项目教学法结合线上微课资源的教学实践——基于《可摘局部义齿修复工艺技术》课程

在项目教学法教学过程中融入微课资源利用职教云线上平台进行混合式教学,根据可摘局部义齿修复在企业的制作流程和学生的学习实际,对《可摘局部义齿修复工艺技术》课程中的铸造可摘局部义齿的制作设计了 9个教学项目,以"可摘局部义齿模型观测与设计"这一项目为例,分析项目教学法结合线上微课的混合式教学的具体应用.     

Gross Anatomy Education in China during the Covid-19 Pandemic: A National Survey

The Covid-19 pandemic launched the use of online courses in Chinese medical schools during February 2020. To evaluate the state of gross anatomy education in China during the pandemic, a nationwide survey was conducted through convenience sampling by email or respondent invitations on social media. A total of 359 questionnaires were received from the respondents. The first response from a given school was included in the study to represent that school, thus, 77 questionnaires were used for analyses. Schools represented were from all provinces in mainland China as well as Hong Kong and Macao. The survey found that before the pandemic, 74.0% and 33.8% of the 77 schools conducted online theoretical and practical sessions, respectively, on gross anatomy, and 36 (46.8% of 77) had temporarily suspended practical sessions at the time the survey was conducted. Body donation programs were also affected with 26.0% and 27.3% of the 77 schools having suspended donation programs or saw a decreased number of donations. During the pandemic, 40.3% of the 77 schools kept or initiated the implementation of active learning, and online assessment was continued in 49.4% of the 77 medical schools. Another 26 (33.8%) schools initiated online assessment during the pandemic. A total of 359 answers were included for the analysis of the “teachers’ perception of the online teaching experience.” Over half (51.0%) of the 359 responded teachers were very statisfied or satisfied with the effectiveness of online teaching during the pandemic. A total of 36.2% of these respondents preferred to implement online teaching of theoretical sessions after the pandemic, and 89 (24.8%) teachers were keen to return to traditional face-to-face anatomy education.     

Irony detection via sentiment-based transfer learning

Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection.     

A novel intelligent classification model for breast cancer diagnosis

Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are clearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer intelligent diagnosis approach has been proposed, which employed information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future. Moreover our proposed method could also be applied to other illness diagnosis.     

匡亚明的大学办学思想

匡亚明是当代中国著名的高等教育家,在高等教育方面有着卓越的建树,其大学办学思想突出表现为：立足现实,以培养社会主义建设人才为大学使命;尊重、爱惜人才,依靠教授办学;辩证统一,坚持教学与科研并重。匡亚明的大学办学思想既传承了传统大学的精神,又彰显了现代的时代特征,对于今天的大学建设有重要借鉴意义。     

“后方法”教学理论及其对高校英语课堂教学的启示

中国高校英语课堂存在诸多亟待解决的问题.研究依据“后方法”教学理论的教学参量与宏观策略,通过调查问卷方式调查高校英语课堂教学现状并诊断其存在的问题,在此基础上总结出如下四方面的启示：转换教师角色;从实践循环到理论创造;注重课堂活动,打造和谐氛围：将语言与文化有机结合.这些启示对于提高课堂教学质量将起到一定的促进作用.     

现代空间焦虑症与洪州禅学的解决办式

现代人关于物与空间的现实迷信及由此形成的生存方式使一种普遍性的社会心理顽症——空间焦虑症越演越烈.对现代人的这种心理焦虑症,洪州禅学有神奇的解决之道.     

Framework for educational robotics: a multiphase approach to enhance user learning in a competitive arena

Educational robotics involves using robots as an educational tool to provide a long term, and progressive learning activity, to cater to different age group. The current concern is that, using robots in education should not be an instance of a one-off project for the sole purpose of participating in a competitive event. Instead, it should be a sustainable long-term progression spanning the primary school to pre-university level. This article presents a framework for robotics using a multiphase approach to enhance user learning in a competitive arena.