Motivation and relationship of the student with the school as factors involved in the perceived learning

Motivation should be seen as a very important factor in the learning process. The motivated student has the inner strength to learn, to discover and capitalize on capabilities, to improve academic performance and to adapt to the demands of the school context. Contextual factors like the psychological sense of school membership may be also especially important to students’ classroom engagement, their motivation and learning success. So with this study we intend to examine how the sense of school belonging and intrinsic motivation influences perceived learning.A structural model reveals that the negative sense of school belonging has a negative impact on intrinsic motivation and on perceived learning. In turn, intrinsic motivation positively and significantly influences perceived learning in the course.

MULTI-WORD VERBS IN DIGITAL ENVIRONMENTS FOR ENGLISH TEACHING AND LEARNING

This paper aims to analyze the approach of multi-word verbs in free digital resources for English learning. Multi-word verbs, which are widely known as phrasal verbs, are verbal English verbal combinations, formed from a verb and preposition or adverb, or both. From a functional standpoint, these verbal combinations and their different particles behave differently in syntactic terms (Greebaum & Quirk, 1990 and Downing & Locke (2006). Learning about these differences can be of great importance to foster fluency in the language, mainly at higher proficiency levels. At present, with the growing demand for learning English, many digital environments were made available. This paper analyzes 07 major websites for English learning in Brazil, in order to investigate how the topic is addressed. As a result, we argue that more precision and concision are required to approach the theme. This can be achieved, for example, by employing the term multi-word verbs, together with a more precise definition of its functional syntactic behavior. This paper argues that this change of approach is especially important in digital learning environments, in which there is not always a direct mediation of the teacher or specialist.

Development and evaluation of an integrated clinical learning model to inform continuing education for acute care nurses

Background Significant ongoing learning needs for nurses have occurred as a direct result of the continuous introduction of technological innovations and research developments in the healthcare environment. Despite an increased worldwide emphasis on the importance of continuing education, there continues to be an absence of empirical evidence of program and session effectiveness. Few studies determine whether continuing education enhances or develops practice and the relative cost benefits of health professionals’ participation in professional development. The implications for future clinical practice and associated educational approaches to meet the needs of an increasingly diverse multigenerational and multicultural workforce are also not well documented. There is minimal research confirming that continuing education programs contribute to improved patient outcomes, nurses’ earlier detection of patient deterioration or that standards of continuing competence are maintained. Crucially, evidence-based practice is demonstrated and international quality and safety benchmarks are adhered to. An integrated clinical learning model was developed to inform ongoing education for acute care nurses. Educational strategies included the use of integrated learning approaches, interactive teaching concepts and learner-centred pedagogies. A Respiratory Skills Update education (ReSKU) program was used as the content for the educational intervention to inform surgical nurses’ clinical practice in the area of respiratory assessment. The aim of the research was to evaluate the effectiveness of implementing the ReSKU program using teaching and learning strategies, in the context of organisational utility, on improving surgical nurses’ practice in the area of respiratory assessment. The education program aimed to facilitate better awareness, knowledge and understanding of respiratory dysfunction in the postoperative clinical environment. This research was guided by the work of Forneris (2004), who developed a theoretical framework to operationalise a critical thinking process incorporating the complexities of the clinical context. The framework used educational strategies that are learner-centred and participatory. These strategies aimed to engage the clinician in dynamic thinking processes in clinical practice situations guided by coaches and educators. Methods A quasi experimental pre test, post test non–equivalent control group design was used to evaluate the impact of the ReSKU program on the clinical practice of surgical nurses. The research tested the hypothesis that participation in the ReSKU program improves the reported beliefs and attitudes of surgical nurses, increases their knowledge and reported use of respiratory assessment skills. The study was conducted in a 400 bed regional referral public hospital, the central hub of three smaller hospitals, in a health district servicing the coastal and hinterland areas north of Brisbane. The sample included 90 nurses working in the three surgical wards eligible for inclusion in the study. The experimental group consisted of 36 surgical nurses who had chosen to attend the ReSKU program and consented to be part of the study intervention group. The comparison group included the 39 surgical nurses who elected not to attend the ReSKU program, but agreed to participate in the study. Findings One of the most notable findings was that nurses choosing not to participate were older, more experienced and less well educated. The data demonstrated that there was a barrier for training which impacted on educational strategies as this mature aged cohort was less likely to take up educational opportunities. The study demonstrated statistically significant differences between groups regarding reported use of respiratory skills, three months after ReSKU program attendance. Between group data analysis indicated that the intervention group’s reported beliefs and attitudes pertaining to subscale descriptors showed statistically significant differences in three of the six subscales following attendance at the ReSKU program. These subscales included influence on nursing care, educational preparation and clinical development. Findings suggest that the use of an integrated educational model underpinned by a robust theoretical framework is a strong factor in some perceptions of the ReSKU program relating to attitudes and behaviour. There were minimal differences in knowledge between groups across time. Conclusions This study was consistent with contemporary educational approaches using multi-modal, interactive teaching strategies and a robust overarching theoretical framework to support study concepts. The construct of critical thinking in the clinical context, combined with clinical reasoning and purposeful and collective reflection, was a powerful educational strategy to enhance competency and capability in clinicians.

Assessment for learning - Why the theory needs the practice

Assessment for Learning is a pedagogical practice with anticipated gains of increased student motivation, mastery and autonomy as learners develop their capacity to monitor and plan their own learning progress. Assessment for Learning (AfL) differs from Assessment of learning in its timing, occurring within the regular flow of learning rather than end point, in its purpose of improving student learning rather than summative grading and in the ownership of the learning where the student voice is heard in judging quality. Since Black and Wiliam (1998) highlighted the achievement gains that AfL practices seem to bring to all learners in classrooms, it has become part of current educational policy discourse in Australia, yet teacher adoption of the practices is not a straightforward implementation of techniques within an existing classroom repertoire. As can be seen from the following meta-analysis, recent research highlights a more complex interrelationship between teacher and student beliefs about learning and assessment, and the social and cultural interactions in and contexts of the classroom. More research is needed from a sociocultural perspective that allows meaning to emerge from practice. Before another policy push, we need to understand better the many factors within the assessment relationship. We need to hear from teachers and students through long-term AfL case studies both to inform AfL theory and to shed light on the complexities of pedagogical change for enhancing learner autonomy.

Perceptions of online learning in an Australian university : Malaysian students' perspectives

Several researchers have reported that cultural and language differences can affect online interactions and communications between students from different cultural backgrounds. Other researchers have asserted that online learning is a tool that can improve teaching and learning skills, but, its effectiveness depends on how the tool is used. Therefore, this study aims to investigate the kinds of challenges encountered by the international students and how they actually cope with online learning. To date little research exists on the perceptions of online learning environments by international Asian students, in particular Malaysian students who study in Australian Universities; hence this study aims to fill this gap. A mixed-method approach was used to collect quantitative and qualitative data using a modified Online Learning Environment Survey (OLES) instrument and focus group interviews. The sample comprised 76 international students from a university in Brisbane. Thirty-five domestic Australian students were included for comparison. Contrary to assumptions from previous research, the findings revealed that there were few differences between the international Asian students from Malaysia and Australian students with regard to their perceptions of online learning. Another cogent finding that emerged was that online learning was most effective when included within blended learning environments. The students clearly indicated that when learning in a blended environment, it was imperative that appropriate features are blended in and customised to suit the particular needs of international students. The study results indicated that the university could improve the quality of the blended online learning environment by: 1) establishing and maintaining a sense of learning community; 2) enhancing the self motivation of students; and 3) professional development of lecturers/tutors, unit coordinators and learning support personnel. Feedback from focus group interviews, highlighted the students‘ frustration with a lack of cooperative learning, strategies and skills which were expected of them by their lecturers/tutors in order to work productively in groups. They indicated a strong desire for lecturers/tutors to provide them prior training in these strategies and skills. The students identified four ways to optimise learning opportunities in cross-cultural spaces. These were: 1) providing preparatory and ongoing workshops focusing on the dispositions and roles of students within student-centred online learning environments; 2) providing preparatory and ongoing workshops on collaborative group learning strategies and skills; 3) providing workshops familiarising students with Australian culture and language; and 4) providing workshops on strategies for addressing technical problems. Students also indicated a strong desire for professional development of lecturers/tutors focused on: 1) teacher attributes, 2) ways to culturally sensitive curricula, and 3) collaborative learning and cooperative working strategies and skills, and 4) designing flexible program structures. Recommendations from this study will be useful to Australian universities where Asian international students from Malaysia study in blended learning environments. An induction program (online skills, collaborative and teamwork skills, study expectations plus familiarisation with Australian culture) for overseas students at the commencement of their studies; a cultural awareness program for lecturers (cultural sensitivity, ways to communicate and a better understanding of Asian educational systems), upskilling of lecturers‘ ability to structure their teaching online and to apply strong theoretical underpinnings when designing learning activities such as discussion forums, and consistency with regards to how content is located and displayed in a learning management system like Blackboard. Through addressing the research questions in this study, the researcher hopes to contribute to and advance the domain of knowledge related to online learning, and to better understand how international Malaysian students‘ perceive online learning environments. These findings have theoretical and pragmatic significance.

Enriching students’ intellectual diet through inquiry based learning

Traditionally, Science education has stressed the importance of teaching students to conduct ‘scientific inquiry’, with the main focus being the experimental model of inquiry used by real world scientists. Current educational approaches using constructivist pedagogy recognise the value of inquiry as a method for promoting the development of deep understanding of discipline content. A recent Information Learning Activity undertaken by a Grade Eight Science class was observed to discover how inquiry based learning is implemented in contemporary Science education. By analysing student responses to questionnaires and assessment task outcomes, the author was able to determine the level of inquiry inherent in the activity and how well the model supported student learning and the development of students’ information literacy skills. Although students achieved well overall, some recommendations are offered that may enable teachers to better exploit the learning opportunities provided by inquiry based learning. Planning interventions at key stages of the inquiry process can assist students to learn more effective strategies for dealing with cognitive and affective challenges. Allowing students greater input into the selection of topic or focus of the activity may encourage students to engage more deeply with the learning task. Students are likely to experience greater learning benefit from access to developmentally appropriate resources, increased time to explore topics and multiple opportunities to undertake information searches throughout the learning activity. Finally, increasing the cognitive challenge can enhance both the depth of students’ learning and their information literacy skills.

Self-regulated learning and executive function : exploring the relationships in a sample of adolescent males

This study investigated relationships between SRL and EF in a sample of 254 school-aged adolescent males. Two hypotheses were tested: that self-reported measures of SRL and EF are closely related and that as different aspects of EF mature during adolescence, the corresponding components of SRL should also improve, leading to an age-related increase in the correlation between EF and SRL. Two self-report instruments were used: the strategies for self-regulated learning survey (SSRLS) and the behavioural rating instrument of executive function (BRIEF). Strong correlations between the measures of EF and SRL were found, especially in areas associated with metacognitive processes. Correlations between EF and SRL were found, with weaker correlations between behavioural regulation and SRL were found to be weaker for the younger participants in the sample while the relationship between EF and SRL appears to grow stronger during the initial years of high school even though self-reported levels of EF along with motivation for SRL and important components of SRL such as goal setting and planning were found to decrease with age. Decreasing levels of motivation for learning during adolescence are speculated to moderate the deployment of SRL and EF in a school context.

Analysing reflective text for learning analytics : an approach using anomaly recontextualisation

Reflective writing is an important learning task to help foster reflective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.

Curiosity driven reinforcement learning for motion planning on humanoids

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.

Rethinking Culture and Diversity in Early Childhood Preservice Teacher Education: Looking at Teaching and Learning Through an Interdisciplinary Lens

Early Childhood Education (ECE) has a long history of building foundations for children to achieve their full potential, enabling parents to participate in the economy while children are cared for, addressing poverty and disadvantage, and building individual, community and societal resources. In so doing, ECE has developed a set of cultural practices and ways of knowing that shape the field and the people who work within it. ECE, consequently, is frequently described as unique and special (Moss, 2006; Penn, 2011). This works to define and distinguish the field while, simultaneously, insulating it from other contexts, professions, and ideas. Recognising this dualism illuminates some of the risks and challenges of operating in an insular and isolated fashion. In the 21st century, there are new challenges for children, families and societies to which ECE must respond if it is to continue to be relevant. One major issue is how ECE contributes to transition towards more sustainable ways of living. Addressing this contemporary social problem is one from which Early Childhood teacher education has been largely absent (Davis & Elliott, 2014), despite the well recognised but often ignored role of education in contributing to sustainability. Because of its complexity, sustainability is sometimes referred to as a ‘wicked problem’ (Rittel & Webber, 1973; Australian Public Service Commission, 2007) requiring alternatives to ‘business as usual’ problem solving approaches. In this chapter, we propose that addressing such problems alongside disciplines other than Education enables the Early Childhood profession to have its eyes opened to new ways of thinking about our work, potentially liberating us from the limitations of our “unique” and idiosyncratic professional cultures. In our chapter, we focus on understandings of culture and diversity, looking to broaden these by exploring the different ‘cultures’ of the specialist fields of ECE and Design (in this project, we worked with students studying Architecture, Industrial Design, Landscape Architecture and Interior Design). We define culture not as it is typically represented, i.e. in relation to ideas and customs of particular ethnic and language groups, but to the ideas and practices of people working in different disciplines and professions. We assert that different specialisms have their own ‘cultural’ practices. Further, we propose that this kind of theoretical work helps us to reconsider ways in which ECE might be reframed and broadened to meet new challenges such as sustainability and as yet unknown future challenges and possibilities. We explore these matters by turning to preservice Early Childhood teacher education (in Australia) as a context in which traditional views of culture and diversity might be reconstructed. We are looking to push our specialist knowledge boundaries and to extend both preservice teachers and academics beyond their comfort zones by engaging in innovative interdisciplinary learning and teaching. We describe a case study of preservice Early Childhood teachers and designers working in collaborative teams, intersecting with a ‘real-world’ business partner. The joint learning task was the design of an early learning centre based on sustainable design principles and in which early Education for Sustainability (EfS) would be embedded Data were collected via focus group and individual interviews with students in ECE and Design. Our findings suggest that interdisciplinary teaching and learning holds considerable potential in dismantling taken-for-granted cultural practices, such that professional roles and identities might be reimagined and reconfigured. We conclude the chapter with provocations challenging the ways in which culture and diversity in the field of ECE might be reconsidered within teacher education.

Learning automata in feedforward connectionist systems

This paper analyses the behaviour of a general class of learning automata algorithms for feedforward connectionist systems in an associative reinforcement learning environment. The type of connectionist system considered is also fairly general. The associative reinforcement learning task is first posed as a constrained maximization problem. The algorithm is approximated hy an ordinary differential equation using weak convergence techniques. The equilibrium points of the ordinary differential equation are then compared with the solutions to the constrained maximization problem to show that the algorithm does behave as desired.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Inferring visuomotor priors for sensorimotor learning.

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations--the mapping between actual and visual location of the hand--during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior.

Learning Euclidean Embeddings for Indexing and Classification

BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original feature-based distance measure.

A defeasible reasoning model of inductive concept learning from examples and communication

This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of examples, and study its properties, showing they correspond to a rather well-behaved non-monotonic logic. We will also show that with the addition of a preference relation on inductive theories we can characterize the inductive bias of ICL algorithms. The second part of the paper shows how this logical characterization of inductive generalization can be integrated with another form of non-monotonic reasoning (argumentation), to define a model of multiagent ICL. This integration allows two or more agents to learn, in a consistent way, both from induction and from arguments used in the communication between them. We show that the inductive theories achieved by multiagent induction plus argumentation are sound, i.e. they are precisely the same as the inductive theories built by a single agent with all data. © 2012 Elsevier B.V.