Pervasive Learning Environment Research Articles

Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments

This article proposes a context-aware course-planning model for pervasive learning environments. The model considers learners’ preferences, including their learning styles, locations, activities, and devices. The model generates four-dimensional contexts based on these preferences, each dimension being a weighted vector of visual, aural, read/write, and kinesthetic (VARK) features. Using a content-based similarity algorithm, the model adapts the course content to each learner’s context. The courses are created in a sequence of rings, each containing all possible learning materials (LMs) that have the same content in different formats. Each LM is represented by a vector with weights like those of the learners’ context dimensions. The model generates all possible plans based on the predefined contexts of the learner, detects the learner’s actual context, and adapts the content accordingly. The goal of the model is to enable learners to learn what they want, in the way they prefer, and to complete courses efficiently, at any time, place, and during any activity, in the appropriate format.

Pervasive Learning Environment for Educational Makerspaces with Emerging Technologies and Teaching and Learning Transformation

In 21st century the technological innovation has reshaped our educational system. Different educational methods and environments are used to facilitate students and teachers. Educational makerspaces are also one of them. This new trend has shifted educational system to student centered instead of teacher centered. Pervasive Learning (P-Learning) environment for educational makerspaces is one of the emerging environments for teaching and learning due to price reduction in handheld devices’, facility for sharing the technological resources, support, and smartness of smartphone technology. Educational Makerspaces environment provide the facility to teachers and learners to create a virtual environment fully equipped with latest ICT tools to share ideas, to perform experiments and collaborate with each other whenever and wherever they are. Thus, P-learning for educational makerspaces can access and share the resources for teaching and learning material beyond the boundaries of the traditional classroom arrangement. This paper presents the idea of P-learning environment for educational makerspaces which is not limited to a single geographic location or mobile or location-based technologies; rather, it accesses, shares the resources, and facilitates teaching and learning from anywhere and at any time with any handheld device means 24*7*12. The purpose of this paper is to propose a fully equipped makerspace classroom so that the students from remote areas or who cannot afford laboratory expenses can access virtually labs, share their ideas, collaborate with each other, and perform experiments

Moodle como um ambiente de aprendizagem amplo e útil

This study provides updated data on the evolution of Moodle , which was initially seen as an e-learning platform intended to become a pervasive learning environment (PLE) with its multiple possibilities for higher education students and teachers. An online anonymous survey has been administered to a sample of 100 students of different degrees, and 32 teachers of different subject areas to find out their perceptions on the usefulness and improvement of our virtual learning platform at the Universidad de Las Palmas de Gran Canaria (ULPGC), Spain. Results show that this Learning Management System (LMS) is still regarded as a motivating space to support face-to-face instruction, but it has also been pointed out that it encourages knowledge building, pervasive learning and constructive interaction among its participants. Keywords: Moodle , e-learning, ICT, pervasive learning environment, higher education.

Network resource management in support of QoS in ubiquitous learning

Ubiquitous learning involves a large-scale service network organized as a social infrastructure. These environments weave together context service dissemination, learner profiling as well as autonomic control of the induced network traffic. The objective of the research presented in this paper is to derive a Quality of Service aware model of ubiquitous learning services based on typical learning schemes. These pedagogical patterns are designed to match various learning situations in terms of learning context, learner profile and network infrastructure. They particularly represent classes of services in ubiquitous learning environments to prioritize traffic so that less important traffic does not consume network bandwidth and slow down or halt the delivery of more important traffic. We analyze formally and empirically the network traffic requirements of a proposed learning service quality controller to support providers of learning services allocating resources in a pervasive learning environment.

Mobile Learning Application for Basic Router and Switch Configuration on Android Platform

This paper presents the design and development of Mobile Learning Application for Basic Router and Switch Configuration on Android Platform using Java Programming Language to help students in computer networking courses at the Department of Computer Technology and Networking, Faculty of Computer and Mathematical Sciences, University of Technology Mara, Malaysia. Our approach is to incorporate multimedia animations concept with command langguage to create the pervasive learning environment in presenting the Router and Switch Configuration systematically. With this mobile learning application, student could learn at his or her own pace, anywhere and anytime. This mobile learning application intends to complement the current traditional classroom and e-learning systems. Initial testing has shown that a well-presented multimedia animations capability that is delivered through a mobile phone has a great potential to promote and enhance learning process.

Deploying learning designs across physical and web spaces: Making pervasive learning affordable for teachers

Pervasive computing devices and communication infrastructures enable learning situations that occur in both the physical and the virtual world. However, deploying these pervasive situations is still a challenge for teachers. This paper presents GLUEPS-AR, a system for deploying learning designs across physical and web spaces, using mainstream Virtual Learning Environments, Web 2.0 artifacts and Augmented Reality applications. GLUEPS-AR has been evaluated through a mixed methods study on the deployment of three authentic pervasive learning situations. Results highlight that GLUEPS-AR supports teachers in deploying their pedagogical ideas on pervasive learning environments, overcoming the main limitations of existing approaches.

Learning, Engagement, and Technology: Middle School Students' Three-Year Experience in Pervasive Technology Environments in South Korea

This study explored an initiative that created a pervasive learning environment in a middle school in South Korea and examined its impact on student academic achievement and learning engagement. Forty students received a laptop to use for class projects, online collaboration, and lesson reviews over a 3-year period. To measure the effect of laptop use, data were collected from surveys, test scores, interviews with students, and teacher reflections. The findings indicate that students demonstrated greater learning engagement, but no significant difference was found in test scores between students who had been given laptops and those who had not. Moreover, student engagement decreased as the novelty effect waned, while the number of students who used laptops inappropriately (e.g., playing online games during class) increased as the project progressed. Detailed explanations for this lack of influence on learning and decreased engagement are provided.

Semantic retrieval: multiple response model for context-aware learning services

With the consistent adoption of pervasive computing thoroughly integrated into our daily learning activities, seamless learning services can be provided for learners corresponding to their needs at any time and any place. The adapted learning service is delivered to learners based on learning situations and learning response/feedback, which are success and failure in practice. But because the learning needs are changing under available rich contexts, learning services generally make the learners ‘cognition overloads’ and ‘disoriented’ under context-aware ubiquitous learning environments. In order to deliver the most suitable learning service, this paper proposes a multiple response approach to realise context-aware learning services under pervasive learning environments. Based on six learning statuses from sharable content object reference model (SCORM), six responses of learning feedback are used to reward or penalise the preferred learning service according to the learning context. Experimental results show that the proposed methods perform well in practice and the prototype system can successfully deliver the learning services adapted to the learning contextual situations of learners.

A context-aware personalized resource recommendation for pervasive learning

As it is difficult for learners to discover and obtain the most appropriate resources from massive education resources according to traditional keyword searching method, the context-aware based resource recommendation service becomes a significant part of pervasive learning environments. At present, recommendation mechanisms are widely used in e-commerce field, where content-based or collaborative-based filter strategies are usually considered separately. However, in these existing recommendation mechanisms, the dynamic interests and preference of learners, the access pattern and the other attributes of pervasive learning environments (such as multi-modes connecting and resources distribution) are always neglected. Thus, these mechanisms can not effectively reflect learners' actual preference and can not adapt to pervasive learning environments perfectly. To address these problems, a context-aware resource recommendation model and relevant recommendation algorithm for pervasive learning environments are proposed. Therein, with taking into account the relevant contextual information, the calculation of relevant degree between learners and resources can be divided into two main parts: logic-based RRD (resource relevant degree) and situation-based RRD. In the first part, content-based and collaborative-based recommendation mechanisms are combined together, where the individual preference tree (IPT) is introduced to take into account the multi-dimensional attributes of resources, learners' rating matrix and the energy of access preference. Meanwhile, the learners' historical sequential patterns of resource accessing are also considered to further improve the accuracy of recommendation. In the second part, in order to enhance the validation of recommendation, the connecting type relevance and time satisfaction degree are calculated according to other relevant contexts. Then, the candidate resources can be filtered and sorted via combining these two parts to generate (Top-N) recommendation results. The simulations show that our newly proposed method outperforms other state of-the-art algorithms on traditional and newly presented metrics and it may also be more suitable for pervasive learning environments. Finally, a prototype system is implemented based on SEU-ESP to demonstrate the relevant recommendation process further.

Survey on Context-Aware Pervasive Learning Environments

Context-aware pervasive learning environments consist of interconnected, embedded computing devices such as portable computers, wireless sensors, auxiliary input/output devices and servers. Until this study there has been no survey that has evaluated and presented information regarding these environments. In this paper, we conducted a survey to identify the commonly used technologies, methods and models behind these systems, and evaluated the role of mobile devices in the reviewed papers. As a result, we made five observations: (i) RFID was the most common sensor technology; (ii) several learning models were suggested, but none was validated properly; (iii) client-server architectures are prevalent in the systems and mobile devices were used most commonly to represent information; (iv) most of the systems supported multiple simultaneous users, but few facilitated virtual communication; and (v) possible roles for physical environments in pervasive learning systems are: contexts for learning, content for learning, and system resources. Evidence indicates that suitable learning models have yet to be validated, and that more roles of mobile devices could be emphasised.

A SCORM Compliant Courseware Authoring Tool for Supporting Pervasive Learning

The sharable content object reference model (SCORM) includes a representation of distance learning contents and a behavior definition of how users should interact with the contents. Generally, SCORMcompliant systems were based on multimedia and Web technologies on PCs. We further build a pervasive learning environment, which allows users to read SCORM-compliant textbooks with multimodal learning devices. Respecting the learning contents for supporting such learning environment, an efficient authoring tool was developed for serving this goal. Some specific tags were defined to specify the corresponding information or interactions that cannot be performed in the hardcopy books. These tags can be printed in SCORM-compliant textbooks and recognized by Hyper Pen to facilitate the affinity between the physical textbooks and digital world. Therefore, users can read the SCORM-compliant hardcopy textbooks in a traditional manner. The authored course contents will be the same while applying to the multimodal learning devices with different layouts.

Extending and Augmenting Scientific Enquiry through Pervasive Learning Environments

Extending and Augmenting Scientific Enquiry through Pervasive Learning Environments