Chatbot for communicating with university students in emergency situation.

Chatbots have arrived in higher education, and professors are trying to make the most of them. Typically, chatbots are used to help students learn academic subjects. In times of crisis, such as the COVID-19 pandemic, students who were not living with their families during the course, especially international students, were isolated and in critical situations. The student services offices were in constant contact with these students to solve problems, advise them and support them during their stay, within the constraints of confinement and the guidelines dictated by the country at the time. The student services offices were overwhelmed trying to help these students because, although the students' problems were very recurrent, the government guidelines changed from one day to the next. This article proposes the use of a chatbot to provide initial support to students during crisis situations, and facilitate communication between them and the university. The chatbot was tested by more than 160 students and student services staff. The findings support the use of chatbots as a potential tool to facilitate communication with students in emerging emergency situations, and encourage universities to adopt these types of smart tools to be prepared to respond quickly and efficiently to students in times of crisis.

A privacy-preserving design for sharing demand-driven patient datasets over permissioned blockchains and P2P secure transfer.

Sharing patient datasets curated by health institutions is critical for the advance of monitoring, surveillance and research. However, patient data is sensitive data and it can only be released under certain conditions and with previous explicit consent. Privacy preserving data sharing provides techniques to distribute datasets minimizing the risk of identification of patients. However, the sharing of datasets is typically done without considering the needs or requests of data consumers. Blockchain technologies provide an opportunity to gather those requests and share and assemble datasets using privacy-preserving methods as data and requirements on anonymity match. The architecture and design of such a solution is described, assuming an underlying permissioned blockchain network where providers such as healthcare institutions deal with consent, patient preferences and anonymity guarantees, playing a mediator role to a network of organizations.

Block-Based Development of Mobile Learning Experiences for the Internet of Things.

The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.

Motivation, students' needs and learning outcomes: a hybrid game-based app for enhanced language learning.

In the context of European Higher Education students face an increasing focus on independent, individual learning-at the expense of face-to-face interaction. Hence learners are, all too often, not provided with enough opportunities to negotiate in the target language. The current case study aims to address this reality by going beyond conventional approaches to provide students with a hybrid game-based app, combining individual and collaborative learning opportunities. The 4-week study was carried out with 104 German language students (A1.2 CEFR) who had previously been enrolled in a first-semester A1.1 level course at a Spanish university. The VocabTrainerA1 app-designed specifically for this study-harnesses the synergy of combining individual learning tasks and a collaborative murder mystery game in a hybrid level-based architecture. By doing so, the app provides learners with opportunities to apply their language skills to real-life-like communication. The purpose of the study was twofold: on one hand we aimed to measure learner motivation, perceived usefulness and added value of hybrid game-based apps; on the other, we sought to determine their impact on language learning. To this end, we conducted focus group interviews and an anonymous Technology Acceptance Model survey (TAM). In addition, students took a pre-test and a post-test. Scores from both tests were compared with the results obtained in first-semester conventional writing tasks, with a view to measure learning outcomes. The study provides qualitative and quantitative data supporting our initial hypotheses. Our findings suggest that hybrid game-based apps like VocabTrainerA1-which seamlessly combine individual and collaborative learning tasks-motivate learners, stimulate perceived usefulness and added value, and better meet the language learning needs of today's digital natives. In terms of acceptance, outcomes and sustainability, the data indicate that hybrid game-based apps significantly improve proficiency, hence are indeed, effective tools for enhanced language learning.