A systems approach to students' motivation and academic achievement

This research followed earlier work (reported in a thesis presented in 1970) on factors associated with the academic performance of a sample of technical college students, which recommended the further study of students' motivation. The technical college then became part of a polytechnic, but the courses chosen for the continuation of the research were all of a specifically vocational character. The approach was influenced by Angyal (1941) in seeking to relate symbolic processes to broader behaviour patterns within a systems framework. Forms of semantic differential were developed to obtain the students' responses to words representing various activities and various people both within and outside the academic environment. Also, a "!growth motivation questionnaire" was produced using ideas from self-actualisation, job satisfaction and expectancy theory and examination marks were recorded. From pre-coded responses to the growth motivation questionnaire, scores on a 'study satisfaction' factor were calculated, and subsamples of students were taken at the extremes of this scale. Wriitten responses from the same questionnaire and semantic differential factor scores showed contrasting patterns between the two subsamples. Interpretation of these patterns suggested a diversity of approach to academic work among the students which calls for greater flexibility in the educational system serving them.

Self-determination theory and student motivation in social studies in an urban Broward County public high school

This study investigated how students perceived their motivation in high school social studies classes in school and to determine if a correlation exists between students’ grade level, race, gender, and their motivation. The sample included 337 high school students in Broward County, Florida. To assess students’ perceptions on their motivation the academic self-regulation questionnaire was utilized. Results indicate that social studies students show high levels of external regulation, with a mean score at 22.31 on a scale of 36 points. The results show a mean score of 24 on a scale of 28 points for identified regulation among social studies students. Findings revealed that student motivation could be gauged. No statistical significance was found between high school students’ grade level, race, gender, and their motivation in social studies classes. The findings of this study have shown that students at Boyd H. Anderson High School want to learn social studies.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

The goal orientations scale (GOS): Validation for portuguese students

Current research on achievement goals acknowledges that students can manifest different goal patterns. This study aimed to adapt and validate a self-report scale to assess the goal orientations of Portuguese students. A total of 2675 (age range 9–24 years) Portuguese students completed the Goal Orientations Scale (GOS). Through a cross-validation procedure, confirmatory factor analysis and descriptive statistics supports the existence of four different goal orientations: task, self-enhancing, self-defeating and avoidance orientations. The reliability and the internal validity estimates confirm that the GOS is an adequate instrument in assessing student goal orientations.

Feasibility, design and conduct of a pragmatic randomized controlled trial to reduce overweight and obesity in children: the electronic games to aid motivation to exercise (eGAME) study

BACKGROUND: Childhood obesity has reached epidemic proportions in developed countries. Sedentary screen-based activities such as video gaming are thought to displace active behaviors and are independently associated with obesity. Active video games, where players physically interact with images onscreen, may have utility as a novel intervention to increase physical activity and improve body composition in children. The aim of the Electronic Games to Aid Motivation to Exercise (eGAME) study is to determine the effects of an active video game intervention over 6 months on: body mass index (BMI), percent body fat, waist circumference, cardio-respiratory fitness, and physical activity levels in overweight children.

METHODS/DESIGN: Three hundred and thirty participants aged 10-14 years will be randomized to receive either an active video game upgrade package or to a control group (no intervention).

DISCUSSION: An overview of the eGAME study is presented, providing an example of a large, pragmatic randomized controlled trial in a community setting. Reflection is offered on key issues encountered during the course of the study. In particular, investigation into the feasibility of the proposed intervention, as well as robust testing of proposed study procedures is a critical step prior to implementation of a large-scale trial.

Can a health coaching intervention delivered during pregnancy help prevent excessive gestational weight gain?

This study evaluated: (1) the efficacy of a health coaching (HC) intervention designed to prevent excessive gestational weight gain (GWG); and (2) whether there were improved psychological, motivational, and behavioural outcomes for women in the HC intervention compared to a "usual care" control group. In this quasi-experimental study, 267 pregnant women ≤18 weeks gestation were recruited between August 2011 and June 2013 from two hospital antenatal clinics in Melbourne, Australia. Intervention women received four individual HC and two group HC/educational sessions informed by theories of behaviour change. Women completed questionnaires assessing psychological, motivational and behavioural outcomes at 16-18 (baseline) and 33 (post-intervention) weeks gestation. Weight measures were collected. Compared to usual care, the intervention did not limit GWG or prevent excessive GWG. However, HC women reported greater use of active coping skills post-intervention. Despite lack of success of the HC intervention, given the risks associated with excessive weight gain in pregnancy, health professionals should continue to recommend appropriate GWG.

A review of the game-centred approaches to teaching and coaching literature since 2006

Background: In 2006, Oslin and Mitchell published a review of the game-centred approaches (GCAs) to teaching and coaching literature highlighting a number of core concepts thought to provide justification for the use of GCAs including (a) its potential to enhance participant motivation, (b) potential for tactical transfer, and (c) development of decision-making skills and effective decision-makers. Oslin and Mitchell also suggested recommendations for future GCA research.Purpose: The purpose of this paper was threefold: (a) to present a review of Anglophone research into GCAs building on the previous review of Oslin and Mitchell published in 2006; (b) to identify new trends in research since 2006; and (c) to investigate the extent to which the initial suggestions and future research directions suggested by Oslin and Mitchell have been addressed.Data collection: GCA literature since 2006 was searched systematically using a three-phase approach. Phase 1 included initial searches of the EBSCO database using terms associated with GCAs and their acronyms (e.g. TGfU (teaching games for understanding), GS (Game Sense), etc.). Phase 2 expanded the search adopting more generic terms from keywords located in the recent literature (e.g. teaching games, tactical development, game performance, etc.). Multiple searches through the EBSCO database were conducted, whereby key terms were cross-referenced until a saturation point was reached. Phase 3 involved removing those publications that were not empirical, peer reviewed, intervention studies or published in English.Findings: Forty-four studies on GCA implementation were identified and the methodological and substantive nature of these studies was examined. The review noted two positive trends: (a) the expansion of research which included the growth of research on GCAs in Europe and Southeast Asia and (b) an increased amount of research in the affective domain. The review found, however, that a number of key challenges remain within GCA research, which include (a) the need for improved articulation of GCA verification procedures; (b) further assessment of tactical awareness development; (c) extended inquiry about GCAs in coaching contexts; (d) more research into ‘newer’ GCAs (i.e. PP (play practice), IGCM (invasion game competence model) and TDLM (tactical decision learning model)); (e) use of longitudinal research designs; (f) inadequate length of GCA induction and training for teachers and coaches, and (g) examination of GCAs in terms of fitness and special populations.Conclusions: GCA pedagogies are of significant importance as they have the potential to promote change within current adult-centric cultures of youth sport and encourage engagement in physical activity over the life course. To meet these needs, it is recommended that GCA research undergo continued expansion with the use of research designs and data collection techniques that aid the examination of different philosophical understandings of GCAs (e.g. ethnographic, phenomenological and psycho-phenomenological). These are paramount to the exploration of ‘who the individual is’ and ‘how the learner is motivated to continue to participate’ and further permit the in-depth, contextual and ecological analysis of GCA interventions that Oslin and Mitchell recommended in their previous review.

Implementation of innovative technologies in small-scale construction firms: five Australian case studies

Purpose - The users of construction technologies such as builders and trades people have been acknowledged as sources of potentially important innovations. These innovations may be in the form of safer, less labour intensive, or cheaper methods and processes. The purpose of this paper is to assess whether the Australian construction industry is providing an environment where user-based innovation is being supported and implemented. Design/methodology/approach - An explorative study was undertaken to provide an insight into actual experiences of the implementation of user-based innovation. The data were collected through faceto- face semi-structured interviews providing case studies on multiple aspects of the implementation of innovative construction technologies. The cases involved a cross section of advances, including product, tool, and system technologies. Findings - The main motivation behind developing the technologies was problem solving. The associated industries of manufacturing and retail, as well as consultants within the construction industry present the greatest barriers to implementation. Originality/value - This research provides a better understanding of the factors that are preventing the successful implementation of user-based innovative construction technologies in small firms.

Scaling-up an efficacious school-based physical activity intervention: study protocol for the 'Internet-based professional learning to help teachers support activity in youth' (iPLAY) cluster randomized controlled trial and scale-up implementation evaluat

BACKGROUND: Despite the health benefits of regular physical activity, most children are insufficiently active. Schools are ideally placed to promote physical activity; however, many do not provide children with sufficient in-school activity or ensure they have the skills and motivation to be active beyond the school setting. The aim of this project is to modify, scale up and evaluate the effectiveness of an intervention previously shown to be efficacious in improving children's physical activity, fundamental movement skills and cardiorespiratory fitness. The 'Internet-based Professional Learning to help teachers support Activity in Youth' (iPLAY) study will focus largely on online delivery to enhance translational capacity.

METHODS/DESIGN: The intervention will be implemented at school and teacher levels, and will include six components: (i) quality physical education and school sport, (ii) classroom movement breaks, (iii) physically active homework, (iv) active playgrounds, (v) community physical activity links and (vi) parent/caregiver engagement. Experienced physical education teachers will deliver professional learning workshops and follow-up, individualized mentoring to primary teachers (i.e., Kindergarten - Year 6). These activities will be supported by online learning and resources. Teachers will then deliver the iPLAY intervention components in their schools. We will evaluate iPLAY in two complementary studies in primary schools across New South Wales (NSW), Australia. A cluster randomized controlled trial (RCT), involving a representative sample of 20 schools within NSW (1:1 allocation at the school level to intervention and attention control conditions), will assess effectiveness and cost-effectiveness at 12 and 24 months. Students' cardiorespiratory fitness will be the primary outcome in this trial. Key secondary outcomes will include students' moderate-to-vigorous physical activity (via accelerometers), fundamental movement skill proficiency, enjoyment of physical education and sport, cognitive control, performance on standardized tests of numeracy and literacy, and cost-effectiveness. A scale-up implementation study guided by the RE-AIM framework will evaluate the reach, effectiveness, adoption, implementation, and maintenance of the intervention when delivered in 160 primary schools in urban and regional areas of NSW.

DISCUSSION: This project will provide the evidence and a framework for government to guide physical activity promotion throughout NSW primary schools and a potential model for adoption in other states and countries.