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PurposeMassive open online courses (MOOCs) have received wide publicity and many institutions have invested considerable effort in developing, promoting and delivering such courses. However, there are still many unresolved questions relating to MOOCs and their effectiveness in a blended-learning context. One of the major recurring issues raised in both academic literature and in the press about MOOCs is the consistently high dropout rate of MOOC learners.Design/methodology/approachIn this study, we applied mixed methods as an exploratory case study, which prioritised the quantitative and qualitative approaches for the data collection processes. The data were collected using a MOOC Online Self-regulated Learning Questionnaire (MOSLQ) adapted and created from an existing measuring instrument. The quantitative data was analysed using Statistical Package for the Social Sciences (SPSS Version 22) tool to conduct descriptive analysis. The qualitative results obtained from the transcribed focus group interviews in this study revealed the various behavioural patterns of how undergraduate students self-directed their learning. This focus group interview was conducted to reveal the various ways students organised and strategised their learning patterns in order to derive satisfaction in their distinctive learning behaviours and encourage motivation within their study approaches. Quantitative data collected online included a 30 items survey of which 17 respondents completed the survey items in the blended-learning study. The online course survey included 19 items of which data were gathered from 11 respondents.FindingsAcross the data, it is noticeable and clear that time management and goal setting were among the dimensions that are highly rated close to high level among SRL skills investigated in this study. We found that goal setting and task strategies predicted much better attainment of individuals controlling personal course goals, while help seeking was associated with lower goal attainment among majority of the participants.Research limitations/implicationsThe study also identified several challenges. For example, there were some challenges in learners completing the survey questions even when several reminders were sent out forth nightly. At this preliminary stage, learners participated as lurkers without engaging fully with other non-academic and academic interactive activities such as surveys, in course quizzes and forums. Most of the participants in this course said they enrolled to know more about the new trend MOOC, to make friends, to have fun and so on. Although, these are some of their intentions for participating, some of the participants at some points contributed to discussion forums.Practical implicationsOur platform currently allows learners to direct their learning within the course and also allow the choice of content prerequisite in order to recommend resources necessary for their learning. This study indicates the necessity to support SRL skills and directing development of self-determination skills among the participants. This study when applied to a larger sample will demonstrate effective measurement on areas of reliability and validity as results from this small sample has indicated some high SRL skill levels for individual learners within the research.Social implicationsHowever, the success of any e-learning or MOOC platform should consider the following best practices and objectives: the learners' entire learning experience, the strategies used in developing the course content, the planning of the course delivery and the methods of delivery. Therefore, all e-learning platforms should be designed with a primary focus on the way students learn to improve their own learning skills and help them regulate their own independent learning habits. In another related study, the success of any e-learning course implementation should be carefully considered with regards to the course's underlying pedagogy and how learners engage with the content.Originality/valueThere are many e-learning platforms in existence globally, but little has been mentioned about the development of a MOOC platform in general that could allow independent learning and also adequately demonstrating the components and features used in these MOOC designs. This research's implication is to aid instructional designers to apply best practices in the development of an online course. The best approach in designing a good course is to consider the learners and how they could engage with the course resources independently and develop the ability to self-direct their learning. One of the main goals of e-learning platforms is primarily based on developing learning resources that would be suitable for linear course structure as directed by the course developer or instructor.

PurposeThe aims of this study are to examine affective responses of university students when viewing their own predictive learning analytics (PLA) dashboards, and to analyse how those responses are perceived to affect their self-regulated learning behaviour.Design/methodology/approachA total of 42 Northern Irish students were shown their own predicted status of academic achievement on a dashboard. A list of emotions along with definitions was provided and the respondents were instructed to verbalise them during the experience. Post-hoc walk-through conversations with participants further clarified their responses. Content analysis methods were used to categorise response patterns.FindingsThere is a significant variation in ways students respond to the predictions: they were curious and motivated, comforted and sceptical, confused and fearful and not interested and doubting the accuracy of predictions. The authors show that not all PLA-triggered affective states motivate students to act in desirable and productive ways.Research limitations/implicationsThis small-scale exploratory study was conducted in one higher education institution with a relatively small sample of students in one discipline. In addition to the many different categories of students included in the study, specific efforts were made to include “at-risk” students. However, none responded. A larger sample from a multi-disciplinary background that includes those who are categorised as “at-risk” could further enhance the understanding.Practical implicationsThe authors provide mixed evidence for students' openness to learn from predictive learning analytics scores. The implications of our study are not straightforward, except to proceed with caution, valuing benefits while ensuring that students' emotional well-being is protected through a mindful implementation of PLA systems.Social implicationsUnderstanding students' affect responses contributes to the quality of student support in higher education institutions. In the current era on online learning and increasing adaptation to living and learning online, the findings allow for the development of appropriate strategies for implementing affect-aware predictive learning analytics (PLA) systems.Originality/valueThe current study is unique in its research context, and in its examination of immediate affective states experienced by students who viewed their predicted scores, based on their own dynamic learning data, in their home institution. It brings out the complexities involved in implementing student-facing PLA dashboards in higher education institutions.

PurposeThis paper presents a reclassification of markers for mixed reality environments that is also applicable to the use of markers in robot navigation systems and 3D modelling. In the case of Augmented Reality (AR) mixed reality environments, markers are used to integrate computer generated (virtual) objects into a predominantly real world, while in Augmented Virtuality (AV) mixed reality environments, the goal is to integrate real objects into a predominantly virtual (computer generated) world. Apart from AR/AV classifications, mixed reality environments have also been classified by reality; output technology/display devices; immersiveness as well as by visibility of markers.Design/methodology/approachThe approach adopted consists of presenting six existing classifications of mixed reality environments and then extending them to define new categories of abstract, blended, virtual augmented, active and smart markers. This is supported with results/examples taken from the joint Mixed Augmented and Virtual Reality Laboratory (MAVRLAB) of the Ulster University, Belfast, Northern Ireland; the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy and Santasco SrL, Regio Emilia/Milan, Italy.FindingsExisting classification of markers and mixed reality environments are mainly binary in nature and do not adequately capture the contextual relationship between markers and their use and application. The reclassification of markers into abstract, blended and virtual categories captures the context for simple use and applications while the categories of augmented, active and smart markers captures the relationship for enhanced or more complex use of markers. The new classifications are capable of improving the definitions of existing simple marker and markerless mixed reality environments as well as supporting more complex features within mixed reality environments such as co-location of objects, advanced interactivity, personalised user experience.Research limitations/implicationsIt is thought that applications and devices in mixed reality environments when properly developed and deployed enhances the real environment by making invisible information visible to the user. The current work only marginally covers the use of internet of things (IoT) devices in mixed reality environments as well as potential implications for robot navigation systems and 3D modelling.Practical implicationsThe use of these reclassifications enables researchers, developers and users of mixed reality environments to select and make informed decisions on best tools and environment for their respective application, while conveying information with additional clarity and accuracy. The development and application of more complex markers would contribute in no small measure to attaining greater advancements in extending current knowledge and developing applications to positively impact entertainment, business and health while minimizing costs and maximizing benefits.Originality/valueThe originality of this paper lies in the approach adopted in reclassifying markers. This is supported with results and work carried out at the MAV Reality Laboratory of Ulster University, Belfast–UK, the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste-Italy and Santasco SrL, Regio Emilia, Milan–Italy. The value of present research lies in the definitions of new categories as well as the discussions of how they improve mixed reality environments and application especially in the health and education sectors.

PurposeThe purpose of this paper is to report on developments and applications of mixed reality cubicles and their impacts on learning in higher education. This paper investigates and presents the cost effective application of augmented reality (AR) as a mixed reality technology via or to mobile devices such as head-mounted devices, smart phones and tablets. Discuss the development of mixed reality applications for mobile (smartphones and tablets) devices leading up to the implementation of a mixed reality cubicle for immersive three dimensional (3D) visualizations.Design/methodology/approachThe approach adopted was to limit the considerations to the application of AR via mobile platforms including head-mounted devices with focus on smartphones and tablets, which contain basic feedback–to-user channels such as speakers and display screens. An AR visualization cubicle was jointly developed and applied by three collaborating institutions. The markers, acting as placeholders acts as identifiable reference points for objects being inserted in the mixed reality world. Hundreds of participants comprising academics and students from seven different countries took part in the studies and gave feedback on impact on their learning experience.FindingsResults from current study show less than 30 percent had used mixed reality environments. This is lower than expected. About 70 percent of participants were first time users of mixed reality technologies. This indicates a relatively low use of mixed reality technologies in education. This is consistent with research findings reported that educational use and research on AR is still not common despite their categorization as emerging technologies with great promise for educational use.Research limitations/implicationsCurrent research has focused mainly on cubicles which provides immersive experience if used with head-mounted devices (goggles and smartphones), that are limited by their display/screen sizes. There are some issues with limited battery lifetime for energy to function, hence the need to use rechargeable batteries. Also, the standard dimension of cubicles does not allow for group visualizations. The current cubicle has limitations associated with complex gestures and movements involving two hands, as one hand are currently needed for holding the mobile phone.Practical implicationsThe use of mixed reality cubicles would allow and enhance information visualization for big data in real time and without restrictions. There is potential to have this extended for use in exploring and studying otherwise inaccessible locations such as sea beds and underground caves. Social implications – Following on from this study further work could be done to developing and application of mixed reality cubicles that would impact businesses, health and entertainment.Originality/valueThe originality of this paper lies in the unique approach used in the study of developments and applications of mixed reality cubicles and their impacts on learning. The diverse composition in nature and location of participants drawn from many countries comprising of both tutors and students adds value to the present study. The value of this research include amongst others, the useful results obtained and scope for developments in the future.

Most universities collect large amounts of students' data to enhance teaching, understand student behaviour, and predict their success. However, such practices raise privacy and ethical issues due to sensitive data harvesting practices. Despite the recognised importance of this topic, few empirical studies address how students perceive the ethical issues related to predictive learning analytics (PLA). To redress this, interview data collected from 42 undergraduate and postgraduate students in a Northern Irish university were thematically analysed. Findings suggest that there are at least three distinct groups of students having varying assumptions about ethics in PLA. They are (1) naïve and trusting, (2) cautious and compromising, and (3) enlightened and demanding, and all of them tend to narrowly focus only on the issue of informed consent. An empirically supported argument for the need for PLA researchers to recognise the within-group variations in student populations and to educate all types of students in issues related to ethics is presented.

The aim of this paper is to discuss the approach undertaken for a number of years at two different universities, by the use of continuous assessment, to require as well as to encourage student motivation and improve learning throughout the units. The additional aim was to improve attendance that normally leads to better results for the students, which was achieved, at least partially, on the three study units considered in this paper. There is a need also to reduce the “strain” on the students as they approach the period of the majority of multiple assessments, often all at the end of the semester or the year, each of these being critical to the students passing the particular units.

Mixed reality environments as in-betweens of real-world and virtual-reality (VR) environments are created by combining real world objects with computer generated ones. In general, mixed-reality along with VR technology provides innovative ways of showing relationships and connections in the real world, mainly by complimenting real objects with additional information (text, audio and video overlays). In Science Technology and Engineering (STE) disciplines, collaborative learning is about learners working together in teams or groups on structured learning tasks, that typically have a clearly defined goal and are arranged in such a manner that all members of the team are involved. A collaborative approach builds individual and group accountability in each learner as well as one or more soft skills such as communicating with peers, managing resources and the ability to make decisions. This paper presents the use of mixed-reality environments for personalised learning along with two approaches based on message passing paradigms for implementing collaborative learning in mixed-reality environments. The first approach shows the use of a pair of geographically distant mixed-reality environments for remote collaborative learning, while the second approach show multiple (independent) mixed-reality tools synchronised for co-located group work/learning. The statistical analysis of a research study with over 70 respondents from STE disciplines who were exposed to mixed-reality tools developed using mobile technology is also presented and discussed in the context of collaborative work within mixed-reality environments. The contributions includes unique implementations of mixed-reality based collaborative learning environments capable of providing similar experience to that obtained from traditional real-world environments.

Smart (ICT based) technologies and applications are increasingly used as teaching/learning aids in various fields of engineering and science capable of supporting both classroom taught courses and hands-on laboratory work as they can potentially encourage creativity and learning in individuals when properly based on adequate learning theories, styles and approaches. This paper presents several smart (mixed reality) technologies and applications for learning in Engineering education and discusses some issues such as knowledge integration and virtual experimentation in the context of applying smart technologies for visualisation in higher science and engineering education along with results from a study carried out at two academic institutions. The contributions of this paper includes a measurable cost-effective use of smart technologies and applications for visualisation based on adequate learning theories, styles and approaches with knowledge integration suitable for engineering disciplines also in developing countries.

E-learning includes elements of both on-line and off-line learning (and/or distance learning). Various Learning and Course Management Systems (LCMS) are used for blended or hybrid learning in Science, Technology and Engineering (STE) disciplines, where they provide a defined (guided) path of study intended for collaborative groups of learners (such as a typical classroom) within the confines of a closed web-based (online) environment. In the blended learning approach, e-learning platforms augment or enhance the face-to-face (traditional) approach to teaching, focusing on the adaptation and delivery of pedagogical material. Key challenges affecting the wider use of e-learning platforms in STE disciplines include lack of adequate support for practical/laboratory work, the need to reformat/transform existing pedagogical content for on-line use, the added demands of on-line interactivity on the part of instructors/providers of content and the accessing them from resource constraint environments. This paper presents two techniques that could be used to reduce the overhead of reformatting or adapting pedagogical material for use as learning objects, as well as, addressing the fundamental challenge of accessing/using online educational resources from resource constrained environments. The results obtained from experimental study are also presented and discussed as potential enhancements for self/personal and group learning.

PurposeThe purpose of this paper is to present an improved concept of software‐based laboratory exercises, namely a Virtual Laboratory for Engineering Sciences (VLES).Design/methodology/approachThe implementation of distance learning and e‐learning in engineering sciences (such as Mechanical and Electrical Engineering) is still far behind current practice in narrative disciplines (Economics, Management, etc.). This is because education in technical disciplines requires laboratory exercises, providing skill‐acquisition and hands‐on experience. In order to overcome this problem for distance‐learning developers and practitioners, a new modular and hierarchically organized approach is needed.FindingsThe concept involves simulation models to emulate system dynamics, full virtual reality to provide visualization, advanced social‐clubbing to ensure proper communication, and an AI tutor to supervise the lab work. Its modularity and hierarchical organization offer the possibility of applying the concept to practically any engineering field: a higher level provides the general framework – it considers lab workplaces as objects regardless of the technical field they come from, and provides communication and supervision – while the lower level deals with particular workplaces. An improved student's motivation is expected.Originality/valueThe proposed concept aims rather high, thus making the work truly challenging. With the current level of information and communication technologies, some of the required features can only be achieved with difficulty; however, the rapid growth of the relevant technologies supports the eventual practicality of the concept. This paper is not intended to present any final results, solutions, or experience. The idea is to promote the concept, identify problems, propose guidelines, and possibly open a discussion.

Nowadays, the digital learning environment has revolutionized the vision of distance learning course delivery and drastically transformed the online educational system. The emergence of MOOCs (Massive Open Online courses) has exposed web technology used in education in a more advanced revolution ushering a new generation of learning environments. The digital learning environment is expected to augment the real world conventional education setting. The educational pedagogy are tailored with the standard practice which has been noticed to increase student success in MOOCs and provide a revolutionary way of self-regulated learning. However, there are still unresolved questions relating to the understanding of learning analytics data and how this could be implemented in educational contexts to support individual learning. One of the major issue in MOOCs is the consistent high dropout rate which over time has seen courses recorded less than 20% completion rate. This paper explores learning analytics from different perspectives in a MOOC context. Firstly, we review existing literature relating to learning analytics in MOOCs, bringing together findings and analyses from several courses. We explore meta-analysis of the basic factors that correlate to learning analytics and the significant in improving education. Secondly, using themes emerging from the previous study, we propose a preliminary model consisting of four factors of learning analytics. Finally, we provide a framework of learning analytics based on the following dimensions: descriptive, diagnostic, predictive and prescriptive, suggesting how the factors could be applied in a MOOC context. Our exploratory framework indicates the need for engaging learners and providing the understanding of how to support and help participants at risk of dropping out of the course.

In Cave Automatic Virtual Environments (CAVEs), a computer generated environment is projected all around a user to fully immerse or eliminate all reference to the real world. Typically, Virtual Reality (VR) CAVEs also track and respond to the user's physical orientation, movements and gestures. Mixed reality environments instead focus on combining real world objects with computer generated ones. In this paper, we focus on the application of Augmented Reality (AR) as a mixed reality technology via (or to) mobile devices such as head-mounted devices, smart-phones and tablets. We present the development of mixed reality applications for mobile (smart-phone and tablet) devices leading up to the implementation of an mixed reality (AR) cubicle for immersive Three Dimensional (3D) visualizations. We also present the results of a study on the familiarity with both VR and AR technologies among students from two institutions of tertiary education. The paper concludes with a discussion of planned deployment and upgrade of mixed reality cubicles using mobile VR equipment.

Collaborative learning activities can raise students’ motivation and help them to achieve better results. Faced with many available tools that support these activities, teachers need to choose the most suitable one. This paper presents a criterion-based procedure for selection of a Web 2.0 tool for collaborative activities in the domain of process modeling. The procedure defined a set consisting of domain specific criteria and general criteria important for assessing Web 2.0 tools for any application domain. The importance of each attribute included in the criteria is expressed numerically using weights. The established criteria are applied to nine Web 2.0 tools intended for diagramming in order to select a tool that will be used for process modeling education as part of the e-learning environment consisting of Moodle LMS and the educational recommender system ELARS.

International audience; In a learning situation, gamification is a way to motivate learners and enhance their participation to learning activities by adding game elements. But it still pays little attention to the individual differences among learners’ preferences as players. This paper presents a generic and adaptive gamification system that can be plugged on various learning environments. The game elements can be automatically adapted, based on an analysis of the interaction traces. The architecture of the proposed system is based on the separation between the control of the pedagogical elements and the control of the game elements. To support the adaptivity of the game elements, we refer to a user model based on a list of player types. The user model is initialized thanks to the traces of interactions, and finally used to predict which game feature will be relevant for the user.