SPOCs for Remedial Education: Experiences at the Universidad Carlos III de Madrid

The Universidad Carlos III de Madrid has been offering several face-to-face remedial courses for freshmen to review or learn concepts and practical skills that they should know before starting their degree programme. During the last two years, our University has adopted MOOC-like technologies to support some of these courses so that a "fipping the classroom" methodology can be applied to a particular small educational context. This paper gathers a list of issues and challenges encountered when using Khan Academy technologies for small private online courses (SPOCs). These issues and challenges include the absence of a single platform that supports all the requirements, the need for integration of different learning platforms, the complexity of the authoring process, the need for an adaptation of gamifcation during the learning process and the adjustment of the learning analytics functionality. In addition, some lessons learned are presented, as well as specifc actions taken in response, where MOOCs do not replace teachers and classrooms for these remedial courses, but improve their effectiveness.This work was partially funded by the EEE project, “Plan Nacional de I+D+i TIN2011-28308-C03-01” and the “eMadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650)”. The last author wishes to acknowledge support from Fundación CajaMadrid to visit Harvard University and MIT in the academic year 2012-13

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems

[EN] Rapid developments in deep learning (DL) and the Internet-of-Things (IoT) have enabled vision-based systems to efficiently detect fires at their early stage and avoid massive disasters. Implementing such IoT-driven fire detection systems can significantly reduce the corresponding ecological, social, and economic destruction; they can also provide smart monitoring for intelligent transportation systems (ITSs). However, deploying these systems requires lightweight and cost-effective convolutional neural networks (CNNs) for real-time processing on artificial intelligence (AI)-assisted edge devices. Therefore, in this paper, we propose an efficient and lightweight CNN architecture for early fire detection and segmentation, focusing on IoT-enabled ITS environments. We effectively utilize depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy with an optimal number of convolution kernels per layer, significantly reducing the model size and computation costs. Extensive experiments on our newly developed and other benchmark fire segmentation datasets reveal the effectiveness and robustness of our approach against state-of-the-art fire segmentation methods. Further, the proposed method maintains a balanced trade-off between the model efficiency and accuracy, making our system more suitable for IoT-driven fire disaster management in ITSs.Muhammad, K.; Ullah, H.; Khan, S.; Hijji, M.; Lloret, J. (2023). Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 24(11):13141-13150. https://doi.org/10.1109/TITS.2022.32038681314113150241

Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research

Critical analysis of Big Data Challenges and analytical methods

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline

Tracking Data in Open Learning Environments

The collection and management of learning traces, metadata about actions that students perform while they learn, is a core topic in the domain of Learning Analytics. In this paper, we present a simple architecture for collecting and managing learning traces. We describe requirements, different components of the architecture, and our experiences with the successful deployment of the architecture in two different case studies: a blended learning university course and an enquiry based learning secondary school course. The architecture relies on trackers, collecting agents that fetch data from external services, for flexibility and configurability. In addition, we discuss how our architecture meets the requirements of different learning environments, critical reflections and remarks on future work

Big Data-driven Value Creation for Organizations

The past few years have been characterized by an enormous increase in data coming from different sources in real time and in many diverse forms. The term commonly used for such data is Big Data (BD). Much of this BD has a high business value and, if properly utilized, can become an important organizational asset helping the organization to achieve competitive advantage. However, many organizations make a limited use of BD because they lack necessary tools and/or do not understand the value of this data. The main contribution of this study is to investigate an issue of Big Data and elements shaping creation of BD-based business value. In particular, the outcome of this research is to build and verify a framework to provide business value based on BD. The proposed framework is distinguished by three components: (1) dynamic capabilities of organizations, (2) integrated process of BD resource exploration and exploitation, and (3) identification and measurement of business value creation based on BD

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented

Learning analytics visualizations of student-activity time distribution for the open Edx platform

MOOCs are one of the current trending topics in educational technology. They surged with the vision of a democratization in education worldwide by removing some access barriers. As every technology, MOOCs have promoters and detractors but truth is, they are an invaluable source of data related to student interaction with courses and their resources as has been available never before. This data is susceptible to shed light on the learning process in this online environment and potentially in uence in a positive way the learning outcomes. Students can be presented with visual, friendly information that enable them to re ect on their performance and gain awareness of their own learning style based on data beyond intuition. Teachers can be given the same metrics augmented with student aggregates for their courses. Thus, they can tune their pedagogical approach and resource quality for the better. In this context, Open edX is one of the most prominent MOOC platforms. However, its learning analytics support is low at present. This project extends the learning analytics support of the Open edX platform by adding new six visualizations related to time on video and problem modules, namely: 1) video time watched, 2) video and 3) problem time distributions, 4) video repetition pro le, 5) daily time on video and problem and 6) distribution of video events. The main technologies used have been Python, Django, MySQL, JavaScript, Google Charts and MongoDBLos MOOCs están de moda en lo que se refiere a tecnología educativa. Surgieron con la visión de remover algunas barreras de acceso en aras de la democratización de la educación en cada rincón del mundo. Como toda tecnología, tienen sus promotores y detractores, pero lo cierto es que constituyen una valiosa fuente de datos como no ha habido antes en lo que respecta a la interacción de los estudiantes con estos cursos y sus recursos. Estos datos pueden ayudarnos a entender el proceso de aprendizaje en estos entornos. Tienen además el potencial de in uir positivamente en los resultados del aprendizaje. Se puede presentar a los estudiantes una información visual fácil de entender, que les permita re exionar sobre su rendimiento y ganar conciencia de su estilo de aprendizaje a partir de los datos, más allá de lo que les pueda indicar la intuición. Las mismas métricas se pueden poner a disponibilidad de los profesores, en conjunto con valores agregados de la clase. De esta manera, los profesores pueden ajustar el enfoque pedagógico del curso y mejorar la calidad de los recursos. En este contexto, Open edX es una de las plataformas proveedoras de MOOCs más prominentes. Sin embargo, tiene todavía poco soporte para analitica del aprendizaje. Este proyecto extiende ese soporte al incorporar seis visualizaciones nuevas sobre tiempo en vídeos y problemas, especícamente: 1) tiempo visto de vídeos, distribución de tiempo en 2) vídeos y 3) problemas, 4) peril de repetición de vídeo, 5) tiempo diario en vídeos y problemas y 6) distribuci on de eventos de vídeo. Las principales tecnologías usadas son: Python, Django, MySQL, JavaScript, Google Charts y MongoDB.Ingeniería de Telecomunicació

The use of tools of data mining to decision making in engineering education—A systematic mapping study

In recent years, there has been an increasing amount of theoretical and applied research that has focused on educational data mining. The learning analytics is a discipline that uses techniques, methods, and algorithms that allow the user to discover and extract patterns in stored educational data, with the purpose of improving the teaching‐learning process. However, there are many requirements related to the use of new technologies in teaching‐learning processes that are practically unaddressed from the learning analytics. In an analysis of the literature, the existence of a systematic revision of the application of learning analytics in the field of engineering education is not evident. The study described in this article provides researchers with an overview of the progress made to date and identifies areas in which research is missing. To this end, a systematic mapping study has been carried out, oriented toward the classification of publications that focus on the type of research and the type of contribution. The results show a trend toward case study research that is mainly directed at software and computer science engineering. Furthermore, trends in the application of learning analytics are highlighted in the topics, such as student retention or dropout prediction, analysis of academic student data, student learning assessment and student behavior analysis. Although this systematic mapping study has focused on the application of learning analytics in engineering education, some of the results can also be applied to other educational areas