Academic journal article Educational Technology & Society

Interactions between Levels of Instructional Detail and Expertise When Learning with Computer Simulations

Academic journal article Educational Technology & Society

Interactions between Levels of Instructional Detail and Expertise When Learning with Computer Simulations

Article excerpt

Introduction

In recent years, computer-assisted learning in a simulation-based environment has become increasingly available in many areas of education (e.g., Kolloffel, Eysink, de Jong, & Wilhelm, 2009; Liu, Lin, & Kinshuk, 2010; Morris, 2001; Renken & Nunez, 2013; Rutten, van Joolingen, & van der Veen, 2012; van der Meij & de Jong, 2006). Simulation-based learning attempts to model a real-life situation with dynamically linked multiple representations on a computer so that complex concepts can be visualized or modelled (Lee, Plass, & Homer, 2006; van der Meij & de Jong, 2006). Learning with computer simulations can be considered to have similarities with discovery learning (Alfieri, Brooks, Aldrich, & Tenenbaum, 2011; Mayer, 2004). It provides a platform for learners to construct their own mental models about the concepts or knowledge to be learned by interacting with the environment.

Although the advantages of using simulation-based discovery learning have been confirmed by some empirical studies (e.g., Jaakkola, Nurmi, & Lehtinen, 2010; Lindgren & Schwartz, 2009; Urban-Woldron, 2009), many have argued that learning with minimal guidance (Kirschner, Sweller, & Clark, 2006) in a simulation-based environment often has proved to be ineffective (Eckhardt, Urhahne, Conrad, & Harms, 2013; Kanar & Bell, 2013; Mayer, 2004; Swaak & de Jong, 2001). Mayer (2004) reviewed a number of studies conducted from 1950 to the late 1980s comparing guided and unguided learning and suggested that learning was more effective using guided rather than unguided forms of instruction. In another study with two meta-analyses based on a sample of 164 research studies of discovery learning (Alfieri et al., 2011), the findings suggested that unassisted discovery failed to benefit learning unless the instructional supports in the form of feedback, work examples, scaffolding, and elicited explanations were provided.

In light of these results, how to design appropriate guidance for learning in simulation-based environments is critical (Rutten et al., 2012). Van der Meij and de Jong (2011) found that using step-by-step guidance for self-explanations to relate and translate between representations resulted in better learning outcomes than using general guidance in a simulation-based learning environment. Another study examining the sequential effects of high and low instructional guidance on children's acquisition of experimentation skills within a discovery learning environment (Matlen & Klahr, 2013), indicated that learning and transfer were promoted whether high guidance instruction consisting of a combination of direct instruction and inquiry questions was received before or after low guidance that included inquiry questions only. A study by Lazonder and Egberink (2014) found that using segmented inquiry questions to scaffold learning procedures facilitated children's acquisition and use of the control-of-variables strategy as much as directly providing instruction prior to investigating a multivariable inquiry task in a simulation-based learning environment.

These studies indicated how learning guidance can be designed to improve learning processes and outcomes in a simulation-based environment; however, few studies have been conducted based on individual differences of learners. In studies based on cognitive load theory, optimal instructional designs have been found to interact with levels of expertise (Kalyuga, 2007; 2009a, 2009b; Kalyuga, Ayres, Chandler, & Sweller, 2003; Kalyuga, Chandler, & Sweller, 2000, 2001). Cognitive load theory has been associated with learning with technology in recent years (e.g., De Koning, Tabbers, Rikers, & Paas, 2009; 2011; Lee et al., 2006; Liu, Lin, & Paas, 2013; Liu, Lin, Tsai, & Paas, 2012; Rey & Fischer, 2013).

Cognitive load theory

Cognitive load theory describes different sources of cognitive load when information is transited between working memory and long-term memory (Sweller, 1994, 2003, 2004, 2010, 2011, 2012; Sweller, Ayres, & Kalyuga, 2011). …

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