Blockchain technology has been widely adopted in many areas to provide more dependable and trustworthy systems, including digital infrastructure. However, this technology is acknowledged to pose severe threats to the environment; its wide adoption is believed to be among the significant modern technology contributors to greenhouse gas emissions. The fundamentals of the technology and the inefficiencies of its underlying consensus algorithms (e.g., Proof of Work) are cited among the reasons for these shortcomings. In particular, trust guarantees through consensus are expensive and require excessive computational power, leading to considerable carbon emissions. In this paper, we propose a novel self-adaptive model to optimize the environmental sustainability of blockchain-based systems. It balances the systems' energy consumption and carbon emission without compromising the fundamental properties of blockchain technology. The model continuously monitors a blockchain-based system and adaptively selects miners, considering context changes and user needs. The model dynamically selects a subset of miners to perform sustainable mining processes while ensuring the decentralization and trustworthiness of the system. The aim is to minimize blockchain-based systems' energy consumption and carbon emissions while maximizing their decentralization and trustworthiness. We conduct experiments to evaluate the efficiency and effectiveness of the model. The results show that our model for self-optimizing the environmental sustainability of blockchain-based systems can reduce energy consumption by 55.49% and carbon emissions by 71.25% on average while maintaining desirable levels of decentralization and trustworthiness by more than 96.08% and 75.12%, respectively. Furthermore, these enhancements can be achieved under different operating conditions compared to similar models, including the straightforward use of Proof of Work. Also, we have investigated and discussed the correlation between these objectives and how they are related to the number of miners within the blockchain-based systems.

Blockchain technology has gained recognition in industrial, financial, and various technological domains for its potential in decentralizing trust in peer-to-peer systems. A core component of blockchain technology is a consensus algorithm, most commonly Proof of Work (PoW). PoW is used in blockchain-based systems to establish trust among peers; however, it does require the expenditure of an enormous amount of energy that affects the environmental sustainability of blockchain-based systems. Energy minimization, whilst ensuring trust within blockchain-based systems that use PoW, is a challenging problem. The solution has to consider how energy consumption can be minimized without compromising trust, whilst still ensuring, for instance, scalability, security, and decentralization. In this paper, we represent the problem as a subset selection problem of miners in a blockchain-based system. We formulate the problem of blockchain energy consumption as a Search-Based Software Engineering problem with four objectives: energy consumption, carbon emission, decentralization, and trust. We propose a model composed of multiple fitness functions. The model can be used to explore the complex search space by selecting a subset of miners that minimizes the energy consumption without drastically impacting the primary goals of the blockchain technology (i.e., security/trustworthiness and decentralization). We integrate our proposed fitness functions into five evolutionary algorithms to solve the problem of blockchain miners selection. Our results show that the environmental sustainability of blockchain-based systems (e.g. reduced energy use) can be enhanced with little degradation in other competing objectives. We also report on the performance of the algorithms used.

Usability is now widely recognised as a critical factor to the success of e-learning systems. A highly usable e-learning system increases students’ satisfaction and engagement, thereby enhancing learning performance. However, one challenge in e-learning is poor engagement arising from a “one-size-fits-all” approach that presents learning content and activities in the same way to all students. Each student has different characteristics and, therefore, the content should be sensitive to these differences. This study evaluated the students’ perceived level of usability of an e-learning system that matches content to reading skill levels of students with dyslexia. 41 students rated their perceived usability of an e-learning system using the System Usability Scale (SUS). Results indicated that when the e-learning system matches content to students’ skill level, students perceive greater usability than when the learning is not matched. There was also a moderate, positive correlation between perceived usability and learning gain when e-learning was matched to their skill level. Thus, students assessment of the usability of a system is affected by the degree to which it is suited to their needs. This may be reflected in increased engagement and is associated with higher learning gain.

Blockchain technology holds several promises for many application areas; however, it is not without its limitations. One of the most significant weaknesses of blockchain technology is its substantial energy consumption. Many researchers have proposed solutions to reduce the energy demands of this technology - such as the use of alternative consensus algorithms and the use of renewable energy. However, the use of alternative trust and reputation models to improve sustainability (by, for instance, selecting miners based on these trust or reputation values) has not been widely investigated. In this paper, we propose a reputation model that quantifies and compares the trustworthiness of miners based on their behaviours within a blockchain network. The model is evaluated analytically and compared to other trust and reputation models for miners. The evaluation shows that our model fulfils several desirable properties that should always be satisfied by reputation models, whereas other models do not always meet these requirements. In addition, we perform experimental evaluations to represent the performance of our model and its accuracy in detecting malicious miners. We also report the effectiveness of using the model in reducing the energy consumption of blockchain-based systems.

Personal interactions and information access are happening more and more through the mediation of computing devices of various types all around us. In our daily life we use many computing devices running different versions of the same application such as email clients or social media platforms, which alert users about a new piece of information or event on all devices. In this paper we first present a study investigating the factors influencing users' decisions in handling notifications in a multi-device environment. We collected 57,242 in-the-wild notifications from 24 users over a period of 21 days. We found that users' decisions in handling notifications are impacted by their physical activity, location, network connectivity, application category and the device used for handling the previous notification. Finally, we show that an individualized model can predict the device on which the user will handle a notification in the given context with 82% specificity and 91% sensitivity.

Social presence is an essential factor in preventing learners from feeling isolation in online courses and in keeping them connected. Some studies, however, point to the negative impact of the social elements in distracting learners from concentrating on a course’s content. In this study, we investigated the influence on learners of different personality types (using the big five model) of an optional chat added to an online learning platform. The results show that there is a variation in the response of the learners based on their personality. However, some personality classes spent the majority of time in the chat discussing off-topic subjects, such as fashion or travel. Thus, although they enjoyed the features in the system, it negatively affected their knowledge gain. We discuss the implications of our findings for adaptive online learning platforms in catering to learners with diverse personalities.

Abstract Several studies indicate the benefit of mapping gamification elements to personality. However, this mapping requires a strong understanding of the relationship between gamification elements and personality. The existing research that has tried to address this relationship is based on a self-report questionnaire that is obtained from only those learners who complete the entire study. Unfortunately, a bias may result from first forcing learners to complete an entire study and then ignoring learners who drop out in the middle of a study. To overcome this bias, we use a more objective approach to understand the relationship between personality and gamification. In our study, we use the dropout rate as a proxy for learner motivation. We hypothesize that learners who are more motivated by gamification elements will use the gamified website longer. Furthermore, because we use a different method than previous studies used, we analyse our data differently. Our solution is to use survival analysis to analyse our data, which confirms the benefit of using gamification to enhance learner motivation. Our results point to the relationship between the response of different personalities and gamification elements. In further studies, we recommend to use this same approach but with more gamification elements. RESEARCH HIGHLIGHTS Gamification plays an important role in enhancing learners’ motivation and engagement. Different personalities respond differently to gamification elements. The dropout rate can be used to measure learners’ motivation. Enhancing the motivation of learners does not necessarily improve their learning.

Abstract The use of reminders in habit-formation apps could lead to dependency towards the reminders and hinder the automaticity of the behaviour. Implementation intentions (if–then plan) have been found to be an effective technique that can be used to support forming new habits/breaking unwanted habits. However, implementation intentions could suffer due to forgetfulness and lack of strong intention. In this study, we investigated how adding plan reminders could impact implementation intentions by conducting a 4-week study on daily mood report task. Our findings suggest that adding plan reminders leads to better compliance and better recall of the plan but not necessarily increased automaticity. We also discuss how plan reminders of implementation intentions can be improved. RESEARCH HIGHLIGHTS Implementation intentions are specific action planning that have been found to be powerful in supporting behaviour change by helping someone to break unwanted habits or form new habits. Implementation intentions could have a weak effect when there is no strong motivation to perform the intended behaviour. Existing habit formation/behaviour change apps focus on using reminders. This could lead to dependency towards the reminder. We propose an alternative approach by sending specific reminders of implementation intention (plan reminders) in advance before the actual action happens. Adding plan reminders on implementation intentions leads to better compliance, however, it is not necessarily the same in term of automaticity.

Subliminal priming has the potential to influence people's attitudes and behaviour, making them prefer certain choices over others. Yet little research has explored its feasibility on smartphones, even though the global popularity and increasing use of smartphones has spurred interest in mobile behaviour change interventions. This paper addresses technical, ethical and design issues in delivering mobile subliminal priming. We present three explorations of the technique: a technical feasibility study, and two participant studies. A pilot study (n=34) explored subliminal goal priming in-the-wild over 1 week, while a semi-controlled study (n=101) explored the immediate effect of subliminal priming on 3 different types of stimuli. We found that although subliminal priming is technically possible on smartphones, there is limited evidence of impact on changes in how much stimuli are preferred by users, with inconsistent effects across stimuli types. We discuss the implications of our results and directions for future research.

Notifications are extremely beneficial to users, but they often demand their attention at inappropriate moments. In this paper we present an in-situ study of mobile interruptibility focusing on the effect of cognitive and physical factors on the response time and the disruption perceived from a notification. Through a mixed method of automated smartphone logging and experience sampling we collected 10372 in-the-wild notifications and 474 questionnaire responses on notification perception from 20 users. We found that the response time and the perceived disruption from a notification can be influenced by its presentation, alert type, sender-recipient relationship as well as the type, completion level and complexity of the task in which the user is engaged. We found that even a notification that contains important or useful content can cause disruption. Finally, we observe the substantial role of the psychological traits of the individuals on the response time and the disruption perceived from a notification.

Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users' interests and, also for this reason, they are considered disruptive and potentially annoying.

An increasing number of notifications demanding the smartphone user's attention, often arrive at an inappropriate moment, or carry irrelevant content. In this paper we present a study of mobile user interruptibility with respect to notification content, its sender, and the context in which a notification is received. In a real-world study we collect around 70,000 instances of notifications from 35 users. We group notifications according to the applications that initiated them, and the social relationship between the sender and the receiver. Then, by considering both content and context information, such as the current activity of a user, we discuss the design of classifiers for learning the most opportune moment for the delivery of a notification carrying a specific type of information. Our results show that such classifiers lead to a more accurate prediction of users' interruptibility than an alternative approach based on user-defined rules of their own interruptibility.

Initiatives based on learning style adaptation are often marked by a lack of experimental evaluation of their efficacy in general and of student satisfaction in particular. A high level of satisfaction can increase the motivation of students, their engagement and experience, and therefore improve their learning. This paper is concerned with the investigation of the effect of adaptation based on learning style on students' satisfaction. An adaptive approach, which involves the construction of personalised learning paths based on learning style, was implemented in an adaptive e-learning system. A controlled experiment in this learning environment was conducted with sixty undergraduate students to evaluate their level of satisfaction with the system. The results indicate that the students were generally satisfied with their learning experience. It can be concluded that adaptation of learning content according to learning style can improve student satisfaction and enhance their experience and motivation.

Teaching computer security is one of the most challenging tasks in computer science, because of the need to successfully integrate abstract concepts and practical applications. Several e-learning systems have been developed to address this issue. However, they usually provide the same material in the same sequence irrespective of the characteristics of the students, such as their knowledge level and learning style. In this paper, an approach to learning style adaptivity is proposed for the teaching of computer security. An e-learning system was developed to provide more personalised and adaptive learning, based on the information perception style of the Felder-Silverman model. This is the dimension of learning style, which has received the least attention in published research. In the approach, a personalised sequence of learning material is generated based on an individual learning style. The approach is evaluated in order to determine its effectiveness in learning provision. An experiment conducted with sixty subjects produced significant results. They indicate that matching computer security learning material, according to the learning style of the students, yields significantly better learning gain and student satisfaction than without matching.

Traditional e-learning systems have been, typically, designed for a generic learner, irrespective of individual knowledge, skills and learning styles. In contrast, adaptive e-learning systems can enhance learning by taking into account different learner characteristics and by personalising learning material. Although a large number of systems incorporating learning style have been deployed, there is a lack of comprehensive, comparative evaluations. This paper attempts to bridge this gap by comparing a number of adaptive e-learning systems. It considers three main perspectives: the learner model, the domain model and the adaptation model. A set of criteria is generated for each perspective, and applied to a representative sample of adaptive e-learning systems.

We describe work in progress toward a new approach for multimedia annotation in which the system and user work synergistically together. This work in progress is particularly focused on enabling journalists to efficiently annotate articles for submission to news agencies. Initial work on gathering user requirements is detailed along with several interesting findings that resulted from this process: capturing mood and emotion is needed as well as descriptive content. Important areas for future research are also highlighted and discussed.

The increasing availability of open and large e-learning repositories and the advent of distributed technologies such as Web services have led to a renewed interest in distributed e-learning. Composition rather than aggregation is seen as a way forward in the design and implementation of learning paths. In this paper, a framework for courseware composition is presented in terms of workflow management and semantic Web technologies. In order to cater for the needs of learners and to provide flexible learning content, a compositional platform, BPEL4WS, is enhanced by the introduction of virtual Web services, which are initially unbound. The resolution of these services is performed by agents, through search, discovery and late binding. This approach has the advantage that it overcomes some of the limitations of BPEL4WS and that it decouples the design of learning paths from the delivery of learning content. It also enhances adaptivity in e-learning provision.