Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps.

This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app's setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage.

Evaluating the performance of multilingual models in answer extraction and question generation.

Multiple-choice test generation is one of the most complex NLP problems, especially in languages other than English, where there is a lack of prior research. After a review of the literature, it has been verified that some methods like the usage of rule-based systems or primitive neural networks have led to the application of a recent architecture, the Transformer architecture, in the tasks of Answer Extraction (AE) and Question Generation (QG). Thereby, this study is centred in searching and developing better models for the AE and QG tasks in Spanish, using an answer-aware methodology. For this purpose, three multilingual models (mT5-base, mT0-base and BLOOMZ-560 M) have been fine-tuned using three different datasets: a translation to Spanish of the SQuAD dataset; SQAC, which is a dataset in Spanish; and their union (SQuAD + SQAC), which shows slightly better results. Regarding the models, the performance of mT5-base has been compared with that found in two newer models, mT0-base and BLOOMZ-560 M. These models were fine-tuned for multiple tasks in literature, including AE and QG, but, in general, the best results are obtained from the mT5 models trained in our study with the SQuAD + SQAC dataset. Nonetheless, some other good results are obtained from mT5 models trained only with the SQAC dataset. For their evaluation, the widely used BLEU1-4, METEOR and ROUGE-L metrics have been obtained, where mT5 outperforms some similar research works. Besides, CIDEr, SARI, GLEU, WER and the cosine similarity metrics have been calculated to present a benchmark within the AE and QG problems for future work.

Dataset on the learning performance of ECDL digital skills of undergraduate students for comparing educational gaming, gamification and social networking.

This paper reports data about the learning performance of students using four different motivational tools: an educational game, a gamified plugin, a social networking website and a gamified social networking website. It also reports a control group. The data pertain to 379 students of an undergraduate course that covers basic Information and Communication Technology (ICT) skills in Spain. Data corresponds to different learning modules of the European Computer Driving License (ECDL) initiative. The data include variables of four pre-test scores, four post-test scores and a final examination. It was gathered using a quasi-experimental research design during 2014. Data reported here refers to the research paper in (de-Marcos et al., 2016) [1].

An experiment with content distribution methods in touchscreen mobile devices.

This paper compares the usability of three different content distribution methods (scrolling, paging and internal links) in touchscreen mobile devices as means to display web documents. Usability is operationalized in terms of effectiveness, efficiency and user satisfaction. These dimensions are then measured in an experiment (N = 23) in which users are required to find words in regular-length web documents. Results suggest that scrolling is statistically better in terms of efficiency and user satisfaction. It is also found to be more effective but results were not significant. Our findings are also compared with existing literature to propose the following guideline: "try to use vertical scrolling in web pages for mobile devices instead of paging or internal links, except when the content is too large, then paging is recommended". With an ever increasing number of touchscreen web-enabled mobile devices, this new guideline can be relevant for content developers targeting the mobile web as well as institutions trying to improve the usability of their content for mobile platforms.