An Approach for the Personalization of Exercises Based on Contextualized Attention Metadata and Semantic Web technologies

Proocedings of: 10th IEEE International Conference on Advanced Learning Technologies (ICALT 2010). Sousse, Tunisia, 5-7 July 2010.The generation of Contextualized Attention Metadata (CAM) allows to retrieve information about the different actions that users execute over different resources in a specific context. This paper presents how CAM is used within a learning system to personalize help provided to students while working on online exercises. We outline our approach and present two application examples within this framework for the personalization of exercises with hints.Work partially funded by the Learn3 project TIN2008-05163/TSI within the Spanish “Plan Nacional de I+D+I”, and the Madrid regional community project eMadrid S2009/TIC-1650. This research was partially supported by the European Commission within the Role IP (Grant agreement no.:231396).Publicad

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.LinkedU

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

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

Towards PLEs through Widget Spaces in Moodle

Bringing flexibility and extensibility into Learning Management Systems is crucial because it gives teachers and students a free choice of technologies and educational materials they want to use for their courses. This paper presents a solution by enabling widgets (OpenSocial apps) within Mood le. Our first Moodle plugin allows teachers to freely choose a set of tools they want to use in their courses, although students cannot change widgets proposed by teachers. Additionally, the plugin enables the flexible interaction interfaces inside Mood le and improves the interoperability of Mood le with other Web platforms. The environment was evaluated with students within several courses. Even though the environment was perceived as useful by students, they lacked their own personalization. The second Mood le plugin described tackles this problem

MRnews: Design Explorations into Accessibility and News

Creating accessible technology and content is generally seen as beneficial for all users. This is particularly important when the content has a significant societal impact, such as news stories. To find new and innovative ways to engage users, digital news outlets are faced with challenges related to accessibility. In the case of Mixed Reality (MR) technology, the increasing interest emphasizes the need for the technology to be inclusive and accessible. The embodied nature and affordances of MR technology enable users to manipulate virtual objects using real-world knowledge and in real-time and enable them to utilize a wide range of skills when interacting with such systems. In turn, leveraging these affordances can enhance the accessibility of the task at hand. Contributions to developing accessibility guidelines have been made, and the use of MR applications to enhance accessibility is on the rise. However, these contributions are most prominent in education and not for leisurely use. This research project investigates the affordances of MR and of the Augmented Reality (AR) Head Mounted Display (HMD), HoloLens 2 (HL2) in particular, and how these can be leveraged to enhance accessibility when reading digital news. This is a Research through Design (RtD) project carried out in participation with users by conducting design activities and user evaluations. The RtD-process is supported by prototypes developed through an iterative process. MRnews is an application built for Microsoft’s AR HMD, the HL2. The implemented design showcases how news content creators and developers can leverage the affordances of MR technology to achieve accessibility in news stories. The results point toward direct manipulation of virtual content utilizing the spatial nature of MR technology and the use of sensory cues to keep the user oriented and focused impact accessibility.Masteroppgave i medie- og interaksjonsdesignMIX350MASV-MI