Development of a cognition-priming model describing learning in a STEM classroom

Abstract

Successful STEM learning depends on the interaction of affect, cognition, and application of ideas. Simply put students who are unwilling to persist in STEM based endeavors do not suddenly develop into scientists, mathematicians, engineers or computer scientists, nor do they seek out STEM related courses or STEM based careers. The purpose of this study is to investigate content, cognitive, and affective outcomes related to STEM integrated curriculum within the K‐5 arena. Educational and psychological literature tends to focus one aspect of the other when examining the role of affect and cognition on student outcomes. Current trends in educational measurement and psychometrics have begun to address the artificial disconnect that exists between affect, cognition, and content outcomes within the science education literature. The methods used to develop the results within this study are a mixture of quantitative methods to develop a model of learning occurring in a STEM school. Using ANOVA, structural equation modeling, and model analysis, an understanding of the problems presented becomes clear. Analysis of model fit statistics suggests adequate model fit (χ2(21) = 30.91, p = 0.075, CFI = 0.94, TLI = 0.93, RMSEA = 0.04, SRMR = 0.05). The standardized structural coefficients for the path from group to each of the constructs is statistically significant (p < 0.05) thus indicating that the two groups differ on the constructs of self‐efficacy, science interest, spatial visualization, and mental rotation. An estimate of effect size of the mean group difference across the statistically significant constructs reveals self‐efficacy (d = 1.27, large), science interest (d = 1.97, large), spatial visualization (d = 1.30, large), and mental rotation (d = 1.42, large). There is considerable evidence that the inclusion, STEM integrated learning at the earlier elementary level becomes critically important for the students as they progress in school. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 52: 410–437, 2015

The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education

“…They argued that some items in their scale were more associated with being in school by respondents in some age groups than others, such that those items were able to elicit the desired construct better with younger children. By contrast, Lamb, Akmal and Petrie (2015) in a study "to investigate content, cognitive and affective outcomes related to STEM integrated curriculum" (p.410) explain lower reliabilities of an instrument administered to kindergarten pupils (as compared to second and fifth grade pupils) as due to "the varying ability of kindergarteners to interpret questions and responses" (p.422). This reflects how Cronbach's alpha cannot be seen as a measure of a scale or instrument per se but only of its application to a particular sample of respondents.…”

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

“…The model was developed within the framework of connectivism. Identification of the attributes occurred using a modified cognitive diagnostics approach (CDA) [28]. The modification of the CDA is the use of video game server data based on student choices and actions as opposed to talk aloud protocols [29].…”

“…This situation arises because researchers view the processes between input and output as too complex for empirical study [28]. This linear system out of necessity often becomes focused on external environmental systems such as culture without consideration of internal cognitive processing.…”

“…The use of a linear research approaches without testable underlying mechanisms of action and attempts to reconcile across frameworks fragments science education, isolates researchers, and advocates for a singular approach to the study of constructs. A study by Lamb, Cavagnetto, and Akmal (2014) provides direct evidence for the systemic non-linear nature of learning and more importantly provides a testable mechanism of action related to cognition around student learning [28]. Furthermore, several recent computational experiments using cognitive computational model have suggested the power of the computational model approach as means to test and understand cognition related to learning in science.…”

Modeling of a Theoretical Construct in Inquiry-Based Science Education // Modelización de un constructo teórico sobre la percepción del alumnado en procesos de indagación en ciencias

“…Other studies, based on SEM models, including also cognitive and affective variables have emphasized the importance of having adequate meaningful understanding of scientific concepts (Nieswandt, 2007), and the use of learning strategies and social support of students (Lamb, Akmal, & Petrie, 2015).…”