The Effects of Discipline on Deep Approaches to Student Learning and College Outcomes

“Deep learning” represents student engagement in approaches to learning that emphasize integration, synthesis, and reflection. Because learning is a shared responsibility between students and faculty, it is important to determine whether faculty members emphasize deep approaches to learning and to assess how much students employ these approaches. This study examines the effect of discipline on student use of and faculty members’ emphasis on deep approaches to learning as well as on the relationships between deep approaches to learning and selected educational outcomes. Using data from over 80,000 seniors and 10,000 faculty members we found that deep approaches to learning were more prevalent in Biglan’s soft, pure, and life fields compared to their counterparts. The differences were largest between soft and hard fields. We also found that seniors who engage more frequently in deep learning behaviors report greater educational gains, higher grades, and greater satisfaction with college, and that the strength of these relationships is relatively consistent across disciplinary categories.     

重视数学阅读,打造深度学习课堂

许多小学数学教师已然认识到了开展小学数学阅读活动和制定科学的教学方法,对提高学生的计算能力和思维能力的重要作用。所以,教师在教学过程中要积极探索科学的教学方法培养学生的有效阅读能力,这样学生才能够更好地理解和学习数学知识,提高学生的解题思维能力和学习有效性。以下便对数学阅读活动的开展和深度学习课堂的构建进行分析和探究。     

Introducing innovative approaches to learning in fluid mechanics: a case study

The purpose of the current article is to examine the impact of laboratory demonstrations and computer visualizations on learning in a third-year fluid mechanics course at Norwegian University of Science and Technology (NTNU). As a first step, on entering the course, students were exposed to a laboratory demonstration focusing on the nature of waves. Students were subsequently taught the theory of wave mechanics in a regular class along with exercises. Finally, the computer algebra system Maple was used to simulate physical properties of the waves using the mathematical equations. The illustrations throughout the paper are examples of some of the pedagogical devices that students were exposed to during the project. The students enjoyed the demonstrations, but there are some indications that the interventions did not help to improve learning of phenomena.     

研究性学习中的"深生态学"观照——兼论环境伦理教育

运用“深生态学”理论来考察当前的研究性学习,我们会发现,当前的研究性学习课程涉足环境教育时步入了一些误区。基础教育在环境保护中所起的作用主要不是拿出什么方案,做出什么实际成效,而是加强环境伦理教育,使下一代养成正确对待环境的新观念、新德性和新的行为方式。     

An end-to-end deep generative approach with meta-learning optimization for zero-shot object classification

Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks.     

Online portfolio management via deep reinforcement learning with high-frequency data

Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in the field of portfolio management (PM). However, the built-in quadratic complexity of the Transformer prevents its direct application to the PM task. To solve this problem, in this paper, we propose a deep reinforcement learning-based PM framework called LSRE-CAAN, with two important components: a long sequence representations extractor and a cross-asset attention network. Direct Policy Gradient is used to solve the sequential decision problem in the PM process. We conduct numerical experiments in three aspects using four different cryptocurrency datasets, and the empirical results show that our framework is more effective than both traditional and state-of-the-art (SOTA) online portfolio strategies, achieving a 6x return on the best dataset. In terms of risk metrics, our framework has an average volatility risk of 0.46 and an average maximum drawdown risk of 0.27 across the four datasets, both of which are lower than the vast majority of SOTA strategies. In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation.     

User-aware multilingual abusive content detection in social media

Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource languages. This work focuses on providing a novel method for abusive content detection in multiple low-resource Indic languages. Our observation indicates that a post’s tendency to attract abusive comments, as well as features such as user history and social context, significantly aid in the detection of abusive content. The proposed method first learns social and text context features in two separate modules. The integrated representation from these modules is learned and used for the final prediction. To evaluate the performance of our method against different classical and state-of-the-art methods, we have performed extensive experiments on SCIDN and MACI datasets consisting of 1.5M and 665K multilingual comments, respectively. Our proposed method outperforms state-of-the-art baseline methods with an average increase of 4.08% and 9.52% in the F1-score on SCIDN and MACI datasets, respectively.     

基于Deep Web数据集成的个性化信息服务研究

互联网和信息数字化的高度发展为信息查询带来了新的挑战,如何改善查询模式使用户从海量的数据中快速找到真正需要的信息成为了当前业界亟待解决的课题。本文描述了一种个性化信息服务模型,使用DeepWeb数据集成技术设计了一个图书馆数字资源个性化信息服务系统,实现了以用户为中心的图书馆信息服务。     

融合·冲突·缝合——从郭小川《深深的山谷》看知识分子与革命关系

在“十七年”政治——文化语境中,郭小川的《深深的山谷》出现了创作动机与实际效果之间有趣的相悖现象。形成这一现象的诱因在于文本的复杂性,诗歌表层上讲述的是从“合”到“分”的“爱情”故事,而在深层上则以“爱情”故事为依托。反观与思索知识分子与革命之间“融合——冲突——缝合”的动态过程,表层上讲述的是“他者”的悲欢离舍,深层上叙说的却是“自我”历史记忆及现实体验中的欢乐与痛苦。     

论原型批评与主题学研究之关系

世界文学中的原型是可数的,一个原型可以不断地衍生出无数个主题。原型与主题之间是一种渊源与支流的关系。主题学研究同一主题在不同时代和地区的演变,也研究主题在不同文学体系中的表现。主题学的研究必须先探索原型,寻求到同一主题演变的渊源关系。探究原型的根源,进行宏观研究分析,将会为主题学的研究开辟出一个崭新的天地,有利于促进比较文学的研究与发展,推进文学的发展与繁荣。