Decisions made at the strategic level of Higher Educational Institutions (HEIs) affect policies, strategies, and actions that the institutions make as a whole. Decision's structures at HEIs are depicted in this paper and their effectiveness in supporting the institutions' governance. The disengagement of the stakeholders and the lack of using efficient computational algorithms lead to 1) the decision process takes longer; 2) the ''whole picture'' is not involved along with all data necessary; and 3) small academic impact is produced by the decision, among others. Machine learning is an emerging field of artificial intelligence that using various algorithms analyzes information and provides a richer understanding of the data contained in a specific context. Based on the author's previous works, we focus on supporting decision-making at a strategic level, being deans' concerns the preeminent mission to bolster. In this paper, three supervised classification algorithms are deployed to predict graduation rates from real data about undergraduate engineering students in South America. The analysis of receiver operating characteristic (ROC) curve and accuracy are executed as measures of effectiveness to compare and evaluate decision tree, logistic regression, and random forest, where this last one demonstrates the best outcomes.

This paper analyzes the assessment experience as part of curriculum design by learning outcomes of the Master in User Experience Design of the Universidad Nacional Abierta y a Distancia—UNAD Colombia and the University of Lleida—UdL Spain. The article presents the assessment route, which allows for continuous improvement and is tailored to the self-assessment process. Conceptual references on curriculum design, competencies, purposes, constant improvement, and assessment are outlined for presentation. The theoretical line is based on international and national legal references. Likewise, the educational, pedagogical, and curricular implications of learning outcomes are presented, among them: change of paradigm (teaching vs. learning), coherence of curricular design, change of evaluation (qualification vs. assessment), decision-making, professor training, change of professor attitude, sustainability through assessment, and implementation routes, all of them with the aim of continuous improvement and to maintain the high quality of the program. One of the main conclusions indicates that curriculum design based on learning outcomes should be aligned and coherent at the macro-, meso-, and micro-curricular levels in order to meet the needs and requirements of the professional field.

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