Moderating effect of learning styles on a learning management system’s success

This study uses the DeLone and McLean model to determine the moderating impact of learning styles on the success of learning management systems from a student’s point of view. The main objectives of this research are: (1) to evaluate the Delone and McLean model of information system success in the context of learning management systems and, (2) to determine the effect of the learning styles of students on this model. An in-person survey of 258 engineering students was used to evaluate the research model. The analysis is based on structural equation modelling, specifically partial least squares. The results indicate that the research model explains use, user satisfaction, and perceived benefits of a learning management system. In addition, the Felder-Silverman learning styles (sensing-intuitive, visual-verbal; active-reflective; sequential-global) modify the strength of the relationships between the variables of the success model.     

IoTFLiP: IoT-based flipped learning platform for medical education

Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things (IoTs) concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical cases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learing environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastructure is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool (ICBFLT), which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students' and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students' academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.     

Examining the use of YouTube as a Learning Resource in higher education: Scale development and validation of TAM model

The teaching–learning activity was a perishable service which was to be consumed as it was being delivered. It was also constrained previously by geographical location – teacher and learner being at the same place. The advent of technology has allowed some leeway for these time and space constraints. The printed books, recorded lectures, specific learning aids, etc. allow learner’s the freedom to determine the learning speed. YouTube is a content community with provision for individuals to post their own User Generated Content (UGC) for use by other users. Though, popular as an entertainment medium, it has become a valuable learning resource and is being considered as an alternative to written text hosted by different websites and blogs. However, there is no scale to measure the behavioral intentions of the users to use YouTube as a Learning Resource and no model to understand the factors influencing this behavioral intention. This paper aims at two objectives: scale development, and validating the basic Technology Acceptance Model (TAM). It defined constructs and developed scales to measure the factors explaining the behavioral intention and established the significance of relationship between different variables and the behavioral intention validating the TAM.     

Prerequisites between learning objects: Automatic extraction based on a machine learning approach

One standing problem in the area of web-based e-learning is how to support instructional designers to effectively and efficiently retrieve learning materials, appropriate for their educational purposes. Learning materials can be retrieved from structured repositories, such as repositories of Learning Objects and Massive Open Online Courses; they could also come from unstructured sources, such as web hypertext pages. Platforms for distance education often implement algorithms for recommending specific educational resources and personalized learning paths to students. But choosing and sequencing the adequate learning materials to build adaptive courses may reveal to be quite a challenging task.In particular, establishing the prerequisite relationships among learning objects, in terms of prior requirements needed to understand and complete before making use of the subsequent contents, is a crucial step for faculty, instructional designers or automated systems whose goal is to adapt existing learning objects to delivery in new distance courses. Nevertheless, this information is often missing. In this paper, an innovative machine learning-based approach for the identification of prerequisites between text-based resources is proposed. A feature selection methodology allows us to consider the attributes that are most relevant to the predictive modeling problem. These features are extracted from both the input material and weak-taxonomies available on the web. Input data undergoes a Natural language process that makes finding patterns of interest more easy for the applied automated analysis. Finally, the prerequisite identification is cast to a binary statistical classification task. The accuracy of the approach is validated by means of experimental evaluations on real online coursers covering different subjects.     

A global perspective on an emotional learning model proposal

Existing literature argues that emotions have a significant impact on the majority of human activities and functions. The learning process is one of the activities on which emotions have a direct influence. Thus, understanding the manner in which emotions change the students’ learning process is not only very important but it can also allow to improve the existing learning models.Currently, in the majority of situations, the teacher serves as a facilitator between the student and the learning course, and through a constant analysis of the student’s behaviour, emotions and achievements, he constantly performs adjustments to the teaching process in order to meet the students’ needs and goals. Thus far, in online learning environments there is no easy way for teachers to analyse students’ behaviour and emotions. A possible solution to this problem can be the development of mechanisms that enable computers to automatically detect students’ emotions and adapt the learning process in order to meet students’ real needs.An emotional learning model was described and a software prototype was developed and tested, in order to find out whether it performs live identification of the students’ emotions, by using affective computing techniques, and whether it automatically performs adjustments to their individual learning process. Through a deeper analysis and multidisciplinary discussion of the achieved results it is possible to acknowledge that not only emotions impact students’ learning, but also that an application that performs live emotion recognition and which integrates this feature with adjustable online learning environments will trigger improvements in students’ learning.     

An affective and Web 3.0-based learning environment for a programming language

We present a Web-based environment for learning Java programming that aims to provide adapted and individualized programming instruction to students by using modern learning technologies as a recommender and mining system, an affect recognizer, a sentiment analyzer, and an authoring tool. All these components interact in real time to provide an educational setting where the student learn to develop Java programs. The recommender system is an E-Learning 3.0 software component that recommends new exercises to a student based on the actions (ratings) of previous learners. The affect recognizer analyze pictures of the student to recognize learning-centered emotions (frustration, boredom, engagement, and excitement) that are used to provide personalized instruction. Sentiment text analysis determines the quality of the programming exercises based on the opinions of the students. The authoring tool is used to create new exercises with no programming work. We conducted two evaluations: one evaluation used the Technology Acceptance Model to assess the impact of our software tool on student behavior. The second evaluation calculated the student’s t-test to assess the learning gain after a student used the tool. The results of the evaluations show the students perceived enjoyment and are willing to use the tool. The study also show that students using the tool have a greater learning gain than those who learn using a traditional method.     

Active and interactive learning online: a comparison of Web-basedand conventional writing classes

This study examines how students enrolled in two Web-based sections of a technical writing class performed compared to students enrolled in a conventional version of the class. Although no significant difference in student performance was found between the two learning conditions, our data reveal intriguing relationships between students' prior knowledge, attitudes, and learning styles and our Web-based writing environment. One finding that we focus on is that reflective, global learners performed significantly better online than active, sequential learners, whereas there was no difference between them in the conventional class. Our study highlights the complexity of effective teaching and the difficulty of making comparisons between the online and the classroom environments. In particular, we maintain that the transfer of active learning strategies to the Web is not straightforward and that interactivity as a goal of instructional Web site design requires significant elaboration     

基于Felder-Silverman学习风格的自适应e-learning系统

传统e-learning系统缺乏学生个性化特征的定制功能,学习风格是学习过程中较为稳定的学习策略倾向个性特征。将Felder-Silverman学习风格引入e-learning系统,给出了基于Solomon量化表的学习风格生成算法,然后搭建基于.NET分层架构的自适应性e-learning系统。实验结果表明,该系统能够根据学生的学习风格进行个性化的内容呈现和知识导航,具有自适应的特征。     

On-line classroom visual tracking and quality evaluation by an advanced feature mining technique

With the rapid development of computer vision and multimedia technology, especially the visual tracking technology and network transmission, teacher-centered education is popular nowadays. The shortcomings of the conventional classroom teaching mode by manually student behavior analysis are gradually becoming less effective. Aiming at the main problems existing in the application of classroom teaching video resources in multimedia teaching, in this paper, we proposed an online classroom visual data tracking system, associated with an advanced tracking quality evaluation method based on data mining. Our proposed framework can offer a scientific basis for improving the quality of online education by discovering students’ learning patterns from their online learning data. The evaluation results can effectively demonstrated that the mining of various learning information of students is useful, and obtained the classification rules that affect the learning effect toward students. These clues can be adopted to uncover the learning effect of students and provide individual guidance for students’ learning behaviors. This work can reveal the pattern online classroom image teaching behavior from the perspective of behavior chain. We also noticed the online classroom visual tracking behavior can be divided into several components: selection, presentation, mapping, analysis and collection, as well as the analysis from students facial expression.     

VARK Learning Style Classification Using Decision Tree with Physiological Signals

Learning style is deemed crucial for different types of age groups. It is essential, especially for individual learning achievement. Learning is a part of cognitive processes affecting the human central nervous system, which can be monitored by using the physiological signals. In this study, physiological signals thus are proposed as key attributes for the classification of learning styles to avoid biased data from completing the questionnaire and promote the real-time response in the classroom environment. More specifically, heart rate and blood pressure signals are chosen for this study. Following the VARK model, the physiological signals of learners are classified with the decision tree into four different types, including visual, aural, read and write, and kinesthetic learners. There are 40 primary school children and 30 university students involved in the whole study. The results show that the proposed factors obtain 85% and 90% classification accuracy for children and university students, respectively. Both heart rate and blood pressure are thus reasonably impacted as the classification attributes.

     

Online Dictionary Learning Based Intra-frame Video Coding

In this paper, we propose an online learning based intra-frame video coding approach, exploiting the texture sparsity of natural images. The proposed method is capable of learning the basic texture elements from previous frames with convergence guaranteed, leading to effective dictionaries for sparser representation of incoming frames. Benefiting from online learning, the proposed online dictionary learning based codec (ODL codec) is able to achieve a goal that the more video frames are being coded, the less non-zero coefficients are required to be transmitted. Then, these non-zero coefficients for image patches are further quantized and coded combined with dictionary synchronization. The experimental results demonstrate that the number of non-zero coefficients of each frame decreases rapidly while more frames are encoded. Compared to the off-line mode training, the proposed ODL codec, learning from video on the fly, is able to reduce the computational complexity with fast convergence. Finally, the rate distortion performance shows improvement in terms of PSNR compared with the K-SVD dictionary based compression and H.264/AVC for intra-frame video at low bit rates.     

Teaching and learning in cyberspace

From both a technological and educational perspective, cyber education creates a multitude of challenges for students and instructors. Both novice and experienced computer users alike must master the use of Internet tools quickly, while also working to overcome conceptual misunderstandings about the technology and its root metaphors. The technology also makes commenting on student documents cumbersome but does have the benefit of creating a digitized record of students' writing processes, while also allowing for the online publication of students' work. Other benefits include more active learning and better interactive collaboration. Preliminary assessments further indicate that, despite critics' concerns about the rigor and quality of distance learning, for a variety of technical and social reasons, student work is equal to and sometimes better than that of on-campus students     

Efficient web-based open and distance learning services

In this paper, we present data management issues faced during the design and development of an open distance learning system for the University of Patras, Greece. In order to handle data efficiently, as required in a web tele-training application, for each type of information maintained, different strategies must be deployed according to their behaviour and structure. The diversity and complexity of data, the network aspect of the application and web deficiencies impose an architecture design incorporating a plethora of technologies and tools that must be integrated in such a fashion that they efficiently organise these data preserving their relationships. This presents a software engineering challenge requiring coherence of solutions at all levels: structures, consistency, security, models, and protocols. The paper presents the data components of an open and distance learning (ODL) system that access the information stored in a database and the file system, their underlying technology, their interaction with the network services, and features regarding the ways they address issues faced in an open vendor-independent distance learning environment and outlines the system's overall architecture. In addition, this paper presents the architecture, the design and the services of a network-based information system that supports open and distance learning activities. The open and distance learning information system (ODLIS) offers synchronous and asynchronous distance learning and management of information system (MIS) services to support the educational procedure. The ODLIS is a web-based application, which runs over the Internet using real time protocols.     

Patient-centric ICTs based healthcare for students with learning,physical and/or sensory disabilities

The purpose of this review article is to explore a new paradigm on healthcare assessment and intervention practices for students with learning, physical and/or sensory disabilities. The perspective presented here is regarded as patient centric and relies on ICTs, in order to address evidence–based treatment and care, meeting every individual’s health profile, with regard to his or her needs, preferences, goals and culture. Patient-centric ICTs based approach has an impact on healthcare systems, introducing a multi-disciplinary care planning that overcomes individual, professional and organizational barriers, reduces anxiety and establishes better understanding with the student at the center.Generally, results show the impact of the interoperability of healthcare information between patients’ healthcare record and information systems which facilitate healthcare systems to be lifesaving if available at the time of medical examination. When medical treatment is combined with ICTs based methods and applications such as Serious Games, Mobile Assistive Technologies applications, Telehealth Systems and Virtual Training, patients with learning, physical and/or sensory disabilities can benefit from a cost-effective and flexible network model while having their customized needs fully examined and resolved.The articles presented within this review demonstrate that ICTs based patient-centeredness for students with learning, physical and/or sensory disabilities is associated with better (functional) outcomes leading to quality of life and inclusion, improved quality of care, fewer problem behaviors, higher levels of health-related autonomy and greater patient satisfaction.     

A blockchain-based online language learning system

To check students’ daily language learning tasks and give students corresponding reasonable scores based on their daily behavior is hard for teachers. The existing online language learning systems are vulnerable and easy to be modified by teachers or system managers. Blockchain can provide immutable and trusted storage service and automatic calculation service. Therefore, a blockchain-based online language learning system is proposed in this paper to monitor students’ daily study and automatically evaluate their behavior so as to save teachers from tedious and complex homework verification workload and provide trusted and reliable evaluation on students’ behavior. This paper first introduces the current situation of language learning in universities and the related works on blockchain-based online language learning system. Then the system is detailed in its structure and smart contracts. At last, we implement this system and do the analysis and summary.

     

Context-aware mobile learning system: Usability assessment based on a field study

Teaching-learning models supported by technology have evolved since the emergence of personal computers until the present day, where m-learning has been incorporated thanks to mobile technology. In this context, some works have proposed mobile learning systems (MLS). One of the main requirements of this kind of systems in terms of software quality is the usability. Therefore, the analysis and evaluation of the usability of MLS are especially relevant; however, few works have addressed the usability issues using field studies with students and professors in real domains. In this paper, we present a usability assessment of a Context-Aware Mobile Learning System (CAMLS) based on a field study with six professors and forty-eight high school students in a real environment. The results obtained from the usability assessment show that, on average, the services offered by the system have 82.4% acceptance by users (professors and high school students), with the learning reinforcement service through SMS messages having the highest acceptance for teachers, with a positive perception of 91.5%. Meanwhile, for the high school students, the Mobile Learning Objects (MLOs) suggestion service was highest with 81% acceptance. Based on the obtained results, the evaluated mobile learning system holds wide acceptance, satisfaction, and applicability from teachers’ and students’ perspectives. The usability assessment described in this study can serve as a reference for developers seeking to improve mobile learning systems development.     

Satellite-based distance learning using digital video and theInternet

The Boston University College of Engineering Distance Learning Initiative (DLI) integrates computers, digital video, and the Internet to deliver graduate degree courses in engineering to students in companies distant from the Boston University campus. A key objective of the DLI is to support learning wherever it is most convenient, whether by groups in a classroom, at the workplace desk, or at home. The article describes the motivation, technology, and experiences in integrating a satellite-based digital video distance learning system coupled with Web technology     

基于Web的协同学习策略及其应用

对网络教育的现状进行了分析，并针对目前网络教育中师生之间、学生之间缺乏有效的交流的缺点，提出了将协同学习策略应用在的基于Web的协同学习系统中的方法。使用多种同学习策略对协同学习过程进行控制，并且将学习评估与协同学习过程进行结合，以提高学生的学习效果。     

Intelligent laser welding through representation,prediction, and control learning: An architecture with deep neural networks and reinforcement learning

Laser welding is a widely used but complex industrial process. In this work, we propose the use of an integrated machine intelligence architecture to help address the significant control difficulties that prevent laser welding from seeing its full potential in process engineering and production. This architecture combines three contemporary machine learning techniques to allow a laser welding controller to learn and improve in a self-directed manner. As a first contribution of this work, we show how a deep, auto-encoding neural network is capable of extracting salient, low-dimensional features from real high-dimensional laser welding data. As a second contribution and novel integration step, these features are then used as input to a temporal-difference learning algorithm (in this case a general-value-function learner) to acquire important real-time information about the process of laser welding; temporally extended predictions are used in combination with deep learning to directly map sensor data to the final quality of a welding seam. As a third contribution and final part of our proposed architecture, we suggest that deep learning features and general-value-function predictions can be beneficially combined with actor–critic reinforcement learning to learn context-appropriate control policies to govern welding power in real time. Preliminary control results are demonstrated using multiple runs with a laser-welding simulator. The proposed intelligent laser-welding architecture combines representation, prediction, and control learning: three of the main hallmarks of an intelligent system. As such, we suggest that an integration approach like the one described in this work has the capacity to improve laser welding performance without ongoing and time-intensive human assistance. Our architecture therefore promises to address several key requirements of modern industry. To our knowledge, this architecture is the first demonstrated combination of deep learning and general value functions. It also represents the first use of deep learning for laser welding specifically and production engineering in general. We believe that it would be straightforward to adapt our architecture for use in other industrial and production engineering settings.     

Ubiquitous learning: A systematic review

Ubiquitous learning, labeled as u–learning, takes advantage of digital content, physical surroundings, mobile devices, pervasive components, and wireless communication to deliver teaching–learning experiences to users at anytime, anywhere, and anyway. U–learning represents an emergent paradigm that spreads education in diverse settings, where users are situated in authentic learning contexts to face immersive experiences in order to accomplish meaningful learning. With the aim at disseminating such a revolutionary arena, this systematic review analyzes its nature, application, and evolution throughout a longitudinal study, where 176 approaches built since 2010 up to the third quarter of 2017 date are gathered, classified, and characterized to disclose labor traits, outcome patterns, and field tendencies. These five results are grounded respectively in a representative collection, a proposed taxonomy, a suggested pattern, statistical interpretations, mining findings, and critical analysis. The conclusions reveal: u–learning is able to transform traditional education provided at classroom level and by e–learning. Principally, this is because students, pertaining to diverse academic levels experience real and authentic settings, are immersed in dual reality sceneries, benefit from context–aware support, learn diverse educational domains, follow suitable learning paradigms, deal with diverse effects, and interact with different devices and technologies in a blended fashion. All of this with the purpose of enhancing users’ apprenticeship.