基于深度时空残差卷积神经网络的课堂教学视频中多人课堂行为识别

针对课堂教学场景遮挡严重、学生众多,以及目前的视频行为识别算法并不适用于课堂教学场景,且尚无学生课堂行为的公开数据集的问题,构建了课堂教学视频库以及学生课堂行为库,提出了基于深度时空残差卷积神经网络的课堂教学视频中实时多人学生课堂行为识别算法.首先,结合实时目标检测和跟踪,得到每个学生的实时图片流;接着,利用深度时空残...     

机械类专业单片机原理与应用课程考核方法研究

单片机原理与应用是机械类专业教学过程中的重要环节,为提高课程教学质量和学生单片机实际操作水平,分析了单片机原理与应用课程考核方法现状以及存在的问题,提出了一种综合的成绩考核评价体系,并建立了课程的成绩模糊综合评价模型。     

Evaluating the integration of fuzzy logic into the student model of a web-based learning environment

In this paper, we evaluate the effectiveness and accuracy of the student model of a web-based educational environment for teaching computer programming. Our student model represents the learner’s knowledge through an overlay model and uses a fuzzy logic technique in order to define and update the student’s knowledge level of each domain concept, each time that s/he interacts with the e-learning system. Evaluation of the student model of an Intelligent Tutoring System (ITS) is an aspect for which there are not clear guidelines to be provided by literature. Therefore, we choose to use two well-known evaluation methods for the evaluation of our fuzzy student model, in order to design an accurate and correct evaluation methodology. These evaluation models are: the Kirkpatrick’s model and the layered evaluation method. Our system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus, in order to learn how to program in the programming language C. The results of the evaluation were very encouraging.     

《手机游戏开发》课程学习质量多元化过程评价的研究

本文以《手机游戏开发》课程为基础,为了全面提高游戏软件专业职业教育的教学质量和学生综合素质,就学生学习质量评价体系的建立提出了评价原则、设计了考核形式和相应操作方案,并在此基础上构建一套完整的测评软件系统,旨在通过网络在线的软件评测系统对学生学习质量进行全方位、多元化的评价,全面提高游戏软件专业职业教育的教学质量和学生综合素质,使本课程的学生学习质量评价体系能更全面、更科学和更合理。     

慕课授课中的学生听课行为自动分析系统

为了解决在线课程(Massive open online course, MOOC)授课过程中, 缺乏对于学生学习情况的跟踪与教学效果评估问题, 本文依据视频信息对学生行为进行建模, 提出了一种评判学生听课专心程度的行为自动分析算法.该算法能够有效跟踪学生的学习状态, 提取学生的行为特征参数, 并对这些参数进行D-S融合判决, 以获得学生的听课专注度.经过多次实验的结果表明, 本文采用的方法能够有效评判学生在授课期间的专心程度, 在数据融合上, 与贝叶斯推理方法相比, 采用D-S融合方法能有效提高实验结果的准确性和可靠性.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

Software verification and graph similarity for automated evaluation of students’ assignments

ContextThe number of students enrolled in universities at standard and on-line programming courses is rapidly increasing. This calls for automated evaluation of students assignments.ObjectiveWe aim to develop methods and tools for objective and reliable automated grading that can also provide substantial and comprehensible feedback. Our approach targets introductory programming courses, which have a number of specific features and goals. The benefits are twofold: reducing the workload for teachers, and providing helpful feedback to students in the process of learning.MethodFor sophisticated automated evaluation of students’ programs, our grading framework combines results of three approaches (i) testing, (ii) software verification, and (iii) control flow graph similarity measurement. We present our tools for software verification and control flow graph similarity measurement, which are publicly available and open source. The tools are based on an intermediate code representation, so they could be applied to a number of programming languages.ResultsEmpirical evaluation of the proposed grading framework is performed on a corpus of programs written by university students in programming language C within an introductory programming course. Results of the evaluation show that the synergy of proposed approaches improves the quality and precision of automated grading and that automatically generated grades are highly correlated with instructor-assigned grades. Also, the results show that our approach can be trained to adapt to teacher’s grading style.ConclusionsIn this paper we integrate several techniques for evaluation of student’s assignments. The obtained results suggest that the presented tools can find real-world applications in automated grading.     

Combining VIKOR with GRA techniques to evaluate service quality of airports under fuzzy environment

The aim of this paper was to present an effective approach for evaluating service quality of Northeast-Asian international airports by conducting customer surveys. In general, evaluation of service quality is a complex multicriteria decision-making (MCDM) problem; therefore, a complex decision process is often involved in which multiple requirements and fuzzy conditions have to be taken into consideration simultaneously. By combining concepts of VIKOR and grey relational analysis (GRA), a new fuzzy MCDM method was proposed to deal with the evaluation of service quality problems in the international airports. This model was solved by an effective algorithm, which incorporated the decision-maker’s attitude and/or preference for customers’ assessments on weights and performance ratings of each criterion. An empirical study for evaluating service quality of seven major Northeast-Asian international airports was put forth to illustrate an application of the proposed model. The study results showed that this approach is an effective means for tackling MCDM problems involving subjective assessments of qualitative attributes in a fuzzy environment.     

Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis

A huge number of studies attest that learning is facilitated if teaching strategies are in accordance with students learning styles, making the learning process more effective and improving students performances. In this context, this paper presents an automatic, dynamic and probabilistic approach for modeling students learning styles based on reinforcement learning. Three different strategies for updating the student model are proposed and tested through experiments. The results obtained are analyzed, indicating the most effective strategy. Experiments have shown that our approach is able to automatically detect and precisely adjust students’ learning styles, based on the non-deterministic and non-stationary aspects of learning styles. Because of the probabilistic and dynamic aspects enclosed in automatic detection of learning styles, our approach gradually and constantly adjusts the student model, taking into account students’ performances, obtaining a fine-tuned student model.     

项目式教学在职业教育中对学生承诺的考核方法的研究

在中职、高职类院校,项目式教学正在被推广,在项目式教学模式下,我们把实际的企业项目带到课堂,同时,我们也希望借鉴有益的企业对员工的评价方法。基于此,提出在原有的项目式评价学生体系下,增加对学生承诺的考核方法,并给出切实可操作的研究方案。增加对学生承诺的考核旨在充分发掘学生的潜能,对超出学生承诺的专业能力的成绩给予鼓励。     

Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

A method for process condition monitoring and evaluation, which combines the online support vector machine (SVM) regression and the fuzzy sets methods, is proposed. To account for the time dependence, the proposed approach is based on moving windows in order to take into account the past and new data for the model’s adaptation. The fuzzy analysis is then applied to the generated residual data to give an evaluation of the condition monitoring. The proposed approach is applied to hot rolling for constructing a complementary condition monitoring system, which permits an online quality evaluation in the rolling process. Simulation results based on residual data show that the new approach is easily implementable.     

项目驱动式教学法在《网页设计与制作》中的探讨

项目驱动式教学法是以学生自主学习为主,教师指导为辅的一种有效的教学方法。它有助于调动学生的学习积极性、主动性和创造性,通过学生对项目的自主完成,教师的引导从而完成教学目的,提高教学质量。对《网页设计与制作》这门计算机基础课程如何应用项目驱动式教学进行探讨。     

基于能力培养的《软件工程》课程实践教学方案设计

首先提出了该实践课程的定位与培养目标,而后从实践教学体系设计的指导思想、实践教学内容和教学方法设计、考核方式与评价方法、实践教学效果几个方面阐述了《软件工程》课程实践教学方案设计的主要内容。通过设计该方案,进一步培养学生的应用能力和实践能力,即有效培养学生的项目分析、设计与技术开发能力,从而提高学生的就业竞争力。     

An automated approach to dependability evaluation of virtual networks

Network virtualization has been pointed as a promising approach to solve Internet’s current ossification. A major challenge is the mapping of virtual networks (VNs) onto the substrate network due to its NP-hard nature, and, thus, several heuristics have been proposed aiming to achieve efficient allocations. However, the existing approaches only focus on performance, and dependability issues are usually neglected. Dependability involves metrics such as reliability and availability, which directly impact the quality of service, and a limitation is the absence of mechanisms for estimating dependability metrics in virtual networks. This paper proposes an automated approach for estimating dependability metrics in virtual network environments and a mapping algorithm based on GRASP metaheuristic. The approach is based on stochastic Petri nets (SPN) and reliability block diagrams (RBD), and a tool is also presented for automating model generation and evaluation. Experimental results demonstrate the feasibility of the proposed technique, as well as the trade-off between VN’s availability and cost.     

基于深度注意力网络的课堂教学视频中学生表情识别与智能教学评估

为了解决复杂课堂场景下学生表情识别的遮挡的问题,同时发挥深度学习在智能教学评估应用上的优势,提出了一种基于深度注意力网络的课堂教学视频中学生表情识别模型与智能教学评估算法.构建了课堂教学视频库、表情库和行为库,利用裁剪和遮挡策略生成多路人脸图像,在此基础上构建了多路深度注意力网络,并通过自注意力机制为多路网络分配不同权...     

基于数据挖掘技术的高校教学质量监控研究

教学质量监控是学校进行教学质量管理的重要手段,对提高教学质量具有重要意义。随着校园信息化的发展,高校教务系统里积存了大量有价值的数据,但却没有被很好的挖掘利用。鉴此提出将数据挖掘技术应用到高校教学质量监控中,使用因子分析法对计算机专业的学生成绩进行综合评价分析,并提出一种决策树改良方法对学生成绩进行预测,最后针对文中所提出的方法与其他方法进行了对比分析。结果表明,因子分析法相对于平均分排名法,蕴含了更多的信息量可以提供更加全面的评价,另外其突出重要因素,可以对各方面情况进行合理量化;决策树改良方法相对于其他成绩预测方法稳定性好、准确性高,且过拟合情况少。经验证,这个方法可以在高校教学质量监控中发挥一定作用。     

基于项目任务教学模式的《网页设计》课程评价模式研究

随着项目任务教学模式的广泛应用,与其相关的评价模式也应随之改进,以《网页设计》课程为例,针对基于过程的形成性考核评价模式展开深入的分析与研究,同时建立学生平时考核档案。此评价模式不但能提高学生的学习效果与职业能力,而且有利于教学质量的提高。     

求解大尺度优化问题的学生t-分布估计算法

针对处理大尺度全局优化问题,提出一种基于自适应t-分布的分布估计算法(EDA-t).该算法不仅求解效果良好,而且求解速度也比同类型算法快.其基本思想是：在迭代搜索过程,首先利用期望最大化算法对演化种群进行概率主成分分析,然后根据得到的概率隐变量建立算法的概率模型,并通过t-分布自由度自适应方法,在算法收敛停滞时跳出局部最优.由于在构建模型时进行了数据降维,在不影响算法求解精度的前提下,其计算开销得到了明显降低.通过和目前主流的演化算法在大尺度优化测试函数上的仿真实验和分析,验证了所提算法的有效性和适用性.     

基于云模型的智能教学系统中学习质量的评价

提出将云模型理论应用于智能教学系统的学生模型构建中,建立一种基于云模型的学生学习质量评价方法,利用云对概念的贡献程度进行数据离散化,并引入云变换计算隶属云,最后结合极大判定算法求出更加符合实际的学习质量等级划分。实验结果表明,这种新的评价方法得出的隶属概念不仅能够反映出学生对知识点的掌握程度,还能够反映出学生在学习中的发挥稳定性、心理素质等情况,有利于提高智能教学系统的应用效率。     

基于应用型人才培养的“离散数学”考试方法改革

考核是教学的有机组成部分,也是衡量学生学习效果的重要依据。"离散数学"作为计算机等相关专业的一门重要基础课程,在基于应用型人才培养目标下,采取怎样的考核方式来评价教学效果,是一个值得思考的问题。本文针对此问题提出了模块化考核方式思想,分析了这种考核方式的原因、实施步骤及实际效果,很好地体现出以学生为本的教学理念。