Designing e-learning content that is engaging and effective for all students is impossible without an awareness of and consideration for student diversity (Liu, Liu, Lee, & Magjuka, 2010). Before one can begin to effectively address student diversity through instructional design, one must examine the concept as it appears in the research literature. Surprisingly, the characteristics that form the concept of diversity within student populations are not addressed by a unified model or theory in any of the studies consulted for this paper. However, researchers have investigated student diversity from a variety of perspectives, including cultural dimensions, disability, geography, and language (Parrish & Linder-VanBerschot, 2010; Shimoni, Barrington, Wilde, & Henwood, 2013). Despite this apparent diversity of ideas about student diversity, the majority of studies on the topic deal solely with the cultural dimensions of diversity and rely heavily on the work of Geert Hofstede for their theoretical frameworks (e.g., Wang, 2007). Though popular as a measure of cultural difference, the usefulness of Hofstede’s model has been criticized for oversimplification, inconsistency, lack of empirical evidence, and a view of culture as largely static (Signorini, Wiesemes, & Murphy, 2009).
Learning Styles: A Critical Appraisal
Learning styles-based instruction is a method of teaching that matches instructional techniques to a student’s preferred style of learning. The benefit of matching instructional techniques to learning styles is that students will learn more. This claim is known as the matching hypothesis. Despite the theoretical appeal of learning styles, the evidence for the matching hypothesis is minimal in the current research literature (Cuevas, 2015; Rohrer & Pashler, 2012). Of the 31 studies published since 2009, only one—not without its methodological flaws—has supported the matching hypothesis. With the lack of empirical support for learning styles-based instruction, downplaying the need to accommodate learning styles seems not only correct, but also necessary. Educational resources are limited, and time and money need to be spent on interventions that have been shown by empirically-supported research to improve student learning (Newton, 2015). In the following sections, I will examine learning styles and related theories, my own experience with learning and education, my preferred way to learn, and my success with various learning modalities. I will show that learning styles are merely preferences that have little effect on actual learning. Read More …
Even though e-learning has been around for decades, there is still a need to identify best practices in the field for practitioners new to the discipline. While there are many approaches to the subject of how to identify best practices for e-learning, the most common are institutional (Irlbeck, 2008; Stansfield et al., 2009), which looks at the implementation of e-learning from a institution-wide perspective, and pedagogical (Keengwe, Onchwari, & Agamba, 2014; Reilly, Vandenhouten, Gallagher-Lepak, & Ralston-Berg, 2012), which looks at the implementation of e-learning from the more limited scope of classroom integration and practitioner training. Pachler and Daly (2011) identify several different eras of e-learning research, but are careful to caution that the field is fast-moving and is liable to slip the bonds of any classificatory system before it is brought into mainstream use. With these facts in mind, I wish to examine the various findings concerning best practices for e-learning, first from an institutional perspective and second from a pedagogical perspective. Then, with an understanding of the best practices from these two perspectives, I will offer an overview of the challenges, opportunities, and best practices in e-learning as a unified discipline.
Positive and meaningful e-learning experiences are essential for student satisfaction with online courses. However, there are several approaches to identifying and promoting such experiences in practice. A review of the literature on positive and meaningful e-learning experiences revealed not only that course design and human connection are important for high levels of student satisfaction, but also that IT support and institutional infrastructure are vital to student satisfaction as well (Boling, Hough, Krinsky, Saleem, & Stevens, 2012; Carter et al., 2014; Salyers, Carter, Carter, Myers, & Barrett, 2014). All of the studies consulted recommended interactive learning based on socio-constructivist principles as the most appropriate means for ensuring positive and meaningful e-learning experiences (Boling, Hough, Krinsky, Saleem, & Stevens, 2012; Carter et al., 2014; Luyt, 2013; Salyers, Carter, Carter, Myers, & Barrett, 2014; Watkins, 2014). Watkins’ (2014) study even provided examples of learning activities that could be easily integrated into an online course without extensive instructor preparation or training.
The digital divide is often defined in terms of access to computers and the internet. However, this definition of the digital divide does not fully capture the extent of the disconnect between people and the technology that makes information available to them. While the digital divide—in the sense of technology availability—has narrowed significantly in the past decade, there remain a large number of people who are unable to make full use of the internet and its associated applications. Thus, scholarship on the digital divide has shifted away from providing access toward discussing barriers to inclusion in the “digital society.” These barriers are new forms of the digital divide that limit both the adoption of the internet and its useful usage by certain social groups. Consequently, the digital divide has been redefined by van Deursen and van Dijk (2015) to indicate four types of access necessary for utilizing the full potential of the internet and its applications: motivational access, material access, internet skills access, and internet usage access. These new dimensions of access reveal further divides between urban and rural communities, between majority and minority groups, between higher and lower income populations, and between people with higher and lower levels of educational attainment (Armenta, Serrano, Cabrera, & Conte, 2012; Cohron, 2015).