Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

Metadata in architecture education - First evaluation results of the MACE system.

The paper focuses on the MACE (Metadata for Architectural Contents in Europe) system and its usage in architecture education at the university level. We report on the various extensions made to the system, describe some of the new functionality and give rst results on the evaluation of the MACE System. Several universities were involved with signicant student groups in the evaluation, so that the indications described here are already highly trustable. First results show that using MACE increases student performance signicantly. Paper AWARDED BY the EA-TEL (European Association Technology Enhanced Learning

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

Towards the Prediction of User Actions on Exercises with Hints Based on Survey Results

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.The actions a user performs on exercises depending on the different hinting techniques applied, can be used to adapt future exercises. In this paper, we propose a survey for users in order to know their different actions depending on different conditions. The analysis of preliminary results for some questions of the model shows that there is a correlation between some survey questions and the real student actions, but there is a case in which there is not such correlation. For the cases where that correlation exists, this correlation leads to think that some prediction of users actions based on survey results is possible.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

Automatic Discovery of Complementary Learning Resources

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.Students in a learning experience can be seen as a community working simultaneously (and in some cases collaboratively) in a set of activities. During these working sessions, students carry out numerous actions that affect their learning. But those actions happening outside a class or the Learning Management System cannot be easily observed. This paper presents a technique to widen the observability of these actions. The set of documents browsed by the students in a course was recorded during a period of eight weeks. These documents are then processed and the set with highest similarity with the course notes are selected and recommended back to all the students. The main problem is that this user community visits thousands of documents and only a small percent of them are suitable for recommendation. Using a combination of lexican analysis and information retrieval techniques, a fully automatic procedure to analyze these documents, classify them and select the most relevant ones is presented. The approach has been validated with an empirical study in an undergraduate engineering course with more than one hundred students. The recommended resources were rated as "relevant to the course" by the seven instructors with teaching duties in the course.Work partially funded by the Learn3 project, “Plan Nacional de I+D+I TIN2008-05163/TSI”, the Acción Integrada Ref. DE2009-0051, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650) and TELMA Project (Plan Avanza TSI-020110-2009-85)

Why decision support systems are important for medical education

During the last decades the inclusion of digital tools in health education has rapidly lead to a continuously enlarging digital era. All the online interactions between learners and tutors, the description, creation, reuse and sharing of educational digital resources and the interlinkage between them in conjunction with cheap storage technology has led to an enormous amount of educational data. Medical education is a unique type of education due to accuracy of information needed, continuous changing competences required and alternative methods of education used. Nowadays medical education standards provide the ground for organizing the educational data and the paradata. Analysis of such education data through education data mining techniques is in its infancy, but decision support systems for medical education need further research. To the best of our knowledge, there is a gap and a clear need for identifying the challenges for decision support systems in medical education in the era of medical education standards. Thus, in this paper the role and the attributes of such a decision support system for medical education are delineated and the challenges and vision for future actions are identified

[11C]acetate PET/CT Visualizes Skeletal Muscle Exercise Participation, Impaired Function, and Recovery after Hip Arthroplasty; First Results

Based on skeletal muscle acetate physiology we aimed studying muscle function after hip arthroplasty with [(11)C]acetate PET

A multidimensional evaluation framework for personal learning environments

Evaluating highly dynamic and heterogeneous Personal Learning Environments (PLEs) is extremely challenging. Components of PLEs are selected and configured by individual users based on their personal preferences, needs, and goals. Moreover, the systems usually evolve over time based on contextual opportunities and constraints. As such dynamic systems have no predefined configurations and user interfaces, traditional evaluation methods often fall short or are even inappropriate. Obviously, a host of factors influence the extent to which a PLE successfully supports a learner to achieve specific learning outcomes. We categorize such factors along four major dimensions: technological, organizational, psycho-pedagogical, and social. Each dimension is informed by relevant theoretical models (e.g., Information System Success Model, Community of Practice, self-regulated learning) and subsumes a set of metrics that can be assessed with a range of approaches. Among others, usability and user experience play an indispensable role in acceptance and diffusion of the innovative technologies exemplified by PLEs. Traditional quantitative and qualitative methods such as questionnaire and interview should be deployed alongside emergent ones such as learning analytics (e.g., context-aware metadata) and narrative-based methods. Crucial for maximal validity of the evaluation is the triangulation of empirical findings with multi-perspective (end-users, developers, and researchers), mixed-method (qualitative, quantitative) data sources. The framework utilizes a cyclic process to integrate findings across cases with a cross-case analysis in order to gain deeper insights into the intriguing questions of how and why PLEs work