Improving quality of educational processes providing new knowledge using data mining techniques

1. Improving quality of educational processes providing new knowledge using data mining techniques Manolis Chalaris, Stefanos Gritzalis, Manolis Maragoudakis, Cleo Sgouropoulou and Anastasios Tsolakidis Technological Educational Institute of Athens, Ag. Spyridonos, 12210 Aigaleo, Athens, Greece University of the Aegean, Department of Information and Communication Systems, Samos GR-83200, Greece

2. Presentation Outline • Introduction • Data Mining in Higher Education Institutes • Case Study - Application of Data Mining Techniques in TEI of Athens • Conclusion - Future Work

3. Introduction Our aim in this work is • to demonstrate the ability of data mining in improving the quality of educational processes and thus supporting the administration of an educational institute in the decision-making procedure • to conduct some experiments in using data mining techniques like clustering analysis, correlation analysis and association rules in educational data of the Technological Educational Institute of Athens (TEIA)

4. Introduction Educational Data Mining: describes a new research field concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.

5. Data Mining in Higher Education Institutes Related work • Romero and Ventura, survey between 1995 and 2005 to present application of data mining techniques in different types of educational systems and then grouping them by task. • Al-Radaidehet al. applied decision tree as classification method to evaluate student data in order to find which attributes affect the performance of a courses. • Mohammed M. Abu Tair & Alaa M. El-Haleesuse educational data mining to discover knowledge that may affect the students’ performance • Baradwajand Pal applied decision tree for evaluating students’ performance • KarelDejaeger et al. investigated the construction of data mining models to identify the main attributes of students satisfaction and • DursunDelenexamines the institution-specific nature of the attrition problem through models developed by educational data using machine learning techniques.

6. Data Mining in Higher Education Institutes Benefits and Success Factor of EDM Benefits • Extracting knowledge that will support the HEI administration in decision making procedure for improving the quality of educational processes. • Clustering can offer comprehensive characteristics of students, while Prediction (classification and regression) and Relationship Mining (association, correlation, sequential mining) can help the university to decrease student's drop-out rate or to increase student's retention rate and learning outcome. • Provide more personalized education, maximize educational system efficiency, and reduce the cost of education processes Success Factors • Existence of an appropriate infrastructure for finding and collecting all educational data in a centralized system (Q.A.I.S. of MODIP TEIA) • Analysis Model as a roadmap for the institute to identify which part of the educational processes can be improved through data mining and how to obtain each strategic goal

7. Application of data mining techniques in TEI of Athens Methodology: CRISP-DM methodology- the CRoss Industry Standard Process for Data Mining. The methodology consists of six steps or phases: • Business (Organizational) Understanding. This phase focuses on understanding the project objectives and requirements from a business or organizational perspective. • Data Understanding where initial data is collected, data quality problems are identified and/or interesting subsets to form hypotheses for hidden information are detected. • Data Preparation. In this phase all necessary tasks like data cleansing, data transformation and data selection are performed in order to construct the final dataset. • Modeling phase where various modeling techniques are selected and applied • Evaluation phase in which you determine how valuable your model is, and if it achieves the business objectives you have set. • Deployment phase. In this phase we have the actions that should be carried out in order to use the created models.

8. Business (Organizational) Understanding Ultimate target concerning TEIA Quality improvement of Educational Processes In the framework of this paper, the main objective is to conduct some experiments in data of the evaluation process of the institute using data mining techniques and thus extract knowledge concerning aspects of educational processes.

9. Data Understanding In this research • we focus on data collected by QAU of TEIA (MODIP TEIA) for the evaluation process of the spring semester of the acad. year 2011 - 2012 between 8th – 10th week of courses. • 2 questionnaires – theoretical courses with 35 questions (attributes) and laboratory courses with 22 questions (attributes) • The sections – directions of the 2 questionnaires are: • course-centred items (e.g. is the course well structured?) • lecturer & teaching effectiveness (e.ghow s/he explains the content of the course) • student-centred items (e.g. how often you attend the course?) and • laboratory work (e.g. are the facilities of the labs sufficient?) • sample of 26.000 questionnaires, approximately 10.000 for the theoretical courses and 16.000 for the laboratory from students of 5 Faculties and 27 (33) Departments

10. Data Preparation • Data Integration (Collecting all data in one dataset) • Data Cleansing (Handling of missing values or inaccurate records) • Selecting attributes for conducting the experiments

11. Modeling and Evaluation phase Cluster Analysis (k-means) to the data of the theoretical questionnaire to examine which faculty has better averages in attributes of all three directions and compare it with the percentage of graduates of each Faculty

12. Modeling and Evaluation phase

13. Modeling and Evaluation phase Correlation Analysis to examine if the student concepts' understanding (q28_student_understanding in theory questionnaire) interacts with other attributes

14. Modeling and Evaluation phase Association Rules (FP-Growth) to examine the student understanding (q17) considering the laboratory courses.

15. Modeling and Evaluation phase Cluster Analysis (k-means) to the data of the laboratory questionnaire to examine how students in the three Faculties (SEYP, SDO, STEF) evaluate the facilities of the labs (q13 and q14).

16. Deployment phase Student Understanding relates mainly with the lecturer and teaching effectiveness especially in the theoretical courses while in the laboratory courses, lab facilities are considered as a premise for the student to understand. Students of the Faculty of SEYP are more consistent with their studies (attendance, studying, and understanding) than those of other Faculties. On the other hand, SEYP has the biggest problem concerning the lab facilities. • Focus in the improvement of the lab facilities (input indicators) in SEYP (e.g better resource allocation) • In other Faculties the focus should be mainly in the educational process (process indicators) such as better organization of the course, improving teaching effectiveness and student understanding (e.g Organizing pedagogical seminars for the lectures in order to enhance their teaching effectiveness)

17. Conclusion – Future work Present a more integrate approach of applying data mining techniques in a HEI. • Definition and Modelling all educational processes • Defining all indicators that determine the quality of them. • Proposal of an Analysis Model as guideline for the application of data mining in educational data of TEIA Create a strategic tool to support the decision making procedure for enhancing the quality of educational processes

18. Thank You! Manolis Chalaris Quality Assurance Unit (MODIP) TEI of Athens Email: modip@teiath.gr