KnowSe: Fostering user interaction context awareness

The CSCW area has recognized the concept of awareness as a critical issue to focus on (Schmidt et al., 2002) since “users who work together require adequate information about their environment” (Gross and Prinz, 2003). The environment of an individual encompasses her connections with other people, as well as with digital resources and actions (tasks or processes). If connections are not clear or hidden to the individual or to the group, the cost is a lack of awareness in the organization (McArthur and Bruza, 2003), which not only leads to inefficient cooperation but can even prevent it from being started. Unveiling the relations between persons, topics, tasks and processes to computer workers facilitates cooperative work by increasing the awareness of the personal social networks and the role of an individual in the organization, a project, or a group. These connections can be created and modeled manually but a better approach is to develop semi-automatic or even automatic tools to create and share them (McArthur and Bruza, 2003). Based on emails, McArthur and Bruza (2003) have computed such kind of connections, and suggest using more global corpora as well as taking into account dynamic ones

Detecting real user tasks by training on laboratory contextual attention metadata

Detecting the current task of a user is essential for providing her with contextualized and personalized support, and using Contextual Attention Metadata (CAM) can help doing so. Some recent approaches propose to perform automatic user task detection by means of task classifiers using such metadata. In this paper, we show that good results can be achieved by training such classifiers offline on CAM gathered in laboratory settings. We also isolate a combination of metadata features that present a significantly better discriminative power than classical ones

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

Decision support for multi-component systems: visualizing interdependencies for predictive maintenance

Taking dependencies between components seriously and considering the multi-component perspective instead of the single-system perspective could help to improve the results of predictive maintenance (PdM). However, modeling and identifying the interdependencies in complex industrial systems is challenging. A way to tackle this challenge and to identify interdependencies is using visualization. To the best of our knowledge, existing research on visualizing interdependencies is not applied to multi-component systems (MCS) so far. Further, it is not clear how visualization approaches can provide suitable decision support to identify interdependencies in PdM tasks. We evaluate three key visualization approaches to represent interdependencies in the context of PdM for MCS using a crowd-sourced design study in a questionnaire survey involving 530 participants. Based on our study, we were able to rank these approaches based on performance and usability for our given PdM task. The multi-line approach outperformed other approaches with respect to performance

Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

D5.2 - Stakeholder engagement plan, report on Guidelines and Monitoring tools-metrics

Barak, N., Burgos, D., Specht, M., De Vries, F., Camilleri, A., Lindstaedt, S., & Persico, D. (2010). D5.2 - Stakeholder engagement plan, report on Guidelines and Monitoring tools-metrics. STELLAR-project.This document describes the Guidelines, strategy, tools and methods for establishing the STELLAR stakeholders’ network and engaging with the TEL stakeholders. The overall aim of WP5 is to build a TEL community level capacity, with five main objectives: The STELLAR consortium aims to unify the leading institutions and projects in European Technology-Enhanced Learning (TEL) in a Network of Excellence (NoE). As part of this, it aims to reduce community fragmentation by bringing together the key stakeholders in European TEL and stimulate ongoing knowledge exchange between them. This is the work of WP5.European Union 7th Framework Programm

dataTEL - Datasets for Technology Enhanced Learning

The dataTEL white paper develop during the dataTEL workshop at the ARV2011. The workshop was motivated by the issue that very less educational datasets are publicly available in TEL, so that the outcomes of different TEL adaptive applications and recommender systems that support personalised learning are hardly comparable. In other domains like in e-commerce it is a common practise to use different datasets as benchmarks to evaluate recommender systems algorithms to make the results comparable (MovieLens, Book-Crossing, EachMovie dataset). So far, no universally valid knowledge exists in TEL on algorithm that can be successfully applied in a certain learning setting to personalise learning. Having a collection of datasets could be a first major step towards a theory of personalisation with in TEL that can be based on empirical experiments with verifiable and valid results. Therefore, the main objective of the dataTEL workshop was to explore suitable datasets for TEL with a specific focus on recommender and adaptive information systems that can take advantage of these datasets. In this context, new challenges emerge like unclear legal protection rights and privacy issues, suitable policies and formats to share data, required preprocessing procedures and rules to create sharable datasets, common evaluation criteria for recommender systems in TEL and how a dataset driven future in TEL could look like