How ‘other people matter’ in a classroom-based strengths intervention: Exploring interpersonal strategies and classroom outcomes

Interventions that identify and develop character strengths have been shown to benefit well-being, academic engagement and achievement. Strengths research within positive psychology has focused primarily on individual outcomes with less attention on group or relationships effects. This study (N?=?193) examined the effect of a classroom-based strengths intervention on class cohesion and friction, relatedness, engagement, well-being and strengths use. A six-session programme was trialled with 9–12-year-old students. Students learned to recognise strengths and practised strengths-related goal setting. At three-months post-test, multi-level modelling indicated that intervention group participants scored significantly higher on class cohesion and relatedness need satisfaction, and lower on class friction than the non-randomised control group. Programme participants also reported higher levels of positive affect, classroom engagement, autonomy need satisfaction and strengths use. School-based strengths interventions may influence individual perceptions of class climate, engagement and student relatedness in addition to individual well-being.     

Managing Religion and the Judicialization of Religious Freedom

Religious freedom is a highly valued goal for many citizens and political leaders around the world, especially in Western‐oriented nations. Much ink has been spilled in defense of religious freedom and many have waxed eloquent about the virtues of promoting religious freedom. Most constitutions and other international documents around the world guarantee religious freedom even if those guarantees are sometimes honored in the breach. Why this focus on religious freedom is occurring and how it is being addressed are the focus of this article.     

Conceptions of learning and approaches to studying among White and ethnic minority students in distance education

Background. The attainment of White students at UK institutions of higher education tends to be higher than that of students from other ethnic groups, but the causes of this are unclear. Aims. This study compared White students and students from other ethnic groups in their conceptions of learning, their approaches to studying, and their academic attainment. Sample. A stratified sample of 1,146 White students and 1,146 students from other ethnic groups taking courses by distance learning with the UK Open University. Methods. The Mental Models section of the Inventory of Learning Styles and the Revised Approaches to Studying Inventory were administered in a postal survey. Results. The students' questionnaire scores were contaminated by response bias, which varied across different ethnic groups. When adjusted to control for response bias, the scores on the two questionnaires shared 37.2% of their variance and made a significant contribution to predicting the students' attainment. White students were more likely to exhibit a meaning‐directed learning pattern, whereas Asian and Black students were more likely to exhibit a reproduction‐directed learning pattern. However, the variation in attainment across different ethnic groups remained significant when their questionnaire scores and prior qualifications were taken into account. Conclusions. There is a strong relationship between students' conceptions of learning and their approaches to studying, and variations in conceptions of learning in different ethnic groups give rise to variations in approaches to studying. However, factors other than prior qualifications and conceptions of learning are responsible for variation in attainment across different ethnic groups.     

Estimation and visualization of confusability matrices from adaptive measurement data

We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach.We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme-grapheme connections) and larger units of language is visualized in an easily understood way with color cues and explicit indication of the accuracy of the estimates. Visualization of learning-related changes in the player’s performance is considered.The empirical validity of the choice axiom is evaluated using the game data itself. The axiom appears to hold reasonably well although a small systematic violation is observable for the smallest choice-set sizes.     

First graders' literacy and self-regulation gains: The effect of individualizing student instruction

We examined the effect of individualizing student instruction (ISI; N = 445 students, 46 classrooms) on first graders' self-regulation gains compared to a business-as-usual control group. Self-regulation, conceptualized as a constellation of executive skills, was positively associated with academic development. We hypothesized that the ISI intervention's emphasis on teacher planning and organization, classroom management, and the opportunity for students to work independently and in small groups would promote students' self-regulation. We found no main effect of ISI on self-regulation gains. However, for students with weaker initial self-regulation, ISI was associated with greater self-regulation gains compared to peers in control classrooms. The ISI effect on self-regulation was greater when the intervention was more fully implemented.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

How to have a radically minimal ontology

In this paper I further elucidate and defend a metaontological position that allows you to have a minimal ontology without embracing an error-theory of ordinary talk. On this view ‘there are Fs’ can be strictly and literally true without bringing an ontological commitment to Fs. Instead of a sentence S committing you to the things that must be amongst the values of the variables if it is true, I argue that S commits you to the things that must exist as truthmakers for S if it is true. I rebut some recent objections that have been levelled against this metaontological view.     

Public Storylines in the British Transition from Rail to Road Transport (1896–2000): Discursive Struggles in the Multi-Level Perspective

ABSTRACT

An analysis of the transition from railways to highways as the dominant British transport system during the twentieth century shows that public storylines about competing niche and regime technologies can have a powerful influence on socio-technical transitions. These storylines are developed by supporters and opponents of the competing technologies, with each group attempting to frame their favoured technology positively. The public salience of these storylines can be evaluated by assessing how highly they score on four elements of frame resonance: empirical fit, experiential commensurability, actor credibility, and macro-cultural resonance. These storylines can be seen at play across the entirety of the transition to a road-based transport system, from the very early history of the automobile through to the turn of the millennium, when public opposition to road transport was becoming increasingly pronounced. This case study uniquely traces discursive conflict over the entire course of a multi-decade transition. While existing literature in the multi-level perspective typically emphasises the disadvantages faced by niche-innovations, this case study shows that powerful storylines, enabled by the right cultural repertoires and possibly negative storylines about existing socio-technical systems, can create powerful political support for a new technology, giving it an advantage against more established incumbents.     

Modeling the Dynamics of Risky Choice

Individuals make decisions under uncertainty every day. Decisions are based on incomplete information concerning the potential outcome or the predicted likelihood with which events occur. In addition, individuals' choices often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision making are difficult to capture using conventional, linear mathematical models. Here, we present data from a 2-choice task with variable risk between sure loss and risky loss to illustrate how a simple nonlinear dynamical system can be employed to capture the dynamics of human decision making under uncertainty (i.e., multistability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlinear complexities of human decision making are discussed as well as the degree to which the theory of nonlinear dynamical systems might offer an alternative framework for understanding human decision making processes.     

Age Discrimination in the Evaluation of Job Applicants

This study investigated the nature of age discrimination against older job applicants. One hundred fifty‐six participants (102 students; 54 organization based) evaluated a hypothetical job applicant's (aged 33–66 years) work‐related competences and likelihood of being hired. Applicant age affected hiring decisions for both samples where there was a preference for hiring applicants aged 42–48 years. Applicants at both the older and younger ends of the continuum were less likely to be hired, with the oldest applicants (over 54 years) being the least likely to be hired. Although the applicants' age negatively affected evaluations of their trainability and sociability, the effect of applicant age on hiring evaluations was not mediated by these work‐related competencies, suggesting that age discrimination occurs via direct bias against older workers.