维、汉语被动句的深层句法对比

深层结构是表层结构的基础部分,但只能通过表层结构才能体现出来。论文以Van Buren的深层句法对比理论为基础,对维、汉语被动句的深层句法进行了对比。     

教学范式转型是否可能——《教学设计的理论与模型：教学理论的新范式（第2卷）》解读

《教学设计的理论与模型：教学理论的新范式（第2卷）》在深入分析信息时代不同于工业时代特征的基础上,提出面向信息时代的教学范式,主张以定制化的、聚焦于学习的教学取代工业时代的标准化的、聚焦于分类的教学,变被动学习为主动学习,从教师的主动、控制和责任转变为分享的主动、控制和责任;从去境脉的学习转变为真实的、意义丰富的任务,以高层级的学习目标取代低层级的学习目标。新教学设计理论从多个不同的视角构建设计模型,并彰显教学方法的情境化考量和教学设计向＂用户设计者＂模式的转变,为在实践上进行教学范式转变提供了一种发展方向。     

寻根文学的叙事时间——以《爸爸爸》为例

寻根文学的叙事文本综合运用了时间顺序的各种变异形态,如倒叙,预叙,插叙和补叙等。另外,寻根文学作品包含一个表层时间和一个深层时间。表层时间被作者淡化或者直接模糊,为的是摆脱现实时间所带来的沉重感。而深层时间却是作家们着力突出和渲染的。在深层时间里,寻根作家们深入挖掘了民族传统文化,重新审视了历史。     

Chicken or the egg: Longitudinal analysis of the causal dilemma between goal orientation,self-regulation and cognitive processing strategies in higher education

The aim of this study was to investigate the direction of the effect between goal orientation, self-regulation and deep processing strategies in order to understand the impact of these three constructs on students’ achievement. The participants were 110 freshmen from the engineering faculty at the Université catholique de Louvain in Belgium, who were followed during the first three years of their university studies. Data were analyzed through structural equation modeling. The main finding was that mastery goal orientation increased students’ subsequent deep processing which in turn enhanced subsequent self-regulation. Deep processing and self-regulation also appeared to be mutually influential from year 2 to year 3. The implications of our results for the understanding of the interplay between cognitive and motivational processes in higher education are discussed.     

生态文化建设与深生态意识

生态文化建设包括生态制度的建立健全和生态意识的培育。我国现阶段生态文化建设的关键是培育公民的深生态意识。深生态意识的内容包括整体论自然观、生态社会观、生态优位的科技观、生态生活观等。     

Enquiry-based learning workshop for deep learning in Middle Eastern classrooms – an action research approach

The senior year design students and I were dismayed when my linear teaching and their habitual rote learning failed in a Middle Eastern University. The gulf between the curricular objectives and our teaching-learning methods intrigued me. I turned this into an action research project that sought to answer the questions, ‘What paradigm shift might we need to migrate from traditional rote learning to deep learning? What attitudinal change and philosophical beliefs would that call for in an instructor?’ The search for a solution metamorphosed me from a disengaged instructor into an empathizing reflecting practitioner. It led my students to active engagement in an enquiry-based learning workshop, which significantly improved their performance. This paper celebrates the journey of our collective deep learning. It explicates how I built my personal theory of teaching praxis through critical consciousness and meta reflection. This knowledge-creation process is empowering and may draw many teacher researchers towards meta-reflexive engagement with the social systems around. These change drivers can initiate institutional overhaul to effect systemic reforms.     

Data fusion with factored quantization for stock trend prediction using neural networks

As compared to the continuous temporal distributions, discrete data representations may be desired for simplified and faster data analysis and forecasting. Data compression can introduce one of the efficient ways to reduce continuous historical stock market data and present them in discrete forms; while predicting stock trend, a primary concern is towards up and down directions of the price movement and thus, data discretization for a focused approach can be beneficial. In this article, we propose a quantization-based data fusion approach with a primary motivation to reduce data complexity and hence, enhance the prediction ability of a model. Here, the continuous time-series values are transformed into discrete quantum values prior to applying them to a prediction model. We extend the proposed approach and factorize quantization by integrating different quantization step sizes. Such fused data can reduce the data to mainly concentrate on the stock price movement direction. To empirically evaluate the proposed approach for stock trend prediction, we adopt long short-term memory, deep neural network, and backpropagation neural network models and compare our prediction results with five existing approaches on several datasets using ten performance metrics. We analyze the impact of specific quantization factors and determine the individual best as well as overall best factor sizes; the results indicate a consistent performance enhancement in stock trend prediction accuracy as compared to the considered baseline methods with an improvement up to 7%. To evaluate the impact of quantization-based data fusion, we analyze time required to execute the experiments along with percentage reduction in the number of unique numeric terms. Further, these results are statistically evaluated using Wilcoxon signed-rank test. We discuss the superiority and applicability of factored quantization-based data fusion approach and conclude our work with potential future research directions.     

MRC-Sum: An MRC framework for extractive summarization of academic articles in natural sciences and medicine

Extractive summarization for academic articles in natural sciences and medicine has attracted attention for a long time. However, most existing extractive summarization models often process academic articles with sentence classification models, which are hard to produce comprehensive summaries. To address this issue, we explore a new view to solve the extractive summarization of academic articles in natural sciences and medicine by taking it as a question-answering process. We propose a novel framework, MRC-Sum, where the extractive summarization for academic articles in natural sciences and medicine is cast as an MRC (Machine Reading Comprehension) task. To instantiate MRC-Sum, article-summary pairs in the summarization datasets are firstly reconstructed into (Question, Answer, Context) triples in the MRC task. Several questions are designed to cover the main aspects (e.g. Background, Method, Result, Conclusion) of the articles in natural sciences and medicine. A novel strategy is proposed to solve the problem of the non-existence of the ground truth answer spans. Then MRC-Sum is trained on the reconstructed datasets and large-scale pre-trained models. During the inference stage, four answer spans of the predefined questions are given by MRC-Sum and concatenated to form the final summary for each article. Experiments on three publicly available benchmarks, i.e., the Covid, PubMed, and arXiv datasets, demonstrate the effectiveness of MRC-Sum. Specifically, MRC-Sum outperforms advanced extractive summarization baselines on the Covid dataset and achieves competitive results on the PubMed and arXiv datasets. We also propose a novel metric, COMPREHS, to automatically evaluate the comprehensiveness of the system summaries for academic articles in natural sciences and medicine. Abundant experiments are conducted and verified the reliability of the proposed metric. And the results of the COMPREHS metric show that MRC-Sum is able to generate more comprehensive summaries than the baseline models.     

高职室内设计专业发展之我见

本文论述了高职教育室内设计专业目前的现状和将遇到的问题及其解决方法，并结合作者在实际工作中所做的一些操作进行探讨．     

深度非弹性散射与自由核子的结构函数

介绍了深度非弹性散射的概念 ,引出了自由核子的结构函数并给出了其具体的函数形式