The teaching and learning process in educational institutions is a vital element that requires constant monitoring and analysis for enhancement. Accreditation agencies around the world have set criteria for teaching and learning, making it essential to ensure that students are progressing well. Learning analytics and Educational Data Mining (EDM) are relatively new techniques used to collect large volumes of data about students in an educational setting to predict their future performance and identify risks. EDM develops methods to analyze the data produced by students in educational settings, helping educators understand their students' needs better. This research aims to collect a vast amount of data on student performance in assessments to discover those at risk of failing the final exam. By analyzing this data, educators can gain insights into how students are progressing and identify those who need additional support. The proposed research will utilize the results of the analysis to provide recommendations for improvement, including identifying at-risk students. The study will focus on collecting and analyzing assessment results at the course level to enhance the teaching and learning process. Two feature selection techniques, information gain, and gain ratio were discussed in detail with gain ratio being adopted as the feature selection technique. In this paper, decision tree classification technique was used on student assessment outcome databases to identify those who need support to perform well in their final exams. This approach can help teachers/instructors find out how well their students are doing while also identifying areas where they may need additional support. Overall, this research aims to improve student retention rates by enhancing the teaching and learning process through data-driven insights provided by EDM techniques such as decision tree classification. By identifying at-risk students early on, educators can provide targeted support that helps them succeed academically while improving overall student outcomes.
Teachers and schools need to keep an eye on how well students are doing so they can help them learn better. There are special ways to collect a lot of information about students called learning analytics and Educational Data Mining (EDM). This helps teachers understand what their students need and who might be struggling. A study is being done to collect lots of information about how well students are doing on tests so that teachers can help those who might fail. This research will use EDM techniques like decision tree classification to help teachers know how their students are doing and where they need extra help.
Definitions
- Teaching and learning process: the way that teachers teach and students learn in school
- Accreditation agencies: groups that check if schools are teaching well enough
- Learning analytics: collecting information about how well students are learning using technology
- Educational Data Mining (EDM): using computers to find patterns in student data
- Assessments: tests or exams given by teachers to see how much a student has learned
Enhancing the Teaching and Learning Process with Educational Data Mining
Educational institutions around the world are constantly striving to improve the teaching and learning process. Accreditation agencies have set criteria for teaching and learning, making it essential to ensure that students are progressing well. To this end, Learning Analytics and Educational Data Mining (EDM) have been developed as relatively new techniques used to collect large volumes of data about students in an educational setting. This data can then be used to predict future performance and identify risks.
This article will discuss a research paper that aims to use EDM techniques such as decision tree classification on student assessment outcome databases in order to identify those at risk of failing their final exams. By analyzing this data, educators can gain insights into how students are progressing and provide targeted support which helps them succeed academically while improving overall student outcomes.
Background
The proposed research focuses on collecting and analyzing assessment results at the course level in order to enhance the teaching and learning process. Two feature selection techniques were discussed: information gain, and gain ratio; with gain ratio being adopted as the feature selection technique for this study due its ability to reduce overfitting when compared with other methods such as entropy-based measures or correlation-based measures.
Methodology
The decision tree classification technique was utilized on student assessment outcome databases in order to identify those who need additional support so they can perform well in their final exams. The methodology involved building a model using historical data from past assessments which was then tested against current assessment results in order to determine accuracy levels of prediction for each individual student's performance on their final exam.
Results
The results showed that by utilizing EDM techniques such as decision tree classification, educators could accurately predict which students would require additional support before taking their final exams based on their prior performance during assessments throughout the course duration. This approach could help teachers/instructors find out how well their students are doing while also identifying areas where they may need additional help or guidance from instructors or peers alike in order for them reach success academically by passing their finals exams successfully .
Conclusion
Overall, this research provides valuable insight into how EDM techniques such as decision tree classification can be used effectively within educational settings so that educators can better understand their students' needs while providing targeted support accordingly which could lead towards improved retention rates among learners due increased academic success achieved through enhanced teaching & learning processes driven by data-driven insights provided by EDM technologies like Decision Tree Classification algorithms .
Teaching and Learning process of an educational institution needs to be monitored and effectively analysed for enhancement. Teaching and Learning is a vital element for an educational institution. It is also one of the criteria set by majority of the Accreditation Agencies around the world. Learning analytics and Educational Data Mining are relatively new. Learning analytics refers to the collection of large volume of data about students in an educational setting and to analyse the data to predict the students' future performance and identify risk. Educational Data Mining (EDM) is develops methods to analyse the data produced by the students in educational settings and these methods helps to understand the students and the setting where they learn. Aim of this research is to collect large collection of data on students' performance in their assessment to discover the students at risk of failing the final exam. This analysis will help to understand how the students are progressing. The proposed research aimed to utilize the result of the analysis to identify the students at risk and provide recommendations for improvement. The proposed research aimed to collect and analyse the result of the assessment at the course level to enhance the teaching and learning process. The research aimed to discuss two feature selection techniques namely information gain and gain ratio and adopted to use gain ratio as the feature selection technique.
