Development of a Short-Form Measure of Science and Technology Self-efficacy Using Rasch Analysis

Lamb, Richard; Vallett, David; Annetta, Leonard A.

doi:10.1007/s10956-014-9491-y

Cited by 42 publications

(21 citation statements)

References 55 publications

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“…The content test consisted of state‐created science and mathematics end‐of‐course tests prepared by Pearson. Affective tests consisted of the Science Interest Survey (Lamb et al, ) and the Self‐Efficacy in Science and Technology Short Form (Lamb et al, ). Cognitive tests consisted of the Paper Folding Task (Ekstrom, French, Harman, & Dermen ) and the Shepard Metzler Test of Mental Rotation Task (Shepard & Metzler, 1971).…”

Section: Methodsmentioning

confidence: 99%

“…Psychologists see self‐efficacy and interest governed under an area of psychology known as self‐referent thought (Kappes, Oettingen, & Pak, ). Psychometricians and measurement experts suggest measurement of self‐referent constructs occur most readily via self‐reporting measures as the constructs are internal and latent (Bandura, ; Berry, West, & Dennehey, , Lamb, Vallett, & Annetta, ). Self‐efficacy related to addressing one's environment is not fixed but is semi‐stable, nor is self‐efficacy a matter of knowledge related to the environment (Bandura, ).…”

Section: Review Of Literaturementioning

confidence: 99%

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

2015

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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

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Section: Methodsmentioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

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

2015

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“…Critical thinking in the biological sense is a cognitive attribute and supporting cognitive architecture responsible for the analysis, evaluation, and conclusions drawn from incoming data streams via the senses, internal memory, and psychological affect [9,21]. Modeling of the nonlinear dynamics associated with critical thinking is difficult requiring the use of complex measurement methods and applications of theories of nonlinear dynamics such as Chaos Theory [21,22]. With this level of complexity, conventional modeling optimization techniques are often difficult to extend to even simple models of human cognitive functioning.…”

Section: Multiobjective Evolutionary Algorithmsmentioning

confidence: 99%

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Lamb

Premo

2015

Computation

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Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA) can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M) and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a "virtual" student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M) a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning.

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“…Rasch model has been widely used i n s u r v e y i n s t r u m e n t v a l i d a t i o n f o r educational studies, such as self-efficacy (Lamb, Vallett, & Annetta, 2014), success of instructional intervention (Royal & Tabor, 2008), and perceptions of instructors or students (Kyriakides, Kaloyirou, & Lindsay, 2006). For example, Lamb, Vallett, and Annetta (2014) validated an instrument called SETS-SF aimed to investigate selfefficacy related to scientific reasoning, computer technology, and video gaming on adolescent students.…”

Section: Rasch Modelmentioning

confidence: 99%

“…For example, Lamb, Vallett, and Annetta (2014) validated an instrument called SETS-SF aimed to investigate selfefficacy related to scientific reasoning, computer technology, and video gaming on adolescent students. The authors collected s u r v e y r e s p o n s e s f r o m 6 5 1 s t u d e n t s in 15 schools.…”

Section: Rasch Modelmentioning

confidence: 99%

The Validation of an Instrument for Evaluating the Effectiveness of Professional Development Program on Teaching Online

Hung¹,

Yang²

2015

JETDE

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Development of a Short-Form Measure of Science and Technology Self-efficacy Using Rasch Analysis

Cited by 42 publications

References 55 publications

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

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

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

The Validation of an Instrument for Evaluating the Effectiveness of Professional Development Program on Teaching Online

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