Implicit Theories, Epistemic Beliefs, and Science Motivation: A Person-Centered Approach

The purpose of this study was to explore (a) the individual belief profiles that naturally arise among middle and high school science students (n = 1225); (b) the relationships between these profiles to science achievement and other prominent motivation variables; and (c) the demographic and developmental differences among the belief profiles. Results revealed that a four-class solution fit the data the best. These profiles were differentially related to achievement goal orientations, science self‐efficacy, and science achievement. Differences in profiles also arose as a function of minority status, grade level, and gender. Findings support and refine Schommer-Aikins\u27s (2004) Embedded Systemic Model of epistemic beliefs. Results are discussed in relation to theory and implications for science instruction

Conceptual Issuess and Assessment of Implicit Theories

We reviewed fundamental conceptual issues and the state of research on the definition and assessment of implicit theories. We grappled with the following controversies related to the construct: (a) Are entity theory and incremental theory opposite ends of the same continuum? (b) How can scholars use more sophisticated methodologies to classify individuals into either the entity or incremental theory? (c) Given shifting conceptions of what intelligence is, how can scholars refine the implicit theory of intelligence construct? Given these conceptual issues, we then addressed practical issues related to the assessment of implicit theories. We point to the need for more sophisticated methods such as implicit association tests and the use of virtual environments as more “stealthy” ways to assess the construct

Do High Ability Students Disidentify With Science? A Descriptive Study of U. S. Ninth Graders in 2009

The present study describes science expectancy-value motivation classes within a nationally representative sample of students who were U.S. ninth graders in 2009. An expectancy-value model was the basis for science-specific profile indicators (self-efficacy, attainment value, utility value, interest-enjoyment value). Using exploratory latent class analysis, a four-class model was identified as the best model, based on model fit and interpretability. Although the low and typical profiles had uniform levels of indicators, the two high motivation profiles (high self-efficacy and high utility value) had mixed levels. The profile characterized by very high self-efficacy had lower values, while the profile characterized by high utility value had lower self-efficacy. The differences in math achievement between profiles were small. High-ability students disidentified with science; only 29% of high-ability students had high science expectancy-value profiles. The implications for science talent development are discussed

Implicit Theories of Ability and Self-Efficacy: Testing Alternative Social Cognitive Models to Science Motivation

Our overall goal was to empirically test what we called the “growth mindset as inoculation” hypothesis using a series of latent profile analytical approaches. This inoculation hypothesis, which is consistent with the way in which Dweck and Leggett (1988) described their social cognitive approach, states that believing in the malleability of intelligence serves a protective role against negative motivational and achievement outcomes. Participants were Grade 6 students (n = 504) from a middle school and Grade 10 students (n = 354) from two high schools in the Southeastern part of the United States. Two distinct patterns emerged, which corresponded to a growth mindset profile, and an all moderate profile. Our findings did not completely confirm or disconfirm the inoculation hypothesis – rather, a more nuanced conclusion should be drawn. Although there was evidence that the growth mindset profile evinced more adaptive outcomes compared to the all moderate alternative, which reinforced Dweck and Leggett’s claims, there was no evidence of any profiles with a distinct fixed theory of ability. This was true even when we forced our data to conform to such a model. Results refine Dweck and Leggett’s social cognitive approach to motivation

A Virtual Internship to Prepare High School Students for Civic and Political Action

We explored the impact of participating in a Virtual Internship (VI) computer-supported collaborative learning simulation, on high school students’ (n = 43) development of knowledge and skills for critiquing the political media with which they engage. Second, we evaluated the effect of this intervention on students’ self-efficacy for using specific media strategies to take political action. Finally, we explored the epistemic (knowledge-seeking) and non-epistemic aims that students set for themselves while participating within our VI, which was designed specifically to address students’ epistemic cognition. Analyses of both the quantitative and qualitative data revealed that students: (1) evinced gains in knowledge about what “fracking” is and also knowledge about why it is a controversial topic; (2) evinced gains in self-efficacy for civic engagement—a key indicator to students’ likelihood for acting; and (3) were able to understand the politicized nature of a social media post, and therefore reported wanting to pursue knowledge-seeking goals to understand both sides of the argument and the trustworthiness of the information sources. We discuss these results vis-à-vis the literature on epistemic games, which can help students develop the knowledge, skills, and values of a profession

Epistemic Cognition and Motivation

Why do you want to teach? What are the reasons you decided to major in philosophy in college? Why did you consult three different physicians and comb through hundreds of medical journals just to find out whether you should have your daughter vaccinated—isn’t asking your own doctor sufficient? Motivation is at the root of all of these types of questions. Motivation researchers are primarily concerned with the cognitive processes by which people initiate and sustain behaviors. For example, if a group of teachers indicate they decided to teach because they believe ensuring the next generation of young people enters their adult lives prepared to face the challenges of the 21st century, then these teachers are likely describing a belief in the utility of what they do. On the other hand, if a student said she decided to major in philosophy because she took introductory courses in logic and in ethics and earned superior marks in these classes, then her competence beliefs are likely the most salient aspect of her motivation. Although motivation historically has been presented in many different ways (e.g., need satisfaction, innate drives), in this chapter we frame the most commonly studied constructs of motivation as important cognitive structures and processes that guide our behaviors. We conceive of behaviors in a broad sense of the word to also include cognitive behaviors such as asking oneself whether a certain strategy is the best approach to solve a problem. This focus is in line with the purpose of this chapter and handbook—to focus on cognitive structures and processes that guide behaviors related specifically to building and evaluating knowledge. Given this focus on the cognitive basis of motivation, we then explore how motivational aspects of cognition relate to aspects of cognition that concern the nature of knowledge and knowing. Although the literature about the intersection of motivation and epistemic cognition is relatively small, scholars are becoming increasingly interested in questions such as, “why might some students refer to a politician about whether vaccines are effective and safe rather than refer to their family doctor?” At the heart of these types of questions is the assumption that cognitive behavior (including epistemic cognition) is motivated. That is, might some students refer to their teachers as the definitive source for an answer because they believe that it is not worth the time and effort to find more nuanced answers from multiple sources of information? Or might other students seek out alternative answers that are different from their textbook because they want to show off to their peers and teachers about how smart they are? To understand the linkages between motivation and epistemic cognition, however, we must first understand the theoretical frameworks that guide research in motivation as well as the empirical findings that have supported them. Motivation is a very broad construct that can include competence beliefs (i.e., “Am I able to do this task?”), value beliefs (i.e., “Do I find this task compelling?”), and goal orientations (i.e., “What is the reason I am engaging in this task?”). Given the large number of constructs included under the umbrella term of motivation, clarification is necessary regarding which constructs are typically included when researchers describe motivation. From there, we explore the studies that have examined the links between epistemic cognition and motivation, we consider ways that theory on epistemic cognition has implicitly enveloped motivational constructs, and we delineate how clear motivational constructs might inform such research. We conclude by exploring areas where future research is needed, and offer comments about the types of studies that may be productive for the field.https://scholarworks.wm.edu/bookchapters/1001/thumbnail.jp

A Survey of School Counselor Multicultural Education Behaviors and the Obstacles that Impede Them

In this study, researchers examined the frequency with which school counselors enact multicultural education behaviors and the obstacles preventing those behaviors. Using theoretical dimensions and approaches to multicultural education, they developed an instrument measuring school counseling multicultural education behavior. After pilot testing the instrument (n = 114), they distributed a refined instrument to a state school counselor database, and 594 school counselors participated in the primary data collection. Researchers used exploratory factor analysis to determine five factors comprising 72% combined variance of school counselor multicultural education behaviors. Participants enacted behaviors in two factors (Classroom Guidance with Multicultural Education Emphases and Human Relations) occasionally and behaviors in three factors (Professional Development with Multicultural Education Emphases, Knowledge Construction, and Teaching the Exceptional and Culturally Different) rarely. The most common obstacles preventing behaviors were not enough time and not needed. Researchers discuss implications for engaging in and promoting multicultural education in schools

Profiles of the Sources of Science Self-Efficacy

The purpose of this study was to investigate (a) the latent profiles that arise from middle and high school students’ (N = 1225) reported exposure to information from the four hypothesized sources of self-efficacy; (b) the relationships between these latent profiles and science self-efficacy and science achievement; and (c) the differences in latent profiles as a function of implicit theory of science ability, gender, and grade level. Results revealed that a four-class solution fit the data the best. Results support past findings indicating that mastery experiences are a powerful source of self-efficacy. Furthermore, there seemed to be an additive benefit of drawing from multiple sources simultaneously. Gender did not predict membership in these four profiles, but implicit theory of ability and grade level did. The results show that students in the most adaptive profiles drew from multiple sources of efficacy-relevant information and espoused a strong belief in the plasticity of their science abilities, whereas those who were in the least adaptive profiles exhibited a high degree of negative affect and held a fixed view of science ability

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice