Evaluating the quality of studenbts actions in a distance learning programming language academic discipline

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Adaptive Information Visualization for Personalized Access to Educational Digital Libraries

Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

GPUVerify: A Verifier for GPU Kernels

We present a technique for verifying race- and divergence-freedom of GPU kernels that are written in mainstream ker-nel programming languages such as OpenCL and CUDA. Our approach is founded on a novel formal operational se-mantics for GPU programming termed synchronous, delayed visibility (SDV) semantics. The SDV semantics provides a precise definition of barrier divergence in GPU kernels and allows kernel verification to be reduced to analysis of a sequential program, thereby completely avoiding the need to reason about thread interleavings, and allowing existing modular techniques for program verification to be leveraged. We describe an efficient encoding for data race detection and propose a method for automatically inferring loop invari-ants required for verification. We have implemented these techniques as a practical verification tool, GPUVerify, which can be applied directly to OpenCL and CUDA source code. We evaluate GPUVerify with respect to a set of 163 kernels drawn from public and commercial sources. Our evaluation demonstrates that GPUVerify is capable of efficient, auto-matic verification of a large number of real-world kernels

A Study of Learning-by-Doing in MOOCs through the Integration of Third-Party External Tools:Comparison of Synchronous and Asynchronous Running Modes

Many MOOCs are being designed replicating traditional passive teaching approaches but using video lectures as the means of transmitting information. However, it is well known that learning-by-doing increases retention rates and, thus, allows achieving a more effective learning. To this end, it is worth exploring which tools fit best in the context of each MOOC to enrich learners' experience, including built-in tools already available in the MOOC platform, and third-party external tools which can be integrated in the MOOC platform. This paper presents an example of the integration of a software development tool, called Codeboard, in three MOOCs which serve as an introduction to programming with Java. We analyze the effect this tool has on learners' interaction and engagement when running the MOOCs in synchronous (instructor-paced) or asynchronous (self-paced) modes. Results show that the overall use of the tool is similar, regardless of the course running mode, although in the case of the synchronous mode the use of the tool is concentrated in a shorter period of time. Results also show that in the synchronous mode there is a higher percentage of accesses to the tool from registered learners (who can save their advances and continue the work later); this finding suggests that learners in the synchronous running mode are more engaged with the MOOC.The authors acknowledge the eMadrid Network, which is funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. S2013/ICE-2715. This work also received partial support from the Spanish Ministry of Economy, Industry and Competitiveness, Project RESET (TIN2014-53199-C3-1-R), Project SYMBHYO-TIC (PTQ-15-07505), Project SIMLAP (RTC-2014-2811-1), Project SMARTLET (TIN2017-85179-C3-1-R), and from the European Commission through Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ESEPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-1-BEEPPKA3-PI-FORWARD), COMPASS (2015-1-EL01-KA203-014033), and COMPETEN-SEA (574212-EPP-1-2016-1-NLEPPKA2-CBHE-JP)

Blockchain technology for the construction industry

One of the challenges that the construction industry faces is the lack of trust between participants and information sharing processes. Blockchain is a disruptive and emerging technology that can be used to add immutability, trust and transparency to information. This dissertation proposes a platform that aims to mitigate the problem of information sharing in the construction industry using blockchain technology. The platform allows to keep an immutable record of file interactions between construction participants and simulate document signatures that can later be verified. A proof-of-concept was developed using the Ethereum network, which was also used to evaluate the gas price influence in the execution duration of the transaction and its cost. It is concluded that blockchain technology can support information sharing in the construction industry.Um dos desafios que a indústria da construção enfrenta é a falta de confiança entre os intervenientes e os sistemas de partilha de informação. Blockchain é uma tecnologia disruptiva e emergente que pode ser usada para adicionar imutabilidade, confiança e transparência à informação. A presente dissertação propõe uma plataforma que pretende mitigar o problema de partilha de informação na indústria da construção utilizando a tecnologia blockchain. A plataforma permite manter um registo imutável das alterações efetuadas em ficheiros partilhados entre os vários intervenientes da obra e simular assinaturas de documentos que possam ser, posteriormente, verificadas. Foi desenvolvida uma prova de conceito utilizando a rede Ethereum sendo, de seguida, utilizada para avaliar a influência do preço unitário do gas na duração de execução da transação e o seu custo. Conclui-se que a tecnologia blockchain pode auxiliar a partilha de informação na indústria da construção

Monitoring Students at the University: Design and Application of a Moodle Plugin

Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities.Consejería de Educación de la Junta de Castilla y León (Spain) (Department of Education of the Junta de Castilla y León), grant number BU032G19, and grants from the University of Burgos for the dissemination and the improvement of teaching innovation experiences of the Vice-Rectorate of Teaching and Research Staff, the Vice-Rectorate for Research and Knowledge Transfer, 2020, and the Departamento de Ciencias de la Salud the University of Burgos (Spain)