Open University of Catalonia

About: Open University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Collaborative learning & Educational technology. The organization has 1943 authors who have published 4646 publications receiving 64200 citations. The organization is also known as: Universitat Oberta de Catalunya & UOC.

Lessons learned from large-scale dense IEEE802.15.4 connectivity traces

Abstract: Time Synchronized Channel Hopping (TSCH) is a technique known to efficiently combat external interference and multi-path fading. TSCH is at the heart of industrial low-power wireless standards such as WirelessHART and IEEE802.15.4e, and the focus of the current standardization activities at IETF 6TiSCH. In a TSCH network, communicating nodes send successive link-layer frames at different frequencies. The performance of such frequency-agile communication is not well understood. In this paper, we propose an empirical approach. We conduct an experiment to record the connectivity between 350 nodes in a typical office environment, simultaneously on each of the 16 frequencies at 2.4 GHz. Analysis reveals the impact of WiFi interference on the reliability of the IEEE802.15.4 wireless links: even when the WiFi network sits idle, IEEE802.11 beaconing causes a significant number of links to drop from 90% to 70–80% packet delivery ratio. It also reveals the impact of multi-path fading, showing how moving a pair of nodes can cause their link to go from perfect to non-existing. Results show that the quality of each link depends heavily on the communication frequency. The paper shows the effectiveness of channel hopping: by simply changing the communication frequency between successive transmissions, less nodes are required to cover a geographical area. The paper discusses the importance of the frequency to retransmit on. The 5-hour connectivity trace reveals a wealth of information. This paper is only a first step towards a much larger dataset collected over a representative number of real-world deployments.

Designing online interaction to address disciplinary competencies: A cross-country comparison of faculty perspectives

Abstract: This study was conducted at colleges in three countries (United States, Venezuela, and Spain) and across three academic disciplines (engineering, education, and business), to examine how experienced faculty define competencies for their discipline, and design instructional interaction for online courses. A qualitative research design employing in-depth interviews was selected. Results show that disciplinary knowledge takes precedence when faculty members select competencies to be developed in online courses for their respective professions. In all three disciplines, the design of interaction to correspond with disciplinary competencies was often influenced by contextual factors that modify faculty intention. Therefore, instructional design will vary across countries in the same discipline to address the local context, such as the needs and expectations of the learners, faculty perspectives, beliefs and values, and the needs of the institution, the community, and country. The three disciplines from the three countries agreed on the importance of the following competencies: knowledge of the field, higher order cognitive processes such as critical thinking, analysis, problem solving, transfer of knowledge, oral and written communication skills, team work, decision making, leadership and management skills, indicating far more similarities in competencies than differences between the three different applied disciplines. We found a lack of correspondence between faculty’s intent to develop collaborative learning skills and the actual development of them. Contextual factors such as faculty prior experience in design, student reluctance to engage in collaborative learning, and institutional assessment systems that focus on individual performance were some of these reasons.

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

Abstract: This research characterized how Facebook deals with rare diseases This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis The Facebook analysis shows that posts with photos and positive comments have the highest engagement We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day

On the Effect of Observed Subject Biases in Apparent Personality Analysis From Audio-Visual Signals

Abstract: Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender, and appearance. Approaches developed for automatic personality perception are not expected to predict the real personality of the target but the personality external observers attributed to it. Hence, they have to deal with human bias, inherently transferred to the training data. However, bias analysis in personality computing is an almost unexplored area. In this article, we study different possible sources of bias affecting personality perception, including emotions from facial expressions, attractiveness, age, gender, and ethnicity, as well as their influence on prediction ability for apparent personality estimation. To this end, we propose a multimodal deep neural network that combines raw audio and visual information alongside predictions of attribute-specific models to regress apparent personality. We also analyze spatio-temporal aggregation schemes and the effect of different time intervals on first impressions. We base our study on the ChaLearn first impressions dataset, consisting of one-person conversational videos. Our model shows state-of-the-art results regressing apparent personality based on the Big-Five model. Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions.