Implications for educational practice of the science of learning and development

Abstract

This article draws out the implications for school and classroom practices of an emerging consensus about the science of learning and development, outlined in a recent synthesis of the research. Situating the review in a developmental systems framework, we synthesize evidence from the learning sciences and several branches of educational research regarding well-vetted strategies that support the kinds of relationships and learning opportunities needed to promote children’s well-being, healthy development, and transferable learning. In addition, we review research regarding practices that can help educators respond to individual variability, address adversity, and support resilience, such that schools can enable all children to find positive pathways to adulthood.     

Progress bias versus status quo bias in the ethics of emerging science and technology

How should we handle ethical issues related to emerging science and technology in a rational way? This is a crucial issue in our time. On the one hand, there is great optimism with respect to technology. On the other, there is pessimism. As both perspectives are based on scarce evidence, they may appear speculative and irrational. Against the pessimistic perspective to emerging technology, it has been forcefully argued that there is a status quo bias (SQB) fuelling irrational attitudes to emergent science and technology and greatly hampering useful development and implementation. Therefore, this article starts by analysing the SQB using human enhancement as a case study. It reveals that SQB may not be as prominent in restricting the implementation of emergent technologies as claimed in the ethics literature, because SQB (a) is fuelled by other and weaker drivers than those addressed in the literature, (b) is at best one amongst many drivers of attitudes towards emergent science and technology, and (c) may not be a particularly prominent driver of irrational decision-making. While recognizing that SQB can be one driver behind pessimism, this article investigates other and counteracting forces that may be as strong as SQB. Progress bias is suggested as a generic term for the various drivers of unwarranted science and technology optimism. Based on this analysis, a test for avoiding or reducing this progress bias is proposed. Accordingly, we should recognize and avoid a broad range of biases in the assessment of emerging and existing science and technology in order to promote an open and transparent deliberation.     

Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space

Abstract

The use of modern data science has recently emerged as a promising new path to tackling the complex challenges involved in the creation of next-generation chemistry and materials. However, despite the appeal of this potentially transformative development, the chemistry community has yet to incorporate it as a central tool in every-day work. Our research program is designed to enable and advance this emerging research approach. It is centred around the creation of a software ecosystem that brings together physics-based modelling, high-throughput in silico screening and data analytics (i.e. the use of machine learning and informatics for the validation, mining and modelling of chemical data). This cyberinfrastructure is devised to offer a comprehensive set of data science techniques and tools as well as a general-purpose scope to make it as versatile and widely applicable as possible. It also emphasises user-friendliness to make it accessible to the community at large. It thus provides the means for the large-scale exploration of chemical space and for a better understanding of the hidden mechanisms that determine the properties of complex chemical systems. Such insights can dramatically accelerate, streamline and ultimately transform the way chemical research is conducted. Aside from serving as a production-level tool, our cyberinfrastructure is also designed to facilitate and assess methodological innovation. Both the software and method development work are driven by concrete molecular design problems, which also allow us to assess the efficacy of the overall cyberinfrastructure.     

Pathogen roid rage: Cholesterol utilization by Mycobacterium tuberculosis

Abstract

The ability of science and medicine to control the pathogen Mycobacterium tuberculosis (Mtb) requires an understanding of the complex host environment within which it resides. Pathological and biological evidence overwhelmingly demonstrate how the mammalian steroid cholesterol is present throughout the course of infection. Better understanding Mtb requires a more complete understanding of how it utilizes molecules like cholesterol in this environment to sustain the infection of the host. Cholesterol uptake, catabolism and broader utilization are important for maintenance of the pathogen in the host and it has been experimentally validated to contribute to virulence and pathogenesis. Cholesterol is catabolized by at least three distinct sub-pathways, two for the ring system and one for the side chain, yielding dozens of steroid intermediates with varying biochemical properties. Our ability to control this worldwide infectious agent requires a greater knowledge of how Mtb uses cholesterol to its advantage throughout the course of infection. Herein, the current state of knowledge of cholesterol metabolism by Mtb is reviewed from a biochemical perspective with a focus on the metabolic genes and pathways responsible for cholesterol steroid catabolism.     

Bears in a boat: Science content and language development through a problem-based learning experience

In Problem-Based Learning (PBL), a problem is posed before students have learned how to solve it. Language and PBL pair well for effective learning environments. Informed by language-based theories of learning and strategically adding methods for working with English Language Learners (ELLs), we developed an approach called Problem-Based Enhanced-Language Learning. In this article, we provide the background for the approach and explain the specifics of implementing Bears in a Boat, which we have used in many contexts including a second-grade urban classroom and a third-grade US-Mexico border classroom with significant numbers of ELLs. The science content learner outcomes are that students will be able to: (a) describe what it means to sink and float and (b) explain that the more weight you add to a boat, the deeper it will float.     

FOUNDING A NEW SCIENCE: MIND GENOMICS

We present in this article our vision for a new science, modeled on the emerging science of genomics and the technology of informatics. Our goal in this new science is to better understand how people react to ideas in a formal and structured way, using the principles of stimulus–response (from experimental psychology), conjoint analysis (from consumer research and statistics), Internet‐based testing (from marketing research) and multiple tests to identify patterns of mind‐sets (patterned after genomics). We show how this formal approach can then be used to construct new, innovative ideas in business. We demonstrate the approach using the development of new ideas for an electronic color palette for cosmetic products to be used by consumers.     

Children’s book series and associated curricula support elementary education and outreach in water resources

Background: Water resources are of fundamental importance to society, and are better managed by stakeholders who understand resource issues. Gaining such knowledge is a lifelong process best begun at an early age and best supported by educational approaches integrating across science, technology, engineering and mathematics (STEM). Research scientists can bring resource education to young audiences through children’s books and curricula that emphasise and integrate across STEM principals.

Aims: To encourage empathy for the environment in younger students, researchers at the Niwot Ridge Long Term Ecological Research site have developed a children’s book series and methods for training teachers in water science education.

Methods: Children’s books in the My Water series are paired with curricula, hands-on learning kits, teacher development training and dissemination of materials through school districts to further water science education.

Results: Thousands of children and educators have received training through the My Water book series, and a more broadly focused, federally funded Schoolyard Children’s Book Series has grown out of these efforts towards water resource education.

Conclusions: Children’s books and curricula that integrate STEM principals can play a key role in the development of environmental empathy and lifelong learning to support resource management.     

AI applications to medical images: From machine learning to deep learning

PurposeArtificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context.MethodsA narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections.ResultsWe first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way.ConclusionsBiomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.     

On the application of the information concept to learning theory

A learning curve derived by H. D. Landahl (Bull. Math. Biophysics,3, 71–77, 1941) from postulated neurological structures is shown to be derivable from simplified assumptions by introducing the information measure of the uncertainty of response. The possible significance of this approach to learning theory is discussed.     

综述: 果蝇复杂脑功能研究进展

全面揭示脑的奥秘是现代科学所面临的最大挑战.通过脑研究,我们可以获得防治脑疾病、认知及心理障碍的线索和工具,找到提高人类智力和心理健康水平的途径,并发展出具备高等智能的机器人.果蝇作为研究基因-神经回路-行为关系的首选模式动物,日益得到重视.本文围绕果蝇复杂脑功能包括视觉学习记忆、欲望与动机、情感相关行为和社会行为的研究意义及前景、已知调控基因及神经回路以及未来研究方向展开综述,便于读者把握相关领域的全貌.     

Drama as a valuable learning medium in early childhood

ABSTRACT

Theater arts encompass unique art forms comprising highly developed pedagogical applications apart from theater as performance. Specifically, the use of drama as a learning medium, referred to in the field as process drama, is most emphatically applicable to the education of young children. Young children actively gain skills in dialogue, collaboration, and creative problem solving, by collectively pretending, with teacher guidance, to be in an imaginative elsewhere—something most are well adept at from their own natural dramatic play. This article argues that ages 3–8 represent a critical time for children to benefit from process drama integrated into the curriculum and highlights the unique impact process drama can make when it is used to explore a particular problem, situation, or theme, referencing related national core arts. Documentation from well-established programs is presented, and recommendations for training drama teachers on how to work in early childhood and for early childhood teachers on how to integrate process drama into their curriculum are presented.     

Mathematical biology of learning to drive an automobile

In learning to drive, an individual must learn to rapidly make small corrective turns to the right or to the left as the car comes too close correspondingly to the left or to the right edges of the lane. The magnitude of the corrective turn depends on the angle at which the edge is approached. Thus, the individual must learn to produce a quantitatively correct response (corrective turn) to any one of an infinite number of possible stimuli (angles of approach). By making a number of highly oversimplifying assumptions, the problem can be reduced to a learning situation, studied previously by H. D. Landahl (Bull. Math. Biophysic,3, 13–26, 71–77, 1941). This is used not so much to obtain any relation that might be considered practically applicable immediately as toillustrate what kind of relation can be obtained from such considerations. It is shown how the safe speed of a driver depends on his total driving experience (total distance driven) as well as on his psychophysical parameters.     

Science,industry and the laity: Towards a knowledgeable society for biotechnology

This paper is concerned with the task of developing a 'knowledgeable society' for biotechnology. This is hindered by the fact that the scientific and commercial communities often engage in separate discourses from that which engages the general public. Different concepts of knowledge ('knowing') and learning underpin preoccupations and concerns in these three spheres, and different logics and assumptions about knowledge often apply. The paper begins by highlighting the key issues that exercise people in the public domain, and in relation to science and industry. It then applies concepts of knowledge, communication and learning to show how people in these domains construct issues differently. Finally, it makes some predictions and suggestions for how a more knowledgeable society may evolve.     

Effects of pulsed electromagnetic fields on learning and memory abilities of STZ-induced dementia rats

Introduction: Recent studies have shown that pulsed electromagnetic field (EMF) has therapeutic potential for dementia, but the associated neurobiological effects are unclear. This study aimed to determine the effects of pulsed EMF on Streptozotocin (STZ)-induced dementia rats.Methods: Forty Sprague-Dawley rats were randomly allocated to one of the four groups: (i) control, (ii) normal saline injection (sham group), (iii) STZ injection (STZ group) and (iv) STZ injection with pulsed EMF exposure (PEMF, 10 mT at 20 Hz) (STZ + MF group). Morris water maze was used to assess the learning and memory abilities. Insulin growth factors 1 and 2 (IGF-1 and IGF-2) gene expression were determined by quantitative PCR. Results: The results showed that the mean escape latency in STZ-induced dementia rats was reduced by 66% under the exposure of pulsed EMF. Compared with the STZ group, the swimming distance and the time for first crossing the platform decreased by 55 and 41.6% in STZ + MF group, respectively. Furthermore, the IGF-2 gene expression significantly increased compared to that of the STZ group. Conclusions: Our findings indicate that the pulsed EMF exposure can improve the ability of learning and memory in STZ-induced dementia rats and this effect may be related to the process of IGF signal transduction, suggesting a potential role for the pulsed EMF for the amelioration of cognition impairment.     

Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella

The goal of this study was to identify and differentiate the influence of multiple habitat types that span a spectrum of suitability for Hyla squirella, a widespread frog species that occurs in a broad range of habitat types. We collected microsatellite data from 675 samples representing 20 localities from the southeastern USA and used machine‐learning methodologies to identify significant habitat features associated with genetic structure. In simulation, we confirm that our machine‐learning algorithm can successfully identify landscape features responsible for generating between‐population genetic differentiation, suggesting that it can be a useful hypothesis‐generating tool for landscape genetics. In our study system, we found that H. squirella were spatially structured and models including specific habitat types (i.e. upland oak forest and urbanization) consistently explained more variation in genetic distance (median pR² = 47.78) than spatial distance alone (median pR² = 23.81). Moreover, we estimate the relative importance that spatial distance, upland oak and urbanized habitat have in explaining genetic structure of H. squirella. We discuss how these habitat types may mechanistically facilitate dispersal in H. squirella. This study provides empirical support for the hypothesis that habitat‐use can be an informative correlate of genetic differentiation, even for species that occur in a wide range of habitats.     

A derivation of a rote learning curve from the total uncertainty of a task

The derivation of H. D. Landahl’s learning curve (1941,Bull. Math. Biophysics,3, 71–77) from a single information-theoretical assumption obtained previously (Rapoport, 1956,Bull. Math. Biophysics,18, 317–21) is extended to obtain the entire family of such curves with the number of stimuliM (to each of which one ofN responses is to be associated) as a parameter. No additional assumptions are required. The entire family thus appears as a function of a single free parameter,k, all other parameters being experimentally determined. The theory is compared with a set of experiments involving the learning of artificial languages. An alternative quasi-neurological model leading to the same equation is offered.     

A neural model for conditioned avoidance learning

A mathematical model for learning of a conditioned avoidance behavior is presented. An identification of the net excitation of a neural model (Rashevsky, N., 1960.Mathematical Biophysics. Vol. II. New York: Dover Publications, Inc.) with the instantaneous probability of response is introduced and its usefulness in discussing block-trial learning performances in the conditioned avoidance situation is outlined for normal and brain-operated animals, using experimental data collected by the author. Later, the model is applied to consecutive trial learning and connection is made with the approach of H. D. Landahl (1964. “An Avoidance Learning Situation. A Neural Net Model.”Bull. Math. Biophysics,26, 83–89; and 1965, “A Neural Net Model for Escape Learning.”Bull. Math. Biophysics,27, Special Edition, 317–328) wherein lie further data with which the model can be compared.     

Drosophila learning and memory: Recent progress and new approaches

The processes of learning and memory have traditionally been studied in large experimental organisms (Aplysia, mice, rats and humans), where well-characterized behaviors are easily tested. Although Drosophila is one of the most experimentally tractable organisms, it has only recently joined the others as a model organism for learning and memory. Drosophila behavior has been studied for over 20 years; however, most of the work in the learning and memory field has focused on initial learning, because establishing memory in Drosophila has not been as straightforward as in other organisms. A major recent advance in this field has been the development of a training protocol that induces long-term memory in flies. This made possible experiments that implicated the Drosophila CREB gene as a critical component in the consolidation of long-term memory, and paves the way for future experiments utilizing the well developed tools in Drosophila. This review will briefly summarize what is known in the field of Drosophila learning and memory to date, and discuss why the unique aspects of this field make traditional approaches difficult and reward the use of alternative paths of experimentation.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.