A Cognitive Curriculum: Improving Statistics Cognition Online

A Cognitive Curriculum: Improving Statistics Cognition Online

DOI: 10.4018/978-1-5225-2420-5.ch004
(Individual Chapters)
No Current Special Offers


This chapter provides a discussion of developing a curriculum for a modern statistics course which aims to improve statistics cognition. We begin by examining micro-level curricular considerations, such as designing learning objectives and assessments which can allow transfer of cognitive processes. Then, we discuss the implications of macro-level curricular considerations, such as tracking, and the need to search for a mismatch between the learner and their environment. Collectively, we argue that such practices allow educators to develop a cognitive curriculum. We conclude the chapter with a discussion of how online learning environments inherently lend themselves to a cognitive curriculum and provide numerous benefits for the educator and learner.
Chapter Preview

The design of the physical environment itself is equally important as content and pedagogy. - J.D. Mills, 2015, p. 62


1. Introduction

So far, discussion has centered on statistics cognition as internal constructs researchers and educators can empirically measure (Chapter 2), and as a central mechanism by that scholars can shed light on student learning (Chapter 3). However, cognitions is not static, and may often depend on the situation (see Brown, Collins, Duguid, 1989; Kishner & Whitson, 1997). Indeed, a common assumption made by educational scholars is that learning itself is situated, for if we did not believe that formal settings enhanced education, we probably would not have schools.

In order to understand how cognition can be situated, we look toward the field of curriculum (see Tanner & Tanner, 1980). While the definition of curriculum has changed over time (Kelly, 2009), it may often be thought of as the culmination of an educational experience (Pinar, 1995). That is, how students learn knowledge or skills, and what they end up doing with this information. While deciding what curriculum to employ can be a major undertaking in of itself (see Gall, 1981), an array of resources have emerged to help educators select curriculum for disciplines, such as statistics (Bakker, 2004; Cheatham, 2000; Cobb, 1993; 2007), as well as modalities, such as online learning (Edelson, 2001; Edelson, Gordin, & Pea, 1999; Quintana et al., 2004).

Although relatively fewer studies have examined curriculum of online statistics courses (Summers, Waigandt, & Whittaker, 2005), it is imperative to consider how quality education may be independent of certain environmental features, depending on the student. As previously mentioned, while meta-analyses have found students in online courses are relatively equal in terms of their performance and affect (see Allen, Bourhis, Burrell, & Mabry, 2002; Allen, Mabry, Mattrey, Bourhis, Titsworth, & Burrell, 2004), results vary widely depending upon the quality of the curriculum (Zhao, Li, Yan, Lai, Tan, 2005).

Although more recent meta-analyses have suggested that technology may provide a slight advantage to students, relative to classes that only rely on face-to-face instruction (Means, Toyama, Murphy, Bakia, & Jones, 2009; Sitzmann, Kraiger, Stewart, & Wisher, 2006), this Chapter discusses the overarching framework for developing a cognitive curriculum, or rather, a curriculum that assesses current levels of cognitive ability, and implements materials that prompt the student to go beyond their current means. In a classical social-constructivist sense, the educator assesses the limits of a student’s cognition, and targets instruction on what the student can do with assistance (Vygotsky, 1986).

While this Chapter integrates research on statistics curriculum, many comprehensive reviews of curriculum and resources for statistics education exist elsewhere (Cobb, 1993; 2007; Bryce, Gould, Notz, & Peck, 2001; Franklin et al., 2005). Whether developing assessments (Gal & Garfield, 1997), aligning statistics curriculum with related disciplines (Bargagliotti, 2012), or examining the role of technology in improving statistics education (Chance, Ben-Zvi, Garfield, & Medina, 2007), researchers have amassed a wealth of knowledge about designing statistics courses (Bradstreet, 1996; Dietz-Uhler, Fisher, & Han, 2007; Remsburg, Harris, & Batzli, 2014). Thus, instead of reiterating this material, our focus is on research exploring the ways online curriculum can be used to facilitate cognitive growth. This topic will be approached from two primary perspectives.

Complete Chapter List

Search this Book: