Predicting student's psychomotor domain on the vocational senior high school using linear regression

Harimurti, Rina; Yamasari, Yuni; Ekohariadi,; Munoto,; Asto, B. I. G. P.

doi:10.1109/icoiact.2018.8350768

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…Harimurti. [43] The article focuses on the processing of educational data to predict the psychomotor domain of students. In this case, the method of linear regression is used.…”

Section: Iiia Review On (Linear Regression)mentioning

confidence: 99%

A Review on Linear Regression Comprehensive in Machine Learning

Maulud

Abdulazeez

2020

JASTT

655

145

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Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

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“…Harimurti. [43] The article focuses on the processing of educational data to predict the psychomotor domain of students. In this case, the method of linear regression is used.…”

Section: Iiia Review On (Linear Regression)mentioning

confidence: 99%

A Review on Linear Regression Comprehensive in Machine Learning

Maulud

Abdulazeez

2020

JASTT

655

145

View full text Add to dashboard Cite

show abstract

“…In VET, linear regression is used for prediction, classification, and decision support systems. In prediction use for student's psychomotor domain [33] [36] and potential dropout students [32], in classification roles use for developing vocational research [35] and in decision support system used for potential dropouts [34].…”

Section: Linear Regressionmentioning

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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“…However, if the impact of separation on the clustering validation is ignored, the compactness measure alone may not be effective in evaluating partitions of different sizes due to the first issue. On the other hand, Internal CVIs require no such prior knowledge, and have extensive practical applications in information retrieval, text and image analysis, biological engineering, and other domains of data mining [9][10][11][12][13][14][15][16]. The quality of clustering results is usually inspected internally from two aspects-the intra-cluster compactness, and the inter-cluster separation (also known as isolation) [5,[17][18][19][20][21][22][23].…”

Section: Do Internal Cvis For Categorical Data Show Monotonicity Withmentioning

confidence: 99%

Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data

Gao

Yang

2018

Algorithms

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Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and can act as the objective function of clustering algorithms. In this paper, we first studied several well-known internal CVIs for categorical data clustering, and proved the ineffectiveness of evaluating the partitions of different numbers of clusters without any inter-cluster separation measures or assumptions; the accurateness of separation, along with its coordination with the intra-cluster compactness measures, can notably affect performance. Then, aiming to enhance the internal clustering validation measurement, we proposed a new internal CVI—clustering utility based on the averaged information gain of isolating each cluster (CUBAGE)—which measures both the compactness and the separation of the partition. The experimental results supported our findings with regard to the existing internal CVIs, and showed that the proposed CUBAGE outperforms other internal CVIs with or without a pre-known number of clusters.

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Predicting student's psychomotor domain on the vocational senior high school using linear regression

Cited by 15 publications

References 7 publications

A Review on Linear Regression Comprehensive in Machine Learning

A Review on Linear Regression Comprehensive in Machine Learning

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

Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data

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