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

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 dr...

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Autores principales:	Pratya Nuankaew, Patchara Nasa-ngium, Wongpanya Sararat Nuankaew
Formato:	article
Lenguaje:	EN
Publicado:	International Association of Online Engineering (IAOE) 2021
Materias:	learning analytics dropping out educational data mining eruptive technology disruptive technology Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/d23a391801ea4e27806dc5e433ef78f9
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id	oai:doaj.org-article:d23a391801ea4e27806dc5e433ef78f9
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spelling	oai:doaj.org-article:d23a391801ea4e27806dc5e433ef78f92021-11-26T17:08:33ZApplication for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning1865-792310.3991/ijim.v15i22.24069https://doaj.org/article/d23a391801ea4e27806dc5e433ef78f92021-11-01T00:00:00Zhttps://www.online-journals.org/index.php/i-jim/article/view/24069https://doaj.org/toc/1865-7923The 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. 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.Pratya NuankaewPatchara Nasa-ngiumWongpanya Sararat NuankaewInternational Association of Online Engineering (IAOE)articlelearning analyticsdropping outeducational data miningeruptive technologydisruptive technologyTelecommunicationTK5101-6720ENInternational Journal of Interactive Mobile Technologies, Vol 15, Iss 22, Pp 22-43 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	learning analytics dropping out educational data mining eruptive technology disruptive technology Telecommunication TK5101-6720
spellingShingle	learning analytics dropping out educational data mining eruptive technology disruptive technology Telecommunication TK5101-6720 Pratya Nuankaew Patchara Nasa-ngium Wongpanya Sararat Nuankaew Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning
description	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. 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.
format	article
author	Pratya Nuankaew Patchara Nasa-ngium Wongpanya Sararat Nuankaew
author_facet	Pratya Nuankaew Patchara Nasa-ngium Wongpanya Sararat Nuankaew
author_sort	Pratya Nuankaew
title	Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning
title_short	Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning
title_full	Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning
title_fullStr	Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning
title_full_unstemmed	Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning
title_sort	application for identifying students achievement prediction model in tertiary education: learning strategies for lifelong learning
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/d23a391801ea4e27806dc5e433ef78f9
work_keys_str_mv	AT pratyanuankaew applicationforidentifyingstudentsachievementpredictionmodelintertiaryeducationlearningstrategiesforlifelonglearning AT patcharanasangium applicationforidentifyingstudentsachievementpredictionmodelintertiaryeducationlearningstrategiesforlifelonglearning AT wongpanyasararatnuankaew applicationforidentifyingstudentsachievementpredictionmodelintertiaryeducationlearningstrategiesforlifelonglearning
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Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning

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