A learning achievement prediction model for meaningful learning designs

2013 ◽  
pp. 525-528
Author(s):  
Kuo-Kuang Fan ◽  
Chung-Ho Su ◽  
Shuh-Yeuan Deng ◽  
Wei-Jhung Wang
Keyword(s):  
Prediction Model ◽  
Meaningful Learning ◽  
Learning Achievement ◽  
Achievement Prediction

scholarly journals ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK MEMPREDIKSI PRESTASI SISWA SEKOLAH MENENGAH KEJURUAN

2019 ◽  
Vol 5 (1) ◽  
pp. 23-28
Author(s):  
Astrid Noviriandini ◽  
Nurajijah Nurajijah
Keyword(s):  
Student Achievement ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tree Model ◽  
Learning Achievement ◽  
Achievement Prediction ◽  
C4.5 Decision Tree ◽  
Auc Value ◽  
Bayes Algorithm

This research informs students and teachers to anticipate early in following the learning period in order to get maximum learning outcomes. The method used is C4.5 decision tree algorithm and Naïve Bayes algorithm. The purpose of this study was to compare and evaluate the decision tree model C4.5 as the selected algorithm and Naïve Bayes to find out algorithms that have higher accuracy in predicting student achievement. Learning achievement can be measured by the value of report cards. After comparison of the two algorithms, the results of the learning achievement prediction are obtained. The results showed that the Naïve Bayes algorithm had an accuracy value of 95.67% and the AUC value of 0.999 was included in Excellent Clasification, for the C4.5 algorithm the accuracy value was 90.91% and the AUC value of 0.639 was included in the state of Poor Clasification. Thus the Naïve Bayes algorithm can better predict student achievement.

scholarly journals Teacher´s role in the meaningful learning achievement based on ICT

2019 ◽  
Vol 17 (02) ◽  
Author(s):  
Luz Estela Gómez Vahos ◽  
Luz Enid Muriel Muñoz ◽  
David Alberto Londoño-Vásquez
Keyword(s):  
Meaningful Learning ◽  
Learning Achievement ◽  
Medios De Comunicacion

En este artículo se analizan el papel del docente que busca en los estudiantes el desarrollo de competencias, como las formuladas por la didáctica crítica para el desarrollo de los conocimientos, habilidades y valores, las cuales se deben apoyar en el buen uso de las Tecnologías de Información y Comunicación, para así facilitar un aprendizaje que le sea significativo y útil en su contexto familiar, social y laboral; de igual forma, invita a la discusión sobre como desde las prácticas educativas se accede a la construcción del conocimiento de una forma crítica, argumentativa, propositiva  y comunicativa,  donde los intereses de cada sujeto que interviene en el proceso educativo, se centra en el aprendizaje.  Asimismo, se expone el papel del docente en el desarrollo del aprendizaje significativo con el acercamiento al conocimiento que permiten hoy los medios de comunicación y de información, con la cual se pretende afianzar el proceso de enseñanza aprendizaje desde los intereses, necesidades y oportunidades que presenta el contexto.

scholarly journals Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning

2021 ◽  
Vol 15 (22) ◽  
pp. 22
Author(s):  
Pratya Nuankaew ◽  
Patchara Nasa-ngium ◽  
Wongpanya Sararat Nuankaew
Keyword(s):  
Prediction Model ◽  
Learning Strategies ◽  
Tertiary Education ◽  
Confusion Matrix ◽  
Dropping Out ◽  
Future Research ◽  
Application Development ◽  
Achievement Prediction ◽  
Academic Year ◽  
Very High

<span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">The purpose of the research is to identify the risk of dropping out in tertiary students with an application. The components of the research goal aim (1) to develop the students’ achievement prediction model and (2) to construct a prototype application for the predictions of the tertiary students dropping out. <a name="_Hlk71439748"></a>The research tools consisted of three parts, (1) tool for developing predictive prototypes uses a tool called the CRISP-DM process with Decision Tree Classification, Feature Selection methods, Confusion Matrix performance, Cross-Validation methods, Accuracy, Precision and Recall measurements, (2) tool for application development used the SDLC with V-method, and (3) tool to assess application satisfaction used questionnaires and statistical analysis. Data sample were collected from 401 students enrolled in the Business Computer Program at the School of Information and Communication Technology, University of Phayao during the academic year 2012-2016. The results showed that the prediction model had a very high percentage of accuracy (82.29%). The prototype test results with the data gathered had a very high score level (84.04%; correct 337 out of 401 training examples). An overview of the underlying application with the utmost integrity by the researchers planned to put the application to the test in the first semester of the academic year 2021 at the School of Information Technology and Communication, University of Phayao. For future research, the researchers plan to create a mobile application for mentors in the University of Phayao to monitor learner on both Android and iOS systems.</span>

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