Trends in educational studies on personal learning environments (PLE)

Se presenta una revisión de investigaciones publicadas en los últimos años sobre Entornos Personales de Aprendizaje (Personal Learning Environments -PLE-); entre los documentos analizados se incluyen artículos, capítulos de libros y actas de congresos. Como resultado de la revisión se proponen tres tópicos relacionados con la investigación sobre PLE. En primer lugar, se muestra cómo algunos estudios empíricos se agrupan en torno al discurso teórico y pedagógico de PLE, que suele justificar estas investigaciones. En segundo lugar, alrededor del concepto de PLE se ofrecen tres líneas de significación que son útiles para categorizar investigaciones empíricas. Finalmente, se analiza la forma en que se han orientado las investigaciones empíricas hacia algunos objetos de investigación y se utilizan algunos elementos del PLE para clasificar publicaciones.This article presents a review of documents published in recent years. All publications are empirical researches about Personal Learning Environments (PLE). The analysis includes journal articles, chapters of books, doctoral theses and conference papers. The authors have established three topics about trends of research from the exam of theoretical and empirical documents. First, the article show how some empirical studies are grouped around the discourse of PLE, which is used for justify these researches. Second, the article shows three lines of PLE's significations; they are useful for categorising empirical researches. Finally, the authors analyze how the empirical researches have been oriented towards some objects of investigation, and then, the authors use some elements of PLE to categorizing the publications

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Obrazovni sustavi preporučivanja: pregled stanja sa smjernicama za daljnja istraživanja i razvoj

Educational Recommender Systems (ERS) are increasingly used as tools to help students and teachers during the implementation of the learning process. The main difference between ERS and their commercial counterparts is in the pedagogical principles appropriate for the learning and teaching process. The differences in the educational methods used in a variety of educational situations, and their dependence on the field of study, set initial guidelines for ERS design. This paper reviews the evolution of ERS up to the currently achieved level of development and presents the basic techniques used in ERS design and the common problems they encounter in their work. Examples of classification of different ERS, according to their specific characteristics and basic approaches in their work, are presented. Based on this analysis, along with the training and upgrading of the existing algorithms, five specific areas in which future research and development can be expected are defined: construction of universal ERS, ERS intended primarily for teachers, ERS that links student achievements across different courses, ERS which take into account physical distance between students and use of ERS to motivate students to work continuously.Obrazovni sustavi preporučivanja (ERS) sve se više koriste kao alati za pomoć studentima i nastavnicima tijekom implementacije procesa učenja. Najvažnija razlika između ERS-ova i komercijalnih inačica sustava preporučivanja u pedagoškim je principima koji odgovaraju procesima učenja i poučavanja. Razlike u obrazovnim metodama koje se koriste u različitim obrazovnim situacijama, kao i njihova povezanost s područjem koje se uči, postavljaju polazne parametre za dizajniranje ERS-a. U ovom je članku dan pregled evolucije ERS-ova do trenutno postignutog nivoa razvoja, prikazane su osnovne metode na kojima su ERS-ovi dizajnirani, kao i uobičajeni problemi s kojima se ERS-ovi susreću u svojem radu. Na primjerima danas korištenih ERS-ova raspravljano je o klasifikaciji ERS-ova po njihovim posebnostima i osnovnim pristupima na kojima počiva njihov rad. Na temelju provedene analize, uz usavršavanje i nadograđivanje postojećih algoritama, određeno je pet područja u kojima se može očekivati buduće istraživanje i razvoj: izgradnja univerzalnog ERS-a, ERS-ovi namijenjeni primarno nastavnicima, ERS-ovi koji povezuju studentske uspjehe u različitim kolegijima, ERS-ovi koji uvažavaju fizičku udaljenost studenata i primjena ERS-ova za motiviranje studenata na kontinuirani rad

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad