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Abstract-This paper presents results of a large pilot project among Croatian primary and secondary schools focused on digital maturity of schools. It explores relationships between indicators that influence the overall digital maturity level of a school with the main aim to identify the main drivers of digital maturity. It also reveals key steps in the development of the Framework for Digitally Mature Schools in Croatia and the instrument for assessing digital maturity of schools. The instrument evaluation involved 151 primary and secondary schools in Croatia that were assessed against maturity levels. Descriptive statistics is used to identify and explain correlations between 38 indicators of digital maturity of schools. Results obtained from the instrument show that 50 percent of schools in Croatia are in the initial phase of maturity and 43 percent of them are e-enabled which in respect to 5 different maturity levels corresponds to levels 2 and 3 respectively. An on-line system developed for this purpose, besides it features the instrument itself and enables schools to benchmark between themselves, identifies critical indicators for each school that require improvement in order for school to make progress against maturity level.

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In this paper, a process of determining the criteria and sub-criteria weights for the selection of Regional Centers of Competence (RCC) in sector of vocational education is described. In 2018, the Croatian Ministry of Science and Education was responsible for creating a document comprising all parameters and criteria for this selection, named The Network of Regional Centers of Competence, and requested assistance in determining the criteria and sub-criteria weights to be incorporated within. The criteria were predefined pursuant to the national Vocational Education and Training Act and, to determine the weights, the AHP decision making group of the members of the Council for Vocational Education were engaged. There were 10 criteria with the total of 64 sub-criteria in this process, and to make it more efficient, pairwise comparison was made for the criteria and an inverse ranking method for the sub-criteria weights. Because of the enhanced number of pairwise comparisons, it was quite challenging for some decision makers to stay consistent. Therefore, the inconsistency coefficient was used to calculate the decision makers' weights, to incorporate the reliability of their comparisons in the overall process.

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