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• W2152891933 abstract "As the technology-enriched learning environments and theoretical constructs involved in instructional design become more sophisticated and complex, a need arises for equally sophisticated analytic methods to research these environments, theories, and models. Thus, this paper illustrates a comprehensive approach for analyzing data arising from experimental studies using structural equation modeling (SEM) procedures that can formulate and test theories regarding how interventions affect observed outcomes, in comparison to traditional MANCOVA design. Researchers in the field of instructional systems and educational technology are encouraged to incorporate this method into their analyses of experimental investigations, because this method allows for a close examination of mediating processes that are responsible for the outcomes observed and for the estimation of both random and correlated measurement errors. Conventionally, studies related to educational technology have used statistical techniques to test mean differences between groups. The t-test, analysis of variance (ANOVA), and analysis of covariance (ANCOVA) allow researcher to determine the effects of interventions. However, cognitive functioning and processes related to learning are intricate and human learning involves various psychological constructs. In other words, theoretical advances for understanding human cognition and learning processes require the consideration of more psychological constructs when designing learning environments. Moreover, with technological advances, educators infuse more technologies into learning environments to improve students' learning. However, the effectiveness of these innovations cannot simply explained by testing mean differences, because these interventions could be related to underlying mediating processes that might be responsible for the desired outcomes (Koetting & Malisa, 2004, Delialioglu, et al., 2010). Thus, researchers in the field of educational technology should be more interested in explaining how interventions affect learning (Alenezi, Abdul Karim, V Kline, 2005). To illustrate this statistical approach, this paper analyzes data drawn from an actual experimental study. The Experimental Context To demonstrate the use of structural equation modeling for MANCOVA designs, this paper applies the procedures to data derived from an actual experiment that examined the effects of generative learning strategy prompts and metacognitive feedback on learners' self-regulation, use of learning strategies, and learning performance (Lee, Lim, & Grabowski, 2010). In that experiment, the researchers wanted to create experimental conditions where interventions would influence students' learning performance directly and indirectly. The prediction was that the interventions would positively affect learning performance, but through their effect on self-regulation and use of learning strategies." @default.
• W2152891933 created "2016-06-24" @default.
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• W2152891933 date "2011-01-01" @default.
• W2152891933 modified "2023-09-28" @default.
• W2152891933 title "An Application of Latent Variable Structural Equation Modeling for Experimental Research in Educational Technology." @default.
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