Enhanced recommendations for e-learning authoring tools based on a proactive context-aware recommender

Authoring tools are powerful systems in the area of e-Learning that make easier for teachers to create new learning objects by reusing or editing existing educational resources coming from learning repositories or content providers. However, due to the overwhelming number of resources these tools can access, sometimes it is difficult for teachers to find the most suitable resources taking into account their needs in terms of content (e.g. topic) or pedagogical aspects (e.g. target level associated to their students). Recommender systems can take an important role trying to mitigate this problem. In this paper we propose a new model to generate proactive context-aware recommendations on resources during the creation process of a new learning object that a teacher carries out by using an authoring tool. The common use cases covered by the model for having recommendations in online authoring tools and details about the recommender model itself are presented

Facilitating the creation of interactive multi-device Learning Objects using an online authoring tool

Learning Objects facilitate reuse leading to cost and time savings as well as to the enhancement of the quality of educational resources. However, teachers find it difficult to create or to find high quality Learning Objects, and the ones they find need to be customized. Teachers can overcome this problem using suitable authoring systems that enable them to create high quality Learning Objects with little effort. This paper presents an open source online e-Learning authoring tool called ViSH Editor together with four novel interactive Learning Objects that can be created with it: Flashcards, Virtual Tours, Enriched Videos and Interactive Presentations. All these Learning Objects are created as web applications, which can be accessed via mobile devices. Besides, they can be exported to SCORM including their metadata in IEEE LOM format. All of them are described in the paper including an example of each. This approach for creating Learning Objects was validated through two evaluations: a survey among authors and a formal quality evaluation of 209 Learning Objects created with the tool. The results show that ViSH Editor facilitates educators the creation of high quality Learning Objects

Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations. A pedagogical and technical perspective

Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. In this paper, we review the state-of-the-art approaches for defining a user learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations

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

Organization and Usage of Learning Objects within Personal Computers

Research report of the ProLearn Network of Excellence (IST 507310), Deliverable 7.6To promote the integration of Desktop related Knowledge Management and Technology Enhanced Learning this deliverable aims at increasing the awareness of Desktop research within the Professional Learning community and at familiarizing the e-Learning researchers with the state-of-the-art in the relevant areas of Personal Information Management (PIM), as well as with the currently on-going activities and some of the regular PIM publication venues

A hybrid recommendation model for learningo object repositories

Learning Objects (LOs) have emerged as a cornerstone approach for the development and distribution of educational content. These resources are distributed by Learning Object Repositories (LORs), which can make it easier for users to find suitable LOs by using Recommender Systems (RSs). This paper presents a hybrid recommendation model for LORs that combines content-based, demographic and context-aware techniques, along with the use of quality and popularity metrics. This article also describes how the model has been used to implement two RSs for two real LORs: ViSH and Europeana. Each of these RSs was evaluated in terms of accuracy, utility,usability and satisfaction perceived by end users. Besides, an A/B testing was performed in ViSH to compare the recommendations of the RS with random suggestions. The results showed that the RSs had a high user acceptance in terms of utility, usability and satisfaction, and that the RSs significantly exceeded the performance achieved by the random recommendations

Entity Recommendation for Everyday Digital Tasks

Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitoring, (2) introducing entity-based computing and interaction, and (3) investigating the previously overlooked usefulness of entity recommendations and their actual impact on user behavior in real tasks. The methodology exploits context from screen frames recorded every 2 seconds to recommend information entities related to the current task. We embodied this methodology in an interactive system and investigated the relevance and influence of the recommended entities in a study with participants resuming their real-world tasks after a 14-day monitoring phase. Results show that the recommendations allowed participants to find more relevant entities than in a control without the system. In addition, the recommended entities were also used in the actual tasks. In the discussion, we reflect on a research agenda for entity recommendation in context, revisiting comprehensive monitoring to include the physical world, considering entities as actionable recommendations, capturing drifting intent and routines, and considering explainability and transparency of recommendations, ethics, and ownership of data