Learning analytics beyond the LMS: The connected learning analytics toolkit

We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. A number of implementation issues are discussed, and a mapping that will enable the consistent storage and then analysis of xAPI verb/object/activity statements across different social media and online environments is introduced. A set of example learning activities are proposed, each facilitated by the Learning Analytics beyond the LMS that the toolkit enables

Integration of a recommender system into an online video streaming platform

The ultimate goal of this project is to develop a recommender system for the SmartVideo platform. The platform streams different content of local channels for the Grand Est Region of France to a large public. So, we aim to propose a solution to alleviate the data representation and data collection issue of recommender systems by adopting and adjusting the xAPI standard to fit our case of study and to be able to represent our usage data in a formal and consistent format. Then, we will propose and implement a bunch of recommendation algorithms that we are going to test in order to evaluate our developed recommender system.Le but ultime de ce projet est de développer un système de recommandation dédié à la plateforme SmartVideo de diffusion de vidéo en ligne. En effet, la plateforme met à disposition diverses contenus des chaînes locales de la région Grand Est du France. Alors, nous allons présenter une solution pour alléger le problème de représentation et de collecte de données d’usages par adopter et ajuster le standard xAPI pour représenter et collecter les données de façon simple et formelle. Ensuite, nous allons proposer et implanter des algorithmes de recommandation que nous allons les tester pour évaluer notre système de recommandation

A Learning Ecosystem for Linemen Training based on Big Data Components and Learning Analytics

Linemen training is mandatory, complex, and hazardous. Electronic technologies, such as virtual reality or learning management systems, have been used to improve such training, however these lack of interoperability, scalability, and do not exploit trace data generated by users in these systems. In this paper we present our ongoing work on developing a Learning Ecosystem for Training Linemen in Maintenance Maneuvers using the Experience API standard, Big Data components, and Learning Analytics. The paper describes the architecture of the ecosystem, elaborates on collecting learning experiences and emotional states, and applies analytics for the exploitation of both, legacy and new data. In the former, we exploit legacy e-Learning data for building a Domain model using Text Mining and unsupervised clustering algorithms. In the latter we explore self-reports capabilities for gathering educational support content, and assessing students emotional states. Results show that, a suitable domain model for personalizing maneuvers linemen training path can be built from legacy text data straightforwardly. Regarding self reports, promising results were obtained for tracking emotional states and collecting educational support material, nevertheless, more work around linemen training is required

Variability Handling in Educational Context

Today there are many different forms of educational activities present, e.g., traditional lecturing, e-learning, blended learning and living labs. Also, the audience becomes more and more international and heterogeneous in terms of background knowledge of students, their educational purposes, capabilities and expectations. This introduces a high level of variability in educational settings and requires new methods and tools for managing this variability. Customized application of feature models, known in software product line management, is one possible solution applicable for variability handling in educational context. This paper proposes the development of a feature model as the method for variability handling

Gaming learning analytics for serious games

Los juegos serios (o videojuegos educativos), se consideran una herramienta importante para la educación en el futuro. Por ello, se está invirtiendo mucho esfuerzo en el análisis de su corrección e idoneidad para alcanzar los objetivos educativos pretendidos. El campo de análisis de aprendizaje con juegos pretende proporcionar herramientas que verifiquen estas características mejorando la calidad y efectividad de los juegos serios. Para ello, se necesitan normalmente tres etapas: 1), monitorizar los datos de la interacción del jugador con el juego; 2), analizar esos datos recolectados; y 3), visualizar los resultados. En este contexto, hay algunos asuntos importantes a considerar: nivel de conocimiento del juego, receptor de las visualizaciones finales o cantidad y complejidad de los datos. Estas ideas se ponen en práctica con dos ejemplos de juegos serios centrándonos en las dos últimas etapas del proceso. Se realizan varios análisis y visualizaciones con ellos considerando los diferentes aspectos antes mencionados. Entre las conclusiones que se pueden extraer, destaca que, a pesar de haber algunos aspectos aún por mejorar, el análisis de aprendizaje con juegos es una herramienta esencial para muchos usuarios con una amplia variedad de intereses en juego serios

An Integrated Learning Analytics Approach for Virtual Vocational Training Centers

Virtual training centers are hosted solutions for the implementation of training courses in the form of e.g. Webinars. Many existing centers neglect the informal and social dimension of vocational training as well as the legitimate business interests of training providers and companies sending their employees. In this paper, we present the virtual training center platform V3C that blends formal, certified virtual training courses with self-regulated and social learning in synchronous and asynchronous learning phases. We have developed an integrated learning analytics approach to collect, store, analyze and visualize data for different purposes like certification, interventions and gradual improvement of the platform. The results given here demonstrate the ability of the platform to deliver data for key performance indicators like learning outcomes and drop-out rates as well as the interplay between synchronous and asynchronous learning phases on a very large scale. Since the platform implementation is open source, results can be easily transferred and exploited in many contexts

Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below

On the Potential of Emerging Learning Technology Standards and Specifications to Enhance Learning Experience

Master's thesis in Information- and communication technology IKT590 - University of Agder 2016Emerging educational technologies had a significant impact on the learning environment. Nowadays, classrooms are no longer as simple as it used to be, like using chalks and blackboards. Lots of technologies (e.g. video projectors, electronic boards) have been integrated to learning environments to better support students and teachers. eLearning is one the technologies that provided virtual classrooms environments on the Web. eLearning helped teachers and students to have a better control over courses and learning process respectively. In this thesis, I had an investigation on the potential of some eLearning standards and specifications that enhance interoperability as well as learning experience. I studied xAPI specification in more details and the use of this specification in learning environments for tracking students’ learning experience and mobile learning scenarios. I proposed a mobile learning solution that can help teachers to easily track their students’ learning activities based on xAPI specification and give their students quick feedback. For designing a mobile learning solution, first, I had a research on the existing mobile learning dashboards to find out how they are supporting teachers and students and to identify what are the existing lacks. Second, I followed the Human-centred Design activities. I specified the context of use, who are the users and how they are going to use this application. Then, I had interviews with teachers to specify their requirements for using a mobile learning dashboard. I evaluated a paper prototype and tested it with teachers. During the test, teachers gave me great comments and feedback. Finally, I designed a mobile learning dashboard prototype that met some of the teachers’ requirements. This study identified the use of eLearning technologies by teachers and how much they are engaged with these technologies and how they prefer to track their students’ learning experience. The potential of the xAPI specification in tracking learning experiences were studied and how this specification is used in mobile learning scenarios

Spatial Acumen in the Digital Age: The Transformative Power of STEM-Enriched Mobile Textbooks in Geography

Integrating technology into learning has become a pivotal necessity in the digital age. This underscores the imperative for educators, serving as facilitators, to organize and implement digital technology within instructional processes adeptly. Central to this application is educators’ perceptions of the utilization of mobile digital textbooks. Crucially, this study, rooted in a qualitative research design, sought to elucidate teachers’ perceptions regarding the employment of the Mobile Digital Textbook, integrated with a STEM approach, to enhance students’ spatial thinking and Higher-Order Thinking Skills (HOTS). Data collection was meticulously executed through semi-structured interviews involving fifteen geography educators from eight East Java, Indonesia, secondary institutions. With the aid of NVIVO software, the data were systematically organized and categorized based on pre-established criteria. The findings revealed a predominantly positive perception among the fifteen participants towards using the Mobile Digital Textbook with the STEM approach in fostering students’ spatial thinking and HOTS. The implications of this research underscore the significance of continuous training and support for educators in assimilating digital technology, particularly mobile digital textbooks, to elevate instructional quality and cultivate students’ advanced cognitive abilities