多元教学模式在分析化学教学中的应用探索

构建了以培养学生主动学习为中心,以能力提升为核心的分析化学多元教学模式,并探索了适应多元教学模式的学业评价机制的建立。     

拓展学生专业实践知识，夯实高分子化学理论基础

以有机化学、物理化学为基础,构建高分子化学的理论知识体系,通过将课堂教学与实验和生产实践相结合,拓展学生在高分子化学前沿领域的专业知识,强调发展学生的素质教育与创新精神,进而提高学生的学习兴趣。     

有机锗对铅所致小鼠学习记忆障碍的拮抗作用

用避暗法和水迷宫法研究了对铅所致的小鼠记忆障碍的影响。ＩＣＲ系小鼠隔日腹腔注射羧乙基锗 半氧化物（Ｇｅ－１３２,２５ｍｇ／ｋｇ）,和／或醋酸铅（１５ｍｇ／ｋｇ）２８ｄ。结果表明暴露于铅２０ｄ后小鼠学习记忆能力受障碍,同时给予Ｇｅ－１３２能明显拮抗铅的毒性作用。试验提示Ｇｅ－１３２能拮抗所致的神经行为为功能的破坏作用。     

ELVQ算法实现宽参数偏移的多故障电路诊断

该文提出了一种强化自适应策略的学习矢量量化(Enhanced Learning Vector Quantization, ELVQ)算法,并设计了基于SOM(Self-Organizing Map)-LVQ模型的故障分类方法,用于实现宽参数偏移的模拟电路多故障诊断。该文算法具有两方面的优势：一方面利用获胜神经元数目的自适应,均衡了神经元的获胜概率;另一方面根据样本分类结果计算作用因子修正神经元的权值,增强了类别边界决策性能。仿真结果表明,所提出的算法具有收敛速度快,分类误差小等特点。     

深度学习在微机原理实践课程中的应用

“深度学习”强调学习者主动、批判地学习知识,通过深度加工来构建知识体系、实现迁移应用。在高校课堂上则体现在教学过程中教师的引导和跟踪、学习过程中学生的主动及创造。本研究在深入分析“深度学习”的基础上,在《微机原理及接口技术实践》课程中进行深度学习尝试,同时引入MOOC(大型开放式网络课程)教学模式,从教学内容、教学方法、考核措施等方面着手设计,增强学生实践能力、创新意识、创新精神及团队合作意识等综合能力,从而促进学生高阶认知能力、科创能力的发展,完成教育个性化需求的落实。     

Learning Electricity with NIELS: Thinking with Electrons and Thinking in Levels

Electricity is regarded as one of the most challenging topics for students of all ages. Several researchers have suggested that na?ve misconceptions about electricity stem from a deep incommensurability (Slotta and Chi 2006; Chi 2005) or incompatibility (Chi et al. 1994) between na?ve and expert knowledge structures. In this paper we argue that adopting an emergent levels-based perspective as proposed by Wilensky and Resnick (1999), allows us to reconceive commonly noted misconceptions in electricity as behavioral evidences of “slippage between levels,” i.e., these misconceptions appear when otherwise productive knowledge elements are sometimes activated inappropriately due to certain macro-level phenomenological cues only. We then introduce NIELS (NetLogo Investigations In Electromagnetism), a curriculum of emergent multi-agent-based computational models. NIELS models represent phenomena such as electric current and resistance as emergent from simple, body-syntonic interactions between electrons and other charges in a circuit. We discuss results from a pilot implementation of NIELS in an undergraduate physics course, that highlight the ability of an emergent levels-based approach to provide students with a deep, expert-like understanding of the relevant phenomena by bootstrapping, rather than discarding their existing repertoire of intuitive knowledge.

Comments on “A note on minimizing maximum lateness in an m-machine scheduling problem with a learning effect”

The purpose of this paper is to point out that if there are some machines that do not process any job then the mathematical programming model provided by Eren [T. Eren, A note on minimizing maximum lateness in an m-machine scheduling problem with a learning effect, Applied Mathematics and Computation 209 (2009) 186-190] may not be a valid one. A simple way to fix this problem is given. Furthermore, based on the idea of Eren’s model, a general mathematical programming model is proposed.     

Ein "Learning Network" als innovative Form der firmeninternen Weiterbildung

Im Bereich PSE der Siemens AG ?sterreich wurde dazu übergegangen, die interne Aus- und Weiterbildung zu Fach- und Methodikthemen nicht mehr in Form eines klassischen firmeninternen Training Centers zu organisieren, sondern in Form eines lose gekoppelten "Learning Network". Dies hat gerade bei internationaler Aufstellung gro?e Vorteile hinsichtlich Kosten, Aktualit?t von Inhalten, Berücksichtigung von "local content" und auch p?dagogischer Effizienz. Im vorliegenden Artikel werden die Herausforderungen an innerbetriebliches Lernen in einer gro?en EDV-Firma skizziert, das Vorgehen detailliert dargestellt (anhand von zwei konkreten Beispielen), die kritischen Erfolgsfaktoren herausgearbeitet und ein Resümee gezogen, auch hinsichtlich der Anwendbarkeit dieses Konzepts im weiteren Kontext.      

Robust learning in social networks via matrix scaling

The influence vanishing property in social networks states that the influence of the most influential agent vanishes as society grows. Removing this assumption causes a failure of learning of boundedly rational dynamics. We suggest a boundedly rational methodology that leads to learning in almost all networks. The methodology adjusts the agent's weights based on the Sinkhorn-Knopp matrix scaling algorithm. It is a simple, local, Markovian, and time-independent methodology that can be applied to multiple settings.