Abstract
We report an integration process that involves the Moodle learning management system and an in-house developed e-portfolio system - SPD - and the institution information system. SPD is a system developed to create, evaluate and maintain digital portfolios assigned and assessed by a jury to keep a high quality level of the projects registered. The SPD system uses information imported from Moodle's database, in order to fill in its own database for users and courses and for propagating the existing session between systems. It also keeps projects ordered by development phases, determining whatever can be done and by whom, making them available for consult only after being accepted by the jury. To aid the rapid creation of projects and development of its documentation a set of pre-defined templates are made available.

Abstract
The digital transformation associated with the huge volume of data that healthcare organizations deal with today is based on transforming this complex knowledge-driven industry to turn data into knowledge. The healthcare industry requires comprehensive models that help identify priorities to implement business intelligence (BI) solution. Business intelligence can help organizations make better decisions by showing current and historical data within their business context. This paper systematizes and analyzes three business intelligence maturity models into one and also attempts to understand the main key performance indicators in adopting business intelligence maturity model in healthcare organizations. For this purpose, we present a questionnaire that was based on the systemized business intelligence maturity model that was sent to X% of the Portuguese hospitals with the objective of identifying not only the business intelligence maturity stage of the Portuguese hospitals but also to infer the most important key performance indicators that will characterize each stage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract

Abstract

Abstract
Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student's final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation-which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment.