Extracting Relationships between Students' Academic Performance and Their Area of Interest Using Data Mining Techniques

Ktona, Ana; Xhaja, Denada; Ninka, Ilia

doi:10.1109/cicsyn.2014.18

Cited by 13 publications

(6 citation statements)

References 4 publications

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“…In the second phase, the results of the study can also be described on disciplines and subjects as a two-dimensional matrix (19).…”

Section: Fig 8 Requirements For Disciplines On Specialtiesmentioning

confidence: 99%

“…Forming individual learning trajectories based on student's achievements and functional state analysis provides the possibility to build educational process automatically that will be suitable for a specific student and satisfy the requirements of the educational program [18].Guzman-Ramirez and Enrique propose the education system modeling based on the Gate matrix using multiple preceptors [11]. In the article [19], the authors use Data Mining technology to explore students' interests and their successes in these areas. The article [9] sets out the criteria for the international assessment of IT knowledge using artificial intelligence in education.…”

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confidence: 99%

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Modelling Support Systems for Selecting Professions for Applicants in the Content of Personalization of Education

Gasimov

2020

EUREKA: Physics and Engineering

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Various methods are currently being used in examining the initial "START" knowledge of applicants and their placement for specialties. Studies show that applicants are placed on the decreasing principle in terms of their overall scores at universities. In this case, applicants with a high level of knowledge are placed in the prestigious specialties as medicine and law as they require high results. Though, while applying for other professions, the applicants do not perform enough results on the key disciplines for the profession, they are placed in those professions when the general results enable it. This causes them to face a number of problems while working both in education process and in the industry.To avoid this problem and to place applicants in a specialty that is more relevant to their level of knowledge, the introduction of an individual approach to the evaluation of initial level of knowledge may be more promising.This article presents a modeling of the "evaluation -placement" support system for the individual approach to assessing applicants' knowledge and positioning them in relevant specialties. The main goal of the system is to give each applicant the opportunity to choose and study the specialty that is more relevant to their knowledge and skills, as well as to analyze the results for each discipline along with the overall results. The system is implemented using fuzzy logic based artificial neural networks.The network consists of 100 neurons in the input layer, two hidden layers and one output layer. The number of neurons at the output is the same as the number of specialties taught at university. one of the most promising technologies in assessing students' competence [8]. Intelligent analysis in the educational environment is based on decision making, statistics, pattern recognition, cluster analysis, classification, regression and factor analysis, fuzzy logic, artificial neural networks and other methods [9][10][11][12][13][14][15]. The personalization of education or the determination of individual learning trajectories for each student is also possible through the use of artificial intelligence in the learning process. One of the characteristics of learners is their learning style [16].The trajectory of the personal education is a three-stage process that involves evaluating the basic knowledge and directing to occupations, from the initial assessment to the beginning of education and the stages of the educational process [17].There are many articles in the scientific literature on the use of intellectual systems at different stages of the educational process. A creation concept has been specified for a learning management system of new generation that allows forming individual learning trajectories based on analysis of student's achievements, wishes and peculiarities. Forming individual learning trajectories based on student's achievements and functional state analysis provides the possibility to build educational process automatically that will be suitable for a specific student and satisf...

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“…In the second phase, the results of the study can also be described on disciplines and subjects as a two-dimensional matrix (19).…”

Section: Fig 8 Requirements For Disciplines On Specialtiesmentioning

confidence: 99%

mentioning

confidence: 99%

Modelling Support Systems for Selecting Professions for Applicants in the Content of Personalization of Education

Gasimov

2020

EUREKA: Physics and Engineering

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“…Ktona, Xhaja and Ninka in [2] stated that data mining is an area in computer science that can be used in education and at the same time can provide a findings that will help to increase the education quality. It's a combination of tools of statistics with database management and artificial intelligence.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Recently, tutors and researches have been utilized data mining techniques in analyzing students learning in order to get a broad view of it and at the same time can improve the quality of the educational procedures [1]. Data mining also can be described as a combination of tools taken from artificial intelligence area and statistic together with database management in computer science field [2]. KPTM is an institution that practices in maintaining the quality improvement in providing the best teaching and learning service in a conducive environment in order to fulfill clients' satisfaction with excellent reputation and at the same time will lead a holistic education through global acknowledgement and acceptance.…”

Section: Introductionmentioning

confidence: 99%

A Student Performance Prediction Model Using Data Mining Technique

Razak¹,

Omar²,

Ahmad³

2018

IJET

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Predicting performance is very significant in the education world nowadays. This paper will describe the process of doing a prediction of student performance by using data mining technique. 257 data sets were taken from the student of semester 6 KPTM that involved four (4) academic programs which are Diploma in Computer System and Networking, Diploma in Information Technology, Diploma in Business Management and Diploma in Accountancy. Knowledge Discovery in Database (KDD) was used as a guide to the process of finding and extracting a knowledge from the dataset. A decision tree and linear regression were used to analyze the dataset based on variables selected. The variables used are Gender, Financing, SPM, GPASem1, GPASem2, GPASem3, GPASem4, GPASem5 and CGPA as a dependent variable. The result from this indicate the significant variable that contribute most to the students' performance. Based on the analysis, the decision tree shows that GPASem1 has a strong significant to the CGPA final semester of the student and the prediction accuracy is 82%. The linear regression shows that the GPA for each semester has a highly significant with the dependent variable with 96.2% prediction accuracy. By having this information, the management of KPTM can make a plan to ensure that the student can maintain a good result and at the same time to make a strategic plans for those without a good result.

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“…Pengetahuan ini diperoleh dari beberapa aktivitas dari data mining, antara lain klasifikasi, klaster, dan analisis asosiasi. Beberapa penelitian tentang klasifikasi antara lain prediksi mahasiswa nonaktif dengan menggunakan beberapa metode klasifikasi [6], klasifikasi untuk memprediksi prestasi belajar siswa dengan menggunakan Naïve Bayesian [7], dan klasifikasi dengan decision tree untuk mengetahui hubungan kinerja akademik dan bidang minat [8]. Untuk penelitian pengklasteran, penelitian yang telah dilakukan antara lain pengklasteran untuk mengevaluasi kualitas pengajaran [9] dan penggunaan pengklasteran untuk mengevaluasi kinerja mahasiswa [10].…”

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Meta-Algoritme Adaptive Boosting untuk Meningkatkan Kinerja Metode Klasifikasi pada Prestasi Belajar Mahasiswa

Yamasari¹,

Nugroho²,

Suyatno³

et al. 2017

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Abstract-Determining the right class on student achievement is important in an evaluation process, because placing students in the right class helps lecturer in reflecting the successfullness of learning process. This problem relates to the performance of classification method which is measured by the classifier metrics. High performance is indicated by the optimality of these classifier's metrics. Besides, meta-algorithm adaptive boosting has been proven to be able to improve the performance of classifier in various fields. Therefore, this paper employs adaptive boosting to reduce the number of incorrect student placement in a class. The experimental results of implementing adaptive boosting in classifying student achievement shows that there is an increase of performance of all classification metrics, i.e., Kappa, Precision, Recall, F-Measure, ROC, and MAE. In terms of accuracy, J-48 is able to rise about 3.09%, which means this method reduces three misclassified students. Additionally, decisionStump increases 12.37% of accuracy. This also means this method is able to decrease 12 misclassified students. Finally, Simple Cart reaches the highest accuracy of about 23.71%, while the number of misclassified students is reduced to 24 students. However, there is no improvement in Random Forest method by using this adaptive boosting. Intisari-Penetapan kelas yang tepat pada prestasi belajar mahasiswa adalah suatu hal yang penting dalam proses evaluasi.Hal ini disebabkan keberadaan mahasiswa pada kelas yang tepat dapat membantu dosen untuk merefleksikan berhasil tidaknya proses belajar mengajar yang telah dilakukan. Masalah ketepatan kelas ini berkaitan dengan kinerja metode klasifikasi yang diukur dengan ukuran-ukuran classifier. Kinerja yang tinggi diindikasikan dengan optimalnya ukuran-ukuran classifier tersebut. Di sisi lain, meta-algoritme adaptive boosting telah terbukti mampu meningkatkan kinerja classifier pada berbagai bidang. Oleh karena itu, makalah ini menerapkan adaptive boosting untuk meningkatkan kinerja classifier agar keberadaan mahasiswa pada kelas yang tidak sesuai bisa direduksi. Hasil uji coba penerapan adaptive boosting pada klasifikasi prestasi belajar mahasiswa ini menunjukkan peningkatan kinerja pada semua ukuran classifier, Penggunaan sistem e-learning dan sistem evaluasi ini akan menghasilkan tumpukan data pada tempat penyimpanan. Tumpukan data ini dapat digunakan untuk pengoptimalan pada proses-proses tersebut dengan penerapan data mining. Hal ini dapat dilakukan karena penerapan ini menghasilkan pengetahuan yang sangat berguna. Pengetahuan ini diperoleh dari beberapa aktivitas dari data mining, antara lain klasifikasi, klaster, dan analisis asosiasi. Beberapa penelitian tentang klasifikasi antara lain prediksi mahasiswa nonaktif dengan menggunakan beberapa metode klasifikasi [6], klasifikasi untuk memprediksi prestasi belajar siswa dengan menggunakan Naïve Bayesian [7], dan klasifikasi dengan decision tree untuk mengetahui hubungan kinerja akademik dan bidang minat [8]. Untuk penelitian pen...

show abstract

Extracting Relationships between Students' Academic Performance and Their Area of Interest Using Data Mining Techniques

Cited by 13 publications

References 4 publications

Modelling Support Systems for Selecting Professions for Applicants in the Content of Personalization of Education

Modelling Support Systems for Selecting Professions for Applicants in the Content of Personalization of Education

A Student Performance Prediction Model Using Data Mining Technique

Meta-Algoritme Adaptive Boosting untuk Meningkatkan Kinerja Metode Klasifikasi pada Prestasi Belajar Mahasiswa

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