Effective Online Learning for Older People: A Heuristic Design Approach

Effective Online Learning for Older People: A Heuristic Design Approach

Robert Z. Zheng (University of Utah, USA)
DOI: 10.4018/978-1-4666-1966-1.ch008
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Abstract

This chapter examines the cognitive constraints related to older people in learning, particularly in e-learning, and proposes a new design approach that: (1) assists the instructional designer and Web development in identifying issues related to older people’s involvement in e-learning; (2) helps reduce the mental load in designing and developing e-learning for older people; and (3) uses heuristics to systematically support the designers in making decisions about meeting the needs of older people in their learning and searching for information online.
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Introduction

The aging process is associated with gradual declines in cognitive functioning, which include reduced working memory capacity, processing speed, and physical and mental coordination (Gardner & Hill, 2012; Rhodes & Kelley, 2005). Because of the decline in cognitive functioning, older adults face increasing challenges when learning new materials, especially learning new materials via the Word Wild Web. Research shows that the extent to which how well older people learn is dependent on the amount of cognitive load imposed on the learner during the learning process (Hawthorn, 2007; Low, Jin, & Sweller, 2012; Ouwehand, van Gog, & Paas, 2012; van Gerven, Paas, van Merrienboer, & Schmidt, 2002). In an early study, Sweller and Chandler (1994) identified the relationship between the level of element interactivity and the challenges associated with learning. They noted that high-level element interactivity could impose high cognitive load, which makes the learning process difficult. It is thus agreed that reducing cognitive load by reducing the level of element interactivity in complex learning can significantly improve the effectiveness and efficiency in learning (Zheng & Cook, 2011; Low, et al., 2012; Ouwenhand, et al., 2012; Tindall-Ford, Chandler, & Sweller, 1997). Previous research has established that cognitive strategies such as worked examples, integrative instructional format, cueing, gesturing and signaling can significantly alleviate the cognitive load that the learner experiences in learning (Kirschner, Sweller, & Clark, 2006; Ouwenhand, et al., 2012; Sweller, 2010).

Along the same line researchers investigated ways to apply heuristics to E-learning design in an effort to reduce cognitive load in learning (Darabi, Arrastia, & Nelson, 2011; Hwang, Kuo, & Yin, 2010; Sweet & Ellaway, 2010). Hwang et al. (2010) employed a heuristic algorithm to guide the learning activities in a natural science course. The researchers found that students' learning behaviors had significantly improved through a heuristic design that aimed at personalizing the support for learners. Sweet and Ellaway (2010) argued that heuristic design, compared to conventional instructional design, results in heightened levels of critical thinking and sensitivity to learners’ cognitive needs. They further pointed out that heuristics which use simple, experience based rules to guide instructional design not only serve as an effective tool for diagnosing the usability issues related to the physical design of the e-Learning but also function as a cognitive walk-through which identifies malpractices in e-Learning that often cause cognitive dysfunction and misunderstanding in learning. Lee and Reigeluth (2009) pointed out the benefits of applying heuristics to the design of e-learning. They demonstrated that heuristic task analysis, a method developed for eliciting, analyzing, and representing expertise in complex cognitive tasks, provides a clear path for effective learning.

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