The use of explicit algorithms and episodic context to teach subtraction to students with learning problems in mathematics /

This research attempted to address the question of the role of explicit algorithms and episodic contexts in the acquisition of computational procedures for regrouping in subtraction. Three groups of students having difficulty learning to subtract with regrouping were taught procedures for doing so through either an explicit algorithm, an episodic content or an examples approach. It was hypothesized that the use of an explicit algorithm represented in a flow chart format would facilitate the acquisition and retention of specific procedural steps relative to the other two conditions. On the other hand, the use of paragraph stories to create episodic content was expected to facilitate the retrieval of algorithms, particularly in a mixed presentation format. The subjects were tested on similar, near, and far transfer questions over a four-day period. Near and far transfer algorithms were also introduced on Day Two. The results suggested that both explicit and episodic context facilitate performance on questions requiring subtraction with regrouping. However, the differential effects of these two approaches on near and far transfer questions were not as easy to identify. Explicit algorithms may facilitate the acquisition of specific procedural steps while at the same time inhibiting the application of such steps to transfer questions. Similarly, the value of episodic context in cuing the retrieval of an algorithm may be limited by the ability of a subject to identify and classify a new question as an exemplar of a particular episodically deflned problem type or category. The implications of these findings in relation to the procedures employed in the teaching of Mathematics to students with learning problems are discussed in detail.

Developing and nurturing professional learning communities: principles used by elementary administrators /

This study examined the strategies used by elementary school principals to facilitate and nurture the development of professional learning communities (PLC) within their school settings. Using a reputational sample of administrators whose schools were demonstrating observable characteristics of PLCs, this study documented and described the strategies and actions taken by the principals to move their schools forward. Data collection included the use of open-ended interviews as well as observations capturing the means by which the principals addressed the areas of culture, processes, and structures within their school setting. A grounded theory approach to data analysis uncovered 4 guiding principles used by the principals to facilitate the development of the PLCs within their school: (a) protecting the purpose; (b) attending to relationships; (c) sharing the responsibility; and (d) valuing the journey. The guiding principles were used by each administrator to anchor the decisions they made and develop responsive, contextspecific strategies to support the PLC at their school. The results highlighted the complex role of the principal and the supports required to tackle the difficult work of facilitating PLCs.

Anxiety and learning / response behaviour in the context of intellectual problem solving by young children

Twenty-eight grade four students were ca.tegorized as either high or low anxious subjects as per Gillis' Child Anxiety Scale (a self-report general measure). In determining impulsivity in their response tendencies, via Kagan's Ma.tching Familiar Figures Test, a significant difference between the two groups was not found to exist. Training procedures (verbal labelling plus rehearsal strategies) were introduced in modification of their learning behaviour on a visual sequential memory task. Significantly more reflective memory recall behaviour was noted by both groups as a result. Furthermore, transfer of the reflective quality of this learning strategy produced significantly less impulsive response behaviour for high and low anxious subjects with respect to response latency and for low anxious subjects with respect to response accuracy.

Conceptualizing Institutional Capacity Building Through a Learning Process Set in Motion by a Foundation

Employing critical pedagogy and transformative theory as a theoretical framework, I examined a learning process associated with building capacity in community-based organizations (CBOs) through an investigation of the Institutional Capacity Building Program (ICBP) initiated by a Foundation. The study sought to: (a) examine the importance of institutional capacity building for individual and community development; (b) investigate elements of a process associated with a program and characteristics of a learning process for building capacity in CBOs; and (c) analyze the Foundation’s approach to synthesizing, systematizing, and sharing learning. The study used a narrative research design that included 3 one-on-one, hour-long interviews with 2 women having unique vantage points in ICBP: one is a program facilitator working at the Foundation and the other runs a CBO supported by the Foundation. The interviews’ semistructured questions allowed interviewees to share stories regarding their experience with the learning process of ICB and enabled themes to emerge from their day-to-day experience. Through the analysis of this learning process for institutional capacity building, a few lessons can be drawn from the experience of the Foundation.

How Immigrant Mothers Contribute to Their Children’s Learning Inside and Outside of School

Abstract The main focus of this qualitative research was to explore how parents from different national backgrounds see their role in their children’s education inside and outside of school. Although greater recruitment was described and sought after, this qualitative research gathered data from two immigrant female parents from a community parents’ group located in Ontario, Canada. Data were collected through face-to-face interviews with each participant using open-ended questions asking about the different ways these mothers, along with their spouses, were involved in their children’s education. Moreover, questions were designed to find out what alternatives parents use to support their children’s learning. The main question driving this research was “How are immigrant families currently involved with their children’s education inside and outside of school?” NVivo, 10 was used to code the transcripts giving rise to themes which could then be utilized to explain and explore the research question. The findings of this research are congruent with past research and demonstrate that immigrant mothers are more involved than the fathers are in their children’s education (Grolnick & Slowiaczek 1994; Peters, Seeds, Goldstein, & Coleman, 2008). A specifically important finding in this research is that schools are perceived by the immigrant mothers in this study as not doing enough to actively engage immigrant parents in their children’s education. On the other hand, findings also show that parents are eager to find different avenues to get involved and help their children succeed.

Learning analysis by reduction from positive data

Analysis by reduction is a linguistically motivated method for checking correctness of a sentence. It can be modelled by restarting automata. In this paper we propose a method for learning restarting automata which are strictly locally testable (SLT-R-automata). The method is based on the concept of identification in the limit from positive examples only. Also we characterize the class of languages accepted by SLT-R-automata with respect to the Chomsky hierarchy.

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

Investigating the use of Virtual Learning Environments by teachers in schools and colleges

Investigating the use of Virtual Learning Environments by teachers in schools and colleges

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.

Providing pervasive learning eXperiences by combining Internet of Things and e-Learning standard

Resumen basado en el de la publicaci??n

Language learning strategy use by Spanish EFL students : the effect of proficiency level, gender, and motivation

Resumen basado en el de la publicaci??n

The formation of learning sets by the chinchilla in an auditory discrimination procedure

This paper discusses a study conducted to test sound discrimination abilities of the chinchilla.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.