A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

Implementasi Algoritma K-nearest Neighbors untuk Menentukan Kemungkinan Tingkat Ketercapaian Kompetensi Pembelajaran Keterampilan Komputer dan Pengelolaan Informasi

Nilai keterampilan komputer, nilai matematika dan bahasa Inggris pada saat kelas 3 SMP yang tersimpan dalam basis data digunakan untuk menentukan tingkat ketercapaian kompetensi pembelajaran Keterampilan Komputer dan pengelolaan Informasi (KKPI) di kelas I SMK pada masa lampau. Nilai keterampilan komputer, nilai matematika dan bahasa Inggris pada masa sekarang digunakan untuk menentukan kemungkinan tingkat ketercapaian kompetensi pembelajaran KKPI di kelas I SMK pada masa sekarang sebagai informasi untuk meminimalisir dan mendiagnosa awal faktor-faktor yang bisa diantisipasi lebih dini. Sistem digunakan sebagai salah satu media komunikasi antar guru pembina dan guru muda dalam mengelola pembelajaran. Algoritma K-Nearest Neigbohr digunakan untuk menentukan kemiripan kondisi masa lampau dan kondisi sekarang dengan menggunakan rumus similarity. Kasus sekarang dibandingkan kasus lampau kemudian diukur similarity-nya. Nilai similarity tertinggi menunjukkan kemiripan antara kasus yang dibandingkan, sehingga menjadi dasar pengambilan kesimpulan kemungkinan tingkat pencapaian kompetensi. Hasil penelitian menunjukkan sistem dapat menghitung nilai similarity dan membuat kesimpulan kemungkinan pencapaian tingkat kompetensi KKPI berdasarkan pada nilai keterampilan komputer, nilai matematika dan bahasa Inggris. Sistem dapat digunakan sebagai model komunikasi guru muda dan guru pembina dalam proses pembelajaran

Teknik Klastering untuk Menganalisa Peminatan Sekolah pada Penerimaan Peserta Didik Online Tingkat SMA di Kota Semarang

Sejak pemerintah kota Semarang menyelenggarakan penerimaan peserta didik baru secara online, khususnya untuk tingkat SMA Negeri di Kota Semarang, banyak permasalahan dapat diatasi dengan adanya sistem tersebut. Orang tua/wali dan calon peserta didik tidak lagi harus banyak mengeluarkan tenaga dan pikiran untuk melakukan proses pendaftaran, seperti pergi jauh menuju lokasi sekolah dan menunggu antrian yang cukup lama, tetapi cukup dengan melakukan proses secara online melalui internet. Yang sangat perlu diperhatikan adalah bagaimana proses yang berjalan secara real-time tersebut dapat dipantau secara transparan oleh masyarakat luas, sehingga keputusan yang harus diambil oleh orang tua/wali maupun calon peserta didik sepenuhnya tepat berdasarkan kesadaran dan kondisi yang ada pada saat proses penerimaan siswa baru secara online berlangsung. Menjadi unik ketika mengamati bagaimana jumlah SMA yang ada di Kota Semarang diserbu oleh calon peserta didik yang begitu banyak, yang berasal dari negeri maupun swasta dan dari dalam kota maupun luar kota. Kuota yang ada pada setiap sekolah tentunya tidak dapat menampung semua jumlah pendaftar, sehingga ketentuan pada proses seleksilah yang akan menjadi dasar akhir penerimaan. Tidak semua calon peserta didik yang berasal dari sekolah negeri bisa diterima di sekolah yang dituju, demikian pula calon peserta didik yang berasal dari sekolah swasta tetap memiliki kesempatan yang sama untuk bisa diterima di sekolah negeri tujuannya. Menggunakan metode data mining dan teknik klustering diperoleh gambaran hasil bagaimana proses seleksi masuk di SMA Negeri di Kota Semarang yang cukup ketat namun transparan. Data yang diolah dapat mengetahui sejauh mana calon peserta didik berkompetisi memperebutkan kursi di sekolah yang diidamkannya tersebut. Gambaran hasil berikutnya adalah bagaimana porsi sebenarnya antara calon peserta didik laki-laki dan perempuan yang berkesempatan untuk melanjutkan studi di SMA Negeri. Prosentase pendaftar dari Dalam Rayon, Luar Rayon dan Luar Kota juga tampak jelas dalam hasil analisa. Terakhir adalah bagaimana dengan data mining dan teknik klustering diperoleh gambaran kelompok calon peserta didik dengan nilai tinggi, sedang dan rendah, serta kelompok jumlah calon peserta didik dari asal sekolah mereka

Determinants of Efficacy of Studying in the Republic Croatia - Comparing Neural Networks and Decision Trees: Research Framework Proposition

Rapid technological development and progress lead to the need for better and more efficient education which should prepare the applicant for increasingly flexible labour market. The goal of this research is to create models for prediction of student’s efficacy, compare them, find the key factors that contribute to more efficient studying in the Republic of Croatia, and finally determine how efficient studying is related to first employment. Models will be based on students’ data and hypothesis will be tested using multivariate statistical methods (multiple regressions, Cronbach’s alpha), decision trees and neural networks. Data will be collected by structured questionnaire and will consist of demographic and economic data, information about previous education, attitudes towards learning, and goals after completing studies and information about the first employment. Students’ efficacy will be measured by grade point average in college. This research will try to increase our understanding of how different factors influence students’ performance and how students’ efficacy affects the speed and conditions of finding the first employment. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Self Organizing Map (SOM) based Modelling Technique for Student Academic Performance Prediction

Over the years, student academic performance mapping is considered an important issue for academic institutions and designing such system is very complicated. However, the student performances rely on various factors such as attendance, marks, family background, curriculum activities, social behavior etc. and mapping of all these attributes is very complicated. In the past, various data mining software and techniques have been proposed to classify student data set. These software�s and techniques have been failed to classify student dataset correctly. Now advances of Artificial Intelligence (AI) and data mining techniques made it possible to classify student data set and draw useful patterns efficiently. In this study, real data set of Government Girls College (GGC) vidisha of 250 students is considered. The main concern of this study is to apply SOM clustering approach to classify student dataset. Finally, experimental results demonstrated that 4 clusters have been formed based on category like very good, good, average, and poor

Predicting student performance in higher education using multi-regression models

Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the student’ grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, learning management system (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

INTRODUCTION OF EDUCATIONAL DATA MINING BY USING A VARIETY OF TECHNIQUES IN ORDER TO ACHIEVE THE GOAL FROM THE MOODLE LMS

Different works relating to this specialty have been done in recent years and several data extraction approaches have been used to solve numerous educational problems. This analysis compares the Felder-Silverman Learning Style Model component of student activity in Moddle class with three data mining algorithms for the identification of knowledge presentation dimension (visual/verbal) learning style. This study analyzes Moodle LMS student log data using data mining strategies to identify their learning styles that rely on one aspect of the learning style of Feld-Silverman: visual/verbal. The WEKA compares various classification algorithms as classified J48 Decision Tree, Naive Bayes and Portion. The selected classifiers were evaluated using a 10-fold cross validation. The tests revealed that at 71.18 percent the Naive Bays achieve the strongest score. Article visualizations