Search Personalization: Knowledge-Based Recommendation in Digital Libraries

Recommendation engines have made great strides in understanding and implementing search personalization techniques to provide interesting and relevant documents to users. The latest research effort advances a new type of recommendation technique, Knowledge Based (KB) engines, that strive to understand the context of the user’s current information need and then filter information accordingly. The KB engine proposed in this paper requires less effort from the user in representing the search task and is the first of its kind implemented in a digital library setting. The KB engine performance was compared with Content Based (CB) and Collaborative Filtering (CF) recommendation techniques and the text search engine Lucene by asking sixty subjects to perform two different tasks to find relevant documents in a database of 212,000 documents from 22 National Science Digital Library (NSDL) collections. Our KB engine design outperforms CB, CF, and text search techniques in nearly all areas of evaluation

Improving Access to Digital Library Resources by Automatically Generating Complete Reading Level Metadata

Digital library collections usually hold resources describing a limited set of topics spanning a wide range of reading levels, requiring complete reading level metadata to filter relevant resources from the collection. In order to suggest the reading level for all resources in the test collection, we propose an SVM-based classification tool which predicts the specific reading level with an F-Measure of 0.70 for all resources, outperforming other classification methods and readability formulas under evaluation. To measure the impact of reading level metadata completeness on retrieval performance, a knowledge based system retrieves documents from three collections containing different reading level completeness: one with complete reading level information generated by the proposed SVM method, one missing all reading level information, and the final collection containing limited, human-expert provided metadata. The dataset with automatically identified complete reading level exceeds the performance of collection-provided reading level metadata for all five sample tasks

An Asynchronous, Personalized Learning Platform―Guided Learning Pathways (GLP)

The authors propose that personalized learning can be brought to traditional and non-traditional learners through asynchronous learning platform that recommends to individual learners the learning materials best suited for him or her. Such a platform would allow learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner’s interests and motivations. This paper describes the authors’ vision and design for a modular, personalized learning platform called Guided Learning Pathways (GLP), and its characteristics and features. We provide detailed descriptions of and propose frameworks for critical modules like the Content Map, Learning Nuggets, and Recommendation Algorithms. A threaded user scenario is provided for each module to help the reader visualize different aspects of GLP. We discuss work done at MIT to support such a platform

Análisis de metodologías de recomendación y ensamblado de Objetos de Aprendizaje, a partir de la definición de criterios

Uno de los principales obstáculos que tienen los docentes y estudiantes al momento de seleccionar material educativo es la cantidad ingente disponible en la Web. A esto se le suma la necesidad de poder evaluarlos en busca de reconocer su pertinencia. Aunque los metadatos facilitan la búsqueda y recuperación de Objetos de Aprendizaje (OA), existen otros mecanismos como recomendadores y ensambladores de OA que también favorecen estas tareas y permiten organizarlos en una secuencia didáctica. En este artículo se presenta una revisión de las metodologías de recomendación y ensamblado de OA y una serie de criterios de análisis, propuestos por los autores, que permitirán a los docentes tomar decisiones acorde a sus necesidades, al momento de seleccionar una de dichas metodologías.Red de Universidades con Carreras en Informática (RedUNCI

Panorama of Recommender Systems to Support Learning

This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229

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

System Design and Architecture of an Online, Adaptive, and Personalized Learning Platform

The authors propose that personalized learning can be brought to traditional and nontraditional learners through a new type of asynchronous learning platform called Guided Learning Pathways (GLP). The GLP platform allows learners to intelligently traverse a vast field of learning resources, emphasizing content only of direct relevance to the learner and presenting it in a way that matches the learner’s pedagogical preference and contextual interests. GLP allows learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner’s interests and motivations. Learning communities would support learners moving through similar topics. This report describes the software system design and architecture required to support Guided Learning Pathways. The authors provide detailed information on eight software applications within GLP, including specific learning benefits and features of each. These applications include content maps, learning nuggets, and nugget recommendation algorithms. A learner scenario helps readers visualize the functionality of the platform. To describe the platform’s software architecture, the authors provide conceptual data models, process flow models, and service group definitions. This report also provides a discussion on the potential social impact of GLP in two areas: higher education institutions and the broader economy