scholarly journals Analyzing the Accuracy of Answer Sheet Data in Paper-based Test Using Decision Tree

2019 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Edy Suharto ◽  
Aris Puji Widodo ◽  
Suryono Suryono
Keyword(s):  
Decision Tree ◽  
Evaluation Method ◽  
Single Layer ◽  
Answer Sheet ◽  
Data Mining Technique ◽  
Web Based ◽  
Tree Models ◽  
Computer Based ◽  
Using Data ◽  
Education Quality Assurance

In education quality assurance, the accuracy of test data is crucial. However, there is still a problem regarding to the possibility of incorrect data filled by test taker during paper-based test. On the contrary, this problem does not appear in computer-based test. In this study, a method was proposed in order to analyze the accuracy of answer sheet filling out in paper-based test using data mining technique. A single layer of data comprehension was added within the method instead of raw data. The results of the study were a web-based program for data pre-processing and decision tree models. There were 374 instances which were analyzed. The accuracy of answer sheet filling out attained 95.19% while the accuracy of classification varied from 99.47% to 100% depend on evaluation method chosen. This study could motivate the administrators for test improvement since it preferred computer-based test to paper-based.

Research on Teaching Quality Evaluation Using Data Mining Technique

2014 ◽  
Vol 926-930 ◽  
pp. 4582-4585
Author(s):  
Ai Feng Li ◽  
Ying Hu ◽  
Wen Jing Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Association Rule ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Data Mining Technique ◽  
Higher Education System ◽  
Potential Factors ◽  
Rule Method ◽  
Using Data

—In this paper, we employ data mining (DM) technique to analyze various potential factors which impact the in-class teaching quality evaluation. Based on an effective dataset, we first exploit association rule method to mine the relationship between the teacher’s attributions, such as title, degree, age, seniority, and load, and the in-class teaching quality evaluation results. Then, we construct the decision tree of course’s attributions to reveal how the course’s attributions, such as property, credit, week hour, and number of students, impact the in-class teaching quality evaluation results. Our mined rules can provide effective guidance to talent development, teaching management, and input of talent in higher education system. Index Terms—data mining, decision tree, association rule, teaching quality evaluation

The Road to Digitally-Driven Mental Health Services

2022 ◽  
pp. 42-71
Author(s):  
Artemisa Rocha Dores ◽  
Andreia Geraldo ◽  
Helena Martins
Keyword(s):  
Mental Health ◽  
Data Mining ◽  
Technological Development ◽  
Data Mining Technique ◽  
Web Based ◽  
Positive Approach ◽  
Primordial Prevention ◽  
The Road ◽  
Computer Based ◽  
Theoretical Foundations

Intervention in mental health urges new solutions that merge solid theoretical foundations and new possibilities provided by technological development. This chapter is structured around results from a data mining technique using VOSViewer, which organized the field into five clusters of published literature: (1) most affected populations, (2) mental illness/disorders and their impact, (3) the expansion of remote interventions, (4) ICT potential to overcome limitations and (5) a positive approach to ICTs in mental health care. Solutions and recommendations are presented to overcome the issues identified, including how future interventions should consider old and new issues as the ones raised by the COVID-19 pandemic. Computer-based or web-based interventions are hereby presented as part of the revolution towards digital mental health or e-mental health. This approach has the potential to deconfine interventions, releasing them from the traditional settings and reaching new populations. It also reinforces the path already started, from the secondary to the primary and primordial prevention, towards the modification of the psychopathological trajectories.

scholarly journals A Student Performance Prediction Model Using Data Mining Technique

2018 ◽  
Vol 7 (2.15) ◽  
pp. 61
Author(s):  
Rohaila Abdul Razak ◽  
Mazni Omar ◽  
Mazida Ahmad
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Decision Tree ◽  
Student Performance ◽  
Prediction Accuracy ◽  
Significant Variable ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Predicting Performance ◽  
Using Data

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.  

scholarly journals An Analysis on Student Academic Performance by Using Decision Tree Models

2018 ◽  
pp. 615-620
Author(s):  
Jastini Mohd. Jamil ◽  
Nurul Farahin Mohd Pauzi ◽  
Izwan Nizal Mohd. Shahara Nee
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Decision Tree ◽  
Large Volume ◽  
Educational Data Mining ◽  
Data Mining Techniques ◽  
Student Academic Performance ◽  
Tree Models ◽  
Student’S Performance ◽  
Using Data

Large volume of educational data has led to more challenging in predicting student’s performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student’s achievements and lack of investigations on exploring pattern of student’s behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student’s academic performance by using decision trees is proposed to improve student’s achievements more effectively. The main objective of this paper is to provide an overview on predicting student’s academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student’s behaviour and the most important attributes that impact to the student’s achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student’s achievement.

scholarly journals Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

2019 ◽  
pp. 131-136
Author(s):  
Umar Sidiq ◽  
Syed Mutahar Aaqib ◽  
Rafi Ahmad Khan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Research Work ◽  
Support Vector ◽  
Data Sets ◽  
Data Mining Technique ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Classification Techniques ◽  
Using Data

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

scholarly journals Expert System Selection Topics Thesis Title With Forward Chaining Method Web-Based

Tech-E ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 22
Author(s):  
Ricky Tri Utomo ◽  
Ceng Giap Yo
Keyword(s):  
College Students ◽  
Information Technology ◽  
Expert System ◽  
Literature Study ◽  
Web Based ◽  
Forward Chaining ◽  
Computer Based ◽  
Student Information ◽  
Using Data ◽  
System Selection

Expert Systems are computer-based applications that are used to solve problems as the expert thinks. Many college students majoring in Information Technology are difficult to get the thesis title topic even though it has been reading many journals and looking for some references. Therefore to make it easier college students, then the author wants to create an application where college students majoring in Information Technology can more easily get the thesis title topic so the work of thesis becomes more fluent and not obstructed. This app is web based. In system design, the author used several methods in his research that is method of Analyze, Design, and Implementation. Methods in the design of this expert system even this also used forward chaining method as tracking ahead and best first search method. And also using data collection method mean literature study and questionnaire from system that has been created. The result of Expert System of Thesis Title Topic Selection with Forward Chaining method web based expected to be useful and helpfully college students in getting the thesis title topic. Based on questionnaire that has been shared and filled, it can be said that the Expert System of Thesis Title Topic Selection with Forward Chaining Method Web Based is helpful and beneficial for the college students because it helps college student Information Technology in getting Thesis Title.

scholarly journals Aplikasi Pemetaan Daerah Rawan Kriminalitas Di Manadi Berbasis Web

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Marfel A. Kaseger ◽  
Yaulie D.Y. Rindengan ◽  
Arie S.M. Lumenta
Keyword(s):  
Information System ◽  
Spatial Information ◽  
Geographic Area ◽  
Application Development ◽  
Google Maps ◽  
Web Based ◽  
Spatial Reference ◽  
Rapid Application Development ◽  
Computer Based ◽  
Using Data

Abstrak-Sistem Informasi Geografis adalah merupakan suatu system informasi yang berbasis computer, dan dirancang untuk bekerja dengan menggunakan data yang memiliki informasi spasial (bereferensi keruangan). SIG menggunakan teknologi computer untuk mengintegrasikan, memanipulasi, dan menampilkan informasi atau karakteristik yang ada di suatu daerah geografi. Tindak kejahatan/kriminalitas bukan hanya tanggung jawab pihak kepolisian tetapi tanggung jawab semua lapisan masyarakat, sehingga dengan dipetakannya daerah rawan kriminalitas akan dapat diketahui dimana saja terjadi tindak kejahatan itu. Aplikasi yang akan dibuat adalah “APLIKASI PEMETAAN DAERAH RAWAN KRIMINALITAS DI MANADO BERBASIS WEB’. System ini dibangun menggunakan Google Maps dan MySQL, serta dalam pengembangan system menggunakan metode RAD (Rapid Application Development)      Kata kunci : kriminal, Rapid Application Development, SIG, Web.                    Abstract-Geographic Information System is a computer-based information system, and is designed to work using data that has spatial information (spatial reference). GIS uses computer technology to integrate, manipulate, and display information or characteristics that exist in a geographic area. Crime / criminality is not only the responsibility of the police but the responsibility of all levels of society, so that with the mapping of crime-prone areas will be known where the crime occurred.The application that will be made is "APPLICATION OF WEB-BASED CRIMINAL AREA MAPPING IN MANADO WEB-BASED." This system was built using Google Maps and MySQL, and in the development of systems using the RAD (Rapid Application Development) method    Keywords : Criminal, GIS, Rapid Application Development, Web.

A Practical Tutorial for Decision Tree Induction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-38
Author(s):  
Víctor Adrián Sosa Hernández ◽  
Raúl Monroy ◽  
Miguel Angel Medina-Pérez ◽  
Octavio Loyola-González ◽  
Francisco Herrera
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Machine Learning Techniques ◽  
Evaluation Measures ◽  
Decision Tree Induction ◽  
Learning Techniques ◽  
Tree Models ◽  
Evaluation Measure ◽  
Main Components ◽  
Support Decision Making

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.

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