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ADA009922

ADA009922

ADA009922

ADA009922

ADA009922

ADA009922

Students’ performance analysis basically consists of determining the factors influencing the performance and how it will give impact towards success. It will help us to understand students’ behavior and how to improve their academic performance. The efficiency of this analysis depends on the information given by the user through learning management system (LMS). In order to improve the information, we have applied algorithms on the dataset and prepared a model by using Tableau and RapidMiner. Cross-validation with decision tree also has been applied on datasets. This can help in evaluating statistical computational results into a generalized data set. Based on the calculation of data mining, it can analyze that our model is quite stable since it has high accuracy with lower standard deviation. So, the processes like testing and validation, applying the model and decision tree on RapidMiner generates the output in a specific form. The result shows that the percentage of students who are absence is better than students who are absence more than 7 days. At last, a model is prepared, and it can help the schools, students, and the parents in adapting appropriate measures to ensure the success of students at school.

Students’ performance analysis basically consists of determining the factors influencing the performance and how it will give impact towards success. It will help us to understand students’ behavior and how to improve their academic performance. The efficiency of this analysis depends on the information given by the user through learning management system (LMS). In order to improve the information, we have applied algorithms on the dataset and prepared a model by using Tableau and RapidMiner. Cross-validation with decision tree also has been applied on datasets. This can help in evaluating statistical computational results into a generalized data set. Based on the calculation of data mining, it can analyze that our model is quite stable since it has high accuracy with lower standard deviation. So, the processes like testing and validation, applying the model and decision tree on RapidMiner generates the output in a specific form. The result shows that the percentage of students who are absence is better than students who are absence more than 7 days. At last, a model is prepared, and it can help the schools, students, and the parents in adapting appropriate measures to ensure the success of students at school.

This dissertation addresses the prediction of college student academic performance and persistence of Jefferson County Public School graduates at the University of Louisville. Using multiple regression and discriminant analysis, the research design assesses the predictive potential of six Commonwealth Accountability Testing System (CATS) scores and five college preparation variables beyond the predictive contribution of control variables established by the literature. The control variables are high school GPA, ACT score, race, sex, and socioeconomic status. The criterion variables are first semester college GPA, first year college GPA, and persistence to the sophomore year. High school GPA and socioeconomic status were statistically significant contributors to each prediction equation. CATS scores accounted for a significant proportion of variance in first year college GPA with control variables already in the equation.

This dissertation addresses the prediction of college student academic performance and persistence of Jefferson County Public School graduates at the University of Louisville. Using multiple regression and discriminant analysis, the research design assesses the predictive potential of six Commonwealth Accountability Testing System (CATS) scores and five college preparation variables beyond the predictive contribution of control variables established by the literature. The control variables are high school GPA, ACT score, race, sex, and socioeconomic status. The criterion variables are first semester college GPA, first year college GPA, and persistence to the sophomore year. High school GPA and socioeconomic status were statistically significant contributors to each prediction equation. CATS scores accounted for a significant proportion of variance in first year college GPA with control variables already in the equation.

This dissertation addresses the prediction of college student academic performance and persistence of Jefferson County Public School graduates at the University of Louisville. Using multiple regression and discriminant analysis, the research design assesses the predictive potential of six Commonwealth Accountability Testing System (CATS) scores and five college preparation variables beyond the predictive contribution of control variables established by the literature. The control variables are high school GPA, ACT score, race, sex, and socioeconomic status. The criterion variables are first semester college GPA, first year college GPA, and persistence to the sophomore year. High school GPA and socioeconomic status were statistically significant contributors to each prediction equation. CATS scores accounted for a significant proportion of variance in first year college GPA with control variables already in the equation.

A study was conducted to assess the academic performance of San Mateo County Community College District (SMCCD) students after transfer to a University of California (UC) or California State University (CSU) campus; to compare study results with previous research on College of San Mateo transfer students; and to determine the variables most predictive of academic success at a four-year institution. The study sample of 3,139 SMCCD students was drawn from students who transferred to a UC or CSU campus between fall 1974 and spring 1980. Study findings, based on data obtained from academic performance reports, included the following: (1) the CSU cumulative grade point averages (GPA's) earned by SMCCD transfers were essentially equivalent to those of all CSU undergraduates and all community college transfer students; (2) the CSU 3-year transfer rate of SMCCD transfers was significantly higher than the 5-year graduation rate of CSU freshmen and the 3-year graduation rate of all community college transfers; (3) SMCCD transfers to the UC system experienced some sort of transfer shock as evidenced by a first-year post-transfer drop in GPA of approximately .40 grade points; (4) GPA and graduation rates of SMCCD transfers to UC were higher than those attained by transfers to CSU; and (5) studies conducted since 1932 indicated that the large majority of SMCCD transfers were academically successful after transfer. (LAL)