum of educational reform has swung between these two wings, this book proposes a different answer to that solution, one in line with century old discussions framed on the role of statistical scholars in statistics education (see Fox, 1860; Scates & Deming, 1946; Walker, 1945). The nature of education, as far as a scholar in statistics education may be concerned, is how to improve statistics cognitions (Beyth-marom, Fidler, & Cumming, 2008).
Statistics cognition can be defined as the process of understanding the mental actions behind remembering, understanding, and applying numerical data. Specifically, scholars have theorized statistics cognition consists of three components (Garfield & Ben-Zvi, 2007); literacy, reasoning, and thinking (Chapter 2).
Having only recently emerged as a unique academic field of its own (Ben-Zvi & Garfield, 2008), integrating statistics cognition into statistics education is no simple task. As will be discussed, and is apparent throughout this book, the literature reviewed is highly interdisciplinary, coming from fields such as psychology, education, economics, engineering, and neurology. Individually, these fields have amassed an impressive literature devoted toward studying how students learn statistics within their respective disciplines.
Thanks in part to the accountability movement (Alexander, 2000; Huisman & Currie, 2004), much research has been centered around educational performance (hereto referred to as performance) as a key educational variable; with grades being used as the primary means to assess student learning. However, this book focuses on the wide array of educational outcomes, with considerable discussion directed toward cognitive development.