Search

This empirical study, set in secondary school classrooms, examines theoretical constructs around ownership practices: looking at how personal significance and interest affect effort investment, how the role of the teacher and contextual approaches to learning provide incentives to pursue ownership and how active possession can be enhanced through opportunities for self-determined choice with tools to understand learning strategies. This study focuses mainly on the initial stages of nurturing skills towards a sense of ownership and is more about building confidence in taking steps towards controlling learning through self-regulation, process orientated approaches to improvement and help seeking strategies. Sample groups from six domains undertook the full study providing data through questionnaires completed by students at the beginning and end of the study, semi-structured interviews and observations to monitor interventions implemented by teachers, and reflective group interviews with students at the end of the study. Based on the data collected by the questionnaires and interviews, the strongest contributors to perceived change were: setting learning activities into the context of personal goals and involving students in co-constructing new knowledge; provision of choice, expectations of a readiness to learn, scaffolding and tools for managing tasks proactively; personal impact on achievement through proactive help-seeking; and action based process orientated feedback through honest, positive appraisal. The evidence provided by the data suggested that student’ attitudes towards taking ownership for their learning altered as a consequence of the interventions and the impact was not related to gender, IQ or social background. Teachers reported changes in motivation towards taking ownership in individual students, higher levels of achievement being attained than previously expected, and positive changes in whole class attitudes and learning behaviours. Taken as a whole, the data suggested a positive trend towards improvements in the quality of learning in the classroom and students taking greater active possession (ownership) of their learning.

Salinity intrusion into agricultural lands in rice-producing countries around the world has become a serious threat to food security. Currently, more than 960 million hectares of productive land is adversely affected by salinity and is expected to grow in the future. This problem is exacerbated by the projected increase in world population from the current 7.2 to 9.6 billion by 2050. The cultivated rice (Oryza sativa) tolerates 10 dSm-1. This study used morpho-physiological methods including gas exchange measurement, microelectrode ion flux estimation (MIFE) and agronomic measurements to screen salinity tolerance levels of two Japonica (Koshihikari and Reiziq) rice genotypes and one Indica (Doongara) genotype. In addition, this is the first time that four basic ion fluxes were measured using MIFE in both glasshouse and field conditions to screen rice for salinity tolerance. The effects of salinity stress on photosynthetic activities, ion fluxes and growth parameters of the three rice genotypes under glasshouse conditions and in the field were also examined. Variations in the response of the three cultivars to salinity stress were found, thus providing evidence that morpho-physiological basis of salinity stress tolerance can be applied to improve the salinity stress tolerance of this important crop. This may allow the exploitation of salt affected marginal lands and could positively contribute to global food security.

Many real-world decision processes require solving optimization problems which may involve different types of constraints such as inequality and equality constraints. The hurdles in solving these Constrained Optimization Problems (COPs) arise from the challenge of searching a huge variable space in order to locate feasible points with acceptable solution quality. Over the last decades Evolutionary Algorithms (EAs) have brought a tremendous advancement in the area of computer science and optimization with their ability to solve various problems. However, EAs have inherent difficulty in dealing with constraints when solving COPs. This thesis presents a new Agent-based Memetic Algorithm (AMA) for solving COPs, where the agents have the ability to independently select a suitable Life Span Learning Process (LSLP) from a set of LSLPs. Each agent represents a candidate solution of the optimization problem and tries to improve its solution through cooperation with other agents. Evolutionary operators consist of only crossover and one of the self-adaptively selected LSLPs. The performance of the proposed algorithm is tested on benchmark problems, and the experimental results show convincing performance. The quality of individuals in the initial population influences the performance of evolutionary algorithms, especially when the feasible region of the constrained optimization problems is very tiny in comparison to the entire search space. This thesis proposes a method that improves the quality of randomly generated initial solutions by sacrificing very little in diversity of the population. The proposed Search Space Reduction Technique (SSRT) is tested using five different existing EAs, including AMA, by solving a number of state-of-the-art test problems and a real world case problem. The experimental results show SSRT improves the solution quality, and speeds up the performance of the algorithms. The handling of equality constraints has long been a difficult issue for evolutionary optimization methods, although several methods are available in the literature for handling functional constraints. In any optimization problems with equality constraints, to satisfy the condition of feasibility and optimality the solution points must lie on each and every equality constraint. This reduces the size of the feasible space and makes it difficult for EAs to locate feasible and optimal solutions. A new Equality Constraint Handling Technique (ECHT) is presented in this thesis, to enhance the performance of AMA in solving constrained optimization problems with equality constraints. The basic concept is to reach a point on the equality constraint from its current position by the selected individual solution and then explore on the constraint landscape. The technique is used as an agent learning process in AMA. The experimental results confirm the improved performance of the proposed algorithm. This thesis also proposes a Modified Genetic Algorithm (MGA) for solving COPs with equality constraints. After achieving inspiring performance in AMA when dealing with equality constraints, the new technique is used in the design of MGA. The experimental results show that the proposed algorithm overcomes the limitations of GA in solving COPs with equality constraints, and provides good quality solutions.