Abstract
PurposeThe paper aims to define a dashboard of indicators to assess the quality performance of higher education institutions (HEI). The instrument is termed SMART-QUAL.Design/methodology/approachTwo sources were used in order to explore potential indicators. In the first step, information disclosed in official websites or institutional documentation of 36 selected HEIs was analyzed. This first step also included in depth structured high managers' interviews. A total of 223 indicators emerged. In a second step, recent specialized literature was revised searching for indicators, capturing additional 302 indicators.FindingsEach one of the 525 total indicators was classified according to some attributes and distributed into 94 intermediate groups. These groups feed a debugging, prioritization and selection process, which ended up in the SMART-QUAL instrument: a set of 56 key performance indicators, which are grouped in 15 standards, and, in turn, classified into the 3 HEI missions. A basic model and an extended model are also proposed.Originality/valueThe paper provides a useful measure of quality performance of HEIs, showing a holistic view to monitor HEI quality from three fundamental missions. This instrument might assist HEI managers for both assessing and benchmarking purposes. The paper ends with recommendations for university managers and public administration authorities.

Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

Abstract
This paper describes a study on applying data mining techniques to power transformer failure prediction. The data set used consisted not only on DGA tests, but also in other tests done to the transformer’s insulating oil. This dataset presented several challenges, such as highly imbalanced classes (common in failure prediction problems), and the temporal nature of the observations. To overcome these challenges, several techniques were applied for prediction and better understand the dataset. Pre-processing and temporality incorporation in the dataset is discussed. For prediction, a 1-class and 2-class SVM, decision trees and random forests, as well as a LSTM neural network were applied to the dataset. As the prediction performance was low (high false-positive rate), we conducted a test to ascertain if the amount of data collected was sufficient. Results indicate that the frequency of data collection was not adequate, hinting that the degradation period was shorter than the periodicity of data collection.

Abstract
Implementation of information and communication technologies (ICTs) in education is defined as the incorporation of ICTs into teaching and learning activities, both inside and outside the classroom. Despite widely studied, there is still no consensus on how it affects student performance. However, before evaluating this, it is crucial to identify which factors impact students' use of ICT for educational purposes. This understanding can help educational institutions to effectively implement ICT, potentially improving student results. Thus, adapting the conceptual framework proposed by Biagi and Loi (2013) and using the 2018 database of the Program for International Student Assessment (PISA) and a decision tree classification model developed based on CRISP-DM framework, we aim to determine which socio-demographic factors influence students' use of ICT for educational purposes. First, we categorized students according to their use of ICT for educational purposes in two situations: during lessons and outside lessons. Then, we developed a decision tree model to distinguish these categories and find patterns in each group. The model was able to accurately distinguish different levels of ICT adoption and demonstrate that ICT use for entertainment and ICT access at school and at home are among the most influential variables to predict ICT use for educational purposes. Moreover, the model showed that variables related to teaching best practices of Internet utilization at school are not significant predictors of such use. Some results were found to be country-specific, leading to the recommendation that each country adapts the measures to improve ICT use according to its context.

Abstract
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Abstract
Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage.