A year long learning strategy course was designed and embedded in a Year 8 science curriculum. The Science Learning Strategy (SLS) program aimed to improve student ability to apply learning strategies to science, increase student achievement in science and to augment students' feelings of control over their science learning, so that their perceived competence was maximised. Achievement of these aims was monitored by collecting perceptions from students, parents and the teacher/researcher via a range of devices including questionnaires, work samples and interviews. The program overall was regarded as successfully achieving all three aims by 22 of the 24 students. The other two students found that only some aspects of the course were helpful, and felt they had learned little from the program. Thirty three percent of parents attributed positive changes in their daughter's study and learning strategies to participation in the SLS program (the remainder were unsure, or did not know of any changes). In relation to perception of academic performance, 38% of the parents interviewed believed that the SLS had a positive effect on their daughter's achievement in science. Several of these parents reported very positive effects on performance. The remainder were not sure or did not know if there had been any positive effects. No parents mentioned that the SLS program had caused a drop in science performance. The proportion of parents believing their daughters blamed disappointing results on factors they couldn't control dropped from 45% at the start of the year, to 22% by the end of the SLS program. After the intervention, 33% of parents reported that their daughters had come to believe that their science performance was affected by many factors, most of which they could control. / The teacher/researcher observed strong improvement in student ability to apply learning strategies to science as a result of participation in the program. Students were also observed by the teacher/researcher, to have been assisted by the intervention to realise that their science performance was governed not only by their natural ability, but also by factors such as studying behaviour and affective influences. In particular, the program appeared to the teacher/researcher to have helped students realise that they had control over their use of learning strategies, and that this control could influence their science performance. However, the teacher/researcher found that no statistically significant changes in science achievement resulted from student participation in the SLS course. The other objective of the research was to investigate the extent to which learning strategy education was valued and implemented by Western Australian science teachers. The 218 returned surveys revealed that most respondents recognised the need to teach these skills during science lessons. Seventy six percent of respondents considered the delivery of learning strategy instruction in the lower school science classroom to be as important, or more important, than teaching subject processes and content. Sixty seven percent recognised that improving study strategies can improve confidence and/or motivation. / Many teachers, however, had not been able to convert these views into consistent classroom practice. A moderate proportion of teachers reported teaching a variety of learning strategies; 74% of the respondents agreed that learning strategy instruction could improve performance in science. Only one teacher specifically mentioned incorporating the teaching of learning strategies with instruction in science process and content. As a future outcome of this project, the teacher/researcher will encourage other teachers to embed learning strategy instruction within the science curriculum, so that their students come to feel more in control of their learning and can learn more effectively.

The project was concerned with action research aimed at improving the range and effectiveness of reading-to-learn. Students (Advanced Level and Undergraduates) report reading-to-learn problems but they are only vaguely aware of the cognitive organisation (intuitive tactics and strategy) which underlies and structures their reading behaviour. The research emphasis was therefore primarily learner oriented. 2. This approach clarified such issues as: (i) Learner interpretation of instructional directives to learn for specific tasks. (ii) Learner methods of translating the task definition into an operational plan for reading. (iii) The systematic relationship between the tactics and strategies of reading (the time-structure of reading behaviour), and the variety of reading outcomes, within sentence, paragraph and chapter sized texts. (iv) Training procedures (incorporating feedback of performance) by which a student can explore now tactics of reading-for-learning. 3. This approach has required the development of three now techniques: a) A method for recording reading behaviour. b) A method by which the ‘structure of a text’ can be systematically described. c) A system of training procedures for encouraging students to develop more effective methods of reading-for-learning. 4. The empirical data showed that there were two related aspects in developing more effective reading-for-learning; the first was to develop a clearer definition of instructional directives and the second was the ability to translate these into effective operational plans. As a result of individual differences in cognitive structure and skill, students differ in their operational task definition in relation to specific learning outcomes. The plans of a 'beginner' or an 'expert' may bring about the same outcome but they differ considerably. Students also differ in their training needs within a training procedure for reading-to-learn effectively. This emphasises the need to level a hierarchically organised learner-controlled programme of self-diagnosis and training. 5. The theoretical outcome of the research was a tentative model of the student learning by reading. This model is based on the concept of a dynamic interaction between the learner's cognitive structure and skill, the learner's task definition and how this becomes operational, and the syntactic and semantic structure of the text. The model can be considered as a hierarchically organised multi-level description of the reading process. The reading strategy formed of the tactics and the learning outcome, represent the observables of this interaction. The model was influenced by the theories of J. Bruner, G. Miller, N. Chomsky and R. Gagné. 6. The research was directed towards the identification of strategies and outcomes of reading-to-learn, with the double aim of investigating these areas and training students to increase their skill; both these aims were in line with endeavours to increase self-organisation and individual autonomy in learning. 7. Whilst the goals of the research were largely achieved, the results have illuminated a number of practical and theoretical issues that need further investigation.

Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so cause dangerous consequences, e.g., putting people lives at stake, if no reaction is adopted. Therefore, learning systems need to apply intelligent algorithms to monitor evolvement in their environments and update themselves effectively. Further, we may experience fluctuations regarding the performance of learning algorithms due to the nature of incoming data as it continuously evolves. That is, the current efficient learning approach may become deprecated after a change in data or environment. Hence, the question 'how to have an efficient learning algorithm over time against evolving data?' has to be addressed. In this thesis, we have made two contributions to settle the challenges described above. In the machine learning literature, the phenomenon of (distributional) change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, adaptive learners have been devised which may utilize drift detection methods to locate the drift points in dynamic and changing data streams. A drift detection method able to discover the drift points quickly, with the lowest false positive and false negative rates, is preferred. False positive refers to incorrectly alarming for concept drift, and false negative refers to not alarming for concept drift. In this thesis, we introduce three algorithms, called as the Fast Hoeffding Drift Detection Method (FHDDM), the Stacking Fast Hoeffding Drift Detection Method (FHDDMS), and the McDiarmid Drift Detection Methods (MDDMs), for detecting drift points with the minimum delay, false positive, and false negative rates. FHDDM is a sliding window-based algorithm and applies Hoeffding’s inequality (Hoeffding, 1963) to detect concept drift. FHDDM slides its window over the prediction results, which are either 1 (for a correct prediction) or 0 (for a wrong prediction). Meanwhile, it compares the mean of elements inside the window with the maximum mean observed so far; subsequently, a significant difference between the two means, upper-bounded by the Hoeffding inequality, indicates the occurrence of concept drift. The FHDDMS extends the FHDDM algorithm by sliding multiple windows over its entries for a better drift detection regarding the detection delay and false negative rate. In contrast to FHDDM/S, the MDDM variants assign weights to their entries, i.e., higher weights are associated with the most recent entries in the sliding window, for faster detection of concept drift. The rationale is that recent examples reflect the ongoing situation adequately. Then, by putting higher weights on the latest entries, we may detect concept drift quickly. An MDDM algorithm bounds the difference between the weighted mean of elements in the sliding window and the maximum weighted mean seen so far, using McDiarmid’s inequality (McDiarmid, 1989). Eventually, it alarms for concept drift once a significant difference is experienced. We experimentally show that FHDDM/S and MDDMs outperform the state-of-the-art by representing promising results in terms of the adaptation and classification measures. Due to the evolving nature of data streams, the performance of an adaptive learner, which is defined by the classification, adaptation, and resource consumption measures, may fluctuate over time. In fact, a learning algorithm, in the form of a (classifier, detector) pair, may present a significant performance before a concept drift point, but not after. We define this problem by the question 'how can we ensure that an efficient classifier-detector pair is present at any time in an evolving environment?' To answer this, we have developed the Tornado framework which runs various kinds of learning algorithms simultaneously against evolving data streams. Each algorithm incrementally and independently trains a predictive model and updates the statistics of its drift detector. Meanwhile, our framework monitors the (classifier, detector) pairs, and recommends the efficient one, concerning the classification, adaptation, and resource consumption performance, to the user. We further define the holistic CAR measure that integrates the classification, adaptation, and resource consumption measures for evaluating the performance of adaptive learning algorithms. Our experiments confirm that the most efficient algorithm may differ over time because of the developing and evolving nature of data streams.

A leading theory for why we sleep focuses on memory consolidation - the process of stabilizing and strengthening newly acquired memories into long-term storage. Consolidation of memory for cognitive strategies and problem-solving skills is enhanced as compared to a period of daytime wakefulness. Importantly, sleep preferentially enhances memory for the cognitive strategy per se, over-and-above the motor skills that are used to execute the strategy. Although it has been known for some time that sleep benefits this type of memory, it is not known how this process unfolds during sleep, or how sleep transforms this memory trace in the brain. Sleep is classified into rapid eye movement (REM) sleep and non-REM (NREM) sleep. The role of REM sleep for consolidation of memory for problem-solving skills remains controversial. In addition, little attention has been paid to the possible distinct roles of phasic REM sleep (i.e., when bursts of eye movements occur) and tonic REM sleep (i.e., the presence of isolated eye movements and the absence of eye movement bursts). REM sleep might favour procedural memory consolidation for cognitive strategies and problem-solving skills, and the specific role of REM sleep in this process might be discernible only by differentiating between phasic and tonic REM states. In addition, fMRI studies have revealed that sleep-related consolidation of the memory trace for simple motor procedural skills is associated with strengthened activity of, and functional connectivity between, key memory-related brain areas (i.e., hippocampal, striatal, and neocortex). However, fMRI techniques have not yet been employed to investigate sleep-related consolidation of procedural memory for cognitive strategies and problem-solving skills. Participants (n=60) performed a procedural memory task involving a cognitive strategy while undergoing functional magnetic resonance imaging (fMRI) before and after a condition of Sleep, Nap, or Wake. Those in the Sleep and Nap condition underwent polysomnography (PSG) to further study the learning-related changes in sleep macrostructure and microstructure. This thesis not only shows that a period of sleep or a nap afford a greater benefit to memory consolidation of a procedural strategy than a period of wake, but more specifically: In Study 1, during sleep, phasic REM sleep theta power was directly associated with overnight improvement on the task, whereas tonic REM sleep sensorimotor rhythm power was greater following a night of learning compared to a non-learning control night. In Study 2, we show that distinct hippocampal, striatal, and cortical areas associated with strategy learning are preferentially enhanced. Study 3 reveals that the functional communication among these brain areas is greater following sleep compared to a daytime nap or day of wakefulness. Sleep-related changes in brain activation and functional connectivity were both correlated with improved performance from before to after a period of sleep. Overall, findings from this thesis support the benefit of sleep at the behavioural and systems level for consolidating procedural memory involving cognitive strategies used to solve problems. The findings suggest that the multifaceted nature of REM sleep must be examined separately by its phasic and tonic states, to identify the active role of REM sleep for consolidating memory. Further, the consolidation of the memory trace is reflected through activation of, and communication between hippocampal, striatal, and neocortical brain areas. In summary, this thesis shows that sleep actively consolidates memory for cognitive strategies and problem-solving skills.