Achieve Intended Learning Outcomes and Improving Digital Literacy Skills for Practical-Based Subjects Using Online Teaching via Propagation of OER Materials

As professors are subjected to teaching their classes online due to the recent COVID-19, our local Hong Kong students find it difficult to consult their teachers, and ultimately would fail to achieve the intended learning outcomes, especially for practical-based subjects. In this research, students having online classes of a practical-based fabric design subject were encouraged to self-study from Open Educational Resource (OER) materials for a further and better understanding of their subject. Additionally, online materials were developed to improve students’ understanding via skill of digital literacy. Their learning progress was evaluated and compared to the face-to-face version. The majority of students found online classes combined with self-studying OER materials, potentially be a substitute for face-to-face classes. Most of the students further opined different OER videos assisted them without any face-to-face instructions in practical works, to develop new fabric samples from the inspiration. Analysis of test results, and comparison of students’ final grades with different learning modes, supported these phenomena.

Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al)_(13)-based materials

Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.

A Teaching Case of Subject Clause Based on Implicit Learning

The New English curriculum criteria suggest teaching English grammar based on the students’cognitive characteristics and emotional needs,helping them discover the rules and encouraging them to master the grammar by using it.But due to the limited time in a lesson,many English teachers adopt a simple approach to teach grammar,in which students are required to memorize the rules first and then practice a lot.This approach is effec-

Study on Constructivist Approach of Learning and Teaching from a Perspective of Intersubjectivity

This paper makes an investigation on the constructivist point of view towards learning and teaching from the perspective of subjectivity and intersubjectivity. The process of learning and teaching is conducted by both groups,learners and teachers. Therefore,the intersubjectivity between learners and teachers play an important role. The learner-centered approach in teaching not only weakens teachers 'role,but also is not good for the development of learners'subjectivity.

Prediction and interpretation of photocatalytic NO removal on g-C_(3)N_(4)-based catalysts using machine learning

Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.

A Novel Method for Cross-Subject Human Activity Recognition with Wearable Sensors

Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.

国际E-Learning研究热点演化及趋势探测

E-Learning自诞生以来,受到商业组织和学术机构的广泛关注。利用共词分析、聚类分析、社会网络分析、网络社区分析等方法对Web of Science数据库收录的E-Learning研究文献进行的深度剖析发现:国际E-Learning研究发展大体可以分为三个阶段:1968-1993年,E-Learning相关研究开始出现,但研究文献较少,以案例研究等定性研究方法为主,处于以概念探讨为主的初级研究阶段;1994-2003年,研究文献增多,研究方法逐渐偏向于定量研究,开始形成较为稳定的研究主题领域,并逐渐出现主题分化和主题融合;2004-2013年,研究主题不断细化,研究深度进一步增强。总体来看,国际E-Learning研究经历从"技术导向"到"行为导向"再到"行为和技术导向"相融合、从单一强调"学习者"或"教学者"的自我导向学习研究到同时强调"教学者"和"学习者"的互动协作学习研究等主题演化特征,跨文化、跨学科研究成为全球化背景下国际E-Learning研究方向,研究模型设计越来越注重中介变量和调节变量的作用。混合式网络学习环境研究、强调个性化和智能化的E-Learning系统研究、基于认知心理的学习效能研究可能成为未来E-Learning研究的潜在热点领域。

基于E-learning的高校图书馆嵌入式教学服务研究

论文从E-learning及高校图书馆嵌入式教学服务的现状入手,分析了高校图书馆基于E-learning的嵌入式教学服务的主要模式,提出E-learning环境下高校图书馆开展嵌入式教学服务应建立图书馆与教师及学校相关部门的合作机制,保持高效地协作;鼓励学科馆员积极参加培训,提升自身素质;及时评估,改进教学效果,提高教学质量。

E-Learning环境创建探讨

介绍了E

Advances in the application of action learning in nursing practice

In this paper, we elucidate the concept and characteristics of action learning as well as summarize the re？exivity, cooperativeness, and subjectivity of this approach. Furthermore, we describe the effects and limitations of action learning when applied in nursing management, nursing education, and clinical practice, among various ？elds.

Towards a Collaborative Learning Environment Through ICT： A Case Study

This paper expounds how the possibility of collaboration and construction of knowledge being put into practice in a group of ICT （information and communication technologies）-based teaching and learning programmes for Mother Tongue languages, collectively known as 10＇CMT. 10＇CMT, which is initiated by the ETD （Educational Technology Division） of MOE （Ministry of Education） Singapore, embodies a focus on the development of relevant pedagogy by which web-based technologies are embedded in meaningful learning activities in the classroom. Through a case study of a primary school in Singapore, this paper exemplifies how 10＇CMT has the ability to promote collective knowledge and, by doing so, essentially supporting the growth of the individual student＇s knowledge. It draws on the students＇ engagement in peer editing, peer evaluation, peer interaction, and feedback with self-reflective practices through the affordances of an array of online tools. This paper will also discuss how the 10＇CMT approach promotes the ability to respond flexibly to complex problems, to communicate effectively, to manage information, to work in teams, to use technology, and to produce new knowledge which are deemed to be crucial competencies for 21 st century.

A Study on the Cultivation Path of English Learning Initiative Ability of Xinjiang Students

The“counterpart aid to Xinjiang”is one of the important measures to implement the country’s western development strategy.It aims to shorten the gap between higher education in Xinjiang and the eastern region and to maintain the coordinated development of education,economy and society in the eastern and western regions.This article takes the counterpart support of Xinjiang students from the School of Foreign Languages of Jilin Engineering Normal University as an example to explore the problem of the initiative training of Xinjiang students in English learning in our school,mainly from the aspects of the students themselves,the teachers and the entire education and training environment of our school.The angle explains how to help them improve their learning initiative.This article is divided into three parts.The first is the analysis of the connotation of learning initiative;the second is its meaning;the last is how to build the learning initiative of Xinjiang students.

Enhancement of students' learning with mobile devices: Taking librarian-faculty collaboration in SJTU as an example

Purpose： This paper presents an innovative program of Shanghai Jiao Tong University Library which aims to help engineering students to make use of mobile devices to improve their learning efficiency. Design/methodology/approach： Information literacy training and course learning resources were integrated into students＇ learning process. Surveys on students＇ learning with the touch pads were conducted to help evaluate the program＇s effectiveness. Findings： Our practice showed that collaboration of library staff with faculty members is an effective way to integrate information literacy education and course-specific library resources into students＇ learning with mobile devices, which has greatly improved the efficiency of students＇ learning. Research limitations： First, our literacy training still focused on the use of mobile devices in information access, but not on how to evaluate and manage their information resources with mobile devices. Second, subject librarians need to shift their role from information service providers into information resource instructors while developing the partnership with faculty members and teaching assistants. Practical implications： This paper provides learning efficiency of university students with touch pads conveniently. a new insight into the way of how to enhance such new technical devices as smartphones and Originality/value： Our practice can be used as a valuable guide for libraries that plan to leverage mobile technologies to enhance students＇ learning efficiency.

Exploration and Practice of the Cultivation Model for Top Undergraduate Talents Based on Subject Contests in the AI Era

With the rise of artificial intelligence and the rapid development of industrial applications based on deep learning,the demand for innovative and practical talents is rapidly increased.The cultivation of talents in computer discipline has attracted a lot of attention from universities,research institutions,and Internet giants.Subject contests play a crucial role in cultivating top undergraduate talents.This paper proposes a cultivation model that takes the industry application of deep learning as the background,and guides students to learn,practice,and innovate incrementally from machine learning application to deep learning application by participating in subject contests.It aims to help undergraduates to master the cutting-edge technologies and applications,and ultimately cultivate top,innovative,and practical undergraduate talents in the computer field in the era of artificial intelligence.

学习任务群视域下知识观转型与教学变革

学科知识、主体经验和学习活动是语文教学不可缺少的要素,不能因强调其一而取代另两者。学习任务群视域下的知识观,是从静态知识到动态运用、从知识点到概括性知识、从“知识—人”到“知识—世界—人”。学习任务群以实践活动内联知识类型、以学习单元建立知识结构、以真实情境促进知识—人—世界三者交融,探索出一条知识生成素养的有效路径。研究认为,依据学习任务群的特征,以初中语文为例,教学实践中宜采用概括性知识统领的单元教学,可分为提炼单元的概括性知识、提出引导性问题融入学科知识、设计知识理解与运用的学习过程三个步骤。

高校图书馆数学阅读推广研究

数学阅读是认识和学习数学的主要方法,也是提高数学素养和养成阅读习惯的重要途径。高校图书馆在学科阅读理念下开展数学阅读推广体现了阅读推广的细分化和精准化,对于创新阅读推广主题、丰富阅读推广资源、拓展阅读推广渠道具有重要意义。问卷调查发现,大学生数学阅读存在阅读质量不高、阅读能力薄弱、阅读指导欠缺和宣传推广乏力等问题。基于调查结果,提出建设分众书目、多元主体联动、嵌入课堂教学和借力实践活动等数学阅读推广策略。

赋能还是制约:智能技术如何影响学生学习自由的实现

学习自由是学生在学习过程中自主性、能动性与自为性的重要体现,是促进学生创新发展的重要条件。在智能环境中,智能技术既能为学生提供丰富的学习资源和空间自由,也可能以技术意向性和算法黑箱规训和制约学生学习的自由。如何构建智能技术影响学生学习自由实现的分析框架,成为探析两者辨证关系的关键所在。具体而言,应围绕数据、算力、算法、技术伦理等智能技术要素,权利学习自由、自我实现学习自由两类学习自由,以及隐私与表达、机会与选择、发现与认知、整合与创新、情感与价值塑造5个学习自由层次来构建分析框架,以厘清智能技术要素与不同层次学习自由之间的复杂关系。显然,智能技术的不同要素会从不同层面影响和干预学生的学习权利自由和自我实现自由,限制其身心自然成长和创造性学习与发展。为更好地利用智能技术推动学生学习自由的实现,教育管理部门应以负责任创新治理理念为引导,明确技术作用限度及超越方法;教师应强化与智能技术的高效协同,实现从“技术追新”向“育人创新”的转变;学生应明确在人机协同中的主体作用,推动实现高学习自由度—高创造性学习;信息化行业应面向学生发展规律设计智能学习产品,促进自由开放学习的发生。

学科基础课程提升新工科人才学习质量路径探索

提高大学生学习质量是推进新工科人才培养建设的重点问题。依托河北工业大学电气工程专业国家“双一流”建设学科平台,依据新工科人才培养目标,通过学科基础课程线上线下混合式教学,对刚刚进入专业领域学习的学生,多路径引导他们自主学习、深度参与学习,可获得有意义的学习体验、有效提升学习质量,为面向未来布局的新工科人才培养建设助力,满足社会对新工科人才培养质量更高的要求。

主观题自动评判算法研究综述

在教育教学中,试卷评判是教师获取学生知识点掌握情况的重要途径。然而,试题评分是一个耗时的过程,主观题的评判更需要阅卷人认真、投入、细致地审阅,需要耗费大量精力。要减轻教师工作压力,提高主观题评判的效率,基于人工智能的自动评判技术非常重要,其中主观题的自动评判是难点。随着机器学习和深度学习等技术在自然语言处理领域的发展,主观题自动评判技术有了较大进展。文中将主观题分为常规型和开放型两类进行文献梳理,总结主观题自动评价的标准和公开数据集,归纳涉及的方法和技术路线,并对主观题自动评判技术未来的研究方向进行总结和展望。