The differences in the social competence of children who attend integrated junior infant classes and children who attend segregated learning environments

There are a number of reasons why this researcher has decided to undertake this study into the differences in the social competence of children who attend integrated Junior Infant classes and children who attend segregated learning environments. Theses reasons are both personal and professional. My personal reasons stem from having grown up in a family which included both an aunt who presented with Down Syndrome and an uncle who presented with hearing impairment. Both of these relatives' experiences in our education system are interesting. My aunt was considered ineducable while her brother - my uncle - was sent to Dublin (from Cork) at six years of age to be educated by a religious order. My professional reasons, on the other hand, stemmed from my teaching experience. Having taught in both special and integrated classrooms it became evident to me that there was somewhat 'suspicion' attached to integration. Parents of children without disabilities questioned whether this process would have a negative impact on their children's education. While parents of children with disabilities debated whether integrated settings met the specific needs of their children. On the other hand, I always questioned whether integration and inclusiveness meant the same thing. My research has enabled me to find many answers. Increasingly, children with special educational needs (SEN) are attending a variety of integrated and inclusive childcare and education settings. This contemporary practice of educating children who present with disabilities in mainstream classrooms has stimulated vast interest on the impact of such practices on children with identified disabilities. Indeed, children who present with disabilities "fare far better in mainstream education than in special schools" (Buckley, cited in Siggins, 2001,p.25). However, educators and practitioners in the field of early years education and care are concerned with meeting the needs of all children in their learning environments, while also upholding high academic standards (Putman, 1993). Fundamentally, therefore, integrated education must also produce questions about the impact of this practice on children without identified special educational needs. While these questions can be addressed from the various areas of child development (i.e. cognitive, physical, linguistic, emotional, moral, spiritual and creative), this research focused on the social domain. It investigates the development of social competence in junior infant class children without identified disabilities as they experience different educational settings.

Personal learning artefacts: persistence, ownership and privacy

Traditional classrooms have been often regarded as closed spaces within which experimentation, discussion and exploration of ideas occur. Professors have been used to being able to express ideas frankly, and occasionally rashly while discussions are ephemeral and conventional student work is submitted, graded and often shredded. However, digital tools have transformed the nature of privacy. As we move towards the creation of life-long archives of our personal learning, we collect material created in various 'classrooms'. Some of these are public, and open, but others were created within 'circles of trust' with expectations of privacy and anonymity by learners. Taking the Creative Commons license as a starting point, this paper looks at what rights and expectations of privacy exist in learning environments? What methods might we use to define a 'privacy license' for learning? How should the privacy rights of learners be balanced with the need to encourage open learning and with the creation of eportfolios as evidence of learning? How might we define different learning spaces and the privacy rights associated with them? Which class activities are 'private' and closed to the class, which are open and what lies between? A limited set of set of metrics or zones is proposed, along the axes of private-public, anonymous-attributable and non-commercial-commercial to define learning spaces and the digital footprints created within them. The application of these not only to the artefacts which reflect learning, but to the learning spaces, and indeed to digital media more broadly are explored. The possibility that these might inform not only teaching practice but also grading rubrics in disciplines where public engagement is required will also be explored, along with the need for consideration by educational institutions of the data rights of students.

Automatic detection of learner-style for adaptive eLearning

The advent of modern wireless technologies has seen a shift in focus towards the design and development of educational systems for deployment through mobile devices. The use of mobile phones, tablets and Personal Digital Assistants (PDAs) is steadily growing across the educational sector as a whole. Mobile learning (mLearning) systems developed for deployment on such devices hold great significance for the future of education. However, mLearning systems must be built around the particular learner’s needs based on both their motivation to learn and subsequent learning outcomes. This thesis investigates how biometric technologies, in particular accelerometer and eye-tracking technologies, could effectively be employed within the development of mobile learning systems to facilitate the needs of individual learners. The creation of personalised learning environments must enable the achievement of improved learning outcomes for users, particularly at an individual level. Therefore consideration is given to individual learning-style differences within the electronic learning (eLearning) space. The overall area of eLearning is considered and areas such as biometric technology and educational psychology are explored for the development of personalised educational systems. This thesis explains the basis of the author’s hypotheses and presents the results of several studies carried out throughout the PhD research period. These results show that both accelerometer and eye-tracking technologies can be employed as an Human Computer Interaction (HCI) method in the detection of student learning-styles to facilitate the provision of automatically adapted eLearning spaces. Finally the author provides recommendations for developers in the creation of adaptive mobile learning systems through the employment of biometric technology as a user interaction tool within mLearning applications. Further research paths are identified and a roadmap for future of research in this area is defined.

Using parent report to assess early lexical production in children exposed to more than one language

Limited expressive vocabulary skills in young children are considered to be the first warning signs of a potential Specific Language Impairment (SLI) (Ellis & Thal, 2008). In bilingual language learning environments, the expressive vocabulary size in each of the child’s developing languages is usually smaller compared to the number of words produced by monolingual peers (e.g. De Houwer, 2009). Nonetheless, evidence shows children’s total productive lexicon size across both languages to be comparable to monolingual peers’ vocabularies (e.g. Pearson et al., 1993; Pearson & Fernandez, 1994). Since there is limited knowledge as to which level of bilingual vocabulary size should be considered as a risk factor for SLI, the effects of bilingualism and language-learning difficulties on early lexical production are often confounded. The compilation of profiles for early vocabulary production in children exposed to more than one language, and their comparison across language pairs, should enable more accurate identification of vocabulary delays that signal a risk for SLI in bilingual populations. These considerations prompted the design of a methodology for assessing early expressive vocabulary in children exposed to more than one language, which is described in the present chapter. The implementation of this methodological framework is then outlined by presenting the design of a study that measured the productive lexicons of children aged 24-36 months who were exposed to different language pairs, namely Maltese and English, Irish and English, Polish and English, French and Portuguese, Turkish and German as well as English and Hebrew. These studies were designed and coordinated in COST Action IS0804 Working Group 3 (WG3) and will be described in detail in a series of subsequent publications. Expressive vocabulary size was measured through parental report, by employing the vocabulary checklist of the MacArthur-Bates Communicative Development Inventory: Words and Sentences (CDI: WS) (Fenson et al., 1993, 2007) and its adaptations to the participants’ languages. Here we describe the novelty of the study’s methodological design, which lies in its attempt to harmonize the use of vocabulary checklist adaptations, together with parental questionnaires addressing language exposure and developmental history, across participant groups characterized by different language exposure variables. This chapter outlines the various methodological considerations that paved the way for meaningful cross-linguistic comparison of the participants’ expressive lexicon sizes. In so doing, it hopes to provide a template for and encourage further research directed at establishing a threshold for SLI risk in children exposed to more than one language.

Understanding teacher collaboration in disadvantaged urban primary schools: uncovering practices that foster professional learning communities

This study is set in the context of disadvantaged urban primary schools in Ireland. It inquires into the collaborative practices of primary teachers exploring how class teachers and support teachers develop ways of working together in an effort to improve the literacy and numeracy levels of their student. Traditionally teachers have worked in isolation and therefore ‘collaboration’ as a practice has been slow to permeate the historically embedded assumption of how a teacher should work. This study aims to answer the following questions. 1). What are the dynamics of teacher collaboration in disadvantaged urban primary schools? 2). In what ways are teacher collaboration and teacher learning related? 3). In what ways does teacher collaboration influence students’ opportunities for learning? In answering these research questions, this study aims to contribute to the body of knowledge pertaining to teacher learning through collaboration. Though current policy and literature advocate and make a case for the development of collaborative teaching practices, key studies have identified gaps in the research literature in relation to the impact of teacher collaboration in schools. This study seeks to address some of those gaps by establishing how schools develop a collaborative environment and how teaching practices are enacted in such a setting. It seeks to determine what skills, relationships, structures and conditions are most important in developing collaborative environments that foster the development of professional learning communities (PLCs). This study uses a mixed method research design involving a postal survey, four snap-shot case studies and one in depth case study in an effort to establish if collaborative practice is a feasible practice resulting in worthwhile benefits for both teachers and students.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.