**2. Methodology**

In order to analyze the content of the learning outcomes mentioned by the Portuguese higher education institutions for their courses, the documents that these institutions may have presented to the Assessment and Accreditation Agency for Higher Education (A3ES) were considered. In the initial phase of the accreditation process, the higher education institutions submit to A3ES an accreditation proposal: "Previous Accreditation Request of a New Study Cycle." For each assessed program, the learning outcomes that students are expected to achieve on graduation are listed. By an internal rule of A3ES, the information provided in this context is limited to 1000 characters.

Thus, 2.926 evaluation and accreditation request processes were analyzed for New Study Cycles and Study Cycles already in operation.

A content analysis of the learning outcomes acknowledged in the proposals of study programs proposed to quality accreditation by the Portuguese Agency for Higher Education Accreditation (A3ES) was carried out using the MAXQDA software (version 12). Six steps were taken to identify patterns (themes) within the data. This analysis followed a conceptual framework, in which 24 technical and generic skills were included. This procedure uses a theme-based analysis approach, rather than a data-driven approach [14]. Creativity was one of the categories found and analyzed. Within the category Creativity, 6 sub-categories were considered: to Create, To be Original, To solve new problems, to go beyond, to think out of the box, and To transform. Learning outcomes examples for each sub-category could be respectively:


*Can Creativity Be Taught and/or Learned? A Sketch from Higher Education Learning Outcomes DOI: http://dx.doi.org/10.5772/intechopen.112365*


As already mentioned, the data analysis was focused on the information included in the documents submitted for quality accreditation and that refers to the "intended learning outcomes" that students are expected to achieve at the end of a given learning period. All learning outcomes (n = 2926) included in all proposals of new study cycles submitted to the A3ES for accreditation were analyzed. Of these 619 documents, 54.8% were 2nd study cycle proposals, while 26.2% referred to the 1st study cycle and 18.9% to the 3rd study cycle.

The content analysis of all learning outcomes in isolated categories, that is, according to the conceptual synthesis matrix, made it possible to obtain a remarkable set of descriptive data, both by competence and by independent variable. These data thus made it possible to understand which competences and knowledge are most valued not only by Portuguese Higher Education in general but by each subsystem, sector, scientific area, etc. To this end, the quantitative data resulting from the previous content analysis were explored using the statistical program IBM SPSS for Windows, version 25 (IBM Corp. Released, 2010).

First, the univariate normality of all variables was confirmed using the Kolmogorov-Smirnov test and the asymmetry (values <1.0) and kurtosis (values <3.0) criteria, as defined by Kline [47], and the non-existence of outliers (|z| < 3; [47]). Whenever any of the assumptions were not checked, the corresponding non-parametric tests were performed. When both tests were concordant regarding the rejection versus retention of the null hypothesis, the parametric tests were reported [48].

In the analysis of the results, the statistical procedures used included not only descriptive statistics but also parametric tests for independent samples (Student's t-test and one-factor analysis of variance) and relationships between variables through Pearson's r coefficient.

It should be noted that, in the ANOVAs, after the homogeneity of the variances of the variables used was tested through Levene's test, the Gabriel post hoc test was used given the unequal number of subjects in each of the groups studied [49].
