The Te Kotahitanga Observation Tool: Development, use, reliability and validity.

Te Kotahitanga is a New Zealand school reform project aimed at improving the pedagogical contexts in mainstream classrooms in which the indigenous Māori students have traditionally been marginalised. It does this by assisting teachers to implement an Effective Teaching Profile. Part of this process uses an observation tool to monitor the degree to which participating teachers are incorporating the interactions and relationships described in the Effective Teaching Profile into their day-to-day teaching. Given the central importance of these tasks, the Te Kotahitanga team undertook to test the observation tool for measurement reliability and validity. In order to undertake this study, the team conducted synchronous observations amongst trainers (the Professional Development Coordinator and Regional Coordinators) to ascertain their level of consistency when using the tool. The team then conducted synchronous observations between trainers and 38 in-school facilitators in the 12 schools involved in Phase 3 of the project. In total 41 teachers were observed and over 200 Māori students were involved in these observations. This study suggests that the tool can produce consistent and reliable results when observers have been effectively trained

On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas

Multi-class protein fold classification using a new ensemble machine learning approach.

Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources

The contributions of domain-general and numerical factors to third-grade arithmetic skills and mathematical learning disability

Explanations of the marked individual differences in elementary school mathematical achievement and mathematical learning disability (MLD or dyscalculia) have involved domain-general factors (working memory, reasoning, processing speed and oral language) and numerical factors that include single-digit processing efficiency and multi-digit skills such as number system knowledge and estimation. This study of third graders (N = 258) finds both domain-general and numerical factors contribute independently to explaining variation in three significant arithmetic skills: basic calculation fluency, written multi-digit computation, and arithmetic word problems. Estimation accuracy and number system knowledge show the strongest associations with every skill and their contributions are both independent of each other and other factors. Different domain-general factors independently account for variation in each skill. Numeral comparison, a single digit processing skill, uniquely accounts for variation in basic calculation. Subsamples of children with MLD (at or below 10th percentile, n = 29) are compared with low achievement (LA, 11th to 25th percentiles, n = 42) and typical achievement (above 25th percentile, n = 187). Examination of these and subsets with persistent difficulties supports a multiple deficits view of number difficulties: most children with number difficulties exhibit deficits in both domain-general and numerical factors. The only factor deficit common to all persistent MLD children is in multi-digit skills. These findings indicate that many factors matter but multi-digit skills matter most in third grade mathematical achievement

Understanding of the Mole Concept Achieved by Students in a Constructivist General Chemistry Course

The purpose of this research project was to study the conceptual understanding achieved in a general chemistry course based on a constructivist approach. A group of 28 students participated in repeated measures obtained by means of conceptual maps about the mole concept prepared three times during the course: at the beginning the course, immediately after the concept was studied, and after studying other related concepts. In addition, eight students selected from the group of 28 were interviewed. The interviews were carried out focusing on their conceptual maps. The analysis of the repeated measures indicated significant differences among the three times, especially between the first two. It was evidenced, therefore, that these students obtained a significantly higher level of understanding of the mole concept. The qualitative analysis carried out with students identified a broad range of responses that represent different levels of hierarchical organization, of progressive differentiation, and of formation of significant relations of the mole concept. Some recommendations offered are to develop and implement teaching methods that promote understanding of scientific concepts, and to prepare science professors and teachers to emphasize teaching for conceptual understanding

Learning Tree Distributions by Hidden Markov Models

Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main approaches in literature, focusing in particular on the causality assumptions introduced by the choice of a specific tree visit direction. We will then sketch a novel non-parametric generalization of the bottom-up hidden tree Markov model with its interpretation as a nondeterministic tree automaton with infinite states.Comment: Accepted in LearnAut2018 worksho