What Are University Professors' Motivations? A Realistic Approach to Self-Perception of a Group of Spanish University Professors Belonging to the G-9 Group of Universities.

Universities face challenges on a number of levels. In this scenario, university professors play an important role as facilitators of knowledge. The main objective of this study was to analyse the motivations that influence the professional performance in a sample of 102 university professors from nine Spanish public universities (Male: 54 (52.9%); Female: 48 (47.1%)). For this purpose, a questionnaire of 22 closed-ended Likert-type questions was designed, in which scores ranged from 0 to 10 (do not agree at all, strongly agree). Following analysis, the final questionnaire was composed of 17 items, and showed good internal consistency (Cronbach's alpha = 0.858). The validity analysis showed a value of 0.822 (>0.5) in the sample adequacy measure of Kaiser-Meyer-Olkin and Bartlett's sphericity test (p < 0.0001). The exploratory factor analysis showed a clustering in four factors (two for intrinsic motivations and two for extrinsic motivations), explaining 64.33% of the total variance. Comparisons between each factor score by gender (male and female) showed statistically significant differences for factor F1 (higher for females) and F2 (higher for males). Finally, Q1 and Q13 showed a statistically significant correlation (p ≤ 0.05) with years of teaching experience. The motivations of Spanish university professors appear to be associated with the age and gender of the teacher.

Analysis of Interurban Mobility in University Students: Motivation and Ecological Impact.

The management of mobility in large cities is a complex issue of great interest due to its economic, social, and environmental impact. In this work, the interurban mobility of engineering students from two campuses of the University of Seville is studied. Specifically, this work carries out an analysis of the preferences of students in terms of mobility to their study centres and determines the environmental impact of such mobility in terms of kg of CO2 per student. Three constructs can be found to describe the motivation for their choice of transport: those related to comfort and speed, those related to sustainability and price, and those related to safety. Based on the responses obtained, groups of students are established that enable the design of specific actions in accordance with each of the profiles. From the analysis of the results obtained, recommendations are made for policymakers, and a reflection is given on the impact of the COVID-19 pandemic on this issue.

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.