Academic journal article Educational Technology & Society

Adaptive Instruction to Learner Expertise with Bimodal Process-Oriented Worked-Out Examples

Academic journal article Educational Technology & Society

Adaptive Instruction to Learner Expertise with Bimodal Process-Oriented Worked-Out Examples

Article excerpt

Introduction

Adaptive instruction refers to educational interventions intended to effectively accommodate the needs of individual students (Park & Lee, 2003). This educational approach is generally characterized as one that incorporates alternative instructional procedures by providing built-in flexibility to allow students to take various paths to learning (Park & Lee, 2003). The practice of adaptive instructions has had a long history (e.g., aptitude-treatment interactions, Cronbach & Snow, 1977). More recently, researchers in the cognitive load theory (CLT) have investigated methods of adaptive instructions and cast new insight on this important issue.

CLT offers principles and methods to design and deliver instructional interventions that efficiently utilize the limited capacity of human working memory (Sweller, van Merrienboer, & Paas, 1998). If instructional interventions are designed in a way that causes excessive cognitive load, the limited capacity of human working memory can easily be overloaded. This excessively high cognitive load does not contribute to acquisition or automation of schematic knowledge, but rather impedes it (Paas, Renkl, & Sweller, 2004). One of the instructional methods to control excessive cognitive load is worked-out examples (WOE) (Renkl & Atkinson, 2010). WOE provides an experts' problem solving model for novices to study and emulate, so a substantial amount of unnecessary cognitive load caused by premature problem solving can be reduced by studying with WOE in the early phase of skill acquisition (Sweller, 2006). However, as learner expertise grows, knowledge acquisition from studying WOE becomes a redundant activity that contributes little or nothing to further learning. Instead, problem solving fosters learning more than studying with WOE (Kalyuga, Chandler, Tuovinen, & Sweller, 2001). This reversal of the WOE effect is called the expertise reversal effect (Kalyuga, Ayres, Chandler, & Sweller, 2003). The instructional implication of this expertise reversal effect is continuous optimization of cognitive load.

Adaptive instruction to learner expertise

To continuously optimize cognitive load, instructional designs need be tailored to an individual trajectory of cognitive skill acquisition in a domain (Kalyuga, 2007, 2008; Kalyuga & Sweller, 2004, 2005). In the traditional learning situations, researchers or instructors pick when to modify instructional techniques typically through interviews or think-aloud procedures or observations as learners gain expertise. However, in e-learning environments, such techniques are inconvenient to use (Kalyuga, 2006a). As an alternative, CLT researchers have utilized efficiency measures to decide the right movement. Such measures were originally developed to measure efficiency of instructional conditions. Pass and van Merrienboer (1993) suggested the following formula to calculate instructional efficiency, E = Z performance-Z mental effort/[square root of (2)]. Students' performance and mental effort on the test are first standardized and then mean standardized test performance and test mental effort are entered into this formula to calculate instructional efficiency. According to them, a high performance combined with low mental efforts is called high instructional efficiency while a low performance combined with high mental effort is called low instructional efficiency. However, a combination of performance and mental effort is also indicative of expertise. Learners with more expertise are able to attain equal or higher levels of performance with less investment of mental effort (Kalyuga, 2007). Thus, the efficiency measure is also used to measure levels of learner expertise (van Gog & Paas, 2008).

However, such formula may not be appropriate for e-learning situations. Kalyuga and Sweller (2005) argued that it is not convenient for e-learning applications which require diagnostic assessments of learner expertise for adaptation in real time during an experiment. …

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