Guidelines for facilitating the development of learning communities in online courses

Online learning has grown exponentially in recent years; however, dropout problem remains challenging for some online programmes. The dropout problem can be attributed to a number of reasons, with a lack of interaction between learners and the instructor constituting one of the main reasons. The lack of interaction also leads to learners' feeling of isolation. Learning communities can provide learners with an environment conducive to increased interactions and alleviate their feeling of isolation. Unfortunately, there are no clear rules that instructors can follow to help learners create learning communities. In this paper, we propose guidelines for online instructors to facilitate the development of learning communities in online courses. We first review the definition of a learning community, importance of a learning community and factors affecting the development of a learning community. Afterwards, based on a review of the existing guidelines and other relevant literature, we propose guidelines for facilitating the development of learning communities in online courses.     

基于云计算的在线学习平台的设计与应用

本文将云计算的概念引入到在线学习平台的建设中,提出设计基于云计算的在线学习平台。首先介绍云计算的概念和特点,然后提出一种基于云计算在线学习平台的设计框架,最后实现了一个在线讨论的云计算教育服务平台。     

Effectiveness of learning in online academic courses compared with face‐to‐face courses in higher education

This study examined the effectiveness of 3 online courses compared with the same 3 courses in a face‐to‐face (F2F) format, which had the same characteristics (e.g., the same instructor and final exam content and place). Effectiveness was examined by utilizing a wide range of variables, including 2 objective measures (N = 968): grades and completion rate; and 9 subjective measures (N = 360): 7 measures include instructional aspects (course structure, learning content, lessons watched, assignments, communication), engagement, and satisfaction. Findings indicate significant differences between online and F2F courses in most of the examined variables. Students in the online courses reported better understanding of the course structure, better communication with the course staff, watching the videos lessens more, and higher engagement and satisfaction. Students in the F2F courses reported better contribution of the learning content. Students' final grades were higher in the online courses, and no differences were found in the completion rate. The findings suggest that in many of the examined effectiveness aspects, online courses are as effective as, or more effective than, F2F courses. Interpretations of the findings are discussed.     

OKCM: improving parallel task scheduling in high-performance computing systems using online learning

The Journal of Supercomputing - Task scheduling is becoming increasingly important in large-scale high-performance computing real-time systems as the parallel scale, number and types of task...     

A theory of online learning as online participation

In this paper, an initial theory of online learning as online participation is suggested. It is argued that online learner participation (1) is a complex process of taking part and maintaining relations with others, (2) is supported by physical and psychological tools, (3) is not synonymous with talking or writing, and (4) is supported by all kinds of engaging activities. Participation and learning are argued to be inseparable and jointly constituting. The implication of the theory is straightforward: If we want to enhance online learning, we need to enhance online learner participation.     

云计算在网络综合设计教学中应用初探

针对通用实验机房面临的实验环境安全性问题和维护效率低下问题,根据云计算的优势,探索将云计算平台应用于具体实践课程中。云计算平台在网络综合设计课程中的实践应用表明,云计算可使实验环境维护变得轻松、实验过程变得安全高效、实验组织变得更加灵活。     

Software standards in undergraduate computing courses

Abstract High-quality software must be robust, reliable and maintainable. The design and coding of such software is no longer a craft; it is an engineering discipline, and the teaching of computer programming must reflect this. Consequently, students need to be aware of the importance of formulating accurate specifications for programming tasks, and of coding programs which correctly implement such specifications. However, the increase in student numbers experienced in recent years has caused manual methods of assessing students' programming assignments to become inefficient, and consistency more difficult to enforce. In order to support and motivate a rigorous approach in the context of these difficulties, an innovative on-line assessment system for programming language modules has been developed which addresses both concerns. This paper describes the package and discusses its importance in the delivery and assessment of undergraduate programming modules.     

The evaluation of online courses

Abstract Given the special characteristics of online courses, their quantitative/qualitative evaluation calls for the adoption of specific procedures to assess both the learning process and the participant performance. This paper seeks to shed light on the aspects and issues involved in the evaluation of online courses, focusing on the peculiarities that distinguish such evaluation from that of traditional distance education. Subsequently, a methodological approach will be proposed that was developed and tested during evaluation of online courses for in‐service teacher training and for training of staff in small and medium‐sized enterprises.     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

Position calculation models by neural computing and online learning methods for high-speed train

For high-speed trains, high precision of train positioning is important to guarantee train safety and operational efficiency. By analyzing the operational data of Beijing–Shanghai high-speed railway, we find that the currently used average speed model (ASM) is not good enough as the relative error is about 2.5 %. To reduce the positioning error, we respectively establish three models for calculating train positions by advanced neural computing methods, including back-propagation (BP), radial basis function (RBF) and adaptive network-based fuzzy inference system (ANFIS). Furthermore, six indices are defined to evaluate the performance of the three established models. Compared with ASM, the positioning error can be reduced by about 50 % by neural computing models. Then, to increase the robustness of neural computing models and real-time response, online learning methods are developed to update the parameters in the last layer of neural computing models by the gradient descent method. With the online learning methods, the positioning error of neural computing models can be further reduced by about 10 %. Among the three models, the ANFIS model is the best in both training and testing. The BP model is better than the RBF model in training, but worse in testing. In a word, the three models can reduce the half number of transponders to save the cost under the same positioning error or reduce the positioning error about 50 % in the case of the same number of transponders.     

Integrating software engineering in introductory computing courses

An introductory course for undergraduate computing students aims to integrate data structures and software engineering. In addition to completing assignments involving analysis, modeling, and software design, students work in teams using a 10-step process to develop an object-oriented software system.     

A primal-dual perspective of online learning algorithms

We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universal online bounds as an optimization problem. Using the weak duality theorem we reduce the process of online learning to the task of incrementally increasing the dual objective function. The amount by which the dual increases serves as a new and natural notion of progress for analyzing online learning algorithms. We are thus able to tie the primal objective value and the number of prediction mistakes using the increase in the dual. Editors: Hans Ulrich Simon, Gabor Lugosi, Avrim Blum. A preliminary version of this paper appeared at the 19th Annual Conference on Learning Theory under the title “Online learning meets optimization in the dual”.     

Introductory courses in computing: Aims and languages

There is a discrepancy between common introductory computing courses and the stated or implied aims of such courses. We suggest more precise aims, and courses to meet these, and claim that no such course should consist of programming alone. If, however, an introductory course contains a segment on programming, a language must be selected. Criteria for language selection are therefore suggested, and a number of languages compared according to these criteria. We conclude that:(i) an introductory course in computing should be aimed at problem solving and “mastery of society”;(ii) to achieve this, programming should be taught in a wider context;(iii) the programming language used should satisfy a number of well-defined criteria.     

A review of online learning in supervised neural networks

Learning in neural networks can broadly be divided into two categories, viz., off-line (or batch) learning and online (or incremental) learning. In this paper, a review of a variety of supervised neural networks with online learning capabilities is presented. Specifically, we focus on articles published in main indexed journals in the past 10 years (2003–2013). We examine a number of key neural network architectures, which include feedforward neural networks, recurrent neural networks, fuzzy neural networks, and other related networks. How the online learning methodologies are incorporated into these networks is exemplified, and how they are applied to solving problems in different domains is highlighted. A summary of the review that covers different network architectures and their applications is presented.     

The potential of synchronous communication to enhance participation in online discussions: A case study of two e-learning courses

Computer-mediated communication (CMC) has been adopted in most e-learning settings. However, few research studies have considered the effect of different CMC. This study examined how and why synchronous communication affected participation in online discussions. Two online classes that participated in two asynchronous and two synchronous online discussions were examined. Actual and perceived measures of participation indicated that synchronous communication induced personal participation, which could be regarded as a complement to cognitive participation. Personal participation involves more intense interaction better supported by synchronous communication while cognitive participation is a more reflective type of participation supported by asynchronous communication. In synchronous discussions, the e-learners felt that they worked together and were not restricted to only discuss course content. This was likely to induce arousal and motivation and increased convergence on meaning, especially in small groups.     

A reinforcement learning approach to online clustering

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and significantly improve the performance of those algorithms, as indicated by experimental tests on well-known data sets.     

Students' perceptions of instructors' roles in blended and online learning environments: A comparative study

This study develops an instrument—the Online Instructor Role and Behavior Scale (OIRBS)—and uses it to examine students' perceptions of instructors' roles in blended and online learning environments. A total sample of 750 university students participated in this study. Through a confirmatory factor analysis, the OIRBS was validated in five constructs: course designer and organizer (CDO), discussion facilitator (DF), social supporter (SS), technology facilitator (TF), and assessment designer (AD). The results show that the five factor structures remained invariant across the blended learning and online learning. Both students in blended learning environments and students in online learning environments exhibited the greatest weight in the CDO dimension, followed by the TF and DF dimensions. In addition, students in the online learning environments scored higher in the DF dimension than did those in the blended learning environments.     

Collaborative learning in postgraduate level courses

Nowadays, we are immersed in the social and mobile networks era. As a positive consequence of this, collaborative and mobile learning in educational environments have been encouraged thanks to the use of computing for human learning. By coupling the advantages of collaborative and mobile learning, the teaching-learning processes involved in postgraduate courses may be greatly enhanced. The pedagogical experiences in this regard lived by the authors in the Alpha–Beta Research Group when coupling collaborative and mobile learning in the context of postgraduate level courses, are presented in this paper.     

学分制网上选课系统的数据库设计方案

主要探讨了在真正的学分制管理模式下,开发网上选课系统需要解决的主要问题,重点介绍了后台数据库的整体设计思路。     

一种基于池计算的宽度学习系统

宽度学习系统(BLS)是一种基于RVFLN的高效增量学习系统,具有快速且精度高的特点.为了实现BLS对时间序列的精确预测,结合回声状态网络(ESN)的储备池结构,提出一种基于池计算的宽度学习系统(RCBLS).该系统通过在强化层引入简单环型储备池连接,以并行的储备池代替原系统中的前馈连接,使RCBLS具有一定的回声状态...