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Correlation Between Text Book Usage and Academic Performance of Student in Higher Education Using ‘R’
Abstract
In this paper authors try to focus one of the key factors to improve student performance i.e. text book usage. In this study authors explore association between students final Semester Grades and text books usage. Scope of study is limited to only MCA program of Gujarat Technological University students and the text book of subjects that are not issued for longer period by institute library. Authors also take assumption that books are not purchased by students as they are too costly. The aim of this paper is to use correlation and regression methods to analyze the dataset containing 60 students of MCA semester III students and try to find out that does the text book usage by student affects the academic performance. The primary result of experimental analysis shows that if the text book usage increases, performance of students also increases. The results are important for the teachers to motivate students towards library as well as institutions to focus on library utilization.
... Implementing association rules happened in the education field. There existed an analysis of the relationship between the use of books and student grades [4]. Another example is analyzing the relationship between exercise styles and daily activities that should be done together. ...
Thailand has entered the aging society since 2005 and is now stepping towards a complete aging society in 2021. The elderly’s health problems are difficult to avoid.The research aims to analyze the most influential and most minor powerful factors on the elderly’s health status. The association analysis approach is applied to discover the relationship between various factors that affect good health. 17,804 Thai elderly data from the National Statistical Office (NSO) of Thailand were employed. One hundred ninety-nine features were taken into the association rules mining modeling. FP-growth algorithm is chosen to find the most and the least factors affecting the health of the elderly. The interesting relationships among those factors are also disclosed. As a result, the feature that demonstrates good health is performing daily activities independently. Additionally, several features that are less likely to appear in healthy seniors are hard work, working at risk, living in an urban society, living with unfamiliar caregivers, having a few children, and lacking sufficient government medical care. The knowledge gained from the findings can be considered for preparing health care for the aging society.
... Implementing association rules happened in the education field. There existed an analysis of the relationship between the use of books and student grades [4]. Another example is analyzing the relationship between exercise styles and daily activities that should be done together. ...
Thailand has entered the aging society since 2005 and is now stepping towards a complete aging society in 2021. The elderly’s health problems are difficult to avoid.The research aims to analyze the most influential and most minor powerful factors on the elderly’s health status. The association analysis approach is applied to discover the relationship between various factors that affect good health. 17,804 Thai elderly data from the National Statistical Office (NSO) of Thailand were employed. One hundred ninety-nine features were taken into the association rules mining modeling. FP-growth algorithm is chosen to find the most and the least factors affecting the health of the elderly. The interesting relationships among those factors are also disclosed. As a result, the feature that demonstrates good health is performing daily activities independently. Additionally, several features that are less likely to appear in healthy seniors are hard work, working at risk, living in an urban society, living with unfamiliar caregivers, having a few children, and lacking sufficient government medical care. The knowledge gained from the findings can be considered for preparing health care for the aging society.
Data Mining (DM) is a growing area of research in India. DM was applied in various sectors such as Industry, Education, Medical, Marketing etc. In India number of private university grown exponentially, so there is a strong need of outcome based education. In this paper author applied DM in Higher education Sector. Author selects a data sample of 126 out of 300 students who get admitted in Masters of Computer Applications (MCA) program of Gujarat Technological University, Ahmadabad. Author collect three factors from respondents- HSC, Graduate stream and MCA awarded. The objective of study is to find that is there is any relationship between independent (HSC, Graduate stream) factor and dependent (MCA awarded) using Chi-Square test of independence. The results of survey showed that there is no relationship between Graduation & HSC education with awarded MCA degree.
Students have different levels of motivation, different attitudes about teaching and learning, and different responses to specific classroom environments and instructional practices. The more thoroughly instructors understand the differences, the better chance they have of meeting the diverse learning needs of all of their students. Three categories of diversity that have been shown to have important implications for teaching and learning are differences in students' learning styles (characteristic ways of taking in and processing information), approaches to learning (surface, deep, and strategic), and intellectual development levels (attitudes about the nature of knowledge and how it should be acquired and evaluated). This article reviews models that have been developed for each of these categories, outlines their pedagogical implications, and suggests areas for further study.
Student performance in university courses is of great concern to the higher education managements where several factors may affect the performance. This paper is an attempt to use the data mining processes, particularly classification, to help in enhancing the quality of the higher educational system by evaluating student data to study the main attributes that may affect the student performance in courses. For this purpose, the CRISP framework for data mining is used for mining student related academic data. The classification rule generation process is based on the decision tree as a classification method where the generated rules are studied and evaluated. A system that facilitates the use of the generated rules is built which allows students to predict the final grade in a course under study.
The Myers-Briggs Type Indicator ® (MBTI) was administered to a group of 116 students taking the introductory chemical engineering course at North Carolina State University. That course and four subsequent chemical engineering courses were taught in a manner that emphasized active and cooperative learning and inductive presentation of course material. Type differences in various academic performance measures and attitudes were noted as the students progressed through the curriculum. The observations were generally consistent with the predictions of type theory, and the experimental instructional approach appeared to improve the performance of MBTI types (extraverts, sensors, and feelers) found in previous studies to be disadvantaged in the engineering curriculum. The conclusion is that the MBTI is a useful tool for helping engineering instructors and advisors to understand their students and to design instruction that can benefit students of all types.
Educational data mining concerns with developing methods for discovering knowledge from data that come from educational environment. In this paper we used educational data mining to analyze learning behavior. In our case study, we collected students' data from DataBase course. After preprocessing the data, we applied data mining techniques to discover association, classification, clustering and outlier detection rules. In each of these four tasks, we extracted knowledge that describes students' behavior.
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.
From ancient period in India, educational institution embarked to use class
room teaching. Where a teacher explains the material and students understand
and learn the lesson. There is no absolute scale for measuring knowledge but
examination score is one scale which shows the performance indicator of
students. So it is important that appropriate material is taught but it is
vital that while teaching which language is chosen, class notes must be
prepared and attendance. This study analyses the impact of language on the
presence of students in class room. The main idea is to find out the support,
confidence and interestingness level for appropriate language and attendance in
the classroom. For this purpose association rule is used.
Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set/ data warehouse. In this paper data mining technique named k-means clustering is applied to analyze student's learning behavior. Here K-means clustering method is used to discover knowledge that come from educational environment. The students evaluation factors like class quizzes mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students.
We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. From the 10 cases we find that supervised methods prevail while unsupervised techniques play a supporting role. Further, significant domain knowledge is generally required to achieve a completed solution. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single "aha moments" of knowledge ("nugget") discovery.