A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

Mathematics teachers' pedagogy through technology: A systematic literature review

Mathematics teachers' pedagogy (MTP) is an integral part of classroom instructional mediation through technology or manipulatives. This article describes a logical literature analysis for the MTP and technology with GeoGebra (GG). The findings reveal the intervention impact of MTP with GG and other technologies such as matrix laboratory (MATLAB); an interactive whiteboard (IWB) and computer algebra system (CAS); wxMaxima, which is a CAS; information and communication technologies (ICT); concrete materials as well as other resources in developing students' performances in mathematics which were generally effective too. The systematic literature review (SLR) explored findings from current research between January 2011 and October 2020. Quality assessment screening of the papers was done and alongside further elimination of repeated documents from the analysis, twenty-eight publications met the refinement and inclusion/exclusion criteria out of 110 papers. The modified preferred reporting items for systematic reviews and meta-analyses (PRISMA) outline exemplifies the literature review accordingly. The authors observed, accomplished, and discussed the significance of the SLR. This was followed by the constraints, upcoming directions for MTP with technology and GG, and the MTP consequences for education and research

Examining cost measurements in production and delivery of three case studies using eLearning for Applied Health Sciences: a cross-case synthesis

The World Health Organization World Health Report conveys that a significant increase is needed in global healthcare resourcing to meet current and future demand for health professionals. eLearning presents a possible opportunity to change and optimize training by providing a scalable means for instruction, thus reducing the costs for training health professionals and providing patient education. Research literature often suggests that a benefit of eLearning is its cost-effectiveness compared with face-to-face instruction, yet there is limited evidence comparing design and production costs with other forms of instruction, or the establishment of standards for budgeting for these costs

A study of learner experience design and learning efficacy of mobile microlearning in journalism education

With the increasing number of mobile technologies, people rely on smartphones to connect with the world and obtain news and information. The emergent use of mobile technologies changes the way journalists produce and disseminate news. It is important for journalism educators to know how to support journalists' digital skills development, particularly digital skills of mobile technologies, and understand which new forms of learning are suitable and feasible for those learners in the journalism sector. Previous research has shown that mobile microlearning (MML) can be a promising learning approach for specific learning needs. Mobile microlearning basically means learning no more than five minutes of lessons that are distributed on the smartphone. However, there is only a little evidence on the design and effects of MML in the context of journalism education research. Hence, this dissertation aims to examine whether MML can be a useful approach to facilitate mobile journalists' digital skills learning with smartphones. Adapting a sociotechnical-pedagogical learner experience framework with a usercentered design process, a four-phase formative research cycle was conducted in this dissertation: Phase 1, a systematic literature review of mobile microlearning (Study 1), Phase 2, a needs assessment for an understanding of mobile journalists' learning needs and requirements (Study 2), Phase 3, an iterative design and development of a mobile microcourse and studying its usability and user experience (Study 3), and Phase 4, an examination of the learning efficacy (i.e., effectiveness, efficiency, and appeal) and learner experience of the developed mobile microcourse (Study 4). A mixed-method data collection and analysis approach was applied throughout this dissertation. The results in this research provided evidence-based findings and indicated that MML is a feasible and effective approach to support mobile journalists' just-in-time learning when the MML designs follow four sequential design principles: (a) an aha moment to help with the learners connecting their previous experiences to the importance of current learning topics, (b) interactive content, (c) short exercises, and (d) instant automated feedback. Lastly, the dissertation discussed the results and addressed insights and implications of the MML design to improve learner experience and learning efficacy.Includes bibliographical references

On Data-driven systems analyzing, supporting and enhancing users’ interaction and experience

Tesis doctoral en inglés y resumen extendido en español[EN] The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

[EN]The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human- Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and datadriven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. China, the United States, the European Union, and others have put forward the importance of personalized learning in recent years, emphasizing the realization of the organic combination of large-scale education and personalized training. The development of a personalized learning system oriented to learners' preferences and suited to learners' needs should be accelerated. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education. It discusses the research on personalized learning from multiple perspectives, combining definitions, goals, and related educational theories to provide an in-depth understanding of personalized learning from an educational perspective, analyzing the implications of different theories on personalized learning, and highlighting the potential of personalized learning to meet the needs of individuals and to enhance their abilities. Data applications and assessment indicators in personalized learning are described in detail, providing a solid data foundation and evaluation system for subsequent research. Meanwhile, we start from both student modeling and recommendation algorithms and deeply analyze the cognitive and non-cognitive perspectives and the contribution of personalized recommendations to personalized learning. Finally, we explore the challenges and future trajectories of personalized learning. This review provides a multidimensional analysis of personalized learning through a more comprehensive study, providing academics and practitioners with cutting-edge explorations to promote continuous progress in the field of personalized learning.Comment: 82 pages,5 figure

Establishing Social Learning in an Engineering MOOC : Benefits for Diversity and Inclusion in Engineering Education

Recent Higher Education Statistics Agency data shows that only 20% of engineering students at UK Universities are female, despite the hard work being undertaken by many educational institutions to address this gender imbalance via outreach events and special interventions focussing on girls/women in STEM. It has been argued that student-centred teaching methods, together with changes in the engineering curriculum itself, which emphasise the social, creative, and human-centred aspects of the discipline, are required to effect real change in engaging with those from traditionally underrepresented groups. Through analysing quantitative data on age, gender, learner type, and commenting rates in peer-to-peer discussions, we examine the development and delivery of an engineering MOOC, before, during, and after COVID-19-related lockdowns in the UK, to identify what aspects of online learning might be harnessed to improve diversity in engineering education. The results show that the MOOC attracted a better gender balance than reported for UK-based in-person engineering programmes. In addition, we show that careful structuring of discussion prompts encouraged higher levels of social learning. We recommend the continued use of interactive and discursive elements within a blended learning environment to positively impact diversity and inclusion in engineering education specifically, and STEM education in general

A Common Digital Twin Platform for Education, Training and Collaboration

The world is in transition driven by digitalization; industrial companies and educational institutions are adopting Industry 4.0 and Education 4.0 technologies enabled by digitalization. Furthermore, digitalization and the availability of smart devices and virtual environments have evolved to pro- duce a generation of digital natives. These digital natives whose smart devices have surrounded them since birth have developed a new way to process information; instead of reading literature and writing essays, the digital native generation uses search engines, discussion forums, and on- line video content to study and learn. The evolved learning process of the digital native generation challenges the educational and industrial sectors to create natural training, learning, and collaboration environments for digital natives. Digitalization provides the tools to overcome the aforementioned challenge; extended reality and digital twins enable high-level user interfaces that are natural for the digital natives and their interaction with physical devices. Simulated training and education environments enable a risk-free way of training safety aspects, programming, and controlling robots. To create a more realistic training environment, digital twins enable interfacing virtual and physical robots to train and learn on real devices utilizing the virtual environment. This thesis proposes a common digital twin platform for education, training, and collaboration. The proposed solution enables the teleoperation of physical robots from distant locations, enabling location and time-independent training and collaboration in robotics. In addition to teleoperation, the proposed platform supports social communication, video streaming, and resource sharing for efficient collaboration and education. The proposed solution enables research collaboration in robotics by allowing collaborators to utilize each other’s equipment independent of the distance between the physical locations. Sharing of resources saves time and travel costs. Social communication provides the possibility to exchange ideas and discuss research. The students and trainees can utilize the platform to learn new skills in robotic programming, controlling, and safety aspects. Cybersecurity is considered from the planning phase to the implementation phase. Only cybersecure methods, protocols, services, and components are used to implement the presented platform. Securing the low-level communication layer of the digital twins is essential to secure the safe teleoperation of the robots. Cybersecurity is the key enabler of the proposed platform, and after implementation, periodic vulnerability scans and updates enable maintaining cybersecurity. This thesis discusses solutions and methods for cyber securing an online digital twin platform. In conclusion, the thesis presents a common digital twin platform for education, training, and collaboration. The presented solution is cybersecure and accessible using mobile devices. The proposed platform, digital twin, and extended reality user interfaces contribute to the transitions to Education 4.0 and Industry 4.0