Analisis Faktor–faktor Yang Mempengaruhi Prestasi Akademik Mahasiswa Dengan Menggunakan Metode Analisis Diskriminan

Tujuan dari penelitian empiris ini dilakukan untuk mengetahui faktor-faktor yang mempengaruhi prestasi akademik mahasiswa di Fakultas Teknologi Informasi - Perbanas Institite Jakarta, sebagai studi kasus dalam penelitian ini. Metode Analisis diskriminan sebagai metode analisis statistik utama yang digunakan dalam penelitian ini. Hasil analisis dalam penelitian ini, membentuk fungsi diskriminan : Y = - 4.209 + 0.283x1 + 2.147x3. Hasil validasi > 50% menunjukkan bahwa fungsi diskriminan yang terbentuk jika tidak tepat untuk mengklasifikasikan Prestasi Akademik Mahasiswa. Mahasiswa Prestasi Akademik pada siswa studi kasus Fakultas Teknologi Informasi Perbanas Institute, menggunakan analisis diskriminan dipengaruhi oleh faktor lama belajar dan faktor lingkungan keluarga mahasiswa

Profile Analysis of Students’ Academic Performance in Ghanaian Polytechnics: The Case of Bolgatanga Polytechnic

The main purpose of the study was to examine the changes in the average academic performance of students over time and how these changes are related to student segments, choice of program and the entry qualification of the student. The cohort of students admitted into Bolgatanga Polytechnic during the 2009/2010 academic year formed the sample and only students who successfully completed were used. Data on grade point averages (GPA), demographic and socio-economic features from 131 Female and 271 Male students was obtained from the Examinations Department and the Student Affairs Unit of Bolgatanga Polytechnic. The multivariate analysis of variance technique was used to complement the Hotelling’s T2 to compare the mean vectors of k random samples for significant difference among the levels of Departments, Entry Requirements and Gender. Profile analysis of the data indicated at 5% level of significance that the average GPA scores of the Male and Female students were parallel, level and deviated significantly from flatness whereas the various Departments had their own subject-specific mean response. The Entry Qualifications of students admitted into the Polytechnic were not similar. Keywords: Profile Analysis, Academic Performance, MANOVA, Ghan

A Logistic Regression Model of Students’ Academic Performance in University of Maiduguri, Maiduguri, Nigeria

A Logistic regression model was used to investigate the factors that influence student’s performance in MTH101 (Element of Calculus) course. The data used were the grades of the (200-400) level students in MTH101 course which was collected from the department’s examination record and also by questionnaire administered to the students. The data analysis shows that the factors that significantly influence academic performance in MTH101 course are G.P.A (student’s academic Performance), course challenge (student’s attitude related to the course) and concept in the course relate to real world experience (student’s motivation). It was therefore recommended that intervention strategies to bring about improvement in the course should be focused on how to enhance academic performance, change course related attitudinal problems and provide sufficient motivation.   Key Words: ACADEMIC PERFORMANCE, LOGISTIC REGRESSION, AT RISK STUDENTS, ODDS RATIO, LEARNING IMPROVEMENT STRATEGY, COUNSELING, ATTITUDINAL PROBLEMS AND MOTIVATION

Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments

In this work, it is shown that student access time series generated from Moodle log files contain information sufficient for successful prediction of student final results in blended learning courses. It is also shown that if time series is transformed into frequency domain, using discrete Fourier transforms (DFT), the information contained in it will be preserved. Hence, resulting periodogram and its DFT coefficients can be used for generating student performance models with the algorithms commonly used for that purposes. The amount of data extracted from log files, especially for lengthy courses, can be huge. Nevertheless, by using DFT, drastic compression of data is possible. It is experimentally shown, by means of several commonly used modelling algorithms, that if in average all but 5–10% of most intensive and most frequently used DFT coefficients are removed from datasets, the modelling with the remained data will result with the increase of the model accuracy. Resulting accuracy of the calculated models is in accordance with results for student performance models calculated for different dataset types reported in literature. The advantage of this approach is its applicability because the data are automatically collected in Moodle logs

Modeling student success through data mining on blended learning enironment data using frequency domain time series analysis

Svaki pristup studenta sistemu za upravljanje učenjem (engl. learning management system, LMS) generira podatke o tome tko je pristupio, kada, s koje IP (engl. Internet protocol, IP) adrese i koja je aktivnost provedena. Na taj način prikuplja se mnoštvo podataka o studentima i to tako da se studente pri tom ne ometa u procesu učenja. Dostupni podaci mogu se iskoristiti i tako da se pristupi studenata određenom predmetu na LMS-u promatraju kao vremenski nizovi. U ovom radu pokazano je da promatrani vremenski nizovi nose informaciju o ponašanju studenata koja je indikativna za njihov uspjeh na završnom ispitu. Primjenom Fourierove transformacije (FT) na kreirane vremenske nizove, tj. prebacivanjem vremenskih nizova u frekvencijsku domenu, otvorena je mogućnost da se količina podataka smanji, a proces obrade podataka postane operativniji. Drugi razlog primjene FT je otkriti skrivene periodičnosti koje su ključ modeliranja i predikcije, a treći razlog je smanjenje šuma te postizanje bolje točnosti predikcije uspjeha studenata. Hipoteze ovog rada ispitane su na skupovima podataka o studentima upisanim na 2 fakulteta, na ukupno 4 različita predmeta tijekom dvije akademske godine. Uzorak se sastojao od podataka o približno 1400 studenata. U ovom istraživanju je pokazano da je moguće kreirati model predikcije uspjeha studenata rudarenjem podataka iz hibridnog okruženja za učenje primjenom analize vremenskih nizova pristupa studenata LMS-u u frekvencijskoj domeni te da takav model postiže točnost predikcije koja je jednaka ili veća od točnosti predikcije pomoću sličnih modela opisanih u literaturi. Razvijena je tehnika za sažimanje vremenskih nizova pristupa studenata LMS-u u frekvencijskoj domeni uz uklanjanje šuma, što znači da je korištenjem smanjenog broja koeficijenata postignuta bolja točnost predikcije u odnosu na modele temeljene na punom skupu podataka. Nadalje, pokazano je da je moguće postići točnost predikcije uspješnosti koja je veća od 75%, a temeljem podataka o prve 2/3 ukupnog trajanja predmeta. Temeljni doprinos rada je primjena spektralne analize, odnosno analize vremenskih nizova u frekvencijskoj domeni na skup podataka iz hibridnog okruženja za učenje.Each student log to the learning management system (LMS) generates data on who accessed the LMS, when, from which IP address, and which activity is performed. It is the way to collect the large amount of data on students and not to disturb the process of learning while the data are collected. The available data could be also used in the way that student logs to a certain course on the LMS are regarded as time series. In this paper it is proven that the observed time series carry the information on student behavior which is indicative for the final exam success. By applying the Fourier transform (FT) to the created time series, i.e. by switching time series to the frequency domain, the opportunity occurs to reduce the amount of data and for the data processing to become more operational. The second reason to apply FT is to reveal the hidden periodicities which are the key to modeling and prediction, and the third reason is to reduce the noise and to achieve better accuracy of student success prediction. The hypotheses of this study are tested on data sets on students enrolled in two schools, in four different courses, and during two academic years. The sample consists of data on approximately 1,400 students. This research has demonstrated that it is possible to create the student success prediction model through data mining on blended learning environment data using frequency domain time series analysis of student logs to LMS. The proposed model achieves the prediction accuracy which is equal or bigger of the prediction accuracies achieved by similar models described in the literature. The technique for compressing time series of student logs to LMS is developed. The technique removes the noise, meaning that using the reduced number of coefficients gives better prediction accuracy than using models based on the full amount of data. Furthermore, it is proven that it is possible to achieve the success prediction with the accuracy more than 75% based on data for the first 2/3 of the total length of the course. The fundamental contribution of the paper is the application of spectral analysis, i.e. analysis of time series in the frequency domain to the data set from the hybrid learning environment

Modeling student success through data mining on blended learning enironment data using frequency domain time series analysis

Svaki pristup studenta sistemu za upravljanje učenjem (engl. learning management system, LMS) generira podatke o tome tko je pristupio, kada, s koje IP (engl. Internet protocol, IP) adrese i koja je aktivnost provedena. Na taj način prikuplja se mnoštvo podataka o studentima i to tako da se studente pri tom ne ometa u procesu učenja. Dostupni podaci mogu se iskoristiti i tako da se pristupi studenata određenom predmetu na LMS-u promatraju kao vremenski nizovi. U ovom radu pokazano je da promatrani vremenski nizovi nose informaciju o ponašanju studenata koja je indikativna za njihov uspjeh na završnom ispitu. Primjenom Fourierove transformacije (FT) na kreirane vremenske nizove, tj. prebacivanjem vremenskih nizova u frekvencijsku domenu, otvorena je mogućnost da se količina podataka smanji, a proces obrade podataka postane operativniji. Drugi razlog primjene FT je otkriti skrivene periodičnosti koje su ključ modeliranja i predikcije, a treći razlog je smanjenje šuma te postizanje bolje točnosti predikcije uspjeha studenata. Hipoteze ovog rada ispitane su na skupovima podataka o studentima upisanim na 2 fakulteta, na ukupno 4 različita predmeta tijekom dvije akademske godine. Uzorak se sastojao od podataka o približno 1400 studenata. U ovom istraživanju je pokazano da je moguće kreirati model predikcije uspjeha studenata rudarenjem podataka iz hibridnog okruženja za učenje primjenom analize vremenskih nizova pristupa studenata LMS-u u frekvencijskoj domeni te da takav model postiže točnost predikcije koja je jednaka ili veća od točnosti predikcije pomoću sličnih modela opisanih u literaturi. Razvijena je tehnika za sažimanje vremenskih nizova pristupa studenata LMS-u u frekvencijskoj domeni uz uklanjanje šuma, što znači da je korištenjem smanjenog broja koeficijenata postignuta bolja točnost predikcije u odnosu na modele temeljene na punom skupu podataka. Nadalje, pokazano je da je moguće postići točnost predikcije uspješnosti koja je veća od 75%, a temeljem podataka o prve 2/3 ukupnog trajanja predmeta. Temeljni doprinos rada je primjena spektralne analize, odnosno analize vremenskih nizova u frekvencijskoj domeni na skup podataka iz hibridnog okruženja za učenje.Each student log to the learning management system (LMS) generates data on who accessed the LMS, when, from which IP address, and which activity is performed. It is the way to collect the large amount of data on students and not to disturb the process of learning while the data are collected. The available data could be also used in the way that student logs to a certain course on the LMS are regarded as time series. In this paper it is proven that the observed time series carry the information on student behavior which is indicative for the final exam success. By applying the Fourier transform (FT) to the created time series, i.e. by switching time series to the frequency domain, the opportunity occurs to reduce the amount of data and for the data processing to become more operational. The second reason to apply FT is to reveal the hidden periodicities which are the key to modeling and prediction, and the third reason is to reduce the noise and to achieve better accuracy of student success prediction. The hypotheses of this study are tested on data sets on students enrolled in two schools, in four different courses, and during two academic years. The sample consists of data on approximately 1,400 students. This research has demonstrated that it is possible to create the student success prediction model through data mining on blended learning environment data using frequency domain time series analysis of student logs to LMS. The proposed model achieves the prediction accuracy which is equal or bigger of the prediction accuracies achieved by similar models described in the literature. The technique for compressing time series of student logs to LMS is developed. The technique removes the noise, meaning that using the reduced number of coefficients gives better prediction accuracy than using models based on the full amount of data. Furthermore, it is proven that it is possible to achieve the success prediction with the accuracy more than 75% based on data for the first 2/3 of the total length of the course. The fundamental contribution of the paper is the application of spectral analysis, i.e. analysis of time series in the frequency domain to the data set from the hybrid learning environment