Mining Educational Data Using Classification to Decrease Dropout Rate of Students

In the last two decades, number of Higher Education Institutions (HEI) grows rapidly in India. Since most of the institutions are opened in private mode therefore, a cut throat competition rises among these institutions while attracting the student to got admission. This is the reason for institutions to focus on the strength of students not on the quality of education. This paper presents a data mining application to generate predictive models for engineering student's dropout management. Given new records of incoming students, the predictive model can produce short accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.Comment: 5 pages. arXiv admin note: substantial text overlap with arXiv:1203.2987, arXiv:1203.3832, arXiv:1202.4815, arXiv:1201.3418, arXiv:1201.3417, and with arXiv:1002.1144 by other author

Mining Educational Data to Analyze Students' Performance

The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.Comment: 7 pages. arXiv admin note: substantial text overlap with arXiv:1002.1144 by other authors without attributio

Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student's performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student's data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.Comment: 6 pages, 3 Figures. arXiv admin note: substantial text overlap with arXiv:1202.481

Data Mining : A prediction of performer or underperformer using classification

Now a day's students have a large set of data having precious information hidden. Data mining technique can help to find this hidden information. In this paper, data mining techniques name Byes classification method is used on these data to help an institution. Institutions can find those students who are consistently perform well. This study will help to institution reduce the drop put ratio to a significant level and improve the performance level of the institution.Comment: 5 pages, 1 figur

A comparison algorithm to check LTSA Layer 1 and SCORM compliance in e-Learning sites

The success of e-Learning is largely dependent on the impact of its multimedia aided learning content on the learner over the hyper media. The e-Learning portals with different proportion of multimedia elements have different impact on the learner, as there is lack of standardization. The Learning Technology System Architecture (LTSA) Layer 1 deals with the effect of environment on the learner. From an information technology perspective it specifies learner interaction from the environment to the learner via multimedia content. Sharable Content Object Reference Model (SCROM) is a collection of standards and specifications for content of web-based e-learning and specifies how JavaScript API can be used to integrate content development. In this paper an examination is made on the design features of interactive multimedia components of the learning packages by creating an algorithm which will give a comparative study of multimedia component used by different learning packages. The resultant graph as output helps us to analysis to what extent any LMS compliance LTSA layer 1 and SCORM specification

Mining Education Data to Predict Student's Retention: A comparative Study

The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student retention data.Comment: 5 pages. arXiv admin note: substantial text overlap with arXiv:1202.481

Data Mining Application to Attract Students in HEI

In the last two decades, number of Higher Education Institutions (HEI) grows in leaps and bounds. This causes a cut throat competition among these institutions while attracting the student get admission in these institutions. To make reach up to the students institution makes effort of advertisement. Similarly developing and developed both type of institution launch several services also to attract students. Most of the institutions are opened in self finance mode. So all time they feel short hand in expenditure. Now a day a number of advertisement methods are available. So it is difficult for an institution to make advertisement through all modes and launch all services at the same time due to different constraints. In this paper we use support and confidence method to find out the best way of advertisement.Comment: 6 page

Modeling of scalar dissipation rates in flamelet models for low temperature combustion engine simulations

The flamelet approach offers a viable framework for combustion modeling of homogeneous charge compression ignition (HCCI) engines under stratified mixture conditions. Scalar dissipation rate acts as a key parameter in flamelet-based combustion models which connects the physical mixing space to the reactive space. The aim of this paper is to gain fundamental insights into turbulent mixing in low temperature combustion (LTC) engines and investigate the modeling of scalar dissipation rate. Three direct numerical simulation (DNS) test cases of two-dimensional turbulent auto-ignition of a hydrogen-air mixture with different correlations of temperature and mixture fraction are considered, which are representative of different ignition regimes. The existing models of mean and conditional scalar dissipation rates, and probability density functions (PDFs) of mixture fraction and total enthalpy are a priori validated against the DNS data

Data Mining: A prediction for performance improvement using classification

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.Comment: 5 pages. arXiv admin note: substantial text overlap with arXiv:1002.1144 by other authors without attributio

Mobile Ad Hoc Networks: A Comparative Study of QoS Routing Protocols

This Article presents a thorough overview of QoS routing metrics, resources and factors affecting performance of QoS routing protocols. The relative strength, weakness, and applicability of existing QoS routing protocols are also studied and compared. QoS routing protocols are classified according to the QoS metrics used type of QoS guarantee assured.Comment: 5 page