Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression

Sudrajat, Dadang; Purnamasari, Ade Irma; Dikananda, Arif Rinaldi; Kurnia, Dian Ade; Efendi, Dwi Marisa

doi:10.18280/isi.280116

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Although more studies of the teaching model combining collaborative teaching with AI have been conducted, most of them still have significant limitations in application and practice [17][18][19][20][21][22][23][24], because they usually focus on theoretical discussions and lack empirical support in real-world teaching scenarios. In addition, they have not deeply discussed the application methods and potential value of AI technology in the collaborative teaching model.…”

Section: Ai-based Collaborative Teaching: Strategies and Analysis In ...mentioning

confidence: 99%

AI-Based Collaborative Teaching: Strategies and Analysis in Visual Communication Design

Liu,

Li,

Zheng

2023

Int. J. Emerg. Technol. Learn.

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With the rapid development of technology, AI has been widely applied in multiple fields, especially the field of education. As a discipline involving art, technology and creativity, visual communication design is facing the challenge of keeping up with the times and combining new technologies for innovation. Collaborative teaching model emphasizes multi-party participation and collaborative learning, and its proposal has injected new vitality into traditional educational patterns. However, existing studies, which combine collaborative teaching model with artificial intelligence, still have limitations in application and practice, and most of them remain in the theoretical discussion stage and lack empirical support. This study aimed to make up for this deficiency. After in-depth analysis of educational data, a forecasting model of collaborative teaching demand based on AI was proposed. Course content suitable for the collaborative teaching model was further planned for the education in visual communication design.

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Section: Ai-based Collaborative Teaching: Strategies and Analysis In ...mentioning

confidence: 99%

AI-Based Collaborative Teaching: Strategies and Analysis in Visual Communication Design

Liu,

Li,

Zheng

2023

Int. J. Emerg. Technol. Learn.

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“…The average validation error across all folds provides a reliable indicator of the model's generalization capabilities. The models include linear regression [28] with and without interactions, robust linear regression, stepwise linear regression, decision tree-based models (fine, medium, and coarse trees, boosted trees, and bagged trees), support vector machine (SVM) models with linear, quadratic, and cubic kernels, and Gaussian SVM models with fine, medium, and coarse kernels. The table also includes neural network models with different architectures, such as narrow, medium, wide, bilayered, and trilayered neural networks.…”

Section: Regression Learnermentioning

confidence: 99%

Predicting Used-Vehicle Resale Value in Developing Markets: Application of Machine Learning Models to the Kazakhstan Car Market

Barlybayev,

Sankibayev,

Kadyr

et al. 2023

ISI

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The burgeoning trade in used vehicles has necessitated further research into price prediction. In developing nations, the abundance of second-hand cars and limited supply of new ones has led to a preference for used vehicles. Consequently, the analysis of vendor data becomes imperative for gaining valuable insights. Sellers are increasingly seeking accurate price predictions to maximize their profits. The assessment of used car prices necessitates a thorough understanding of the features that influence value. Although the inclusion of multiple features can enhance prediction accuracy, the list of these features is non-exhaustive. This study seeks to examine the effectiveness of various regression techniques such as Linear, Decision Tree, SVM machines, Neural Network, and Bagged Trees, alongside machine learning algorithms, in predicting the selling price of used cars based on the associated features. Evaluation metrics will be utilized to identify the most proficient model by examining the performance and error rate of each model. The deep neural network model demonstrates exceptional performance, as indicated by its low RMSE and MSE values, suggesting high efficiency. Some models, including cubic SVM, fine Gaussian SVM, and wide neural network, exhibit a robust correlation (R) in accurately connecting input and output variables. Furthermore, narrow, medium, bilayered, and trilayered neural networks display commendable performance in recording variable correlations. After comparing various models, Bagged Trees were identified as the most cost-effective option per square meter, due to their advantageous pricing and performance.

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“…With technological advancements and constant development of informatization education, blended learning, which combines traditional face-to-face teaching with online teaching, is becoming an important direction of educational reform. The blended learning reform aims to better give full play to the professional ability of teachers while providing students with more personalized and diverse learning experiences [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Therefore, one of its important topics is undoubtedly effective utilization of online educational resources, especially accurate assessment of their value.…”

Section: Introductionmentioning

confidence: 99%

Value Assessment of Online Educational Resources in the Context of Blended Learning

Sun

2023

Int. J. Emerg. Technol. Learn.

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In the context of promoting informatization education and reforming blended learning, how to accurately assess the value of online educational resources has become an important research topic. However, most existing assessment methods rely on direct data statistics and experience-based judgment, lacking scientific prediction models and comprehensive consideration of learning cycles. To solve this problem, this study constructed a hit prediction model of online educational resources based on the fusion of extreme learning machine and grey Verhulst model, and a hit-based value assessment model of the resources considering the learning cycles of students. The research results showed that the proposed model not only predicted and assessed the value of online educational resources more accurately, but also provided theoretical support and decision-making reference for the implementation of blended learning reform, thereby promoting the effective utilization of online educational resources.

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Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression

Cited by 4 publications

References 19 publications

AI-Based Collaborative Teaching: Strategies and Analysis in Visual Communication Design

AI-Based Collaborative Teaching: Strategies and Analysis in Visual Communication Design

Predicting Used-Vehicle Resale Value in Developing Markets: Application of Machine Learning Models to the Kazakhstan Car Market

Value Assessment of Online Educational Resources in the Context of Blended Learning

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