Data Mining Applications. A Comparative Study for Predicting Student's Performance

Inhaltsverzeichnis (Table of Contents)

• Knowledge Data Discovery Process, Data Mining Concept, History and Application of Data Mining in Various Industries
• Roadmap of Research Done in Educational Data Mining in Various Segment of Education Sector
• Objective of Effective Advertisement Method to Attract Students in Institutions
• Objective of Predicting the Performance of Students
• Impact of Language on the Performance of Students in Class Room
• Objective of Measuring the Quality of a Teacher

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis aims to develop a process for accurately predicting useful data from large datasets using data mining techniques. The focus is on applying educational data mining to understand and improve the efficiency and effectiveness of the higher education system in India. The research investigates the efficiency, scalability, maintenance, and interoperability of data mining techniques and explores how to implement useful data in daily decision-making processes within the education sector.

• Application of data mining techniques to predict useful data from large datasets
• Understanding and improving the higher education system in India
• Investigating the efficiency, scalability, maintenance, and interoperability of data mining techniques
• Exploring the implementation of useful data in daily decision-making processes within education
• Utilizing educational data mining to uncover hidden information and improve the quality of education

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter one provides an overview of the knowledge data discovery process, data mining concept, its history, and its application in various industries.
• Chapter two reviews existing research in educational data mining across different segments of the education sector, examining various tools and successful applications within the field.
• Chapter three investigates the objective of identifying effective advertising methods to attract students to educational institutions. It analyzes various methods such as newspaper ads, hoardings, pamphlets, radio, advertisement vans, and personal contact using support, confidence, and cosine analysis. The chapter concludes that hoardings and personal contact are more effective than other methods.
• Chapter four focuses on predicting student performance. The chapter utilizes data from previous years to develop a model that can help reduce dropout rates and improve institutional performance. Bayesian classification is employed for this purpose.
• Chapter five examines the impact of language on student performance in the classroom. Support, confidence, added value lift, correlation, and conviction analysis are used to evaluate the effectiveness of Hindi, English, and mixed-medium language instruction. The chapter concludes that mixed-medium classes have the highest level of interest.
• Chapter six aims to measure teacher quality and provide feedback on teacher strengths. A psychometric test is administered to collect student opinions on various teaching qualities, and a model is developed to analyze the results and identify adept teachers for specific tasks.

Schlüsselwörter (Keywords)

The key concepts and topics explored in this thesis include data mining, educational data mining, student performance prediction, advertising effectiveness, language impact on learning, teacher quality evaluation, higher education system in India, business intelligence tools, decision-making processes, knowledge discovery, and data analysis.