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Verbert K.,Catholic University of Leuven | Manouselis N.,Agro Know Technologies | Manouselis N.,University of Alcala | Ochoa X.,ESPOL Polytechnic University | And 4 more authors.
IEEE Transactions on Learning Technologies | Year: 2012

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed. © 2011 IEEE.

Kalz M.,Open Box Technologies
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In a recent study the crossdisciplinarity of the field of Technology-Enhanced Learning was analysed with science-overlay-maps and diversity measures. Results reveal that the crossdisciplinarity of the field has constantly increased over the last 10 years. Only in 2004, a significant decrease of interdisciplinary research could be identified. In this paper we take a closer look at the publications of this year and test our hypotheses for the decrease of crossdisciplinarity. © 2013 Springer-Verlag.

Balslev T.,University of Aarhus | Jarodzka H.,Open Box Technologies | Holmqvist K.,Lund University | De Grave W.,Maastricht University | And 4 more authors.
European Journal of Paediatric Neurology | Year: 2012

Background: Visual expertise relies on perceptive as well as cognitive processes. At present, knowledge of these processes when diagnosing clinical cases mainly stems from studies with still pictures. In contrast, patient video cases constitute a dynamic diagnostic challenge that may simulate seeing and diagnosing a patient in person. Aims: This study investigates visual attention and the concomitant cognitive processes of clinicians diagnosing authentic paediatric video cases. Methods: A total of 43 clinicians with varying levels of expertise took part in this cross-sectional study. They diagnosed four brief video recordings of children: two with seizures and two with disorders imitating seizures. We used eye tracking to investigate time looking at relevant areas in the video cases and a concurrent think-aloud procedure to explore the associated clinical reasoning processes. Results: More experienced clinicians were more accurate in visual diagnosis and spent more of their time looking at relevant areas. At the same time, they explored data less, yet they built and evaluated more diagnostic hypotheses. Conclusions: Clinicians of varying expertise analyse patient video cases differently. Clinical teachers should take these differences into account when optimising educational formats with patient video cases. © 2011 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

It’s not that I don’t like his more surreal works, but for me the best movie David Lynch has ever made is The Straight Story, a tiny little film about a man riding his lawnmower across Iowa. Because when Lynch focuses his talents on the grand adventure of ordinary human experience as opposed to more absurd visions, the result is profoundly affecting. Lynch is not the chief architect behind Interview Project, but the adventure fits easily into that humanist tradition. Co-directed by Austin Lynch (Lynch’s son) and Jason S., Interview Project captures the results of an epic 70-day road trip spent interviewing Americans about their lives. Interviews were conducted with 124 people, which will result in 121 episodes being released every three days for the next year. The filmmaking involved in this series is spare yet elegant, as the directors intercut the interview segments with footage from the road, including lots of (so far, anyway) dry Southwestern landscape. It’s a concept that celebrates the little details that make up a great road trip, as well as the people you meet along the way. The series premiered on Monday, but waiting a bit to review it paid off. Episode 1 features Jess, 64 years old and hanging out on the side of the road in Needles, Calif., who bluntly relates the story of his life — from a stint in the army to being abandoned by his wife and children to… well, to the side of the road in Needles, Calif. He’s not nearly as dynamic as Tommy, the subject for Episode 2, and in comparing the two segments it’s clear the most successful episodes will probably be the ones in which the subject being interviewed isn’t just reflecting on his or her life, but has a story to tell. In Tommy’s case, he’s waiting out his probation before hopefully moving up to Montana with the love of his life — who’s also the reason he’s on probation in the first place. The process of selecting interview subjects, Austin Lynch said via email, “was very organic and based on a variety of factors, for example: the mood we were in, the time of day, the weather, the last person we interviewed, the song on the radio, what clothes the person was wearing…” When you have more than three months and 20,000 miles to cover, this process definitely works. But as simple as the project was, the immense amount of footage that resulted required a high-tech solution, which the team found in Open Box Technologies, which would allow them to upload any variety of file format to be automatically encoded, while also having the capacity to handle “lots and lots of viewers,” he said, adding, “Of course, we hope to push this feature right to the limit!” Will this series find an audience? Its high production values and tight running time give it a fighting chance, as does its inspiring populist message — though the amount of content that it will yield (121 episodes multiplied by 3 minutes is 363 minutes, which even over the course of a year is quite a commitment) might need some curating so that the best episodes aren’t lost in the mix. However, as David Lynch says in his trademark nasal deadpan during the project’s introductory video, “It’s so fascinating to look and listen to people.” Over the next year, you may want to continue checking in. If you’re not inspired to go out and explore the world a little yourself.

Kostons D.,Open Box Technologies | van Gog T.,Open Box Technologies | Paas F.,Open Box Technologies | Paas F.,Erasmus University Rotterdam
Computers and Education | Year: 2010

Learner-controlled instruction is often found to be less effective for learning than fixed or adaptive system-controlled instruction. One possible reason for that finding is that students - especially novices - might not able to accurately assess their own performance and select tasks that fit their learning needs. Therefore, this explorative study investigated the differences in self-assessment and task-selection processes between effective and ineffective learners (i.e., in terms of learning gains) studying in a learner-controlled instructional environment. Results indicated that although effective learners could more accurately assess their own performance than ineffective learners, they used the same task aspects to select learning tasks. For effective learners, who were also more accurate self-assessors, the self-assessment criteria predicted subsequent task selection. The results are discussed, particularly with regard to their potential to provide guidelines for the design of a self-assessment and task-selection training. © 2009 Elsevier Ltd. All rights reserved.

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