839 resultados para Learning. Mathematics. Quadratic Functions. GeoGebra
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This article provides a selective overview of the functional neuroimaging literature with an emphasis on emotional activation processes. Emotions are fast and flexible response systems that provide basic tendencies for adaptive action. From the range of involved component functions, we first discuss selected automatic mechanisms that control basic adaptational changes. Second, we illustrate how neuroimaging work has contributed to the mapping of the network components associated with basic emotion families (fear, anger, disgust, happiness), and secondary dimensional concepts that organise the meaning space for subjective experience and verbal labels (emotional valence, activity/intensity, approach/withdrawal, etc.). Third, results and methodological difficulties are discussed in view of own neuroimaging experiments that investigated the component functions involved in emotional learning. The amygdala, prefrontal cortex, and striatum form a network of reciprocal connections that show topographically distinct patterns of activity as a correlate of up and down regulation processes during an emotional episode. Emotional modulations of other brain systems have attracted recent research interests. Emotional neuroimaging calls for more representative designs that highlight the modulatory influences of regulation strategies and socio-cultural factors responsible for inhibitory control and extinction. We conclude by emphasising the relevance of the temporal process dynamics of emotional activations that may provide improved prediction of individual differences in emotionality.
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This article reviews the psychophysiological and brain imaging literature on emotional brain function from a methodological point of view. The difficulties in defining, operationalising and measuring emotional activation and, in particular, aversive learning will be considered. Emotion is a response of the organism during an episode of major significance and involves physiological activation, motivational, perceptual, evaluative and learning processes, motor expression, action tendencies and monitoring/subjective feelings. Despite the advances in assessing the physiological correlates of emotional perception and learning processes, a critical appraisal shows that functional neuroimaging approaches encounter methodological difficulties regarding measurement precision (e.g., response scaling and reproducibility) and validity (e.g., response specificity, generalisation to other paradigms, subjects or settings). Since emotional processes are not only the result of localised but also of widely distributed activation, a more representative model of assessment is needed that systematically relates the hierarchy of high- and low-level emotion constructs with the corresponding patterns of activity and functional connectivity of the brain.
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This study investigated the effectiveness of incorporating several new instructional strategies into an International Baccalaureate (IB) chemistry course in terms of how they supported high school seniors’ understanding of electrochemistry. The three new methods used were (a) providing opportunities for visualization of particle movement by student manipulation of physical models and interactive computer simulations, (b) explicitly addressing common misconceptions identified in the literature, and (c) teaching an algorithmic, step-wise approach for determining the products of an aqueous solution electrolysis. Changes in student understanding were assessed through test scores on both internally and externally administered exams over a two-year period. It was found that visualization practice and explicit misconception instruction improved student understanding, but the effect was more apparent in the short-term. The data suggested that instruction time spent on algorithm practice was insufficient to cause significant test score improvement. There was, however, a substantial increase in the percentage of the experimental group students who chose to answer an optional electrochemistry-related external exam question, indicating an increase in student confidence. Implications for future instruction are discussed.
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The reported research project involved studying how teaching science using demonstrations, inquiry-based cooperative learning groups, or a combination of the two methods affected sixth grade students’ understanding of air pressure and density. Three different groups of students were each taught the two units using different teaching methods. Group one learned about the topics through both demonstrations and inquirybased cooperative learning, whereas group two only viewed demonstrations, and group three only participated in inquiry-based learning in cooperative learning groups. The study was designed to answer the following two questions: 1. Which teaching strategy works best for supporting student understanding of air pressure and density: demonstrations, inquirybased labs in cooperative learning groups, or a combination of the two? 2. And what effect does the time spent engaging in a particular learning experience (demonstrations or labs) have on student learning? Overall, the data did not provide sufficient evidence that one method of learning was more effective than the others. The results also suggested that spending more time on a unit does not necessarily equate to a better understanding of the concepts by the students. Implications for science instruction are discussed.
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The purpose of the study was to design, implement, and assess the effects of a teaching unit about fuel sources and chemical energy on students’ learning. The unit was designed to incorporate students’ experiences in a way that was aligned with the Michigan High School Content Expectations. The study was completed with all of the students taking General Chemistry in a rural Michigan high school in the 2010-11 school year. There were 138 participants total. The participants were mostly Caucasian and the majority were in the 11th grade. Of these, 77 constituted the experimental group and were taught the unit. The additional 61 participants in the control group were given the posttest only. Data was derived from the results of pre/post tests, final assessment projects, and the researcher’s observations. A pretest that contained questions about the fuel sources was administered at the beginning of the unit. An identical posttest was administered at the completion of the unit. A final assessment project required students to choose the best fuel source for the area, and support their opinion with facts and data from their research or the learning activities and labs performed in class. The results of the study revealed that the teaching unit did produce significant learning gains in the experimental group. The results also indicated that the teaching unit added value to the current General Chemistry curriculum by expanding what students were learning. The instructional goals of the unit were aligned with the Michigan High School Content Expectations. The results also revealed that the students were able to learn to support their thinking and decisions with explanations based on the data and labs. These are essential science literacy skills. The study supported the view that connecting the required curriculum with students’ experiences and interests was effective, and that students can learn important science literacy skills, such as providing support for arguments and communicating scientific explanations, when given adequate teacher support.
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The purpose of this research was to address how culturally informed ethnomathematical methods of teaching can be utilized to support the learning of Navajo students in mathematics. The study was conducted over the course of four years on the Navajo Reservations at Tohatchi Middle School in Tohatchi New Mexico. The students involved in the study were all in 8th grade and were enrolled either in Algebra 1 or a Response to Intervention, RTI, class. The data collected came in the form of a student survey, student observation and student assessment. The teacher written survey, a math textbook word problem, and two original math textbook problems along with their rewritten version were the sources of these three studies. The first year of the study consisted of a math attitude survey and how Navajo students perceived math as a subject of interest. The students answered four questions pertaining to their thoughts about mathematics. The students’ responses were positive according to their written answers. The second year of the study involved the observation of how students worked through a math word problem as a group. This method tested how the students culturally interacted in order to solve a math problem. Their questions and reasoning to solve the problem were shared with peers and the teacher. The teacher supported the students in understanding and solving the problem by asking questions that kept the students focused on the goal of solving the problem. The students worked collaboratively and openly in order to complete the activity. During the iv study, the teacher was more able to notice the students’ deficiencies individually or as a group, therefore was able to support them in a more specific manner. The last study was conducted over a period of two different years. This study was used to determine how textbook bias in the form of its sentence structure or word choice affects the performance of students who are not culturally familiar with one or both. It was found that the students performed better and took less time on the rewritten problem than on the original problem. The data suggests that focusing on the culture, language and education of Navajo students can affect how the students learn and understand math.
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Presentation by Dr. Frank Ackerman. Additional information can be found on Montana Tech's Department of Computer Sciences website.
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Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.
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Second Life (SL) is an ideal platform for language learning. It is called a Multi-User Virtual Environment, where users can have varieties of learning experiences in life-like environments. Numerous attempts have been made to use SL as a platform for language teaching and the possibility of SL as a means to promote conversational interactions has been reported. However, the research so far has largely focused on simply using SL without further augmentations for communication between learners or between teachers and learners in a school-like environment. Conversely, not enough attention has been paid to its controllability which builds on the embedded functions in SL. This study, based on the latest theories of second language acquisition, especially on the Task Based Language Teaching and the Interaction Hypothesis, proposes to design and implement an automatized interactive task space (AITS) where robotic agents work as interlocutors of learners. This paper presents a design that incorporates the SLA theories into SL and the implementation method of the design to construct AITS, fulfilling the controllability of SL. It also presents the result of the evaluation experiment conducted on the constructed AITS.
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Ausgehend von der typischen IT‐Infrastruktur für E‐Learning an Hochschulen auf der einen Seite sowie vom bisherigen Stand der Forschung zu Personal Learning Environments (PLEs) auf der anderen Seite zeigt dieser Beitrag auf, wie bestehende Werkzeuge bzw. Dienste zusammengeführt und für die Anforderungen der modernen, rechnergestützten Präsenzlehre aufbereitet werden können. Für diesen interdisziplinären Entwicklungsprozess bieten sowohl klassische Softwareentwicklungsverfahren als auch bestehende PLE‐Modelle wenig Hilfestellung an. Der Beitrag beschreibt die in einem campusweiten Projekt an der Universität Potsdam verfolgten Ansätze und die damit erzielten Ergebnisse. Dafür werden zunächst typische Lehr‐/Lern‐bzw. Kommunikations‐Szenarien identifiziert, aus denen Anforderungen an eine unterstützende Plattform abgeleitet werden. Dies führt zu einer umfassenden Sammlung zu berücksichtigender Dienste und deren Funktionen, die gemäß den Spezifika ihrer Nutzung in ein Gesamtsystem zu integrieren sind. Auf dieser Basis werden grundsätzliche Integrationsansätze und technische Details dieses Mash‐Ups in einer Gesamtschau aller relevanten Dienste betrachtet und in eine integrierende Systemarchitektur überführt. Deren konkrete Realisierung mit Hilfe der Portal‐Technologie Liferay wird dargestellt, wobei die eingangs definierten Szenarien aufgegriffen und exemplarisch vorgestellt werden. Ergänzende Anpassungen im Sinne einer personalisierbaren bzw. adaptiven Lern‐(und Arbeits‐)Umgebung werden ebenfalls unterstützt und kurz aufgezeigt.
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Given a reproducing kernel Hilbert space (H,〈.,.〉)(H,〈.,.〉) of real-valued functions and a suitable measure μμ over the source space D⊂RD⊂R, we decompose HH as the sum of a subspace of centered functions for μμ and its orthogonal in HH. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.
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We focus on kernels incorporating different kinds of prior knowledge on functions to be approximated by Kriging. A recent result on random fields with paths invariant under a group action is generalised to combinations of composition operators, and a characterisation of kernels leading to random fields with additive paths is obtained as a corollary. A discussion follows on some implications on design of experiments, and it is shown in the case of additive kernels that the so-called class of “axis designs” outperforms Latin hypercubes in terms of the IMSE criterion.