Hybrid resampling to handle imbalanced class on classification of student performance in classroom

2017 ◽  
Author(s):  
Yoga Pristyanto ◽  
Noor Akhmad Setiawan ◽  
Igi Ardiyanto
Keyword(s):  
Student Performance ◽  
Imbalanced Class ◽  
Hybrid Resampling

scholarly journals Integrating k-means clustering into automatic programming assessment tool for student performance analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1389
Author(s):  
Rina Harimurti ◽  
Ekohariadi Ekohariadi ◽  
Munoto Munoto ◽  
I. G. P. Asto Buditjahjanto
Keyword(s):  
Student Performance ◽  
Assessment Tool ◽  
Test Case ◽  
Automatic Programming ◽  
Test Results ◽  
Student’S Performance ◽  
Point Calculation ◽  
Evaluation Module ◽  
Automated Tool

Computer programming is a subject involving a large number of logic programming activities. A programmer is compulsory to master skills of algorithms, logic, and programming language to conduct programming. An automatic programming assessment tool is an automated tool used to assist instructors in assessing programming tasks. The technology used in this application is open-source based with an evaluation module that will evaluate the sent program code, assessment, and classification. The evaluation results were then processed in the assessment module, where a comparison process with the test case was performed along with the point calculation. The classification module was used to divide students into five groups based on the point of each practicum. This study used k-means clustering classification method. The entities included were lecturers, assistants, students, and compilers. This application had 2 levels of users namely admin and students. Scoring results were then used in the process of determining the classification of student’s performance based on the k-means clustering method. In connection with the classification test results with three iterations, three practicum scores resulted that the classification process was successfully carried out with student’s performance divided into five groups covering very good, good, sufficient, less, and very less. The data used in the clustering process consisted of 41 students with 10 attributes which were then grouped into 3 groups (clusters).

scholarly journals K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes

2020 ◽  
Vol 8 (2) ◽  
pp. 89-93 ◽  
Author(s):  
Hairani Hairani ◽  
Khurniawan Eko Saputro ◽  
Sofiansyah Fadli
Keyword(s):  
Naive Bayes ◽  
Class Imbalance ◽  
Naïve Bayes ◽  
Data Sampling ◽  
Minority Class ◽  
Class A ◽  
Positive Class ◽  
Negative Class ◽  
Imbalanced Class

The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.

scholarly journals Percepção dos coordenadores de curso de Nutrição sobre o Exame Nacional de Desempenho do Estudante – ENADE/2004

2019 ◽  
Vol 90 (224) ◽  
Author(s):  
Semíramis Domene ◽  
Helena Maria Simonard-Loureiro ◽  
Lúcia Fátima Schwarzchild ◽  
Maria Margareth Naves ◽  
Rahilda Conceição Tuma ◽  
...  
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Negative Aspect ◽  
National Examination ◽  
The Social ◽  
Student’S Performance ◽  
Superior Education ◽  
Professional Formation ◽  
Positive Evaluation

O trabalho teve por objetivo analisar a percepção dos coordenadores de Cursos de Graduação em Nutrição sobre o Exame Nacional de Desempenho do Estudante – ENADE/2004, por meio de um questionário contendo onze questões, sendo dez objetivas e uma subjetiva, abrangendo os diversos contextos do exame. A avaliação foi positiva quanto ao questionário socioeconômico, seleção, qualidade e abrangência das questões dos componentes de formação geral e específica, e negativa, por induzir a uma classificação hierárquica dos cursos, ao invés de avaliar o desempenho dos estudantes. Estes resultados podem servir para o aprimoramento dos próximos exames, e contribuir para o aperfeiçoamento dos projetos pedagógicos do Curso de Nutrição e melhoria do ensino superior na área. Palavras-chave: formação profissional; ensino superior; nutricionista; avaliação; desempenho acadêmico. Abstract The objective of this study was to analyze the perception of the coordinators of Graduation Courses in Nutrition about Brazilian National Examination of Student Performance – ENADE/2004 by using a questionnaire contained eleven questions, being ten objectives and a subjective one, enclosing the diverse contexts of the exam. The ENADE had positive evaluation to the purpose of the social and economic questionnaire; questions selection, quality and comprehensiveness of the questions of general and specific components. According to the coordinators perceptions, the negative aspect of the exam was the probably manner of inducing to a hierarchic classification of the courses, instead of evaluating the student’s performance. These analyses could be proposals for the next examinations, as well as to contribute for the improvement of the pedagogic projects of Nutrition Courses and to approaching the superior education in this area. Keywords: professional formation; superior education; nutritionist; evaluation; academic performance.

IMPLEMENTATION OF GAIN RATIO AND K-NEAREST NEIGHBOR FOR CLASSIFICATION OF STUDENT PERFORMANCE

2020 ◽  
Vol 16 (1) ◽  
pp. 19-24
Author(s):  
Tyas Setiyorini ◽  
Rizky Tri Asmono
Keyword(s):  
Student Performance ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
K Nearest Neighbor ◽  
High Dimensions ◽  
Gain Ratio ◽  
Average Accuracy ◽  
Predicting Student Performance ◽  
Work Done

Predicting student performance is very useful in analyzing weak students and providing support to students who face difficulties. However, the work done by educators has not been effective enough in identifying factors that affect student performance. The main predictor factor is an informative student academic score, but that alone is not good enough in predicting student performance. Educators utilize Educational Data Mining (EDM) to predict student performance. KK-Nearest Neighbor is often used in classifying student performance because of its simplicity, but the K-Nearest Neighbor has a weakness in terms of the high dimensional features. To overcome these weaknesses, a Gain Ratio is used to reduce the high dimension of features. The experiment has been carried out 10 times with the value of k is 1 to 10 using the student performance dataset. The results of these experiments are obtained an average accuracy of 74.068 with the K-Nearest Neighbor and obtained an average accuracy of 75.105 with the Gain Ratio and K-Nearest Neighbor. The experimental results show that Gain Ratio is able to reduce the high dimensions of features that are a weakness of K-Nearest Neighbor, so the implementation of Gain Ratio and K-Nearest Neighbor can increase the accuracy of the classification of student performance compared to using the K-Nearest Neighbor alone.

scholarly journals Ethical Considerations in the Practical Application of the Unisa Socio-Critical Model of Student Success

2016 ◽  
Vol 17 (6) ◽  
Author(s):  
Angelo Fynn
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Success ◽  
Student Performance ◽  
South African ◽  
Higher Education Institutions ◽  
Moral Arguments ◽  
Improved Learning ◽  
Critical Model

<p class="3">The prediction and classification of student performance has always been a central concern within higher education institutions. It is therefore natural for higher education institutions to harvest and analyse student data to inform decisions on education provision in resource constrained South African environments. One of the drivers for the use of learning and academic analytics is the pressures for greater accountability in the areas of improved learning outcomes and student success. Added to this is the pressure on local government to produce well-educated populace to participate in the economy. The counter discourse in the field of big data cautions against algocracy where algorithms takeover the human process of democratic decision making. Proponents of this view argue that we run the risk of creating institutions that are beholden to algorithms predicting student success but are unsure of how they work and are too afraid to ignore their guidance. This paper aims to discuss the ethics, values, and moral arguments revolving the use of big data using a practical demonstration of learning analytics applied at Unisa.</p>

scholarly journals Analisis Sentimen Mahasiswa untuk Perbaikan Desain Afektif Ruang Kelas Jurusan Teknik Industri, Universitas Trisakti

2021 ◽  
Vol 10 (1) ◽  
pp. 27-34
Author(s):  
Anik Nur Habyba ◽  
Novia Rahmawati ◽  
Triwulandari SD
Keyword(s):  
Student Performance ◽  
Engineering Students ◽  
Support Vector ◽  
Classroom Design ◽  
Linear Kernel ◽  
Performance Achievement ◽  
Design Elements ◽  
And Performance ◽  
Positive Label

Improving the affective classroom design is essential to maximize student performance and learning achievement. The comfort and performance achievement of Trisakti University Industrial Engineering students are influenced by the affective design of classrooms. This study aimed to use sentiment analysis in the classification of students’ perceptions of the affective classroom design. Student sentiment classification is done using a Support Vector Machine (SVM). The questionnaire analysis results also showed perceptions about the subjects that were considered the most difficult (statistics). The classrooms had a positive sentiment: FGTSC, a sample for the next stage of classroom design formulation. The results show what impressions and things the students consider in choosing the FGTSC class. Some examples of the dominant kansei word are “comfortable,” this shows that students really care about the comfort of a classroom in the learning process. The word kansei for the design concept was collected from students' perceptions of the “positive” label. Design elements that need to be improved include equipment that is less comfortable to use, less lighting, walls with graffiti and uncomfortable seating. The classification results using three SVM types Kernel linear, radial and polynomial obtained linear have the best accuracy value (76%). These results indicate that the classification of student sentiment has the maximum results with SVM linear kernel (dot) type. This method will be used in classifying student sentiment on the results of improving classroom design.  

scholarly journals Analyzing Students Performance Using Frequent Item Set Mining, Clustering & Classification

2012 ◽  
Vol 1 (2) ◽  
pp. 31-41
Author(s):  
Rudresh Shirwaikar ◽  
Nikhil Rajadhyax
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Decision Tree ◽  
Student Performance ◽  
Real World ◽  
Scientific Inquiry ◽  
Educational Data Mining ◽  
The Subject ◽  
Hidden Patterns

Educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings , and using those methods to better understand students and the settings which they learn in. Data mining enables organizations to use their current reporting capabilities to uncover and understand hidden patterns in vast databases. As a result of this insight, institutions are able to allocate resources and staff more effectively. In this paper, we present a real-world experiment conducted in Shree Rayeshwar Institute of Engineering and Information Technology (SRIEIT) in Goa, India. Here we found the relevant subjects in an undergraduate syllabus and the strength of their relationship. We have also focused on classification of students into different categories such as good, average, poor depending on their marks scored by them by obtaining a decision tree which will predict the performance of the students and accordingly help the weaker section of students to improve in their academics. We have also found clusters of students for helping in analyzing student  performance and also improvising the subject teaching in that particular subject.

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