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Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session, therefore, they need to be addressed in real time to keep the learner engaged in the learning process. Similarly, long-term learners’ motivation plays an equally important role to retain the learner in the long run and minimize the risk of dropout. Therefore, the purpose of this study is to incorporate a comprehensive learner motivation model that is based on short-term and long-term aspects of the learners' motivation. This approach enables Web-based educational systems to identify the real-time motivational state of the learner and provide personalized interventions to keep the learners engaged in learning process.

Recent research regarding personalized Web-based educational systems demonstrates learner’s motivation to be an essential component of the learning model. This is because of the fact that low motivation results in either students’ less engagement or complete drop out from the learning activities. A learner motivation model is considered to be a set of perceptions and beliefs that the system has developed about a learner. This includes both short-term and long-term motivations of leaners.

This study proposed a framework of a domain independent learners’ motivation model based on firm educational theories. The proposed framework consists of two modules. The primary module deals with real-time identification of motivation and logging off activities such as login, forum participation and adherence to assessment deadline. Secondary module maintains the profile of leaners associated with both short-term and long-term motivation. A study was conducted to verify the impact of learners’ motivation model and personalized interventional strategies based on proposed model, using Systematical Information Education Method assessment standards. The results show an increase in motivational index and the characteristics associated with motivation during the conducted study.

Motivational diagnosis is important for both traditional classrooms and Web-based education systems. It is one of the major elements that contribute in the success of the learning process. However, dropout rate among online students is very high, which leads to incorporate motivational elements in more personalized way because motivated students will retain the course until they successfully complete it. Hence, identifying learner’s motivation, updating learners’ motivation model based on this identification and providing personalized interventions are the key for the success of Web-based educational systems.

The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods.

This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research.

After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments.

This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account.

The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

This chapter focuses on the state-of-the-art modeling approaches used in Intelligent Tutoring Systems (ITSs) and the frameworks for researching and operationalizing individual and group models of performance, knowledge, and interaction. We adapt several ITS methodologies to model team performance as well as individuals’ performance of the team members. We briefly describe the point processes proposed by von Davier and Halpin (2013), and we also introduce the Competency Architecture for Learning in teaMs (CALM) framework, an extension of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg, & Holden, 2012) to be used for team settings.

An integrated Web engine (IWE) has been developed by the Internetworking program at Dalhousie University, Halifax, Canada to deliver remote learning experience to geographically remote Master's students. The University intends to increase its student base through online education, retaining the same quality of interactions as the onsite program. To this end, the IWE accommodates three technology‐enabled learning environments that correlate with the three pedagogical approaches and types of onsite interaction. Discusses the e‐learning metrics, pedagogical and technical considerations that influence the design and implementation of the IWE environment. The IWE uses de facto networking standards, commercial and broadband Internet connectivity to ensure real‐time secure interaction with equipment and deliver lectures respectively. A four‐tier role architecture, consisting of faculty, local, remote facilitators, and students, has been determined to be appropriate and adapted to maintain academic integrity and offer the same quality of interaction as the onsite program.

This paper investigates the moderating-mediation roles of synchronous and asynchronous learning, as well as virtual self-efficacy between digital learning space experience and continuous use among learners in Ghanaian institutions of higher learning.

A convenience sampling technique was used in the selection of 604 students who answered questions on digital learning space experience, synchronous and asynchronous learning, virtual self-efficacy and learner continuous use within the Greater Accra Region of Ghana. The study employed regression analysis to measure the hypothesized paths.

The findings show that asynchronous learning partially mediates between digital learning space experience and learner continuous use, but the mediating effect of synchronous learning between digital learning space experience and learner continuous use was not significant. Further, virtual self-efficacy significantly moderates the mediated relationship between asynchronous learning and learner continuous use, but the moderated mediated role of synchronous learning was not established in the study.

Generalization of the study findings is limited due to the sampling scope, which was restricted to students of IHL in the Greater Accra Region of Ghana.

In this research, the academic scope of digital transformation was expanded from both digital structure elements and psychological perspectives within the domain of higher education literature.

The purpose of this paper is to reveal the preliminary results of the IRMOS Project – an EC project to “extended geo‐learning”, delivered “in‐class”, “in‐house”, “in campus and in building” on an urban, suburban and global GPS basis.

The IRMOS Project research is being carried out by 13 European organisations. The basic premise behind their collaborative efforts is that today's service orientated infrastructures (SOIs) lack real‐time capabilities. IRMOS aims to design, develop and validate an SOI using interactive real‐time applications – enabling time‐constrained operation and dynamic service control and adaptation as well as their efficient integration into the infrastructure.

Using the IRMOS Project to blend open source learning platforms with mobile learning technologies, can produce a real time “extended” location‐based learning experience to learners active both in real and virtual learning worlds. This can make available both context and location sensitive learning experiences. In addition, this can personalise learning to a degree previously unknown except in one‐to‐one tuition.

The results of the IRMOS Project should change the way that: educators develop learning materials and teach; learners interact with technology‐delivered learning materials; films – especially special effects – are made; and people interact via virtual worlds.

This paper outlines the preliminary results of groundbreaking research, which allies the real and virtual worlds to enhance learning experiences. This has value for all students – of any age – anywhere.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites.

A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones.

Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026.

The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection.

The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

Steam turbine final assembly is a dynamic process, in which various interference events occur frequently. Currently, data transmission relies on oral presentation, while scheduling depends on the manual experience of managers. This mode has low information transmission efficiency and is difficult to timely respond to emergencies. Besides, it is difficult to consider various factors when manually adjusting the plan, which reduces assembly efficiency. The purpose of this paper is to propose a knowledge-based real-time scheduling system under cyber-physical system (CPS) environment which can improve the assembly efficiency of steam turbines.

First, an Internet of Things based CPS framework is proposed to achieve real-time monitoring of turbine assembly and improve the efficiency of information transmission. Second, a knowledge-based real-time scheduling system consisting of three modules is designed to replace manual experience for steam turbine assembly scheduling.

Experiments show that the scheduling results of the knowledge-based scheduling system outperform heuristic algorithms based on priority rules. Compared with manual scheduling, the delay time is reduced by 43.9%.

A knowledge-based real-time scheduling system under CPS environment is proposed to improve the assembly efficiency of steam turbines. This paper provides a reference paradigm for the application of the knowledge-based system and CPS in the assembly control of labor-intensive engineering-to-order products.

This study aims to show the relevance of augmented reality (AR) in mobile learning for the 21st century. With AR, any real-world environment can be augmented by providing users with accurate digital overlays. AR is a promising technology that has the potential to encourage learners to explore learning materials from a totally new perspective. Besides, the advancements made in information technology further broaden the scope for educational AR applications. Furthermore, the proliferation of wireless mobile devices such as smartphones and tablets is also introducing AR into the mobile domain.

This discussion paper gives an insight of the different potential fields of application of AR and eventually proposes an AR application that will give a completely different learning experience for learners. This AR mobile application will not only provide learners with supplementary information but will also assist lecturers in their teaching process. There are certain concepts in computer science at the tertiary level that are at times difficult for learners to understand using the traditional classroom approach. Through this AR application developed, the learners are able to see what is happening and experience a different form of learning where the focus is more on “learning by doing” and on the ability of visualizing the complete set of steps involved for a particular operation. Finally what is proposed is a generic framework/process for the development of AR applications for learning purposes.

The AR application developed and tested has proved to be helpful in understanding complex concepts of computer science that average students have much difficulty in understanding. Through AR, learning has been brought to a new dimension where the students can easily visualize what is happening and easily understand complex concepts. This low-cost system that has been proposed can track and detect both markerless and marker-based images. A number of experiments have also been carried out to determine a set of best practices for the development and use of such AR applications.

Learners have been able to have a more interactive and enriching learning experience through two-dimensional and three-dimensional digital augmentations. The AR mobile application has been enhancing the cognitive skills of learners through enabling them to scan images from printed materials with their smartphones. Then, informative digital augmentation has been overlaid in real time on the mobile screen with the image preview still in the background.