The extended Theory of Planned Behaviour and college grades: the role of cognition and past behaviour in the prediction of students’ academic intentions and achievements

Understanding the underlying processes influencing college students’ academic achievement represents an important goal of educational research. The aim of the present study was to examine the utility of the extended Theory of Planned Behaviour (TPB) and the relative influence of cognitive processes and measures of past behaviour in the prediction of grade intentions and actual grades in the context of higher education. Our results show that perceived behavioural control (PBC), subjective norms (SN) and past grades emerged as significant predictors and were able to explain 41% of the variance in grade intentions, while the influence of cognitive processes (planning and future orientation) was not significant. With respect to the prediction of actual grades, we found that habitual school attendance and past grades explained 26% of the variance in actual grades. Again, the influence of cognitive processes (planning and future orientation) was not significant. The main discussion is centred on the utility of the extended TPB in the prediction of academic achievement and the complex relationship between cognitive processes and past behaviour in predicting human actions in general. Limitations and implications of the study are also discussed.     

Part I: Toward a System of Educational Engineering for Traditional Class Elements: A Case Study in an Introductory Physics Course

In this paper, we present a system for formally characterizing elements of an introductory science class, measuring the performance of a class based on this characterization, and modeling the value of the class based on the measurements. This system allows the iterative improvement of any educational presentation through a model, test, iterate cycle. We propose formal practices involved in iteratively improving an educational experience be called educational engineering.     

Early numeracy in low‐performing young children

The aim of this study was to explore the early numeracy of low‐performing young children. The mean age of the children was six years and four months. The 511 participants belonged to three groups: multi‐language children, children with special educational needs and children with average performance. The results showed that there were significant group differences in early numeracy: the children in the reference group had better relational and counting skills than those with a multi‐language background and those with special educational needs. Some differences were found in counting skills between the children with multi‐language background and those with special educational needs. Finally, (special) educational support for low‐performing children is discussed.     

Interdisciplinary scholarly collaboration in data-intensive,public-funded,international digital humanities project work

The information practices of collaborative interdisciplinary researchers, especially in distributed environments, remain understudied. Embracing the naturalistic paradigm, this qualitative case study relies upon semi-structured interviews, snowball sampling, and grounded analysis. Based on interviews with 53 participants from 11 Digging Into Data 3 (DID3, 2014-2016) projects, this study examines the working practices of scholars (library and information science professionals, humanists, and computer scientists) engaging in collaborative, international, data-intensive, publicly-funded interdisciplinary research. Benefits of such work include avoiding redundancy, exploding disciplinary silos, and more ambitious, larger-scale outputs. Challenges and lessons learned center on innovation, flexibility, and failure, translation, intermediaries, divisions of labor and delegation of responsibilities and tasks, conflict resolution, technology, outputs and publication, and timeframe, sustainability, and infrastructure.     

The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust

The online merchant of an e-marketplace consists of an intermediary, providing the market infrastructure, and the community of sellers conducting business within that infrastructure. Typically, consumers willingly buy from unknown sellers within an e-marketplace, despite the apparent risk, since they trust the institutional mechanisms furnished by the relatively well-known intermediary. Consumers’ trust in one component of the e-marketplace merchant may not only affect their trust in the other, but also influence the way consumers make online purchases. This paper explores the impact of trust on consumer behavior in e-marketplaces. An empirical study has been conducted to accomplish our research objectives, using a questionnaire survey of 222 active e-marketplace shoppers in Korea. The results reveal that consumer trust in an intermediary has a strong influence upon both attitudinal loyalty and purchase intentions, although consumer trust in the community of sellers has no significant effect on the two constructs representing consumer behavior. In addition, it was found that trust is transferred from an intermediary to the community of sellers, implying that the trustworthiness of the intermediary plays a critical role in determining the extent to which consumers trust and accept the sellers in the e-marketplace. This paper offers some implications from the findings of the research.     

Making inquiry-based science learning visible: the influence of CVS and cognitive skills on content knowledge learning in guided inquiry

ABSTRACT

Many science curricula and standards emphasise that students should learn both scientific knowledge and the skills associated with the construction of this knowledge. One way to achieve this goal is to use inquiry-learning activities that embed the use of science process skills. We investigated the influence of scientific reasoning skills (i.e. conceptual and procedural knowledge of the control-of-variables strategy) on students’ conceptual learning gains in physics during an inquiry-learning activity. Eighth graders (n?=?189) answered research questions about variables that influence the force of electromagnets and the brightness of light bulbs by designing, running, and interpreting experiments. We measured knowledge of electricity and electromagnets, scientific reasoning skills, and cognitive skills (analogical reasoning and reading ability). Using structural equation modelling we found no direct effects of cognitive skills on students’ content knowledge learning gains; however, there were direct effects of scientific reasoning skills on content knowledge learning gains. Our results show that cognitive skills are not sufficient; students require specific scientific reasoning skills to learn science content from inquiry activities. Furthermore, our findings illustrate that what students learn during guided inquiry activities becomes visible when we examine both the skills used during inquiry learning and the process of knowledge construction. The implications of these findings for science teaching and research are discussed.     

Infant Pathways to Externalizing Behavior: Evidence of Genotype × Environment Interaction

To further the understanding of the effects of early experiences, 9-month-old infants were observed during a frustration task. The analytical sample was composed of 348 linked triads of participants (adoptive parents, adopted child, and birth parent[s]) from a prospective adoption study. It was hypothesized that genetic risk for externalizing problems and affect dysregulation in the adoptive parents would independently and interactively predict a known precursor to externalizing problems: heightened infant attention to frustrating events. Results supported the moderation hypotheses involving adoptive mother affect dysregulation: Infants at genetic risk showed heightened attention to frustrating events only when the adoptive mother had higher levels of anxious and depressive symptoms. The Genotype × Environment interaction pattern held when substance use during pregnancy was considered.     

Chemistry at the Nanoscale

Traditionally the kinetics of a chemical reaction has been studied as a set of coupled ordinary differential equations. The law of mass action, a tried and tested principle for reactions involving macroscopic quantities of reactants, gives rise to deterministic equations in which the variables are species concentrations. In recent years, though, as smaller and smaller systems – such as an individual biological cell, say – can be studied quantitatively, the importance of molecular discreteness in chemical reactions has increasingly been realized. This is particularly true when the system is far from the ‘thermodynamic limit’ when the numbers of all reacting molecular species involved are several orders of magnitude smaller than Avogadro’s number. In such situations, each reaction has to be treated as a probabilistic ‘event’ that occurs by chance when the appropriate reactants collide. Explicitly accounting for such processes has led to the development of sophisticated statistical methods for simulation of chemical reactions, particularly those occurring at the cellular and sub-cellular level. In this article, we describe this approach, the so-called stochastic simulation algorithm, and discuss applications to study the dynamics of model regulatory networks.