Application of machine learning methods to anticipate upcoming trends and understand the education systems of European Union

Recent advancements in artificial intelligence have been significantly inspired by the field of educational data mining, or EDM. According to the needs of the students and instructors, new potential
and Possibilities for the advancement of technology-enhanced learning systems have been created via a range of research and put into practice. The use of modern techniques and application strategies by the EDM is crucial in improving the learning environment. The EDM is essential in building the educational setting for scholars along with ameliorating the working conditions for instructors and giving them appropriate information regarding the necessities of the students by looking at both the educational environment and machine learning approaches. In order to reach that goal, the main focus of this thesis will be on the socioeconomic elements that may influence how various institutions, communities, and social backgrounds function at various educational levels. To evaluate student and teacher data and anticipate the critical factors that affect educational effectiveness, a variety of machine learning techniques are applied. Specialized dimensionality reduction techniques are utilized to display the data from diverse locations. Then, regions and countries are categorized based on their common educational qualities using K-Means and Spectral clustering. Two datasets have been used for this analysis.
Clustering Results Based on the provided information from the first dataset, a clustering analysis was conducted across various characteristics related to lower education levels.
Cluster 1 consists of countries with high working time, teaching time, administrative tasks, additional duties, course planning, amount of lessons, volume of marking, negative impact on mental health, stress, physical health, salary satisfaction, and preparedness for students with disabilities. These countries include the United Kingdom, Sweden, Iceland, Norway, the Netherlands, Croatia, the Czech Republic, and Hungary.
Cluster 2 comprises countries with low working time, teaching time, administrative tasks, additional duties, course planning, amount of lessons, volume of marking, negative impact on
mental health, stress, physical health, salary satisfaction, and preparedness for students with disabilities. Italy, Cyprus, Romania, Finland, and Montenegro are part of this cluster. Cluster 3 includes the remaining countries, Belgium, Bulgaria, Spain, Czech Republic, Estonia, Croatia, Austria, North Macedonia and Slovenia, which are considered to have medium characteristics across the analyzed dimensions.
The second dataset has information for both lower and upper education level. Based on the information provided for the lower education level dataset, here is a summary of the clusters created:
Students: Cluster 1 (High Performance): This cluster includes countries like Spain, Belgium, Sweden, Italy, Hungary, and Ireland. These countries exhibit high student performance in terms of academic
achievements, participation rates, and overall educational outcomes.
Cluster 2 (Moderate Performance): This cluster comprises the United Kingdom and the Netherlands. These countries demonstrate moderate student performance, with relatively average
academic achievements and participation rates compared to other countries in the dataset.
Cluster 3 (Mixed Performance): This cluster consists of the remaining countries. They exhibit a mix of student performance levels, with some countries showing above-average performance
while others have lower performance in terms of academic achievements and participation rates.
Teachers: Cluster 1 (Experienced and Well-Qualified): This cluster includes countries such as Latvia, Lithuania, Malta, and Finland. These countries have highly experienced and well-qualified
teachers who undergo continuous professional development, resulting in high-quality teaching and effective classroom practices.
Cluster 2 (Diverse and Competent): This cluster comprises countries like Iceland, Germany, the United Kingdom, and the Netherlands. These countries have a diverse teacher workforce with a
range of competencies and expertise. They prioritize teacher collaboration, innovation, and continuous improvement in their education systems. Cluster 3 (Varied Teacher Profiles): This cluster encompasses the remaining countries. They exhibit a mix of different teacher profiles, including varying levels of experience, qualifications, and professional development opportunities. The characteristics and quality of their teacher
workforce vary across these countries.