The development of an eBook for teaching and learning mathematics

Mobile devices, smartphones, phablets and tablets, are widely avail‐ able. This is a generation of digital natives. We cannot ignore that they are no longer the same students for which the education system was designed tradition‐ ally. Studying math is many times a cumbersome task. But this can be changed if the teacher takes advantage of the technology that is currently available. We are working in the use of different tools to extend the classroom in a blended learning model. In this paper, it is presented the development of an eBook for teaching mathematics to secondary students. It is developed with the free and open standard EPUB 3 that is available for Android and iOS platforms. This specification supports video embedded in the eBook. In this paper it is shown how to take advantage of this feature, making videos available about lectures and problems resolutions, which is especially interesting for learning mathematics.

Online Resources for Improving EFL Teaching and Learning

El presente artículo plantea el beneficio de utilizar recursos en línea en la enseñanza del inglés. Destaca que los estudiantes fortalecerán, no solo el uso de la lengua meta, sino también el de los recursos tecnológicos. Para ejemplificar, se presenta una serie de ejercicios en línea que desarrollan diversas habilidades de la lengua meta como también las ventajas y desventajas del uso de los mismos. Por último, se comparten los resultados obtenidos de una encuesta aplicada sobre el uso de recursos en línea en las clases de inglés.The benefit of using online sources in the EFL class is analyzed here starting from the perspective that this helps students improve not only their use of the language but also their use of technology. Sample online exercises focusing on the development of different language skills are described here, along with the advantages and disadvantages of using online sources. Finally, the results obtained from a survey on the use of online sources in the EFL classes are presented.

Epothilone-d rescues cognition and attenuates alzheimer’s disease-like pathology in APP/PS1 mice

AIMS: Cognitive decline in Alzheimer's disease (AD) patients has been linked to synaptic damage and neuronal loss. Hyperphosphorylation of tau protein destabilizes microtubules leading to the accumulation of autophagy/vesicular material and the generation of dystrophic neurites, thus contributing to axonal/synaptic dysfunction. In this study, we analyzed the effect of a microtubule-stabilizing compound in the progression of the disease in the hippocampus of APP751SL/PS1M146L transgenic model. METHODS: APP/PS1 mice (3 month-old) were treated with a weekly intraperitoneal injection of 2 mg/kg epothilone-D (Epo-D) for 3 months. Vehicle-injected animals were used as controls. Mice were tested on the Morris water maze, Y-maze and object-recognition tasks for memory performance. Abeta, AT8, ubiquitin and synaptic markers levels were analyzed by Western-blots. Hippocampal plaque, synaptic and dystrophic loadings were quantified by image analysis after immunohistochemical stainings. RESULTS: Epo-D treated mice exhibited a significant improvement in the memory tests compared to controls. The rescue of cognitive deficits was associated to a significant reduction in the AD-like hippocampal pathology. Levels of Abeta, APP and ubiquitin were significantly reduced in treated animals. This was paralleled by a decrease in the amyloid burden, and more importantly, in the plaque-associated axonal dystrophy pathology. Finally, synaptic levels were significantly restored in treated animals compared to controls. CONCLUSION: Epo-D treatment promotes synaptic and spatial memory recovery, reduces the accumulation of extracellular Abeta and the associated neuritic pathology in the hippocampus of APP/PS1 model. Therefore, microtubule stabilizing drugs could be considered therapeutical candidates to slow down AD progression. Supported by FIS-PI12/01431 and PI15/00796 (AG),FIS-PI12/01439 and PI15/00957(JV)

Reading Between the Lines of Teaching and Learning

Some students lack reading skills due to biological or environmental factors. Accordingly, in my role as an educator, I would like to know if Dialogic Reading Strategies can help Spanish speaking caregivers to facilitate an interactive reading routine at home with their child.

Teaching and learning sexuality in nursing education

Sexuality is recognized as part of holistic nursing care, but its inclusion in clinical practice and nursing training is inconsistent. Based on the question "How students and teachers acknowledge sexuality in teaching and learning?", we developed a study in order to characterize the process of teaching and learning sexuality in a micro perspective of curriculum development. We used a mixed methods design with a sequential strategy: QUAN-qual of descriptive and explanatory type. 646 students and teachers participated. The quantitative component used questionnaire surveys. Document analysis was used in the additional component. A curricular dimension of sexuality emerges guided by a behaviourist line and based on a biological vision. The issues considered sage are highlighted and framed in steps of adolescence and adulthood and more attacghed to female sexuality and procreative aspect. There is in emeergence a hidden curriculum by reference to content from other dimensions of sexuality but less often expressed. Theoretical learning follows a communicational model of reality through abstraction strategies, which infers a deductive method of learning, with a behaviourist approach to assessment. Clinical teaching adresses sexuality in combination with reproductive lealth nursing. The influencing factors of teaching and learning of sexuality were also explored. We conclude that the vision of female sexuality taught and learned in relation to women has a projection of care in clinical practice based on the same principles

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.

The Students’ Perspective of Geometry Teaching and Learning in High School

The purpose of this article is to present the results obtained from a questionnaire applied to Costa Rican high school students, in order to know their perspectives about geometry teaching and learning. The results show that geometry classes in high school education have been based on a traditional system of teaching, where the teacher presents the theory; he presents examples and exercises that should be solved by students, which emphasize in the application and memorization of formulas. As a consequence, visualization processes, argumentation and justification don’t have a preponderant role. Geometry is presented to students like a group of definitions, formulas, and theorems completely far from their reality and, where the examples and exercises don’t possess any relationship with their context. As a result, it is considered not important, because it is not applicable to real life situations. Also, the students consider that, to be successful in geometry, it is necessary to know how to use the calculator, to carry out calculations, to have capacity to memorize definitions, formulas and theorems, to possess capacity to understand the geometric drawings and to carry out clever exercises to develop a practical ability.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

Diurnal inhibition of NMDA-EPSCs at rat hippocampal mossy fibre synapses through orexin-2 receptors.

Diurnal release of the orexin neuropeptides orexin-A (Ox-A, hypocretin-1) and orexin-B (Ox-B, hypocretin-2) stabilises arousal, regulates energy homeostasis and contributes to cognition and learning. However, whether cellular correlates of brain plasticity are regulated through orexins, and whether they do so in a time-of-day-dependent manner, has never been assessed. Immunohistochemically we found sparse but widespread innervation of hippocampal subfields through Ox-A- and Ox-B-containing fibres in young adult rats. The actions of Ox-A were studied on NMDA receptor (NMDAR)-mediated excitatory synaptic transmission in acute hippocampal slices prepared around the trough (Zeitgeber time (ZT) 4-8, corresponding to 4-8 h into the resting phase) and peak (ZT 23) of intracerebroventricular orexin levels. At ZT 4-8, exogenous Ox-A (100 nm in bath) inhibited NMDA receptor-mediated excitatory postsynaptic currents (NMDA-EPSCs) at mossy fibre (MF)-CA3 (to 55.6 ± 6.8% of control, P = 0.0003) and at Schaffer collateral-CA1 synapses (70.8 ± 6.3%, P = 0.013), whereas it remained ineffective at non-MF excitatory synapses in CA3. Ox-A actions were mediated postsynaptically and blocked by the orexin-2 receptor (OX2R) antagonist JNJ10397049 (1 μm), but not by orexin-1 receptor inhibition (SB334867, 1 μm) or by adrenergic and cholinergic antagonists. At ZT 23, inhibitory effects of exogenous Ox-A were absent (97.6 ± 2.9%, P = 0.42), but reinstated (87.2 ± 3.3%, P = 0.002) when endogenous orexin signalling was attenuated for 5 h through i.p. injections of almorexant (100 mg kg(-1)), a dual orexin receptor antagonist. In conclusion, endogenous orexins modulate hippocampal NMDAR function in a time-of-day-dependent manner, suggesting that they may influence cellular plasticity and consequent variations in memory performance across the sleep-wake cycle.

Parameter setting and statistical learning

Three main models of parameter setting have been proposed: the Variational model proposed by Yang (2002; 2004), the Structured Acquisition model endorsed by Baker (2001; 2005), and the Very Early Parameter Setting (VEPS) model advanced by Wexler (1998). The VEPS model contends that parameters are set early. The Variational model supposes that children employ statistical learning mechanisms to decide among competing parameter values, so this model anticipates delays in parameter setting when critical input is sparse, and gradual setting of parameters. On the Structured Acquisition model, delays occur because parameters form a hierarchy, with higher-level parameters set before lower-level parameters. Assuming that children freely choose the initial value, children sometimes will miss-set parameters. However when that happens, the input is expected to trigger a precipitous rise in one parameter value and a corresponding decline in the other value. We will point to the kind of child language data that is needed in order to adjudicate among these competing models.

Is affective learning really resistant to extinction? Stimulus ratings and affective priming give different answers

Affective learning, the learning of likes and dislikes, is proposed to differ from signal learning, the learning of relationships between events. However, affective learning research varies in the methodology used, and in addition, researchers concerned primarily with affective learning tend to use different paradigms from those concerned with signal learning. The current research used an affective priming task in addition to verbal ratings to assess changes in the valence of neutral geometric shapes in an aversive differential conditioning procedure. After acquisition, affective learning was present as indexed by ratings and affective priming, whereas after extinction, affective learning remained significant only in the ratings. This study suggests that different measures of affective learning may be differentially sensitive to valence, which has implications for studies that employ verbal ratings as the sole measure of affective learning. Moreover, there is no evidence from the current study that affective learning differs from signal learning.