Rocio, V; Coelho, J; Caeiro, S; Nicolau, P; Teixeira, A;
INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING
MOOCs are a recent phenomenon, although given their impact, they have been subject to a large debate. Several questions have been raised by researchers and educators alike regarding their sustainability, both economically and as an efficient mode of education provision. In this paper we contribute to this discussion by presenting a case study of the MOOC on Lived Experiences of Climate Change, which piloted the iMOOC pedagogical model developed at Universidade Aberta (UAb), the Portugese Distance Learning University. The iMOOC is a hybrid model which incorporates elements from existing MOOCs but adds other features drawn from UAb's experience with online learning and aims at better integrating in the larger context of the institutional pedagogical culture. The iMOOC implied also an integration of platforms - Moodle and Elgg. The pilot course had more than one thousand registrations, and it was the largest MOOC course on Portuguese language delivered so far. We discuss the effort required to design and deliver the course, the technological solution developed, and the results obtained. We registered a moderate effort to create and run the course, ensured by internal staff from the University. The technological solution was a success: an integrated architecture combining well-established, well-tested open software. The completion rate was 3.3%, but the high success of this innovative learning experience was demonstrated by the active involvement of about 50% of the registered participants, that followed the course until the end. Lessons learned from this experience and future research on the field are also discussed.
Pinto, HL; Rocio, V;
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Sentiment analysis is a set of techniques that deal with the verification of sentiment and emotions in written texts. This introductory work aims to explore the combination of scores and polarities of sentiments (positive, neutral and negative) provided by different sentiment analysis tools. The goal is to generate a final score and its respective polarity from the normalization and arithmetic average scores given by those tools that provide a minimum of reliability. The texts analyzed to test our hypotheses were obtained from forum posts from participants in a massive open online course (MOOC) offered by Universidade Aberta de Portugal, and were submitted to four online service APIs offering sentiment analysis: Amazon Comprehend, Google Natural Language, IBM Watson Natural Language Understanding, and Microsoft Text Analytics. The initial results are encouraging, suggesting that the average score is a valid way to increase the accuracy of the predictions from different sentiment analyzers. © Springer Nature Switzerland AG 2019.
Barros, C; Rocio, V; Sousa, A; Paredes, H;
Advances in Intelligent Systems and Computing
Today’s cloud computing techniques are becoming unsustainable for real time applications as very low latency is required with billions of connected devices. New paradigms are arising; the one that offers an integrated solution for extending cloud resources to the edge of the network and addresses current cloud issues is Fog Computing. Performing Fog Computing brings a set of challenges such as: provisioning edge nodes to perform task volumes downloaded from the Cloud; placing task volumes on edge nodes; resource management on edge nodes; need for a new programming model; programming, resource management, data consistency service discovery challenges; privacy and security and improving the quality of service (QoS) and user experience (QoE). This paper aims at introducing the Fog Computing concept and it presents a literature review on the way it is applied: context-sensitive applications and context-sensitive mobile service platforms. The result of the study is presented as the current research challenges for context aware mobile applications in Fog Computing infrastructure. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.
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