Models, Techniques and Applications of e-Learning Personalization

In recent years Web has become mainstream medium for communication and information dissemination. This paper presents approaches and methods for adaptive learning implementation, which are used in some contemporary web-interfaced Learning Management Systems (LMSs). The problem is not how to create electronic learning materials, but how to locate and utilize the available information in personalized way. Different attitudes to personalization are briefly described in section 1. The real personalization requires a user profile containing information about preferences, aims, and educational history to be stored and used by the system. These issues are considered in section 2. A method for development and design of adaptive learning content in terms of learning strategy system support is represented in section 3. Section 4 includes a set of innovative personalization services that are suggested by several very important research projects (SeLeNe project, ELENA project, etc.) dated from the last few years. This section also describes a model for role- and competency-based learning customization that uses Web Services approach. The last part presents how personalization techniques are implemented in Learning Grid-driven applications

On the use of case-based planning for e-learning personalization

This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 60, 1-15, 2016. DOI:10.1016/j.eswa.2016.04.030In this paper we propose myPTutor, a general and effective approach which uses AI planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students’ requirements. myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium/large e-learning communities. Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process. © 2016 Elsevier Ltd. All rights reserved.This work has been partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01, the MINECO and FEDER project TIN2014-55637-C2-2-R, the Mexican National Council of Science and Technology, the Valencian Prometeo project II/2013/019 and the BW5053 research project of the Free University of Bozen-Bolzano.Garrido Tejero, A.; Morales, L.; Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications. 60:1-15. https://doi.org/10.1016/j.eswa.2016.04.030S1156

Virtual learning process environment (VLPE): a BPM-based learning process management architecture

E-learning systems have significantly impacted the way that learning takes place within universities, particularly in providing self-learning support and flexibility of course delivery. Virtual Learning Environments help facilitate the management of educational courses for students, in particular by assisting course designers and thriving in the management of the learning itself. Current literature has shown that pedagogical modelling and learning process management facilitation are inadequate. In particular, quantitative information on the process of learning that is needed to perform real time or reflective monitoring and statistical analysis of students’ learning processes performance is deficient. Therefore, for a course designer, pedagogical evaluation and reform decisions can be difficult. This thesis presents an alternative e-learning systems architecture - Virtual Learning Process Environment (VLPE) - that uses the Business Process Management (BPM) conceptual framework to design an architecture that addresses the critical quantitative learning process information gaps associated with the conventional VLE frameworks. Within VLPE, course designers can model desired education pedagogies in the form of learning process workflows using an intuitive graphical flow diagram user-interface. Automated agents associated with BPM frameworks are employed to capture quantitative learning information from the learning process workflow. Consequently, course designers are able to monitor, analyse and re-evaluate in real time the effectiveness of their chosen pedagogy using live interactive learning process dashboards. Once a course delivery is complete the collated quantitative information can also be used to make major revisions to pedagogy design for the next iteration of the course. An additional contribution of this work is that this new architecture facilitates individual students in monitoring and analysing their own learning performances in comparison to their peers in a real time anonymous manner through a personal analytics learning process dashboard. A case scenario of the quantitative statistical analysis of a cohort of learners (10 participants in size) is presented. The analytical results of their learning processes, performances and progressions on a short Mathematics course over a five-week period are also presented in order to demonstrate that the proposed framework can significantly help to advance learning analytics and the visualisation of real time learning data

Extensión de la especificación IMS Learning Design desde la adaptación e integración de unidades de aprendizaje

IMS Learning Design (IMS-LD) representa una corriente actual en aprendizaje online y blended que se caracteriza porque: a) Es una especificación que pretende estandarizar procesos de aprendizaje, así como reutilizarlos en diversos contextos b) Posee una expresividad pedagógica más elaborada que desarrollos anteriores o en proceso c) Mantiene una relación cordial y prometedora con Learning Management Systems (LMSs), herramientas de autoría y de ejecución d) Existe una amplia variedad de grupos de investigación y proyectos europeos trabajando sobre ella, lo que augura una sostenibilidad, al menos académica Aun así, IMS Learning Design es un producto inicial (se encuentra en su primera versión, de 2003) y mejorable en diversos aspectos, como son la expresividad pedagógica y la interoperabilidad. En concreto, en esta tesis nos centramos en el aprendizaje adaptativo o personalizado y en la integración de Unidades de Aprendizaje, como dos de los pilares que definen la especificación, y que al mismo tiempo la potencian considerablemente. El primero (aprendizaje adaptativo) hace que se puedan abordar itinerarios individuales personalizados de estudio, tanto en flujo de aprendizaje como en contenido o interfaz; el segundo (integración) permite romper el aislamiento de los paquetes de información o cursos (Unidades de Aprendizaje, UoL) y establecer un diálogo con otros sistemas (LMSs), modelos y estándares, así como una reutilización de dichas UoLs en diversos contextos. En esta tesis realizamos un estudio de la especificación desde la base, analizando su modelo de información y cómo se construyen Unidades de Aprendizaje. Desde el Nivel A al Nivel C analizamos y criticamos la estructura de la especificación basándonos en un estudio teórico y una investigación práctica fruto del modelado de Unidades de Aprendizaje reales y ejecutables que nos proporcionan una información muy útil de base, y que mayormente adjuntamos en los anexos, para no interferir en el flujo de lectura del cuerpo principal. A partir de este estudio, analizamos la integración de Unidades de Aprendizaje con otros sistemas y especificaciones, abarcando desde la integración mínima mediante un enlace directo hasta la compartición de variables y estados que permiten una comunicación en tiempo real de ambas partes. Exponemos aquí también las conclusiones de diversos casos de estudio basados en adaptación que se anexan al final de la tesis y que se vuelven un instrumento imprescindible para lograr una solución real y aplicable. Como segundo pilar de la tesis complementario a la integración de Unidades de Aprendizaje, estudiamos el aprendizaje adaptativo: Los tipos, los avances y los enfoques y restricciones de modelado dentro de IMS-LD. Por último, y como complemento de la investigación teórica, a través de diversos casos prácticos estudiamos la manera en que IMS-LD modela la perzonalización del aprendizaje y hasta qué punto. Este primer bloque de análisis (general, integración y aprendizaje adaptativo) nos permite realizar una crítica estructural de IMS-LD en dos grandes apartados: Modelado y Arquitectura. Modelado apunta cuestiones que necesitan mejora, modificación, extensión o incorporación de elementos de modelado dentro de IMS-LD, como son procesos, componentes y recursos de programación. Arquitectura engloba otras cuestiones centradas en la comunicación que realiza IMS-LD con el exterior y que apuntan directamente a capas estructurales de la especificación, más allá del modelado. Aunque se encuentra fuera del núcleo de esta tesis, también se ha realizado una revisión de aspectos relacionados con Herramientas de autoría, por ser este un aspecto que condiciona el alcance del modelado y la penetración de la especificación en los distintos públicos objetivo. Sobre Herramientas, no obstante, no realizamos ninguna propuesta de mejora. La solución desarrollada, se centra en las diversas cuestiones sobre Modelado y Arquitectura encontradas en el análisis. Esta solución se compone de un conjunto de propuestas de estructuras, nuevas o ya existentes y modificadas, a través de las que se refuerza la capacidad expresiva de la especificación y la capacidad de interacción con un entorno de trabajo ajeno. Esta investigación de tres años ha sido llevada a cabo entre 2004 y 2007, principalmente con colegas de The Open University of The Netherlands, The University of Bolton, Universitat Pompeu Fabra y del departamento Research & Innovation de ATOS Origin, y ha sido desarrollada parcialmente dentro de proyectos europeos como UNFOLD, EU4ALL y ProLearn. La conclusión principal que se extrae de esta investigación es que IMS-LD necesita una reestructuración y modificación de ciertos elementos, así como la incorporación de otros nuevos, para mejorar una expresividad pedagógica y una capacidad de integración con otros sistemas de aprendizaje y estándares eLearning, si se pretenden alcanzar dos de los objetivos principales establecidos de base en la definición de esta especificación: La personalización del proceso de aprendizaje y la interoperabilidad real. Aun así, es cierto que la implantación de la especificación se vería claramente mejorada si existieran unas herramientas de más alto nivel (preferiblemente con planteamiento visual) que permitieran un modelado sencillo por parte de los usuarios finales reales de este tipo de especificaciones, como son los profesores, los creadores de contenido y los pedagogos-didactas que diseñan la experienicia de aprendizaje. Este punto, no obstante, es ajeno a la especificación y afecta a la interpretación que de la misma realizan los grupos de investigación y compañías que desarrollan soluciones de autoría. _____________________________________________IMS Learning Design (IMS-LD) is a current asset in eLearning and blended learning, due to several reasons: a) It is a specification that points to standardization and modeling of learning processes, and not just content; at the same time, it is focused on the re-use of the information packages in several contexts; b) It shows a deeper pedagogical expressiveness than other specifications, already delivered or in due process c) It is integrated at different levels into well-known Learning Management Systems (LMSs) d) There are a huge amount of European research projects and groups working with it, which aims at sustainability (in academia, at least) Nevertheless, IMS-LD is roughly an initial outcome (be aware that we are still working with the same release, dated on 2003). Therefore, it can and must be improved in several aspects, i.e., pedagogical expressiveness and interoperability. In this thesis, we concentrate on Adaptive Learning (or Personalised Learning) and on the Integration of Units of Learning (UoLs). They both are core aspects which the specification is built upon. They also can improve it significantly. Adaptation makes personalised learning itineraries, adapted to every role, to every user involved in the process, and focus on several aspects, i.e., flow, content and interface. Integration fosters the re-use of IMS-LD information packages in different contexts and connects both-ways UoLs with other specifications, models and LMSs. In order to achive these goals we carry out a threephase analysis. First, analysis of IMS-LD in several steps: foundations, information model, construction of UoLs. From Level A to Level C, we analyse and review the specification structure. We lean on a theoretical frameword, along with a practical approach, coming from the actual modeling of real UoLs which give an important report back. Out of this analysis we get a report on the general structure of IMS-LD. Second, analysis and review of the integration of UoLs with several LMSs, models and specifications: we analyse three different types of integration: a) minimal integration, with a simple link between parts; b) embedded integration, with a marriage of both parts in a single information package; and d) full integration, sharing variables and states between parts. In this step, we also show different case studies and report our partial conclusions. And third, analysis and review of how IMS-LD models adaptive learning: we define, classify and explain several types of adaptation and we approach them with the specificacion. A key part of this step is the actual modeling of UoLs showing adaptive learning processes. We highlight pros and cons and stress drawbacks and weak points that could be improved in IMS-LD to support adaptation, but also general learning processes Out of this three-step analysis carried out so far (namely general, integration, adaptation) we focus our review of the IMS-LD structure and information model on two blocks: Modeling and Architecture. Modeling is focused on process, components and programming resources of IMS-LD. Architecture is focused on the communication that IMS-LD establishes outside, both ways, and it deals with upper layers of the specification, beyong modeling issues. Modeling and Architecture issues need to be addressed in order to improve the pedagogical expressiveness and the integration of IMS-LD. Furthermore, we provide an orchestrated solution which meets these goals. We develop a structured and organized group of modifications and extensions of IMS-LD, which match the different reported problems issues. We suggest modifications, extensions and addition of different elements, aiming at the strength of the specification on adaptation and integration, along with general interest issues. The main conclusion out of this research is that IMS-LD needs a re-structure and a modification of some elements. It also needs to incorporate new ones. Both actions (modification and extension) are the key to improve the pedagogical expressiveness and the integration with other specifications and eLearning systems. Both actions aim at two clear objectives in the definition of IMS-LD: the personalisation of learning processes, and a real interoperability. It is fair to highlight the welcome help of high-level visual authoring tools. They can support a smoother modeling process that could focus on pedagogical issues and not on technical ones, so that a broad target group made of teachers, learning designers, content creators and pedagogues could make use of the specification in a simpler way. However, this criticism is outside the specification, so outside the core of this thesis too. This three-year research (2004-2007) has been carried out along with colleagues from The Open University of The Netherlands, The University of Bolton, Universitat Pompeu Fabra and from the Department of Research & Innovation of ATOS Origin. In addition, a few European projects, like UNFOLD, EU4ALL and ProLearn, have partially supported it

Rich media content adaptation in e-learning systems

The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become a key factor in enabling people with different access needs to enjoy quality learning experiences and services. Another e-learning challenge is represented by m-learning (which stands for mobile learning), which is emerging as a consequence of mobile terminals diffusion and provides the opportunity to browse didactical materials everywhere, outside places that are traditionally devoted to education. Both such situations share the need to access materials in limited conditions and collide with the growing use of rich media in didactical contents, which are designed to be enjoyed without any restriction. Nowadays, Web-based teaching makes great use of multimedia technologies, ranging from Flash animations to prerecorded video-lectures. Rich media in e-learning can offer significant potential in enhancing the learning environment, through helping to increase access to education, enhance the learning experience and support multiple learning styles. Moreover, they can often be used to improve the structure of Web-based courses. These highly variegated and structured contents may significantly improve the quality and the effectiveness of educational activities for learners. For example, rich media contents allow us to describe complex concepts and process flows. Audio and video elements may be utilized to add a “human touch” to distance-learning courses. Finally, real lectures may be recorded and distributed to integrate or enrich on line materials. A confirmation of the advantages of these approaches can be seen in the exponential growth of video-lecture availability on the net, due to the ease of recording and delivering activities which take place in a traditional classroom. Furthermore, the wide use of assistive technologies for learners with disabilities injects new life into e-learning systems. E-learning allows distance and flexible educational activities, thus helping disabled learners to access resources which would otherwise present significant barriers for them. For instance, students with visual impairments have difficulties in reading traditional visual materials, deaf learners have trouble in following traditional (spoken) lectures, people with motion disabilities have problems in attending on-site programs. As already mentioned, the use of wireless technologies and pervasive computing may really enhance the educational learner experience by offering mobile e-learning services that can be accessed by handheld devices. This new paradigm of educational content distribution maximizes the benefits for learners since it enables users to overcome constraints imposed by the surrounding environment. While certainly helpful for users without disabilities, we believe that the use of newmobile technologies may also become a fundamental tool for impaired learners, since it frees them from sitting in front of a PC. In this way, educational activities can be enjoyed by all the users, without hindrance, thus increasing the social inclusion of non-typical learners. While the provision of fully accessible and portable video-lectures may be extremely useful for students, it is widely recognized that structuring and managing rich media contents for mobile learning services are complex and expensive tasks. Indeed, major difficulties originate from the basic need to provide a textual equivalent for each media resource composing a rich media Learning Object (LO). Moreover, tests need to be carried out to establish whether a given LO is fully accessible to all kinds of learners. Unfortunately, both these tasks are truly time-consuming processes, depending on the type of contents the teacher is writing and on the authoring tool he/she is using. Due to these difficulties, online LOs are often distributed as partially accessible or totally inaccessible content. Bearing this in mind, this thesis aims to discuss the key issues of a system we have developed to deliver accessible, customized or nomadic learning experiences to learners with different access needs and skills. To reduce the risk of excluding users with particular access capabilities, our system exploits Learning Objects (LOs) which are dynamically adapted and transcoded based on the specific needs of non-typical users and on the barriers that they can encounter in the environment. The basic idea is to dynamically adapt contents, by selecting them from a set of media resources packaged in SCORM-compliant LOs and stored in a self-adapting format. The system schedules and orchestrates a set of transcoding processes based on specific learner needs, so as to produce a customized LO that can be fully enjoyed by any (impaired or mobile) student

Mobile Learning: location, collaboration and scaffolding inquiry

Critiques of mobile learning pedagogy are concerned with whether such approaches are technology led. This chapter discusses how the particular features of mobile learning can be harnessed to provide new learning opportunities in relation to collaboration, inquiry and location-based learning. Technology supported inquiry learning is a situation rich with possibilities for collaboration. In particular, mobile learning offers new possibilities for scaffolding collaboration together with its other better-known features such as scaffolding the transfer between settings and making learning relevant by making use of the possibilities of location-based learning. These features are considered as part of mobile learning models, in particular mobile collaborative learning models

Cloud eLearning - Personalisation of learning using resources from the Cloud

With the advancement of technologies, the usage of alternative eLearning systems as complementary systems to the traditional education systems is becoming part of the everyday activities. At the same time, the creation of learning resources has increased exponentially over time. However, the usability and reusability of these learning resources in various eLearning systems is difficult when they are unstandardised and semi-standardised learning resources. Furthermore, eLearning activities’ lack of suitable personalisation of the overall learning process fails to optimize resources’ and systems’ potentialities. At the same time, the evolution of learning technologies and cloud computing creates new opportunities for traditional eLearning to evolve and place the learner in the center of educational experiences. This thesis contributes to a holistic approach to the field by using a combination of artificial intelligence techniques to automatically generate a personalized learning path for individual learners using Cloud resources. We proposed an advancement of eLearning, named the Cloud eLearning, which recognizes that resources stored in Cloud eLearning can potentially be used for learning purposes. Further, the personalised content shown to Cloud Learners will be offered through automated personalized learning paths. The main issue was to select the most appropriate learning resources from the Cloud and include them in a personalised learning path. This become even more challenging when these potential learning resources were derived from various sources that might be structured, semi- structure or even unstructured, tending to increase the complexity of overall Cloud eLearning retrieval and matching processes. Therefore, this thesis presents an original concept,the Cloud eLearning, its Cloud eLearning Learning Objects as the smallest standardized learning objects, which permits reusing them because of semantic tagging with metadata. Further, it presents the Cloud eLearning Recommender System, that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. And it concludes with Cloud eLearning automated planner, which generates a personalised learning path using the output of the CeL recommender system

A model for using learners' online behaviour to inform differentiated instructional design in MOODLE

This thesis proposes a learning analytics-based process model, derived from a web analytics process, which aims to build a learner profile of attributes from Moodle log files that can be used for differentiated instructional design in Moodle. Commercial websites are rife with examples of personalisation based on web analytics, while the personalisation of online learning has not yet gained such widespread adoption. Several Instructional Design Models recommend that, in addition to taking prior knowledge and learning outcomes into account, instruction should also be informed by learner attributes. Learning design choices should be made based on unique learner attributes that influence their learning processes. Learner attributes are generally derived from well-known learning styles and associated learning style questionnaires. However, there are some criticisms of learning style theories and the use of questionnaires to create a learner profile. Attributes that can be inferred from learners’ online behaviour could provide a more dynamic learner profile. Education institutions are increasingly using Learning Management Systems, such as Moodle, to deliver and manage online learning. Moodle is not designed to create a learner profile or provide differentiated instruction. However, the abundant data generated by learners accessing course material presented in Moodle provides an opportunity for educators to build such a dynamic learner profile. Individual learner profiles can be used by educators who desire to tailor instruction to the needs of their learners. The proposed model was developed and evaluated using an iterative design focused approach that incorporates characteristics of a web analytics process, instructional design models, Learning Management Systems, educational data mining and adaptive education technologies. At each iteration, the model was evaluated using a technical risk and efficacy strategy. This strategy proposes a formative evaluation in an artificial setting. Evaluation criteria used include relevance, consistency, practicality and utility. The contributions of this thesis address the lack of prescriptive guidance on how to analyse learner online behaviours in order to differentiate learning design in Moodle. The theoretical contribution is a model for a dynamic data-driven approach to profile building and a phased differentiated learning design in a Learning Management System. The practical contribution is an evaluation of the expected practicality and utility of learner modelling from Moodle log files and the provision of tailored instruction using standard Moodle tools. The proposed model recommends that educators should define goals, develop Key Performance Indicators (KPI) to measure goal attainment, collect and analyse suitable metrics towards KPIs, test optional alternative hypotheses and implement actionable insights. To enable differentiated instruction, two phases are necessary: learner modelling and differentiated learning design. Both phases rely on the selection of suitable attributes which influence learning processes, and which can be dynamically inferred from online behaviours. In differentiated learning design, the selection/creation and sequencing of Learning Objects are influenced by the learner attributes. In learner modelling, the data sources and data analysis techniques should enable the discovery of the learner attributes that was catered for in the learning design. Educators who follow the steps described in the proposed model will be capable of building a learner profile from Moodle log files that can be used for differentiated instruction based on any learning style theory

A model for using learners' online behaviour to inform differentiated instructional design in MOODLE

This thesis proposes a learning analytics-based process model, derived from a web analytics process, which aims to build a learner profile of attributes from Moodle log files that can be used for differentiated instructional design in Moodle. Commercial websites are rife with examples of personalisation based on web analytics, while the personalisation of online learning has not yet gained such widespread adoption. Several Instructional Design Models recommend that, in addition to taking prior knowledge and learning outcomes into account, instruction should also be informed by learner attributes. Learning design choices should be made based on unique learner attributes that influence their learning processes. Learner attributes are generally derived from well-known learning styles and associated learning style questionnaires. However, there are some criticisms of learning style theories and the use of questionnaires to create a learner profile. Attributes that can be inferred from learners’ online behaviour could provide a more dynamic learner profile. Education institutions are increasingly using Learning Management Systems, such as Moodle, to deliver and manage online learning. Moodle is not designed to create a learner profile or provide differentiated instruction. However, the abundant data generated by learners accessing course material presented in Moodle provides an opportunity for educators to build such a dynamic learner profile. Individual learner profiles can be used by educators who desire to tailor instruction to the needs of their learners. The proposed model was developed and evaluated using an iterative design focused approach that incorporates characteristics of a web analytics process, instructional design models, Learning Management Systems, educational data mining and adaptive education technologies. At each iteration, the model was evaluated using a technical risk and efficacy strategy. This strategy proposes a formative evaluation in an artificial setting. Evaluation criteria used include relevance, consistency, practicality and utility. The contributions of this thesis address the lack of prescriptive guidance on how to analyse learner online behaviours in order to differentiate learning design in Moodle. The theoretical contribution is a model for a dynamic data-driven approach to profile building and a phased differentiated learning design in a Learning Management System. The practical contribution is an evaluation of the expected practicality and utility of learner modelling from Moodle log files and the provision of tailored instruction using standard Moodle tools. The proposed model recommends that educators should define goals, develop Key Performance Indicators (KPI) to measure goal attainment, collect and analyse suitable metrics towards KPIs, test optional alternative hypotheses and implement actionable insights. To enable differentiated instruction, two phases are necessary: learner modelling and differentiated learning design. Both phases rely on the selection of suitable attributes which influence learning processes, and which can be dynamically inferred from online behaviours. In differentiated learning design, the selection/creation and sequencing of Learning Objects are influenced by the learner attributes. In learner modelling, the data sources and data analysis techniques should enable the discovery of the learner attributes that was catered for in the learning design. Educators who follow the steps described in the proposed model will be capable of building a learner profile from Moodle log files that can be used for differentiated instruction based on any learning style theory