Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service

A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources

Hybrid human-AI driven open personalized education

Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

Computational Intelligence for the Micro Learning

The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis. The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results

Evaluación de la Accesibilidad y Adaptabilidad de Objetos de Aprendizaje y cursos online a través de estándares y metadatos

El emprendimiento ha adquirido relevancia como tema de investigación en los últimos años. El individuo emprendedor tiene un rol influyente en la economía, por lo que entender sus motivaciones y aspiraciones es clave en esta investigación. El emprendimiento puede considerarse desde dos perspectivas, a nivel individual y desde el punto de vista de la organización, tratando así el intraemprendimiento o emprendimiento corporativo. Así pues, este trabajo tiene como objetivo principal analizar los diferentes niveles en los que se desarrolla la actividad emprendedora y entender el vínculo existente con las soft skills, permitiendo así considerar esta actividad como elemento de dinamización. Para abordar el objetivo general planteado se ha estudiado en primera instancia la literatura anterior vinculada con el concepto de emprendimiento, la cual queda vinculada al entorno individual y organizacional. Posteriormente, se analizan las habilidades o soft skills determinantes como elementos influyentes que permiten el desarrollo y crecimiento emprendedor. De esta forma, se consigue abordar la repercusión de la actividad emprendedora e intraemprendedora desde una perspectiva general. En esta línea, elementos como la creatividad y el conocimiento han quedado vinculados a lo largo de toda la investigación, puesto que los emprendedores requieren la actualización constante de conocimientos, y la búsqueda y el aprovechamiento de las oportunidades existentes. En consecuencia, esta investigación contribuye con el gap existente en la literatura y permite poner en valor las capacidades más relevantes en el entramado laboral, permitiendo con esto analizar los nuevos vínculos entre la sociedad y la iniciativa emprendedora. Las primeras aproximaciones de la investigación se han desarrollado a través del análisis de la base de datos European Skills, Competences, Qualifications and Occupations (ESCO), la cual ha permitido destacar las cualidades y competencias clave en el desarrollo emprendedor. A su vez, se ha realizado, como primer artículo de la tesis un análisis bibliométrico del concepto de emprendimiento. Con esto se ha conseguido destacar a los investigadores más representativos en este ámbito, y entender las redes y conexiones existentes entre ellos. De igual forma, se han destacado las palabras innovación y formación vinculadas a este concepto siendo clave para continuar con la investigación en el tema. A nivel del individuo se ha desarrollado un análisis de las motivaciones del emprendedor, quedando reflejado en el segundo artículo de la tesis. En este caso, la investigación ha examinado cómo influyen las variables de creatividad, comunicación y liderazgo en la decisión de convertirse en emprendedor en una situación prepandémica y en la situación actual, considerada como la nueva normalidad. En este sentido, la motivación emprendedora ha destacado por quedar influenciada por factores como la incertidumbre. Además, las variables creatividad, comunicación y liderazgo no son representativas en la presencia de emprendedores potenciales en la situación post-pandémica de nueva normalidad, sin embargo, sí que lo eran antes de la Covid-19. Por consiguiente, se vuelve necesario mencionar que debido a la Covid-19 se desarrolló un análisis comparativo, enriqueciendo en gran medida los resultados obtenidos. Por último, y debido a la dificultad que supone acceder a los datos estratégicos internos de las organizaciones, se han estudiado las variables que impactan en la estrategia de la empresa a través del desarrollo de una encuesta a 241 pequeñas y medianas empresas (PYMES). Esto, ha permitido considerar la influencia que ha tenido cada variable destacada en el análisis anterior, creatividad, comunicación y liderazgo, en la organización. En consecuencia, a través del tercer artículo se consigue un análisis en profundidad de la repercusión de la formación y las skills determinantes en la estrategia empresarial. Se investiga en este artículo si esas variables impactan directamente en el desarrollo de iniciativas intraemprendedoras. La investigación destaca la relevancia de la formación de los empleados en las organizaciones como componente diferencial y generador de valor. Así pues, la formación en habilidades y competencias les permitirá desarrollar actividades emprendedoras, lo que ayudará a la toma de decisiones estratégicas y a la diferenciación en el actual mercado competitivo y cambiante

Navigation Support for Learners in Informal Learning Networks

Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks

Tags and self-organisation: a metadata ecology for learning resources in a multilingual context

Vuorikari, R. (2009). Tags and self-organisation: a metadata ecology for learning resources in a multilingual context. Doctoral thesis. November, 13, 2009, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC.This thesis studies social tagging of learning resources in a multilingual context. Social tagging and its end products, tags, are regarded as part of the learning resources metadata ecology. The term “metadata ecology” is used to mean the interrelation of conventional metadata and social tags, and their interaction with the environment, which can be understood as the repository in the large sense (resources, metadata, interfaces and underlying technology) and its community of users. The main hypothesis is that the self-organisation aspect of a social tagging system on a learning resource portal helps users discover learning resources more efficiently. Moreover, user-generated tags make the system, which operates in a multilingual context, more robust and flexible. Social tags offer an interesting aspect to study learning resources, its metadata and how users interact with them in a multilingual context. Tags, as opposed to conventional metadata description such as Learning Object Metadata (LOM), are free, non-hierarchical keywords that end-users associate with a digital artefact, e.g. a learning resource. Tags are formed by a triple of (user,item,tag). Tags and the resulting networks, folksonomies, are commonly modelled as tri- partite hypergraphs. This ternary relational structure gives rise to a number of novel relations to better understand, capture and model contextual information. This thesis first provides two exploratory studies to better understand how users tag learning resources in a multilingual context and to find evidence on the “cross-boundary use” of learning resources. The term cross-boundary use means that the user and the resource come from different countries and that the language of the resource is different from that of the user’s mother tongue. The second part introduces a trilogy of studies focusing on self-organisation, flexibility and robustness of a social tagging system using empirical, behavioural data captured from log-files and user’s attention metadata trails on a number of learning resource portals and platforms in a multilingual context

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

European Distance and E-Learning Network (EDEN). Conference Proceedings

Erasmus+ Programme of the European UnionThe powerful combination of the information age and the consequent disruption caused by these unstable environments provides the impetus to look afresh and identify new models and approaches for education (e.g. OERs, MOOCs, PLEs, Learning Analytics etc.). For learners this has taken a fantastic leap into aggregating, curating and co-curating and co-producing outside the boundaries of formal learning environments – the networked learner is sharing voluntarily and for free, spontaneously with billions of people.Supported by Erasmus+ Programme of the European Unioninfo:eu-repo/semantics/publishedVersio