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There is a lack of systematic research assessing the quality and effectiveness of doctoral education in Mongolia. In particular, no studies have been conducted to capture the perspectives and experiences of doctoral students regarding the quality of doctoral programs, which limits opportunities for student participation in the development and policymaking processes of this sector. Students' evaluations and insights into the quality of doctoral education are crucial for identifying the strengths and weaknesses of programs and for informing future improvements. Therefore, this study aims to incorporate the perspectives of doctoral students studying at public universities in Mongolia, assess the quality of doctoral education, and contribute to evidence-based policymaking and development efforts.
The Industrial Revolution urges us to think creatively, attentively, and well planned about educating future generations. Technology will impact the teaching, learning, and environmental experience in many ways in the coming years. With the support of decision support systems, institutions can enhance higher education quality assurance by providing more rich learning and teaching experiences, improved operational efficiency, and real-time, actionable insight into student and staff performance. Understanding the interests and views of stakeholders can lead to more effective decision-making. While the national context may vary, there is consensus on the focus areas of higher education quality assurance. The five quality focus areas are Governance and Management, Academic Quality of Teaching and learning, Research, Industry Engagements, and Internal & External Services. In this study, we have determined stakeholders for five quality focus areas in the National University of Mongolia. In the second step, we used Weka to analyze more than 1000 students, 200 graduate satisfaction results. In developing the stakeholder’s satisfaction survey, we included the above five quality focus areas and the most important factors influencing quality in each area. Finally, we have proposed a survey-based framework for Enhancing Higher Education Quality.
In the branches of science that use mathematics, the work of processing the results of experiments and observations often leads to solving systems with more equations than the number of variables. The solution of a system of linear equations with more equations than the number of variables can be solved using the principle of least squares to approximate the solution. Let us call it a system with redundancy. In order to mathematically process test materials using modern computing techniques, it is important to develop an algorithm based on the least squares principle that meets the requirements for solving a system with redundancy. Consider the possibility of creating a program to create a normal system according to the algorithm.
In the branches of science that use mathematics, the work of processing the results of experiments and observations often leads to solving systems with more equations than the number of variables. The solution of a system of linear equations with more equations than the number of variables can be solved using the principle of leas squares to approximate the solution. Let us call it a system with redundancy. In order to mathematically process test materials using modern computing techniques. It is important to develop an algorithm based on the leas squares principles that meets the requirements for solving a system with redundancy. Consider the possibility of creating a program to create a normal system according to the algorithm.
Utilizing a learning management system within higher education has led to the emergence of learner data as a significant component within the education sector. Consequently, harnessing such educational data enables the anticipation of academic achievements or grades. In the context of this research, it becomes feasible to forecast student evaluations through a multi-class classification approach involving neural networks, using data obtained from students engaged in e-courses facilitated within the learning management system. The outcomes of this predictive analysis offer a pathway for enhancing overall learning outcomes
Боловсрол дахь өгөгдлийн олборлолтын хэрэглээ өсөн нэмэгдсэнээр хиймэл оюуны тусламжтайгаар суралцагчийн суралцахуйн үр дүнг илүү нарийн таамаглах, цахим сургалтын орчинд суралцагч загварыг шинээр байгуулах боломж бүрдэж байна. Энэ судалгаагаар сургалтын удирдлагын систем ашиглан заасан цахим хичээл дэх суралцагчийн өгөгдөлөөр суралцагчийн суралцахуйн үр дүн, амжилтыг нэйрал сүлжээний тусламжтайгаар таамаглахыг зорьлоо.
