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In the university education system, the evaluation and provision of two-way teacher-student feedback are tools within the control function of educational management. Feedback can be defined as the process that takes place in the context of teacher-student interaction during university courses, both individually and in groups, for the development and achievement of the performance of the two actors involved through evaluation, appreciation, support, perception, and teaching. The research aims to develop an innovative feedback tool for the higher education engineering sector to support the improvement of learning outcomeoriented curricula and teaching activities to better meet the learning needs of Gen Z students while being relevant to the labor market and to society in general. The research had a number of subjects: 246 students (67.5% women, 32.5% men) and 7 teachers to whom two feedback instruments were applied (the SKS instrument and the standard instrument of Lucian Blaga University of Sibiu). After testing the four hypotheses, it was observed that the feedback provided by Gen Z is focused on four areas of competence: psychological, pedagogical, education management, and general impression. Each field includes a set of professional and transversal competencies. The SKS (STOP, KEEP, and START) evaluation form is more reliable in evaluating different disciplines than the standard evaluation form by providing a more homogenous type of feedback for each discipline or teacher. [ABSTRACT FROM AUTHOR]

(1) Background: Since the current crises that has inevitably impacted the financial market, market prediction has become more crucial than ever. The question of how risk managers can more accurately predict the evolution of their portfolio, while taking into consideration systemic risks brought on by a systemic crisis, is raised by the low rate of success of portfolio risk-management models. Sentiment analysis on natural language sentences can increase the accuracy of market prediction because financial markets are influenced by investor sentiments. Many investors also base their decisions on information taken from newspapers or on their instincts. (2) Methods: In this paper, we aim to highlight how sentiment analysis can improve the accuracy of regression models when predicting the evolution of the opening prices of some selected stocks. We aim to accomplish this by comparing the results and accuracy of two cases of market prediction using regression models with and without market news sentiment analysis. (3) Results: It is shown that the nonlinear autoregression model improves its goodness of fit when sentiment analysis is used as an exogenous factor. Furthermore, the results show that the polynomial autoregressions fit better than the linear ones. (4) Conclusions: Using the sentiment score for market modelling, significant improvements in the performance of linear autoregressions are showcased. [ABSTRACT FROM AUTHOR]