Longitudinal assessment of progress in reasoning capacity and relation with self-estimation of knowledge base

The aim of the study was to investigate progress in reasoning capacity and knowledge base appraisal in a longitudinal analysis of data from summative evaluation throughout a medical problem-based learning curriculum. The scores in multidisciplinary discussion of a clinical case and multiple choice questionnaires (MCQs) were studied longitudinally for 213 students from years 2 to 5. The capacity of core knowledge delimitation was calculated as the difference between the levels of average ascertainment degrees given for correct and incorrect answers at MCQ. For both multidisciplinary discussion of a clinical case evaluation and self-estimation of core knowledge, the capacity increases throughout the curriculum. The reasoning capacity assessed through multidisciplinary discussion of a clinical case is positively correlated with MCQ scores and the capacity to discriminate the mastered core knowledge. In conclusion, this study indicates that self-estimation of core knowledge is associated with an increase in reasoning performance through a well-organised knowledge base. Since that ability is related to success or failure, it is suggested that student awareness about delimitation of mastered core knowledge is considered as part of learning.     

Characterization of Holmes in The Adventure of Speckled Band

Sherlock Holmes is the fictional creation of Sir Arthur Conan Doyle. In The Adventure of the Speckled Band, Sir Arthur Conan Doyle has succeeded in creating the image of Sherlock Holmes. He is not only...     

How students reason about matter flows and accumulations in complex biological phenomena: An emerging learning progression for mass balance

In recent years, there has been a strong push to transform STEM education at K-12 and collegiate levels to help students learn to think like scientists. One aspect of this transformation involves redesigning instruction and curricula around fundamental scientific ideas that serve as conceptual scaffolds students can use to build cohesive knowledge structures. In this study, we investigated how students use mass balance reasoning as a conceptual scaffold to gain a deeper understanding of how matter moves through biological systems. Our aim was to lay the groundwork for a mass balance learning progression in physiology. We drew on a general models framework from biology and a covariational reasoning framework from math education to interpret students' mass balance ideas. We used a constant comparative method to identify students' reasoning patterns from 73 interviews conducted with undergraduate biology students. We helped validate the reasoning patterns identified with >8000 written responses collected from students at multiple institutions. From our analyses, we identified two related progress variables that describe key elements of students' performances: the first describes how students identify and use matter flows in biology phenomena; the second characterizes how students use net rate-of-change to predict how matter accumulates in, or disperses from, a compartment. We also present a case study of how we used our emerging mass balance learning progression to inform instructional practices to support students' mass balance reasoning. Our progress variables describe one way students engage in three dimensional learning by showing how student performances associated with the practice of mathematical thinking reveal their understanding of the core concept of matter flows as governed by the crosscutting concept of matter conservation. Though our work is situated in physiology, it extends previous work in climate change education and is applicable to other scientific fields, such as physics, engineering, and geochemistry.     

Understanding how student-constructed stop-motion animations promote mechanistic reasoning: A theoretical framework and empirical evidence

Previous studies have documented the promising results from student-constructed representations, including stop-motion animation (SMA), in supporting mechanistic reasoning (MR), which is considered an essential thinking skill in science education. Our current study presents theoretically and empirically how student-constructed SMA contributes to promoting MR. As a theoretical perspective, we propose a framework hypothesizing the link between elements of MR and the construction nature of SMA, that is, chunking and sequencing. We then examined the extent to which this framework was consistent with a multiple-case study in the domain of static electricity involving five secondary school students constructing and using their own SMA creation for reasoning. In addition, students' reasoning in pre- and postconstruction of an SMA was examined. Our empirical findings confirmed our framework by showing that all students identified the basic elements of MR, that is, entities and activities of entities, when engaging in chunking and sequencing. Chunking played a role in facilitating students to identify entities responsible for electrostatic phenomena, and sequencing seemed to elicit students to specify activities of these entities. The analysis of students' reasoning in pre- and postconstruction of SMA found that student-generated SMA has a potential effect on students' retention of the use of MR. Implications for instruction with SMA construction to support MR are discussed.     

基于证据推理的大型海事巡逻救助船船型选择

为科学选择大型海事巡逻救助船船型,从成本、技术、能耗、外观等4个方面构建船型选择评价指标体系,运用层次分析法(Analytical Hierarchy Process,AHP)确定各指标权重,并采用证据推理法,借助IDS软件,对各船型进行综合评价.以3 000吨级海事巡逻救助船船型选择为例,通过评价圆舭船型、穿浪双体船型和三体船型,验证该方法在大型海事巡逻救助船船型选择上的可行性.     

模糊系统理论在预测中的一个应用

按照模糊自适应系统的理论，探讨了一种预测方法。这个方法比神经网络预测方法更为简单且收敛更快。     

A Path to Designing Inquiry Activities in Mathematics

Abstract

This paper aims to illustrate a design cycle of inquiry-based mathematics activities. We highlight a series of questions that we use when creating inquiry-based materials, testing and evaluating those materials, and revising the materials following this evaluation. These questions highlight the many decisions necessary to find just the right tasks for our students. Throughout the paper the use of multiple representations (graphical, numerical, symbolic, and narrative) and the distinction between facts, skills, methods, and conceptual understanding is explained and illustrated with examples. Additionally, we present evidence of student learning through excerpts from student journals and exam analysis.     

基于灰色关联的企业危机预警案例检索模型研究

本文提出了基于灰色关联的危机预警案例检索方法,解决了在复杂决策环境中,加权距离的扩展和案例修正等问题,为完善基于案例推理的企业危机预警提出了一种新的思路和方法。     

类脑智能研究现状与发展思考

近年来人工智能研究的许多重要进展反映了一个趋势:来自脑科学的启发,即使是局部的借鉴都能够有效地提升现有人工智能模型与系统的智能水平。然而,想要真正逼近乃至超越人类水平的人工智能,还需要对脑信息处理机制更为深入的研究和借鉴。类脑智能研究的目标就是通过借鉴脑神经结构及信息处理机制,实现机制类脑、行为类人的下一代人工智能系统。文章从受脑启发的新一代人工神经网络、基于记忆、注意和推理的认知功能模型、基于生物脉冲神经网络的多脑区协同认知计算模型等角度,并结合研究团队在类脑智能领域的研究进展,论述类脑智能的研究进展、发展方向和对未来发展的思考。