Application of Predictive Analytics in Intelligent Course Recommendation

AbstractStudents who pursue admission to colleges usually experience a difficulty to select a course. In this paper, we propose a course recommendation system to find out the courses which are apt for a student pursuing admission to the college. Typically, the prediction is based on the career goal or the present job trend. In this system proposed, the prediction is formulated based on the grades acquired by the student in twelfth standard; which is taken as a sign of the previous academic performance and cognitive ability of the student. A model is generated from the legacy data or data from the students who have completed the course successfully. This model is used for predicting the courses for new students. The idea behind this approach is that when a student with specific set of skills is successful in a course then another student with similar set of skills will have a higher success probability in the said course

Aplikasi sistem pendukung keputusan pengelolaan sumber daya Perguruan Tinggi untuk menentukan jumlah mahasiswa=A decision support system application for higher education resource management to determine number of student

ABSTRACT: A Decision Support System application of higher education resource management has been built to determine numbers of students being accepted in university. This system is used to facilitate, and help higher education institution managers to make decisions in connection with resource management. The model of these system is based on the operational model of the higher education resource management, that considerates both the number of human resources, and other resources belonging to the university to accomodate in new student admission. The experimental result showed that the system is capable helping university managers for making decision the number of student admission. By taking the higher education resources into account in the model, the decision making has been proved to be more accurate in determining the number of students. Key words decision support system, resources, student admissio

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

Validity of the Expert System based VIT Model (Vocational Interest Test)

The purpose of this study was to produce avalid expert system-based VIT (Vocational Interest Test)model. Recruitment or student admission plays animportant role for the success of a vocational education.Therefore, using a valid student admission tool isimportant which will result good and quality students,and this can be seen in the admission process through theright means and tools. The novelty of the productdeveloped was to be able to calibrate vocational interestinstruments developed from Holland's theory withinformation technology and knowledge based system. Thisresearch product is in accordance with the VocationalEducation Spectrum which consists of 9 Areas ofExpertise, 48 Expertise Programs and 142 ExpertCompetencies that are in line with 21st CenturyCompetencies to produce a vocational interest test modeland a vocational interest software based on innovativeexpert system and in supporting the right decision making(Decision Support System). The type of research used isResearch and Development (R & D) using the Four-D(4D) model. To produce valid products, expert validity isused. Based on the data analysis, the results of the studywere obtained: (a) Book of Vocational Interest Testmodel, (b) Vocational Interest Test Software Product, (c)Application Usage Handbook, (d) Vocational Interest TestSocialization Handbook that has fulfilled the valid termsand conditions

Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions

A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. However, this approach may be subject to bias in test scores and selection bias in test-taking with recent trends toward test-optional admission. We explore a machine learning-based approach to replace the role of standardized tests in subset generation while taking into account a wide range of factors extracted from student applications to support a more holistic review. We evaluate the approach on data from an undergraduate admission office at a selective US institution (13,248 applications). We find that a prediction model trained on past admission data outperforms an SAT-based heuristic and matches the demographic composition of the last admitted class. We discuss the risks and opportunities for how such a learned model could be leveraged to support human decision-making in college admissions.Comment: In Proceedings of the ACM Conference on Learning at Scale (L@S) 202

A Revealed Preference Ranking of U.S. Colleges and Universities

We show how to construct a ranking of U.S. undergraduate programs based on students' revealed preferences. We construct examples of national and regional rankings, using hand-collected data on 3,240 high- achieving students. Our statistical model extends models used for ranking players in tournaments, such as chess or tennis. When a student makes his matriculation decision among colleges that have admitted him, he chooses which college "wins" in head-to-head competition. The model exploits the information contained in thousands of these wins and losses. Our method produces a ranking that would be difficult for a college to manipulate. In contrast, it is easy to manipulate the matriculation rate and the admission rate, which are the common measures of preference that receive substantial weight in highly publicized college rating systems. If our ranking were used in place of these measures, the pressure on colleges to practice strategic admissions would be relieved.

Analyzing the Fundamental Aspects and Developing a Forecasting Model to Enhance the Student Admission and Enrollment System of MSOM Program

A forecasting model, associated with predictive analysis, is an elementary requirement for academic leaders to plan course requirements. The M.S. in Operations Management (MSOM) program at the University of Arkansas desires to understand future student enrollment more accurately. The available literature shows that there is an absence of forecasting models based on quantitative, qualitative and predictive analysis. This study develops a combined forecasting model focusing on three admission stages. The research uses simple regression, Delphi analysis, Analysis of Variance (ANOVA), and classification tree system to develop the models. It predicts that 272, 173, and 136 new students will apply, matriculate and enroll in the MSOM program during Fall 2017, respectively. In addition, the predictive analysis reveals that 45% of applicants do not enroll in the program. The tuition fee of the program is negatively associated with the student enrollment and significantly influences individuals’ decision. Moreover, the students’ enrollment in the program is distributed over 6 semesters after matriculation. The classification tree classifies that 61% of applicants with non-military status will join the program. Based on the outcomes, this study proposes a set of recommendations to improve the admission process

Student-optimal interdistrict school choice : district-based versus school-based admissions

Hafalir, Kojima and Yenmez (2022) introduce a model of interdistrict school choice: each district consists of a set of schools and the district’s admission rule places applicants to the schools in the district. We show that any district’s admission rule satisfying their assumptions is uniquely rationalized by a collection of schools’ choice functions satisfying substitutability and acceptance. We then establish that all students weakly prefer the outcome of the cumulative offer process (COP) under the school-based admissions to the outcome under the district-based admissions. This has the implication that if students prefer the interdistrict outcome for the district-based admissions to the intradistrict outcome, then all students are weakly better off under the school-based admissions compared to either of these outcomes. Therefore, for student-optimal interdistrict school choice the introduction of district admission rules hurts students and it suffices to endow schools with usual choice priorities (if students’ welfare is more important than districts’ policy goals) and to (de)centralize district admissions by letting schools choose

Application to Predict The Number of Applicants for New Students With a Time Series Model

Problems that will be faced by higher education institutions, especially in the phase of new student admissions. Careful planning and strategies are needed in dealing with the process of admission of new students. The data for planning can be obtained using the forecasting method. The time series forecasting model is used to get forecasting data. Forecasting data is used for the decision making process. The data of new student admissions obtained is 3-period data (2017 - 2019). The data obtained is stationary. Because the data is stationary, the data does not need differentiation. The data obtained also has a sufficient correlation value, and has a loop on the 7th lag. Before making an application, a test is performed to find a time series model that is suitable for admission data. The tested models are the ARIMA model and the AutoRegression model. In testing the forecast timespan, the ARIMA model gets a smaller error value in almost all tests. In the Cross-validation method, the ARIMA Model also gets a smaller RMSECV or MAECV value than the AR model. The ARIMA model was chosen to be implemented into the application. The auto_arima algorithm is used so that applications can adapt to different data. The ARIMA model is implemented into a prediction application using the Python programming language. Application development uses Django as a web-based web application framework. Bootstrap is used to create application interfaces

Analyzing and Forecasting Admission data using Time Series Model

Problems that will be faced by higher education institutions, especially in the phase of new student admissions. Careful planning and strategies are needed in dealing with the process of admission of new students. The data for planning can be obtained using the forecasting method. The time series forecasting model is used to get forecasting data. Forecasting data is used for the decision making process. The data of new student admissions obtained is 3-period data (2017 - 2019). The data obtained is stationary. Because the data is stationary, the data does not need differentiation. The data obtained also has a sufficient correlation value, and has a loop on the 7th lag. Before making an application, a test is performed to find a time series model that is suitable for admission data. The tested models are the ARIMA model and the Autoregression model. In testing the forecast timespan, the ARIMA model gets a smaller error value in almost all tests. In the Cross-validation method, the ARIMA Model also gets a smaller RMSECV or MAECV value than the AR model. The ARIMA model was chosen to be implemented into the application. The auto_arima algorithm is used so that applications can adapt to different data. The ARIMA model is implemented into a prediction application using the Python programming language. Application development uses Django as a web-based web application framework. Bootstrap is used to create application interfaces. the result from forecasted data is acceptable for short period