scholarly journals A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques

2021 ◽  
Vol 13 (4) ◽  
pp. 2141
Author(s):  
Kyungyeul Kim ◽  
Han-Sung Kim ◽  
Jaekwoun Shim ◽  
Ji Su Park
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Technology Literacy ◽  
Early Prediction ◽  
Ict Literacy ◽  
Educational Strategies ◽  
Achievement Level ◽  
Data Mining Techniques ◽  
Educational Backgrounds ◽  
Customized Learning

It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students’ educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students’ perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students.

Computing and ICT Literacy

2005 ◽  
pp. 37-48
Author(s):  
Antonio Cartelli
Keyword(s):  
Data Mining ◽  
Action Research ◽  
Learning Process ◽  
Ict Literacy ◽  
Research Strategies ◽  
Data Mining Techniques ◽  
Web Technologies ◽  
Computing Skills ◽  
Teaching Learning ◽  
School Curricula

Three main questions guided the author in the writing of this chapter: Is there the need for a widespread and in-depth ICT literacy in mankind? What has to be meant for ICT literacy? And are there special problems in students’ learning of ICT topics? And last but not least: How can ICTs themselves improve teachers’ work and students’ learning on ICTs? The introduction answers the first question and shows how difficult the search can be for solutions to the problem of the digital divide. The answer to the second question comes from a short survey of the experiences that some institutions made for the introduction of basic computing skills and ICT literacy in school curricula. In the meantime the problems that the students usually meet while attending computer programming and ICT literacy courses are described. Finally the author reports the results of some experiences involving the use of ICTs in teaching and describes how he arrived to hypothesize the adoption of action research strategies, of Web technologies and data mining techniques for the monitoring of the teaching-learning process and its improvement.

Computing and ICT Literacy

2008 ◽  
pp. 3338-3347
Author(s):  
Antonio Cartelli
Keyword(s):  
Data Mining ◽  
Action Research ◽  
Learning Process ◽  
Ict Literacy ◽  
Research Strategies ◽  
Data Mining Techniques ◽  
Web Technologies ◽  
Computing Skills ◽  
Teaching Learning ◽  
School Curricula

Three main questions guided the author in the writing of this chapter: Is there the need for a widespread and in-depth ICT literacy in mankind? What has to be meant for ICT literacy? And are there special problems in students’ learning of ICT topics? And last but not least: How can ICTs themselves improve teachers’ work and students’ learning on ICTs? The introduction answers the first question and shows how difficult the search can be for solutions to the problem of the digital divide. The answer to the second question comes from a short survey of the experiences that some institutions made for the introduction of basic computing skills and ICT literacy in school curricula. In the meantime the problems that the students usually meet while attending computer programming and ICT literacy courses are described. Finally the author reports the results of some experiences involving the use of ICTs in teaching and describes how he arrived to hypothesize the adoption of action research strategies, of Web technologies and data mining techniques for the monitoring of the teaching-learning process and its improvement.

Early Prediction of Chronic Kidney Disease Using Data Mining Techniques

2021 ◽  
pp. 947-957
Author(s):  
Hasin Shahed Shad ◽  
Zeeshan Jamal ◽  
S. M. Foysal Ahmed ◽  
Sifat Momen ◽  
Nafees Mansoor
Keyword(s):  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Early Prediction ◽  
Data Mining Techniques ◽  
Using Data

Early Prediction of Diabetes Disease Based on Data Mining Techniques

2021 ◽  
pp. 40-51
Author(s):  
Salma N. Elsadek ◽  
Lama S. Alshehri ◽  
Rawan A. Alqhatani ◽  
Zainah A. Algarni ◽  
Linda O. Elbadry ◽  
...  
Keyword(s):  
Data Mining ◽  
Early Prediction ◽  
Data Mining Techniques ◽  
Prediction Of Diabetes

Early prediction of diabetes by applying data mining techniques

2019 ◽  
Vol 171 ◽  
pp. 3
Author(s):  
Mohammed Zeyad Al Yousef ◽  
Mazen Ferwana ◽  
Sherif Sakr ◽  
Riyad Al Shammari
Keyword(s):  
Data Mining ◽  
Early Prediction ◽  
Data Mining Techniques ◽  
Prediction Of Diabetes

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

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