scholarly journals Nomophobia in teenagers: digital lifestyle, social networking and smartphone abuse

2021 ◽  
Vol 34 (4) ◽  
pp. 17-32
Author(s):  
Irene Ramos-Soler ◽  
Carmen López-Sánchez ◽  
Carmen Quiles-Soler
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Social Networking ◽  
Multivariate Statistical ◽  
Statistical Characterization ◽  
Data Mining Techniques ◽  
Explanatory Variables ◽  
Mobile Social Networking ◽  
Smartphone Use ◽  
Structured Questionnaire

Smartphone use influences teenagers’ behaviors and lifestyles, not always in a positive way. Abuse and dependence on the use of this device is what has led to the study of nomophobia. The objective of this research is to measure the level of nomophobia in adolescents, and to study their digital consumption habits. The study seeks to analyze the relationships between risk of nomophobia, digital behavior, age and smartphone use. A structured questionnaire has been applied to a sample of 850 students aged 12 to 16. The data has been analyzed with SPSS and SPAD. Multivariate statistical characterization, one of the most recent data mining techniques, has been used to study differences in teenagers’ behaviors according to their risk of nomophobia, and to find related explanatory variables. Teenagers’ nomophobia risk ranges from mild to moderate, showing a relation with age, academic performance and intensity of use of mobile social networking apps. The risk of nomophobia responds to differences in students’ digital, social, relational and educational behaviors, and exhibits differences according to academic performance, age, gender, motivation and self-perception.

Application of tree modeling techniques using Weka for the analysis of records of an erosion experiment on cohesive soils carried out by the Federal Highway Administration

2021 ◽  
Author(s):  
Ernesto Garcia Rugerio ◽  
Rabindranarth Romero López ◽  
Gerarld Corzo Pérez
Keyword(s):  
Data Mining ◽  
Coefficient Of Determination ◽  
Cohesive Soils ◽  
Data Mining Techniques ◽  
Explanatory Variables ◽  
Absolute Relative Error ◽  
Information Classification ◽  
Federal Highway ◽  
Analysis Of Results ◽  
Types Of Information

<p>The methodologies applied in the analysis of scour in cohesive soils that exist have been evaluated based on linear or potential regressions of the results of experiments carried out in laboratories, however these procedures do not allow to clearly identify the weight of each variable in the explanation of the response variable, they also do not have the ability to carry out regionalizations of the analyzed data universe so that a better coupling of the resulting equations can be done.</p><p> </p><p>Every day data mining techniques are more usefull for analysis of different problems, in the present case study, the use of these techniques is evaluated in the analysis of results of an erosion experiment in cohesive soils carried out by the Federal Highway Administration (FHWA), these results were published in technical report No. FHWA-HRT-15-033 dated May 2015.</p><p> </p><p>The geotechnical and hydraulic variables and the erosion results obtained during the execution of the experimentation were used, with which it was analyzed using the WEKA software (Waikato Environment for Knowledge Analysis) of the University of Waikato in New Zealand, which uses data mining techniques based on different rules and types of information classification such as decision trees.</p><p> </p><p>Through the application of the tree section, various tests were carried out, this with the intention of determining the most important factors that describe the phenomenon of erosion, on the other hand, a series of classifications and equations were obtained through the M5P model that describe the phenomenon . As a result, it was obtained that the variables that describe the erosion phenomenon better according to the analysis of the M5P model are the shear stress, the plasticity index, the unconfined compression stress of the samples and the content of humidity. The result is a tree with 6 rules that zoning and regressing each zone obtaining a correlation coefficient of 0.9246 with an absolute relative error of 33.5874% and a root of the relative square error of 38.0878%. It is mentioned that with the adjustment through potential regressions obtained by the FHWA, a coefficient of determination (R2) of 0.73 was obtained.</p><p> </p><p>The application of this type of techniques allows a deeper knowledge of the erosion phenomenon by classifying and regionalizing the explanatory variables, as well as carrying out regressions within these classifications, explaining the behavior of soils with content of cohesive material as a function of its variables. The implementation of these data mining techniques has more advantages than simple linear or potential regressions, being of great help in research and experimentation in the field of geotechnics and river hydraulics.</p>

scholarly journals Efficient Data Mining Techniques to Improve Academic Performance of Students

2021 ◽  
Vol 13 (SUP 1) ◽  
pp. 81-84
Author(s):  
Rashmi V. Varade ◽  
Blessy Thankanchan
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Large Volume ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Efficient Data

Predicting the academic performance of students is very challenging due to large volume of data in the educational institutions database. Data mining techniques are implemented to predict students' academic performance in many institutions. Because of predicting students' performance, it will help teachers and institutions to decide strategies to teach to the students who are weak in studies and also they can define different strategies who are good in studies so that these students can perform better, So, aim of this paper is to study such a data mining technique which will help us to predict students' academic performance in advance.

Social Networking Data Analysis Tools and Services

2018 ◽  
pp. 19-34
Author(s):  
Gopal Krishna
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Data Analysis ◽  
Social Networking ◽  
Data Interpretation ◽  
Product Reviews ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Business Interests ◽  
Contents Sharing

Social networks have drawn remarkable attention from IT professionals and researchers in data sciences. They are the most popular medium for social interaction. Online social networking (OSN) can be defined as involving networking for fun, business, and communication. Social networks have emerged as universally accepted communication means and boomed in turning this world into a global town. OSN media are generally known for broadcasting information, activities posting, contents sharing, product reviews, online pictures sharing, professional profiling, advertisements and ideas/opinion/sentiment expression, or some other stuff based on business interests. For the analysis of the huge amount of data, data mining techniques are used for identifying the relevant knowledge from the huge amount of data that includes detecting trends, patterns, and rules. Data mining techniques, machine learning, and statistical modeling are used to retrieve the information. For the analysis of the data, three methods are used: data pre-processing, data analysis, and data interpretation.

Social Networking Data Analysis Tools and Services

2021 ◽  
pp. 406-421
Author(s):  
Gopal Krishna
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Data Analysis ◽  
Social Networking ◽  
Data Interpretation ◽  
Product Reviews ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Business Interests ◽  
Contents Sharing

Social networks have drawn remarkable attention from IT professionals and researchers in data sciences. They are the most popular medium for social interaction. Online social networking (OSN) can be defined as involving networking for fun, business, and communication. Social networks have emerged as universally accepted communication means and boomed in turning this world into a global town. OSN media are generally known for broadcasting information, activities posting, contents sharing, product reviews, online pictures sharing, professional profiling, advertisements and ideas/opinion/sentiment expression, or some other stuff based on business interests. For the analysis of the huge amount of data, data mining techniques are used for identifying the relevant knowledge from the huge amount of data that includes detecting trends, patterns, and rules. Data mining techniques, machine learning, and statistical modeling are used to retrieve the information. For the analysis of the data, three methods are used: data pre-processing, data analysis, and data interpretation.

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 Predicting Academic Performance with Applying Data Mining Techniques (Generalizing the results of two Different Case Studies)

2014 ◽  
Vol 3 (2) ◽  
pp. 79-88 ◽  
Author(s):  
Rozita Jamili Oskouei ◽  
Mohsen Askari
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Case Studies ◽  
Teaching Method ◽  
Probability Of Failure ◽  
Data Mining Techniques

Several research works are attempted to predict students academic performance and assess  the  evaluating students knowledge  or  detecting  students’  weakness and probability of failure in final semester examinations. However, several factors affect the performance of students in different countries or even in different states of one country. Therefore, understanding these factors and analyzing the effects of each one of those factors in each country, is necessary for improving instructors’ decisions in selecting   the best teaching method for helping weak students or   increasing performance  of  other  students. This study is motivated  to  study  the  students’ academic performance in high  school  and  bachelor  degree  studies  in  Iran and comparing these analysis results with the similar study’s results in India.

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