The Man Creates Instruments that Transform Himself: An Overview of GERE Research within Mathematics Education

This paper discusses GERE (Study Group on Resources for Education) members’ research trajectory in three different lines: Teaching practice and practice management, Teacher education and identity, and Support for learning and resource generation. It discusses how researchers seek to understand technology integration in the practice of distance education in Brazil, with the instrumental orchestrations lens, revealing the changes made in the didactic configurations, from the multiplicity of teachers responsible for each discipline. Teachers’ documentation is discussed in the process of resource elaboration and use and guided by design, experimentation, and reflection of the generated instrument, such as digital textbooks and mathematical games. The notion of integrating artefacts is discussed both from the perspective of collaborative learning and of instrumental meta-orchestration, a teacher education model about instrumental orchestration. Finally, thinking about the artefact as a support for learning also made us work on generating devices aimed at specific concepts such as covariation in learning functions

Peanut yield under irrigation levels in off-season cultivation

Water deficit is considered the most critical environmental factor for peanut production in Brazil, as it constitutes one of the major constraints to the expansion of its cultivation in the suitable crop zones of the country. Determining crop water demand is fundamental to increasing yield with lower water consumption. The present study aimed to evaluate the effects of full and deficit irrigation levels (L1 = 8%, L2 = 27%, L3 = 63%, L4 = 94% and L5 = 100% replenishment of crop evapotranspiration) on the development, growth and yield of peanut crop sown in two times, February and March. Treatments were distributed in a split-plot randomized complete block design, with four replicates, using a line-source sprinkler system. Irrigation depths from 65 to 314 mm were applied with the levels L1 to L5 during the first and second cropping cycles. Full irrigation with sowing in March was more advantageous due to yield increase of up to 30% compared to sowing in February, but crop cycle was 25 days longer. Water stress caused by deficit irrigation reduced plant height, seed mass and pod yield, while full irrigation (L5) led to yields from 4,141 to 5,102 kg ha-1 for February and March, approximately three times higher than those obtained with the lowest irrigation level (L1). Highlights Peanut has great importance in the food and industry of several countries. The results of this research apply to regions that require irrigation, which is of about 70% of the areas of the globe with this legume. This paper contributes with information that emphasizes the possibility of cultivation in the drought season, aiming at the expansion of the crop and the production of quality seeds using irrigation.Water deficit is considered the most critical environmental factor for peanut production in Brazil, as it constitutes one of the major constraints to the expansion of its cultivation in the suitable crop zones of the country. Determining crop water demand is fundamental to increasing yield with lower water consumption. The present study aimed to evaluate the effects of full and deficit irrigation levels (L1 = 8%, L2 = 27%, L3 = 63%, L4 = 94% and L5 = 100% replenishment of crop evapotranspiration) on the development, growth and yield of peanut crop sown in two times, February and March. Treatments were distributed in a split-plot randomized complete block design, with four replicates, using a line-source sprinkler system. Irrigation depths from 65 to 314 mm were applied with the levels L1 to L5 during the first and second cropping cycles. Full irrigation with sowing in March was more advantageous due to yield increase of up to 30% compared to sowing in February, but crop cycle was 25 days longer. Water stress caused by deficit irrigation reduced plant height, seed mass and pod yield, while full irrigation (L5) led to yields from 4,141 to 5,102 kg ha-1 for February and March, approximately three times higher than those obtained with the lowest irrigation level (L1). Highlights Peanut has great importance in the food and industry of several countries. The results of this research apply to regions that require irrigation, which is of about 70% of the areas of the globe with this legume. This paper contributes with information that emphasizes the possibility of cultivation in the drought season, aiming at the expansion of the crop and the production of quality seeds using irrigation

Concepções sobre periodicidade em atividades de modelagem

O objetivo deste trabalho foi experimentar e analisar uma seqüência de atividades de ensino, abordando o conceito de periodicidade em situações de simulação por computador, utilizando o software MODELLUS. A seqüência foi elaborada a partir de estudos preliminares, revisão da literatura e de um levantamento das concepções prévias do conceito de periodicidade de alunos do 1º ano do ensino médio em um teste de sondagem. O estudo revelou um conjunto de dez concepções alternativas que os estudantes tinham sobre periodicidade. Após essa fase, aplicamos a seqüência de atividades com dois alunos, trabalhando em par, interagindo entre si e com o computador. Analisamos a evolução do conceito de periodicidade nas respostas e justificativas das duplas escolhidas, durante o desenvolvimento da seqüência, levando em consideração a presença ou não das concepções do conceito identificadas previamente na sondagem. Os resultados mostraram que as simulações foram utilizadas pelos alunos como elemento validador de suas respostas e como ferramenta de exploração do conceito de período quando a identificação deste não lhes parecia imediata em outras formas de representação. Sendo utilizadas como uma forma de representação, as simulações favoreceram a superação de algumas concepções e fizeram surgir outras devido a suas limitações de representar um aspecto de um fenômeno real e devido ao fato de não haver intervenção didática por parte de um professor. Concluímos que a abordagem do conceito de periodicidade, a partir de recursos de simulação, favoreceu a sua compreensão e a sua identificação em diversas formas de representação. Porém, para representação algébrica, mostrou necessitar de uma maior ênfas

The birth of the documentary system of mathematics pre-service teachers in a supervised internship with the creation of a digital textbook chapter

International audienceThis presentation, part of a doctoral research, aims at identifying interconnections between professional development of mathematics pre-service teacher during a supervised internship and the resources used to build and use a chapter of digital mathematics textbook (about the introduction of algebraic functions) in a classroom with 30 pupils from 12 to 13 years-old. Based on the Documental Approach to didactics (Guedet and Trouche, 2009), we bring into discussion partial analysis of one of the two data collection comprised of registers of planning process, prototyping of the chapter and its use in classrooms, regarding the first version of the resource. The partial results suggest that the pre-service teacher took control of part of his own formative path while decided what he needs to produce his own resource, using as reference his interaction with pupils since his first experience of using the digital chapter

The birth of the documentary system of mathematics pre-service teachers in a supervised internship with the creation of a digital textbook chapter

International audienceThis presentation, part of a doctoral research, aims at identifying interconnections between professional development of mathematics pre-service teacher during a supervised internship and the resources used to build and use a chapter of digital mathematics textbook (about the introduction of algebraic functions) in a classroom with 30 pupils from 12 to 13 years-old. Based on the Documental Approach to didactics (Guedet and Trouche, 2009), we bring into discussion partial analysis of one of the two data collection comprised of registers of planning process, prototyping of the chapter and its use in classrooms, regarding the first version of the resource. The partial results suggest that the pre-service teacher took control of part of his own formative path while decided what he needs to produce his own resource, using as reference his interaction with pupils since his first experience of using the digital chapter

A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments

Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual and non-contextual image classifications were semi-automatically compared to find any divergence that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models built to classify a single image were used to classify an image time series due to domain adaptation. The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our strategy can be used to guide computational systems to make different decisions to solve a problem in response to each context. The proposed strategy is relevant for improving machine learning, as its use allows computers to have a more organized learning process. Our strategy is presented with respect to its applicability to help monitor environmental disasters. A minor limitation was found in the results caused by the use of domain adaptation. This type of limitation is fairly common when using domain adaptation, and therefore has no significance. Even so, future work should investigate other techniques for transfer learning

A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments

Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual and non-contextual image classifications were semi-automatically compared to find any divergence that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models built to classify a single image were used to classify an image time series due to domain adaptation. The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our strategy can be used to guide computational systems to make different decisions to solve a problem in response to each context. The proposed strategy is relevant for improving machine learning, as its use allows computers to have a more organized learning process. Our strategy is presented with respect to its applicability to help monitor environmental disasters. A minor limitation was found in the results caused by the use of domain adaptation. This type of limitation is fairly common when using domain adaptation, and therefore has no significance. Even so, future work should investigate other techniques for transfer learning

Trail making and cognitive set-shifting

We tested the hypothesis that Part B of the Trail Making Test (TMT) is a measure of cognitive set-shifting ability in 55 normal subjects with the conventional (written) TMT and a verbal adaptation, the "verbal TMT" (vTMT). The finding of a significant association between Parts B of TMT and vTMT (r = 0,59, p < 0,001), after correcting for age and education, supports the view that Part B of TMT is a valid measure of the ability to alternate between cognitive categories