College of Education and Human Development
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Browsing College of Education and Human Development by Author "Annetta, Leonard A."
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Item Factors Influencing Learner Conceptions of Force: Exploring the Interaction among Visuospatial Ability, Motivation, and Conceptions of Newtonian Mechanics in University Undergraduates from an Evolutionary Perspective(2013-08) Vallett, David Bruce; Vallett, David Bruce; Annetta, Leonard A.This study examined the relationships among visuospatial ability, motivation to learn science, and learner conceptions of force across commonly measured demographics with university undergraduates with the aim of examining the support for an evolved sense of force and motion. Demographic variables of interest included age, ethnicity, and gender, which served to determine the ubiquity of the effects of the exogenous variables. Participants (n=91) self selected from introductory physics courses at a large public university in the Mid-Atlantic region of the United States. Utilizing a single-group exploratory design, all participants completed a series of anonymous online instruments to assess the variables of interest. Analysis consisted of an ANOVA for significance testing of demographic variables and a single-level structural equation model (SEM) to ascertain the causal influence of visuospatial ability and affect in the form of motivation on learner conceptions of force. Results of the SEM indicated that while motivation had a nonsignificant (p>.05) impact with this sample, visuospatial ability had a strong (.5 unit change in physics achievement per unit of VSA, p<.05) influence on Newtonian conceptions of mechanics. The results of this study inform physics educators as to the factors underlying conceptual change in Newtonian physics and generate hypotheses regarding the cognitive processes and corresponding neural substrates associated with successful Newtonian reasoning.Item Relationship between Visual Attention and Flow Experience in a Serious Educational Game: An Eye Tracking Analysis(2014-05) Cheng, Wai Ki Rebecca; Cheng, Wai Ki Rebecca; Annetta, Leonard A.Game-based learning has become a topic of interest in education, especially within the science education community. Although some evidence supporting the effectiveness of digital games for science learning is emerging, the results overall have been largely inconclusive. In order to further advance research on game-based learning, the purpose of this study was to apply an interdisciplinary approach using the cognitive-affective integrated framework, the information-processing model of selective attention (Broadbent, 1958; Lachter et al., 2004), and the dual-process theories of cognition (Kahneman, 2011; Svahn, 2009), to construct a comprehensive view of the mental processes of visual attention during gameplay in relation to the positive affective state of Flow experience. This study utilized a mixed methods design, using a concurrent embedded strategy QUAN/qual (Creswell, 2008) to collect and analyze both quantitative and qualitative data. Thirty-one high-school students (N=31) in the mid-Atlantic region of the United States, between ages 14 and 17, played the Serious Educational Game (SEG) called Neuromatrix. Self-report surveys and an eye tracking method were used to collect quantitative data for statistical analysis. A gaze duration sequence diagram (Raschke, Chen, & Ertl, 2012) was adopted for data visualization and qualitative scanpath analysis. Two Flow scales (FSS-2 and eGameFlow) were used to explore the differences in psychometric properties between the generic and context-specific Flow measures. The results showed a negative linear relationship between visual attention and Flow experience (p < .001). Three visual attention variables were identified and served as the indicators of Flow and perceived science learning in an SEG environment: (a) low fixation counts indicated students' focused attention and immersion in an SEG; (b) short total visit duration represented the efficiency of selective visual attention and may serve as an indicator of Flow experience during gameplay; and (c) total fixation duration illustrated the extent to which students looked at specific learning materials that could possibly pass through the selective filter into conscious attention and thus, lead to learning. The interplay between affective and cognitive processes during gameplay played a key role in students' deep engagement and had an impact on their positive science learning in an SEG. An interactive effect of total fixation duration and Flow on perceived science learning was found (p < .001, pn2 = .324), implying that a well-designed SEG that aligns gameplay and learning objectives may promote synergy between engagement and learning. Moreover, two individual differences factors, science interest and self-efficacy for computer use (p < .01) - that predicted Flow were identified by a stepwise regression analysis; these factors were shown to influence the attentional processes and cognitive processes of gameplay. The evidence of a positive relationship between science interest and Flow in an SEG may encourage teachers and parents to take an active role in instilling students' science interest in their early years, and to support students' ongoing development of science interest through exposure to various formal and informal learning contexts.Item The Application of Cognitive Diagnostic Approaches Via Neural Network Analysis of Serious Educational Games(2013-08) Lamb, Richard L.; Lamb, Richard L.; Annetta, Leonard A.Serious Educational Games (SEGs) have been a topic of increased popularity within the educational realm since the early millennia. SEGs are generalized form of Serious Games to mean games for purposes other than entertainment but, that also specifically include training, educational purpose and pedagogy within their design. This rise in popularity (for SEGs) has occurred at a time when school systems have increased the type, number, and presentations of student achievement tests for decision-making purposes. These tests often task the form of end of course (year) tests and periodic benchmark testing. As the use of these tests, has increased policymakers have suggested their use as a measure for teacher accountability. The change in testing resulted from a push by school districts and policy makers at various component levels for a data-driven decision-making (D3M) approach. With the data-driven decision making approaches by school districts, there has been an increased focus on the measurement and assessment of student content knowledge with little focus on the contributing factors and cognitive attributes within learning that cross multiple-content areas. One-way to increase the focus on these aspects of learning (factors and attributes) that are additional to content learning is through assessments based in cognitive diagnostics. Cognitive diagnostics are a family of methodological approaches in which tasks tie to specific cognitive attributes for analytical purposes. This study explores data derived from computer data logging (n=158,000) in an observational design, using traditional statistical techniques such as clustering (exploratory and confirmatory), item response theory and through data mining techniques such as artificial neural network analysis. From these analyses, a model of student learning emerges illustrating student thinking and learning while engaged in SEG Design. This study seeks to use cognitive diagnostic type approaches to measure student learning while designing science task based SEGs. In addition, the study suggests that it may be possible to use SEGs to provide a means to administer cognitive diagnostic based assessments in real time. Results of this study suggest the confirmation of four families (factors) of traits illustrating a simple factor loading structure. Item response theory (IRT) results illustrate a 2-parameter logistic model (2PLM) fit allowing for parameterization using the IRT-True Score Method (X2=1.70, df=1, p=0.19). Finally, fit statistics for the artificial neural network suggest the developed model adequately fits the current data set and provides a means to explore cognitive attributes and their effect on task outcomes. This study has developed a justification for combining and developing two distinct areas of research related to student learning. The first is the use of cognitive diagnostic approaches to assess student learning as it relates to the cognitive attributes used during science processing. The second area is an examination and modeling of the relationship between attributes as propagated in an artificial neural network. Results of the study provide for an ANN model of student cognition while designing science based SEGs (r2=0.73, RMSE= 0.21) at a convergence of 1000 training iterations. The literature presented in this dissertation work integrates work from multiple field areas. Fields represented in this work range from science education, educational psychology, measurement, and computational psychology.