Predictions of academic achievements of vocational and technical high school students with artificial neural networks in science courses (physics, chemistry and biology) in Turkey and measures to be taken for their failures

Yağci, Ali; Çevik, Mustafa

doi:10.1051/shsconf/20173701057

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…EDM can be used in many ways but perhaps the most common application of EDM is predicting a student's academic performance. Several studies along this area predict students' achievement in their subjects like mathematics [8], physics, chemistry, and biology [9]. All of these studies have the goal to identify at-risk students and identify priority learning needs for different groups of students [4] to create interventions and improve their performance.…”

Section: Introductionmentioning

confidence: 99%

A Decision Tree Approach for Predicting Student Grades in Research Project using Weka

Abana¹

2019

IJACSA

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Data mining in education is an emerging multidiscipline research field especially with the upsurge of new technologies used in educational systems that led to the storage of massive student data. This study used classification, a data mining process, in evaluating computer engineering student's data to identify students who need academic counseling in the subject. There were five attributes considered in building the classification model. The decision tree was chosen as the classifier for the model. The accuracy of the decision tree algorithms, Random Tree, RepTree and J48, were compared using crossvalidation wherein Random Tree returned the highest accuracy of 75.188%. Waikato Environment for Knowledge Analysis (WEKA) data mining tool was used in generating the classification model. The classification rules extracted from the decision tree was used in the algorithm of the Research Project Grade Predictor application which was developed using Visual C#. The application will help research instructors or advisers to easily identify students who need more attention because they are predicted to have low grades.

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Section: Introductionmentioning

confidence: 99%

A Decision Tree Approach for Predicting Student Grades in Research Project using Weka

Abana¹

2019

IJACSA

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“…Each subject had a different factor that contributed the most to affecting students' achievement. For example, for Physics and Chemistry subjects, the factor of "class size" had the highest significance, while for the Biology subject, the factor of "enthusiasm for Biology" had the highest significance (Yağcı & Ḉevik, 2017). Neural Network was also used to predict the student's acceptance of the vocational school's learning management system (LMS).…”

Section: Application Of Data Mining In Tvetmentioning

confidence: 99%

Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Hong¹,

Ch’ng²,

Roslan³

2022

JST

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Technical and Vocational Education and Training (TVET) is an education system that delivers necessary information, skills, and attitudes related to work or self-employment. However, the TVET program is not preferred by most Malaysian students due to several factors such as students’ interest, parental influence, employers’ negative impression, facility in vocational institutions, inexperienced TVET instructors, and society’s negative perception. Consequently, it raises the issue of skilled workers shortage. The gravest threat will be far-reaching, pushing our economy into depreciation. Therefore, it is important to identify the students’ traits and interests before conducting further investigation to turn and thrive in this phenomenon. This study aims to utilise several classifiers (Decision Tree, Neural Network, Logistic Regression and Naïve Bayes) to predict students’ inclination to join TVET programmes. A total of 428 secondary school students from Kedah, Malaysia, are chosen as our survey respondents. The best classifier is determined according to the lowest misclassification rate. The findings revealed that the Decision Tree-based Gini Index with three branches prevail against other classifiers with a misclassification rate of 0.1938. Therefore, the classifier could act as a steer for the Kedah Department of Education (DOE), related parties, and the TVET agency in implementing effective strategies to enliven and inspire students to join TVET programs.

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“…For example, a genetic algorithm (GA) [18] is used for VET students' career guidance. Also, an artificial neural network is used for prediction and classification [19]- [22]. Therefore, this paper focuses on the implementation of computational methods in solving VET problems.…”

Section: Introductionmentioning

confidence: 99%

An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

Dardiri

Dwiyanto

Utama

2020

Int. J. Adv. Intell. Informatics

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Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues.

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Predictions of academic achievements of vocational and technical high school students with artificial neural networks in science courses (physics, chemistry and biology) in Turkey and measures to be taken for their failures

Cited by 5 publications

References 3 publications

A Decision Tree Approach for Predicting Student Grades in Research Project using Weka

A Decision Tree Approach for Predicting Student Grades in Research Project using Weka

Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

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