Contributions of Neuroscience to Develop Teaching Strategies and Learning of Mathematics

The goal of the present work is to develop some strategies based on research in neurosciences that contribute to the teaching and learning of mathematics. The interrelationship of education with the brain, as well as the relationship of cerebral structures with mathematical thinking was discussed. Strategies were developed taking into consideration levels that include cognitive, semiotic, language, affect and the overcoming of phobias to the subject. The fundamental conclusion was the imperative educational requirement in the near future of a new teacher, whose pedagogic formation must include the knowledge on the cerebral function, its structures and its implications to education, as well as a change in pedagogy and curricular structure in the teaching of mathematics.

Perceptions of online tutorials for distance learning in mathematics and computing

We report on student and staff perceptions of synchronous online teaching and learning sessions in mathematics and computing. The study is based on two surveys of students and tutors conducted 5 years apart, and focusses on the educational experience as well as societal and accessibility dimensions. Key conclusions are that both staff and students value online sessions, to supplement face-to-face sessions, mainly for their convenience, but interaction within the sessions is limited. Students find the recording of sessions particularly helpful in their studies.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.

The Robson Ten-group Classification System For Appraising Deliveries At A Tertiary Referral Hospital In Brazil.

To evaluate the distribution of women according to the Robson 10-group classification system (RTGCS) and the occurrence of severe maternal morbidity (SMM) by mode of delivery at a tertiary referral hospital. A retrospective cross-sectional study was conducted of all women admitted to the Women's Hospital at the University of Campinas (Campinas, Brazil) for delivery between January 2009 and July 2013. Women were grouped according to RTGCS. Mode of delivery and SMM (defined as need for admission to the intensive care unit) were assessed. Among 12 771 women, 5957 (46.6%) delivered by cesarean. Overall, 3594 (28.1%) women were in group 1 (nulliparous, single pregnancy, cephalic, term, spontaneous labor), 2328 (18.2%) in group 5 (≥1 previous cesarean, single pregnancy, cephalic, term), and 2112 (16.5%) in group 3 (multiparous excluding previous cesarean, single pregnancy, cephalic, term, spontaneous labor). Group 5 contributed the most cesarean deliveries (1626 [27.3%]), followed by group 2 (nulliparous, single pregnancy, cephalic, term, induced labor or cesarean before labor; 1049 [17.6%]). SMM was more common among women undergoing cesarean delivery than among those delivering vaginally in groups 1-5. The RTGCS allowed the identification of groups with the highest frequency of cesarean delivery and an assessment of SMM. This should be considered in related health policies.

Síndrome do X Frágil com variante de Dandy-Walker: estudo clínico das manifestações comunicativas orais e escritas

A síndrome do X Frágil é a causa mais frequente de deficiência intelectual hereditária. A variante de Dandy-Walker trata-se de uma constelação específica de achados neurorradiológicos. Este estudo relata achados da comunicação oral e escrita de um menino de 15 anos com diagnóstico clínico e molecular da síndrome do X-Frágil e achados de neuroimagem do encéfalo compatíveis com variante de Dandy-Walker. A avaliação fonoaudiológica foi realizada por meio da Observação do Comportamento Comunicativo, aplicação do ABFW - Teste de Linguagem Infantil - Fonologia, Perfil de Habilidades Fonológicas, Teste de Desempenho Escolar, Teste Illinois de Habilidades Psicolinguísticas, avaliação do sistema estomatognático e avaliação audiológica. Observou-se: alteração de linguagem oral quanto às habilidades fonológicas, semânticas, pragmáticas e morfossintáticas; déficits nas habilidades psicolinguísticas (recepção auditiva, expressão verbal, combinação de sons, memória sequencial auditiva e visual, closura auditiva, associação auditiva e visual); e alterações morfológicas e funcionais do sistema estomatognático. Na leitura verificou-se dificuldades na decodificação dos símbolos gráficos e na escrita havia omissões, aglutinações e representações múltiplas com o uso predominante de vogais e dificuldades na organização viso-espacial. Em matemática, apesar do reconhecimento numérico, não realizou operações aritméticas. Não foram observadas alterações na avaliação audiológica periférica. A constelação de sintomas comportamentais, cognitivos, linguísticos e perceptivos, previstos na síndrome do X-Frágil, somada às alterações estruturais do sistema nervoso central, pertencentes à variante de Dandy-Walker, trouxeram interferências marcantes no desenvolvimento das habilidades comunicativas, no aprendizado da leitura e escrita e na integração social do indivíduo.

Development, implementation, and assessment of a distance module in endocrine physiology

Rangel EM, Mendes IA, Carnio EC, Marchi Alves LM, Godoy S, Crispim JA. Development, implementation, and assessment of a distance module in endocrine physiology. Adv Physiol Educ 34: 70-74, 2010; doi: 10.1152/advan.00070.2009.-This study aimed to develop, implement, and assess a distance module in endocrine physiology in TelEduc for undergraduate nursing students from a public university in Brazil, with a sample size of 44 students. Stage 1 consisted of the development of the module, through the process of creating a distance course by means of the Web. Stage 2 was the planning of the module's practical functioning, and stage 3 was the planning of student evaluations. In the experts' assessment, the module complied with pedagogical and technical requirements most of the time. In the practical functioning stage, 10 h were dedicated for on-site activities and 10 h for distance activities. Most students (93.2%) were women between 19 and 23 yr of age (75%). The internet was the most used means to remain updated for 23 students (59.0%), and 30 students (68.2%) accessed it from the teaching institution. A personal computer was used by 23 students (56.1%), and most of them (58.1%) learned to use it alone. Access to a forum was more dispersed (variation coefficient: 86.80%) than access to chat (variation coefficient: 65.14%). Average participation was 30 students in forums and 22 students in the chat. Students' final grades in the module averaged 8.5 (SD: 1.2). TelEduc was shown to be efficient in supporting the teaching- learning process of endocrine physiology.

E-Learning Used in a Training Course on Atraumatic Restorative Treatment (ART) for Brazilian Dentists

The purpose of this study was to assess the benefits of using e-learning resources in a dental training course on Atraumatic Restorative Treatment (ART). This e-course was given in a DVD format, which presented the ART technique and philosophy. The participants were twenty-four dentists from the Brazilian public health system. Prior to receiving the DVD, the dentists answered a questionnaire regarding their personal data, previous knowledge about ART, and general interest in training courses. The dentists also participated in an assessment process consisting of a test applied before and after the course. A single researcher corrected the tests, and intraexaminer reproducibility was calculated (kappa=0.89). Paired t-tests were carried out to compare the means between the assessments, showing a significant improvement in the performance of the subjects on the test taken after the course (p<0.05). A linear regression model was used with the difference between the means as the outcome. A greater improvement on the test results was observed among female dentists (p=0.034), dentists working for a shorter period of time in the public health system (p=0.042), and dentists who used the ART technique only for urgent and/or temporary treatment (p=0.010). In conclusion, e-learning has the potential of improving the knowledge that dentists working in the public health system have about ART, especially those with less clinical experience and less knowledge about the subject.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Demotivation: Understanding Resistance to English Language Learning - The Case of Vietnamese Students

Demotivation in English language learning was investigated, using Vietnam as a case study, with three main foci: (i) the reasons (i.e., the demotives) underlying demotivation; (ii) the degree of influence of different demotives; and (iii) students’ experiences in overcoming demotivation. Using stimulated recall essays from 100 university students of their foreign language learning experiences, the findings indicated that demotivation was a significant issue for EFL learning, and a framework for discussing the different sources of demotives was developed. While some categories of demotives occurred more frequent than others, no category appeared to be more or less difficult to overcome. Rather, students’ awareness of the role of English language and their determination to succeed were critical factors in overcoming demotivation.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Triple objective team mentoring: achieving learning objectives with chemical engineering students

A sophisticated style of mentoring has been found to be essential to support engineering student teams undertaking technically demanding, real-world problems as part of a Project-Centred Curriculum (PCC) at The University of Queensland. The term ‘triple-objective’ mentoring was coined to define mentoring that addresses not only the student’s technical goal achievement but also their time and team management. This is achieved through a number of formal mentor meetings that are informed by a confidential instrument which requires students to individually reflect on team processes prior to the meeting, and a checklist of technical requirements against which the interim student team progress and achievements are assessed. Triple-objective mentoring requires significant time input and coordination by the academic but has been shown to ensure effective student team work and learning undiminished by team dysfunction. Student feedback shows they value the process and agree that the tools developed to support the process are effective in developing and assessing team work and skills with average scores mostly above 3 on a four point scale.

Project to investigate improving literacy and numeracy outcomes of distance education students in the early years of schooling

The project was commissioned to investigate and analyse the issue of effective support for distance education students in the early years of school to maximise literacy and numeracy outcomes. The scope of this project was limited to students living in rural and remote areas who are undertaking education at home and who are in their early years of schooling. For the purpose of this project, the early years are conceptualised as the first three years of formal compulsory schooling in each of the States and Territories. There were a number of key tasks for the project which included: 1. Examining of the role of home tutors/supervisors This included interviewing personnel from the State and Territory distance education providers as well as the principals, teachers, home tutors and children. 2. Describing literacy and numeracy teaching and learning, and the use of information and communication technologies (ICT) in distance education This aspect of the project involved a critical review and analysis of relevant literature and reports in the last five years, and a consideration of the new initiatives that had been implemented in the States and Territories in the last two years. 3. The development of resources Through examination of the role of home tutors/supervisors, and an examination of literacy and numeracy and the use of technology in distance education, three resources were developed: ● A guide for home tutors/supervisors and schools of distance education about effective intervention and assessment strategies to support students’ learning and to assist the home tutors/supervisors in implementing ICT to support the development of literacy and numeracy in the early years. ● A calendar of activities for literacy and numeracy that would act as a stimulus for integrated and authentic activity for young children. ● An embryonic website of resources for the stakeholders in rural and distance education that might act as a catalyst for future resource building and sharing. In this way the final key task of the project, which was to create a context for a strategic dissemination plan, was realised when a strategy to address effective dissemination of the findings of the project so as to maximise their usefulness for the relevant groups was achieved.

The clinical utility of the Hopkins Verbal Learning Test as a screening test for mild dementia

Objective. The purpose of this study was to determine whether the Hopkins Verbal Learning Test (HVLT) could be used as a valid and reliable screening test for mild dementia in older people, and to compare its performance to that of the Mini-Mental State Examination (MMSE). Method. Using a cross-sectional design, we studied three groups of older subjects recruited from a district geriatric psychiatry service: (1) 26 patients with DSM-IV dementia and MMSE scores of 18 or better; (2) 15 patients with psychiatric diagnoses other than dementia; and (3) 15 normal controls. The relationship of each potential cutting point on the HVLT and the MMSE was examined against the independently ascertained DSM-IV diagnoses of dementia using a Receiver Operating Characteristic (ROC) analysis. Results. The subjects consisted of 21 (37.5%) males and 35 (62.5%) females with a mean age of 74.7 (SD 6.1) years and a mean of 8.5 (SD 1.8) years of formal education. ROC analysis indicated that the optimal cutting point for detecting mild dementia in this group of subjects using the HVLT was 18/19 (sensitivity = 0.96, specificity = 0.80) and using the MMSE was 25/26 (sensitivity = 0.88, specificity = 0.93). Conclusions. The HVLT can be recommended as a valid and reliable screening test for mild dementia and as an adjunct in the clinical assessment of older people. The HVLT had better sensitivity than the MMSE in detecting patients with mild dementia, whereas the MMSE had better specificity. Copyright (C) 2000 John Wiley & Sons, Ltd.

Progress in integrated assessment and modelling

Environmental processes have been modelled for decades. However. the need for integrated assessment and modeling (IAM) has,town as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that Lire needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers. stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century. (C) 2002 Elsevier Science Ltd. All rights reserved.