José Coelho

The digital transformation of higher education institutions requires intelligent tools that support data-driven academic decision-making. The ADAPTE project applies statistical modelling and interactive dashboards to enhance institutional planning and promote academic success. Developed using anonymised data from a public higher education institution in the field of Tourism, the system provides accessible analytical resources for faculty members and management bodies. By identifying risk patterns associated with dropout and academic performance, ADAPTE enables more efficient management of study programmes, human resources, and strategic planning processes. Although implemented within a hospitality and tourism education context, the project demonstrates broader potential within the domain of educational tourism, by fostering stronger alignment between learning experiences and data related to students’ academic pathways. This contribution presents the current stage of ADAPTE’s implementation and illustrates how applied information technologies can support the development of a data-oriented educational culture, where academic trajectories meet intelligent analytical and predictive systems.

Image description has been a recurrent topic on web accessibility over the years. With the increased use of social networks, this discussion is even more relevant. Social networks are responsible for a considerable part of the images available on the web. In this context, users are not only consuming visual content but also creating it. Due to this shared responsibility of providing accessible content, major platforms must go beyond accessible interfaces. Additional resources must also be available to support users in creating accessible content. Although many of today's services already support accessible media content authoring, current efforts still fail to properly integrate and guide their users through the authoring process. One of the consequences is that many users are still unaware of what an image description is, how to provide it, and why it is necessary. We present SONAAR, a project that aims to improve the accessibility of user-generated content on social networks. Our approach is to support the authoring and consumption of accessible social media content. Our prototypes currently focus on Twitter and Facebook and are available as an Android application and as a Chrome extension.

This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.

The adoption of social networks by older adults has increased in recent years. However, many still cannot make use of social networks as these are simply not adapted to them. Through a series of direct observations, interviews, and focus groups, we identified recommendations for the design of social networks targeting seniors. Based on these, we developed a prototype for tablet devices, supporting sharing and viewing Facebook content. We then conducted a user study comparing our prototype with Facebook's native mobile application. We have found that Facebook's native application does not meet senior users concerns, like privacy and family focus, while our prototype, designed in accordance with the collected recommendations, supported relevant use cases in a usable and accessible manner.