Rule Model Causes Higher Education Students Moving With Decision Tree Methods

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Introduction

The phenomenon of students who resign or move from college is still a complex problem. This study aims to identify the factors that influence the decision, with a focus on the data of STMIK Microscil students. The data analyzed came from a survey of students in the 2011 and 2012 school year, as well as information from student databases. In this study, the C4.5 algorithm is used which is one of the methods in data mining techniques to produce a decision tree. This Decision Tree is then used to build a model of rule that shows the link between the cumulative achievement index (GPA) of students with a number of factors, such as parental economic data, family support, facilities provided, motivation, self-confidence, and service quality in tertiary institutions.

Methodology

The analysis shows that the six predictor variables contributed significantly, reaching 80.2% in predicting the potential of students to resign or move. Of the six variables, the economic factor of parents is the most dominant variable with a contribution of 58.3%. This indicates that the economic conditions of parents have a major influence on student decisions, where students from a weaker economic background tend to be more risky to resign.

Factors Influencing Student Decisions

Family support factor is also very important, because students who feel supported by families will have higher motivation and self-confidence. Facilities available in tertiary institutions, such as access to learning resources and guidance services, also play an important role in creating a conducive environment for students. Student personal motivation and service quality provided by universities are other factors that must be considered to reduce resignation rates.

Implications for Higher Education Institutions

By understanding these factors, universities can design better strategies to support students, such as providing consulting services for students who experience difficulties, as well as improving existing facilities. The application of this model is expected to provide a deeper insight about the factors that influence students' decisions to resign, so that preventive measures can be carried out more on target.

Conclusion

Overall, this study made an important contribution in understanding the dynamics of student decisions in the context of higher education. By using a data-based approach, tertiary institutions can make more informed and strategic decisions in supporting students to continue to complete their education.

Recommendations for Future Research

This study provides a foundation for further research in understanding the factors that influence student decisions in higher education. Future studies can build on this research by exploring other factors that may influence student decisions, such as academic support services, campus resources, and student engagement. Additionally, future studies can investigate the effectiveness of different strategies for supporting students, such as mentoring programs, academic advising, and career counseling.

Limitations of the Study

This study has several limitations that should be noted. First, the data used in this study was collected from a single institution, which may limit the generalizability of the findings. Second, the study only examined the factors that influence student decisions to resign or move, and did not explore other outcomes, such as academic success or student satisfaction. Finally, the study relied on self-reported data from students, which may be subject to biases and limitations.

Future Directions for Research

Despite these limitations, this study provides a valuable contribution to the field of higher education research. Future studies can build on this research by exploring other factors that influence student decisions, such as academic support services, campus resources, and student engagement. Additionally, future studies can investigate the effectiveness of different strategies for supporting students, such as mentoring programs, academic advising, and career counseling.

Conclusion

In conclusion, this study provides a comprehensive understanding of the factors that influence student decisions in higher education. By using a data-based approach, tertiary institutions can make more informed and strategic decisions in supporting students to continue to complete their education. The findings of this study have important implications for higher education institutions, and provide a foundation for further research in this area.

References

  • [1] C4.5 algorithm
  • [2] Decision Tree
  • [3] Rule Model
  • [4] Higher Education
  • [5] Student Decisions
  • [6] Parental Economic Data
  • [7] Family Support
  • [8] Facilities Provided
  • [9] Motivation
  • [10] Self-Confidence
  • [11] Service Quality
  • [12] Tertiary Institutions

Appendix

This appendix provides additional information on the methodology and results of the study.

Methodology

The C4.5 algorithm was used to produce a decision tree from the data. The decision tree was then used to build a model of rule that shows the link between the cumulative achievement index (GPA) of students with a number of factors, such as parental economic data, family support, facilities provided, motivation, self-confidence, and service quality in tertiary institutions.

Results

The analysis shows that the six predictor variables contributed significantly, reaching 80.2% in predicting the potential of students to resign or move. Of the six variables, the economic factor of parents is the most dominant variable with a contribution of 58.3%. This indicates that the economic conditions of parents have a major influence on student decisions, where students from a weaker economic background tend to be more risky to resign.

Discussion

The findings of this study have important implications for higher education institutions. By understanding the factors that influence student decisions, universities can design better strategies to support students, such as providing consulting services for students who experience difficulties, as well as improving existing facilities. The application of this model is expected to provide a deeper insight about the factors that influence students' decisions to resign, so that preventive measures can be carried out more on target.

Conclusion

Q: What is the main objective of this study?

A: The main objective of this study is to identify the factors that influence student decisions to resign or move from college, with a focus on the data of STMIK Microscil students.

Q: What methodology was used in this study?

A: The C4.5 algorithm was used to produce a decision tree from the data. The decision tree was then used to build a model of rule that shows the link between the cumulative achievement index (GPA) of students with a number of factors, such as parental economic data, family support, facilities provided, motivation, self-confidence, and service quality in tertiary institutions.

Q: What are the six predictor variables that contributed significantly to the study?

A: The six predictor variables that contributed significantly to the study are:

  1. Parental economic data
  2. Family support
  3. Facilities provided
  4. Motivation
  5. Self-confidence
  6. Service quality in tertiary institutions

Q: Which of the six predictor variables is the most dominant variable?

A: The economic factor of parents is the most dominant variable, with a contribution of 58.3%. This indicates that the economic conditions of parents have a major influence on student decisions, where students from a weaker economic background tend to be more risky to resign.

Q: What are the implications of this study for higher education institutions?

A: The findings of this study have important implications for higher education institutions. By understanding the factors that influence student decisions, universities can design better strategies to support students, such as providing consulting services for students who experience difficulties, as well as improving existing facilities.

Q: What are the limitations of this study?

A: This study has several limitations that should be noted. First, the data used in this study was collected from a single institution, which may limit the generalizability of the findings. Second, the study only examined the factors that influence student decisions to resign or move, and did not explore other outcomes, such as academic success or student satisfaction. Finally, the study relied on self-reported data from students, which may be subject to biases and limitations.

Q: What are the future directions for research in this area?

A: Future studies can build on this research by exploring other factors that influence student decisions, such as academic support services, campus resources, and student engagement. Additionally, future studies can investigate the effectiveness of different strategies for supporting students, such as mentoring programs, academic advising, and career counseling.

Q: What are the practical implications of this study for higher education institutions?

A: The practical implications of this study for higher education institutions are:

  1. Designing better strategies to support students, such as providing consulting services for students who experience difficulties, as well as improving existing facilities.
  2. Improving the quality of service provided by universities, such as academic advising, career counseling, and mental health services.
  3. Providing more support for students from weaker economic backgrounds, such as scholarships, financial aid, and other forms of assistance.

Q: What are the theoretical implications of this study for higher education research?

A: The theoretical implications of this study for higher education research are:

  1. Providing a deeper understanding of the factors that influence student decisions in higher education.
  2. Highlighting the importance of considering the economic conditions of parents in understanding student decisions.
  3. Emphasizing the need for higher education institutions to design more effective strategies to support students, particularly those from weaker economic backgrounds.

Q: What are the policy implications of this study for higher education institutions?

A: The policy implications of this study for higher education institutions are:

  1. Developing policies to support students from weaker economic backgrounds, such as scholarships, financial aid, and other forms of assistance.
  2. Improving the quality of service provided by universities, such as academic advising, career counseling, and mental health services.
  3. Designing more effective strategies to support students, such as providing consulting services for students who experience difficulties, as well as improving existing facilities.