Crowdsourcing in Kazakhstan’s higher education in the system of dual education as predictor of universal competencies - Mukhtar Tolegen; Botagoz Baymuhambetova; Irina Rovnyakova; Natalya Radchenko; Svetlana Sakhariyeva; Perizate Anafia (International Journal of Evaluation and Research in Education (IJERE), 2025)
The rapid transformation of professional competencies and the emergence of new professions every 3-5 years have accentuated the quest for effective means to facilitate the process of predicting future universal competencies among university graduates. An empirical study was conducted in three stages: organizational, investigative, and analytical. The crowdsourcing process algorithm comprised information gathering, idea generation, filtering, and voting. The findings suggest the feasibility of applying crowdsourced forecasting in the educational sector, where a clear trend towards alignment with real sectors of the economy and constantly changing market business environment conditions is evident. Calculations revealed that consensus decision-making was achieved regarding competencies such as 3D modeling and computer graphics, multilingualism, emotional intelligence, project management competencies, legal literacy, neural networks and big data, intercultural communication, digital competencies, export potential of the agricultural sector, logistics outsourcing, systems thinking, virtual reality competencies, artificial intelligence proficiency, analytics, and critical thinking, as confirmed by the analysis of variance. Forecasts indicated a predominance of subject-specific competencies associated with the growing volatility of the Kazakhstani labor market. The formulated profile of future universal competency development serves as an additional guideline in the development of educational programs (EPs) in professional training directions. Modified crowdsourcing design and methodology for measuring results can be utilized or adapted for addressing other challenges facing the higher education system that require feedback.