Comparison of Moodle and ATutor LMSs

E-learning is a technology that plays an important role in modern education and training. Its great importance lies in the fact that it makes learning content readily available at any place at any time. This paper examines and evaluates two of current systems Moodle and ATutor. The main aim of this paper is to identify the aspects of theese LMS systems, examine their functional features, modules, standards, hardware and software requirements, and compare them

Moodle HEODAR implementation and its implantation in an academic context.

[EN]One of the most important aspects of a 'continuously in change’ society is to improve everything everywhere. In order to obtain the best products, they should be periodically evaluated and reengineered. So the evaluation task and of course, the adequate results interpretation, can make all the difference between competitors. E-learning is similar to these products. Different issues can be evaluated to make learning process getting better and better, such as tutors, platform software and contents. In this last issue, it can be included the minimum knowledge unit: the learning object (LO) (De Marcos et al., 2008). There exist different models and methods for LO evaluation. What is pretended with this work is to choose one model and implement a singular tool, in order to automatically evaluate these LOs and produce a set of information, that can be used to improve those LOs. In this case, it is implemented in the evaluation model called HEODAR (Morales et al., 2008a) and after that the model is implanted in Studium, the Moodle campus of Salamanca University

Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio

A gentle transition from Java programming to Web Services using XML-RPC

Exposing students to leading edge vocational areas of relevance such as Web Services can be difficult. We show a lightweight approach by embedding a key component of Web Services within a Level 3 BSc module in Distributed Computing. We present a ready to use collection of lecture slides and student activities based on XML-RPC. In addition we show that this material addresses the central topics in the context of web services as identified by Draganova (2003)

Student-oriented planning of e-learning contents for Moodle

We present a way to automatically plan student-oriented learning contents in Moodle. Rather than offering the same contents for all students, we provide personalized contents according to the students' background and learning objectives. Although curriculum personalization can be faced in several ways, we focus on artificial intelligence (AI) planning as a very useful formalism for mapping actions, i.e. learning contents, in terms of preconditions (precedence relationships) and causal effects to find plans, i.e. learning paths that best fit the needs of each student. A key feature is that the learning path is generated and shown in Moodle in a seamless way for both the teacher and student, respectively. We also include some experimental results to demonstrate the scalability and viability of our approach. & 2015 Elsevier Ltd. All rights reservedThis paper is co-funded with support from the European Commission, the European Social Fund and the Regione Calabria. The paper was also 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 and the Valencian Prometeo project II/2013/019.Caputi, V.; Garrido Tejero, A. (2015). Student-oriented planning of e-learning contents for Moodle. Journal of Network and Computer Applications. 53:115-127. https://doi.org/10.1016/j.jnca.2015.04.001S1151275

Experiences on Using Intelligent Planning for Curriculum Personalization in Moodle

In this paper we discuss our experiences on using artificial intelligence to plan customized learning paths in the Moodle platform. In particular, we found some limitations in defining students’ profiles, complex relationships between activities and personalized views of the learning contents. We show how we solved this kind of problems in order to create an integrated system for the application of our planning approach in MoodleValentina Caputi thanks the European Commission, the European Social Fund, and the Regione Calabria for financial support of her PhD fellowship and funding for her stay in Valencia. This paper is co-funded with support from the European Commission, the European Social Fund and the Regione Calabria. The paper was also 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 and the Valencian Prometeo project 2008/051. The European Commission and the Regione Calabria disclaim any responsibility for the use that may be made of the information contained in this publicationCaputi, V.; Garrido Tejero, A. (2013). Experiences on Using Intelligent Planning for Curriculum Personalization in Moodle. En EDULEARN13 Proceedings. IATED. 168-176. http://hdl.handle.net/10251/71817S16817

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

Learner models in online personalized educational experiences: an infrastructure and some experim

Technologies are changing the world around us, and education is not immune from its influence: the field of teaching and learning supported by the use of Information and Communication Technologies (ICTs), also known as Technology Enhanced Learning (TEL), has witnessed a huge expansion in recent years. This wide adoption happened thanks to the massive diffusion of broadband connections and to the pervasive needs for education, highly connected to the evolution in sciences and technologies. Therefore, it has pushed up the usage of online education (distance and blended methodologies for educational experiences) to, even in lately years, unexpected rates. Alongside with the well known potentialities, digital-based educational tools come with a number of downsides, such as possible disengagement on the part of the learner, absence of the social pressures that normally exist in a classroom environment, difficulty or even inability from the learners to self-regulate and, last but not least, depletion of the stimulus to actively participate and cooperate with lectures and peers. These difficulties impact the teaching process and the outcomes of the educational experience (i.e. learning process), being a serious limit and questioning the broader applicability of TEL solutions. To overcome these issues, there is a need of tools to support the learning process. In the literature, one of the known approach to improve the situation is to rely on a user profile, that collects data during the use of the eLearning platforms or tool. The created profile can be used to adapt the behaviour and the contents proposed to the learner. On top of this model, some researches stressed the positive effects stimulated by the disclosure of the model itself for inspection purposes by the learner. This disclosed model is known as Open Learner Model (OLM). The idea of opening learners' profile and eventually integrate them with external on-line resources is not new and it has the ultimate goal of creating global and long-run indicators of the learner's profile. Also the representation aspect of the learner model plays a role, moving from the more traditional approach based on the textual and analytic/extensive representation to the graphical indicators that are able to summarise and to present one or more of the model characteristics in a way that is considered more effective and natural for the user consumption. Relying on the same learner models, and stressing the different aggregation and representation capabilities, it is possible to either support self-reflection of the learner or to foster the tutoring process to allow proper supervision by the tutor/teacher. Both the objectives can be reached through the graphical representation of the relevant information, presented in different ways. Furthermore, with such an open approach for the learner model, the concepts of personalisation and adaptation acquire a central role in the TEL experience, overcoming the previous limits related to the impossibility to observe and explain to the learner the reasons for such an intervention from the tool itself. As a consequence, the introduction of different tools, platforms, widgets and devices in the learning process, together with the adaptation process based on the learner profiles, can create a personal space for a potential fruitful usage of the rich and widespread amount of resources available to the learner. This work aimed at analysing the way a learner model could be represented in visual presentation to the system users, exploring the effects and performances for learners and teachers. Subsequently, it concentrated in investigating how the adoption of adaptive and social visualisations of OLM could affect the student experience within a TEL context. The motivation was twofold. On one side was to show that the approach of mixing data from heterogeneous and not already related data sources could have a meaningful didactic interpretations, whether on the other one was to measure the perceived impact of the introduction on online experiences of the adaptivity (and of social aspects) in the graphical visualisations produced by such a tool. In order to achieve these objectives, the present work analysed and addressed them through an approach that merged user data in learning platforms, implementing a learner profile. This was accomplished by means of the creation of a tool, named GVIS, to elaborate on the collected user actions in platforms enabling remote teaching. A number of test cases were performed and analysed, adopting the developed tool as the provider to extract, to aggregate and to represent the data for the learners' model. The GVIS tool impact was then estimated with self- evaluation questionnaires, with the analysis of log files and with knowledge quiz results. Dimensions such as the perceived usefulness, the impact on motivation and commitment, the cognitive overload generated, and the impact of social data disclosure were taken into account. The main result found by the application of the developed tool in TEL experiences was to have an impact on the behaviour of online learners when used to provide them with indicators around their activities, especially when enhanced with social capabilities. The effects appear to be amplifies in those cases where the widget usage is as simplified as possible. From the learner side, the results suggested that the learners seem to appreciate the tool and recognise its value. For them the introduction as part of the online learning experience could act as a positive pressure factor, enhanced by the peer comparison functionality. This functionality could also be used to reinforce the student engagement and positive commitment to the educational experience, by transmitting a sense of community and stimulating healthy competition between learners. From the teacher/tutor side, they seemed to be better supported by the presentation of compact, intuitive and just-in-time information (i.e. actions that have an educational interpretation or impact) about the monitored user or group. This gave them a clearer picture of how the class is currently performing and enabled them to address performance issues by adapting the resources and the teaching (and learning) approach accordingly. Although a drawback was identified regarding the cognitive overload, the data collected showed that users generally considered this kind of support useful. There is also indications that further analyses can be interesting to explore the effects introduced in the teaching practices by the availability and usage of such a tool