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Abstract The study aimed to perform a literature review to identify the trends, impacts, and challenges associated with the integration of microlearning and social media. A total of seven academic databases were used as sources for searching: Scopus, Web of Science, ACM, EBSCOhost, PubMed, ProQuest, and IEEE. A combination of keywords related to microlearning and social media was employed during the search process. No specific date limit was imposed, but only materials published in English were considered for inclusion. A total of 2312 articles were identified in the first phase of the search. Sixteen articles were selected during phase two after applying the inclusion and exclusion criteria. The reviewed studies encompassed various fields, including computing, programming, language, nursing, surgery, and radiology. Additionally, multiple social media platforms were identified, such as podcasts, chatbots, Facebook, Instagram, LinkedIn, MP3, TikTok, Twitter, YouTube, and Sina Weibo. The results indicate that the integration of microlearning and social media has the potential to enhance learning outcomes positively. These outcomes include increased learner satisfaction, expanded reach, improved learner engagement, and enhanced learning effectiveness. Additionally, the review highlights that the most significant benefits of combining microlearning with social media are increased reach and enhanced learner engagement.

Abstract Learning analytics provides a novel means to examine various aspects of students’ learning and to support them in their individual endeavors. The purpose of this study was to explore the potential of learning analytics to provide insights into non-traditional, vocational practical nurse students’ (N = 132) motivational profiles for choosing their studies, using a mixed-methods approach. Non-traditional students were somewhat older learners than those following a more straightforward educational pathway and had diverse educational or professional backgrounds. Institutional admission data and analytics were used to identify their specific study motives and distinct motivational profiles, and to illustrate the connections between the motives emerging in the motivational profiles. Furthermore, the association between the motivational profiles and study performance was examined. The results of qualitative content analysis indicated that non-traditional practical nurse students pursued such specialized training for various reasons, and that pragmatic, professional rationales were emphasized over prosocial, altruistic factors. Through the adoption of person-centered latent class analysis, three motivational profiles were identified: self-aware goal-achievers, qualification attainers, and widely oriented humanitarians. Additionally, the analyses of epistemic networks for the profiles showed the complex interplay between the motives, confirming that some motive connections appear to be more prominent than others. Moreover, the findings indicated that study motives reported at admission did not seem to dictate students’ later study performance, as no statistically significant associations were found between the motivational profile and the students’ final grade point average or study dropout. This investigation paves the way for more-targeted motivational support and the use of learning analytics in the context of vocational education and training.

Complex dynamic systems offer a rich platform for understanding the individual or the person-specific mechanisms. Yet, in learning analytics research and education at large, a complex dynamic system has rarely been framed, developed, or used to understand the individual student where the learning process takes place. Individual (or person-specific) methods can accurately and precisely model the individual person, create person-specific models, and devise unique parameters for each individual. Our study used the latest advances in complex systems dynamics to study the differences between group-based and individual self-regulated learning (SRL) dynamics. The findings show that SRL is a complex, dynamic system where different sub-processes influence each other resulting in the emergence of non-trivial patterns that vary across individuals and time scales, and as such far from the uniform picture commonly theorized. We found that the average SRL process does not reflect the individual SRL processes of different people. Therefore, interventions derived from the group-based SRL insights are unlikely to be effective in personalization. We posit that, if personalized interventions are needed, modelling the person with person-specific methods should be the guiding principle. Our study offered a reliable solution to model the person-specific self-regulation processes which can serve as a ground for understanding and improving individual learning and open the door for precision education.

In recent years, many proposals of context-aware systems applied to IoT-based smart environments have been presented in the literature. Most previous works provide a generic high-level structure of how a context-aware system can be operationalized, but do not offer clues on how to implement it. On the other hand, there are many implementations of context-aware systems applied to specific IoT-based smart environments that are context-specific: it is not clear how they can be extended to other use cases. In this article, we aim to provide an open-source reference implementation for providing context-aware data analytics capabilities to IoT-based smart environments. We rely on the building blocks of the FIWARE ecosystem and the NGSI data standard, providing an agnostic end-to-end solution that considers the complete data lifecycle, covering from data acquisition and modeling, to data reasoning and dissemination. In other words, our reference implementation can be readily operationalized in any IoT-based smart environment regardless of its field of application, providing a context-aware solution that is not context-specific. Furthermore, we provide two example use cases that showcase how our reference implementation can be used in a variety of fields.

This article presents a comprehensive study of human physiology to determine the impact of body mass index (BMI) on human gait. The approach followed in this study consists of a mathematical model based on the centre of mass of the human body, the inertia of a person in motion and the human gait speed. Moreover, the study includes the representation of a building using graph theory and emulates the presence of a person inside the building when an emergency takes place. The optimal evacuation route is obtained using the breadth-first search (BFS) algorithm, and the evacuation time prediction is calculated using a Gaussian process model. Then, the risk of the building is quantified by using a non-sequential Monte Carlo simulation. The results open up a new horizon for developing a more realistic model for the assessment of civil safety.

Learning strategies are important catalysts of students' learning. Research has shown that students with effective learning strategies are more likely to have better academic achievement. This study aimed to investigate students' adoption of learning strategies in different course implementations, the transfer of learning strategies between courses and relationship to performance. We took advantage of recent advances in learning analytics methods, namely sequence and process mining as well as statistical methods and visualisations to study how students regulate their online learning through learning strategies. The study included 81,739 log traces of students' learning related activities from two different problem-based learning medical courses. The results revealed that students who applied deep learning strategies were more likely to score high grades, and students who applied surface learning strategies were more likely to score lower grades in either course. More importantly, students who were able to transfer deep learning strategies or continue to use effective strategies between courses obtained higher scores, and were less likely to adopt surface strategies in the subsequent course. These results highlight the need for supporting the development of effective learning strategies in problem-based learning curricula so that students adopt and transfer effective strategies as they advance through the programme.

Learning analytics methods have proven useful in providing insights from the increasingly available digital data about students in a variety of learning environments, including serious games. However, such methods have not been applied to the specific context of educational escape rooms and therefore little is known about students' behavior while playing. The present work aims to fill the gap in the existing literature by showcasing the power of learning analytics methods to reveal and represent students' behavior when participating in a computer-supported educational escape room. Specifically, we make use of sequence mining methods to analyze the temporal and sequential aspects of the activities carried out by students during these novel educational games. We further use clustering to identify different player profiles according to the sequential unfolding of students' actions and analyze how these profiles relate to knowledge acquisition. Our results show that students' behavior differed significantly in their use of hints in the escape room and resulted in differences in their knowledge acquisition levels.