The challenges of integrating the discussion of controversial issues in initial teacher training

In the framework of the Bologna process, and with regard to pre-service teacher education, it is necessary to model student-centred learning experiences in order to promote the required competences for future professional practice and critical participation in society. Despite the potential of discussion in promoting several competences, this methodology does not always integrate the teaching practices. This case study sought to: a) understand the experiences and views of future teachers from a School of Education on the use of discussion in their past education; and b) investigate the impact of an educational experience centred on discussion. Data were collected through narratives, questionnaires, interviews and participant observation. The learning situations experienced through this study contributed to the development of citizens more aware of their role in society and allowed the promotion of skills indispensable for an Elementary Education teacher.

The challenges of integrating the discussion of controversial issues in initial teacher training

In the framework of the Bologna process, and with regard to pre-service teacher education, it is necessary to model student-centred learning experiences in order to promote the required competences for future professional practice and critical participation in society. Despite the potential of discussion in promoting several competences, this methodology does not always integrate the teaching practices. This case study sought to: a) understand the experiences and views of future teachers from a School of Education on the use of discussion in their past education; and b) investigate the impact of an educational experience centred on discussion. Data were collected through narratives, questionnaires, interviews and participant observation. The learning situations experienced through this study contributed to the development of citizens more aware of their role in society and allowed the promotion of skills indispensable for an Elementary Education teacher.

A model of macadamia with application to pruning in orchards

A self-organising model of macadamia, expressed using L-Systems, was used to explore aspects of canopy management. A small set of parameters control the basic architecture of the model, with a high degree of self-organisation occurring to determine the fate and growth of buds. Light was sensed at the leaf level and used to represent vigour and accumulated basipetally. Buds also sensed light so as to provide demand in the subsequent redistribution of the vigour. Empirical relationships were derived from a set of 24 completely digitised trees after conversion to multiscale tree graphs (MTG) and analysis with the OpenAlea software library. The ability to write MTG files was embedded within the model so that various tree statistics could be exported for each run of the model. To explore the parameter space a series of runs was completed using a high-throughput computing platform. When combined with MTG generation and analysis with OpenAlea it provided a convenient way in which thousands of simulations could be explored. We allowed the model trees to develop using self-organisation and simulated cultural practices such as hedging, topping, removal of the leader and limb removal within a small representation of an orchard. The model provides insight into the impact of these practices on potential for growth and the light distribution within the canopy and to the orchard floor by coupling the model with a path-tracing program to simulate the light environment. The lessons learnt from this will be applied to other evergreen, tropical fruit and nut trees.

The natural pilot model for flight proficiency evaluation

Cover title.

Examining barriers to internationalisation created by diverse systems and structures in vocational education and training

In a global society, all educational sectors need to recognise internationalism as a core, foundational principle. Whilst most educational sectors are taking up that challenge, vocational education and training (VET) is still being pulled towards the national agenda in terms of its structures and systems, and the policies driving it, disadvantaging those who graduate from VET, those who teach in it, and the businesses and countries that connect with it. This paper poses questions about the future of internationalisation in the sector. It examines whether there is a way to create a VET system that meets its primary point of value, to produce skilled workers for the local labour market, while still benefitting those graduates by providing international skills and knowledge, gained from VET institutions that are international in their outlook. The paper examines some of the key barriers created by systems and structures in VET to internationalisation and suggests that the efforts which have been made to address the problem have had limited success. It suggests that only a model which gives freedom to those with a direct vested interest, students, teachers, trainers and employers, to pursue international co-operation and liaison will have the opportunity to succeed. (DIPF/Orig.)

EDUCATION, TRAINING AND AWARENESS OF LAWS AS DETERMINANTS OF COMPLIANCE WITH PLANT PROTECTION LAW: THE CASE OF PESTICIDE USE PRACTICES IN TANZANIA

Pesticide residues in food and environment pose serious health risks to human beings. Plant protection laws, among other things, regulate misuse of agricultural pesticides. Compliance with such laws consequently reduces risks of pesticide residues in food and the environment. Studies were conducted to assess the compliance with plant protection laws among tomato farmers in Mvomero District, Morogoro Region, Tanzania. Compliance was assessed by examining pesticide use practices that are regulated by the Tanzanian Plant Protection Act (PPA) of 1997. A total of 91 tomato farmers were interviewed using a structured questionnaire. Purposive sampling was used in selecting at least 30 respondent farmers from each of the three villages of Msufini, Mlali and Doma in Mvomero District, Morogoro Region. Simple Random Sampling was used to obtain respondents from the sampling frame. Individual and social factors were examined on how they could affect pesticide use practices regulated by the law. Descriptive statistics, mainly frequency, were used to analyze the data while associations between variables were determined using Chi-Square and logistic regression model. The results showed that respondents were generally aware of the existence of laws on agriculture, environment and consumer health, although none of them could name a specific Act. The results revealed further that 94.5% of the farmers read instructions on the pesticides label. However, only 21% used the correct doses of pesticides, 40.7% stored pesticides in special stores, 68.1% used protective gear, while 94.5% always read instructions on the label before using a pesticide product. Training influenced the application rate of pesticide (p < 0.001) while awareness of agricultural laws significantly influenced farmers’ tendency to read information on the labels (p < 0.001). The results showed further that education significantly influenced the use of protective gears by farmers (p = 0.042). Education also significantly affected the manner in which farmers stored pesticide-applying equipment (p = 0.024). Furthermore, farmers’ awareness of environmental laws significantly (p = 0.03) affected farmers’ disposal of empty pesticide containers. Results of this study suggest the need for express provisions on safe use and handling of pesticides and related offences in the Act, and that compliance should be achieved through education rather than coercion. Results also suggest establishment of pesticide disposal mechanisms and structures to reduce unsafe disposal of pesticide containers. It is recommended that farmers should be educated and trained on proper use of pesticides. Farmers’ awareness on laws affecting food, environment and agriculture should be improved.

Clinical education reflective ecological model for health science majors

The clinical education is an integral part of the Health Science majors’ curriculum programs of the University of Aveiro’s School of Health (i.e., Nursing, Physical Therapy, Radiology, Radiotherapy and Speech-Language Pathology) and aims to develop clinical competences in order to generate excellent health care professionals. The organization was based on the Ecological Model of Clinical-Reflective Training, which was characterized by inter-institutional interaction and student’s reflection on actions on a professional setting. This study encompassed two moments of clinical internships in the Nursing, Physical Therapy, Radiology and Radiotherapy majors. The Clinical Internship I provided the 123 students with a global view of the health care professional activities. The Clinical Internship II, with 119 students, developed competences of each health professional. Questionnaires with categorical scales from 1 to 5 evaluated the organization and efficiency of the two internships. The results revealed averages over 3 in all items. In conclusion, the Ecological Model of Clinical-Reflective Training was well accepted by students and clinical supervisors. Applications in the health care area were demonstrated.

Counseling Self-Efficacy and the Relational Efficacy Model

Relation-inferred self-efficacy (RISE), a relatively new concept, is defined as a target individual’s beliefs about how an observer, often a relationship partner, perceives the target’s ability to perform certain actions successfully. Along with self-efficacy (i.e., one’s beliefs about his or her own ability) and other-efficacy (i.e., one’s beliefs about his or her partner’s ability), RISE makes up a three part system of interrelated efficacy beliefs known as the relational efficacy model (Lent & Lopez, 2002). Previous research has shown this model to be helpful in understanding how relational dyads, including coach-athlete, advisor-advisee, and romantic partners, contribute to the development of self-efficacy beliefs. The clinical supervision dyad (i.e., supervisor-supervisee), is another context in which relational efficacy beliefs may play an important role. This study investigated the relationship between counseling self-efficacy, RISE, and other-efficacy within the context of clinical supervision. Specifically, it examined whether supervisee perceptions about how their supervisor sees their counseling ability (RISE) related to how supervisees see their own counseling ability (counseling self-efficacy), and what moderates this relationship. The study also sought to discover the degree to which RISE mediated the relationship between supervisor working alliance and counseling self-efficacy. Data were collected from 240 graduate students who were currently enrolled in counseling related fields, working with at least one client, and receiving regular supervision. Results demonstrated that years of experience and RISE predicted counseling self-efficacy and that the relationship between RISE and counseling self-efficacy was, as expected, moderated by other-efficacy. Contrary to expectations, however, counseling experience and level of client difficulty did not moderate the relationship between RISE and counseling self-efficacy. These findings suggest that the relationship between RISE and counseling self-efficacy was stronger when supervisees saw their supervisors as capable therapists. Furthermore, RISE was found to fully mediate the relationship between supervisor working alliance and counseling self-efficacy. Future research directions and implications for training and supervision are discussed.

THE INFLUENCE OF MOTIVATION ON EMOTION REGULATION AND MOTOR PERFORMANCE: EXAMINATION OF A NEURO-AFFECTIVE MODEL

Mental stress is known to disrupt the execution of motor performance and can lead to decrements in the quality of performance, however, individuals have shown significant differences regarding how fast and well they can perform a skilled task according to how well they can manage stress and emotion. The purpose of this study was to advance our understanding of how the brain modulates emotional reactivity under different motivational states to achieve differential performance in a target shooting task that requires precision visuomotor coordination. In order to study the interactions in emotion regulatory brain areas (i.e. the ventral striatum, amygdala, prefrontal cortex) and the autonomic nervous system, reward and punishment interventions were employed and the resulting behavioral and physiological responses contrasted to observe the changes in shooting performance (i.e. shooting accuracy and stability of aim) and neuro-cognitive processes (i.e. cognitive load and reserve) during the shooting task. Thirty-five participants, aged 18 to 38 years, from the Reserve Officers’ Training Corp (ROTC) at the University of Maryland were recruited to take 30 shots at a bullseye target in three different experimental conditions. In the reward condition, $1 was added to their total balance for every 10-point shot. In the punishment condition, $1 was deducted from their total balance if they did not hit the 10-point area. In the neutral condition, no money was added or deducted from their total balance. When in the reward condition, which was reportedly most enjoyable and least stressful of the conditions, heart rate variability was found to be positively related to shooting scores, inversely related to variability in shooting performance and positively related to alpha power (i.e. less activation) in the left temporal region. In the punishment (and most stressful) condition, an increase in sympathetic response (i.e. increased LF/HF ratio) was positively related to jerking movements as well as variability of placement (on the target) in the shots taken. This, coupled with error monitoring activity in the anterior cingulate cortex, suggests evaluation of self-efficacy might be driving arousal regulation, thus affecting shooting performance. Better performers showed variable, increasing high-alpha power in the temporal region during the aiming period towards taking the shot which could indicate an adaptive strategy of engagement. They also showed lower coherence during hit shots than missed shots which was coupled with reduced jerking movements and better precision and accuracy. Frontal asymmetry measures revealed possible influence of the prefrontal lobe in driving this effect in reward and neutral conditions. The possible interactions, reasons behind these findings and implications are discussed.

Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Personality and Performance: Assessing the Mediating Role of Mental Model Formation in the Personality-Performance Relationship

Personality has long been linked to performance. Evolutions in this relationship have brought forward new questions regarding the true nature of how personality impacts performance. Both direct and indirect relationships have been proven significant. This study further investigated potential indirect relationships by including a mediating variable, mental model formation, in the personality-performance relationship. Undergraduate students were assessed in a 6-week period, Time 1 - Time 2 experiment. Conceptualizations of personality included measures of the Big 5 model and Self-efficacy, with performance measured by content quiz and overall course scores. Findings showed that the Big 5 personality traits, extraversion and agreeableness, positively and significantly impacted commonality with the instructor’s mental model. However, commonality with the instructor’s mental model did not impact performance. In comparison, commonality with an expert mental model positively and significantly impacted performance for both the content quiz and overall course score. Furthermore, similarity with an expert mental model positively and significantly impacted overall course performance. Hypothesized full mediation of mental model formation for the personality-performance relationship was not supported due to a lack of direct effect relationships required for mediation. However, a revised conceptualization of results emerged. Findings from the current study point to the novel and unique role mental models play in the personality-performance relationship. While personality traits do impact mental model formation, accuracy in the mental models formed is critical to performance.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

The relationship between perceived training and development and employee retention:the mediating role of work attitudes

This paper considers how utilizing a model of job-related affect can be used to explain the processes through which perceived training and development influence employee retention. We applied Russell’s model of core affect to categorize four different forms of work attitude, and positioned these as mediators of the relationship between perceived training and development and intention to stay. Using data from 1,191 employees across seven organizations, multilevel analyses found that job satisfaction, employee engagement, and change-related anxiety were significantly associated with intention to stay, and fully mediated the relationship between perceived training and development and intention to stay. Contrary to our hypotheses, emotional exhaustion was not significantly associated with intention to stay nor acted as a mediator when the other attitudes were included. These findings show the usefulness of Russell’s model of core affect in explaining the link between training and development and employee retention. Moreover, the findings collectively suggest that studies examining employee retention should include a wider range of work attitudes that highlight pleasant forms of affect.