Professional Teaching Practice Through MIAP based Integrated Learning Activities for Electrical Engineering Education

Chumchuen, Nutchanat; Klinbumrung, Kanyawit; Meesomklin, Suwaluck

doi:10.1109/istem-ed50324.2020.9332796

Cited by 11 publications

(1 citation statement)

References 4 publications

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“…Professional internship is the most common way for college students to gain professional experience, and attracts much attention from institutions of higher learning across the globe. It is widely recognized as a golden chance for college students to apply their theoretical knowledge to practice [1][2][3][4][5][6]. As a link of talent cultivation in college, professional internship aims to improve the students' mastery of professional knowledge by combining learning with working, and enhance their adaptability to jobs and professional literacy [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Internship Effect Prediction for Physical Education Majors Based on Artificial Neural Network

Yang

2021

Int. J. Emerg. Technol. Learn.

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Professional internship offers college students a golden chance to apply their theoretical knowledge to practice. Through internship, physical education (PE) majors can match the professional knowledge and skills learned at school with the competencies required by actual jobs. The relevant studies at home and abroad mainly attempt to improve the internship effect. This paper explores the influence of the diversity of job competencies on the internship effect of PE majors, and establishes a prediction model based on artificial neural network (ANN). Firstly, an evaluation index system (EIS) was constructed for the internship quality of PE majors, and a table was prepared for four types of internship jobs for PE majors, as well as their core competences. Then, the sample data for quality evaluation of PE majors’ internship were preprocessed and subjected to feature extraction, in the light of their sequential property. After that, a prediction model was proposed for the internship quality of PE majors, along with its optimization algorithm. The proposed model was proved effective through experiments.

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Section: Introductionmentioning

confidence: 99%

Internship Effect Prediction for Physical Education Majors Based on Artificial Neural Network

Yang

2021

Int. J. Emerg. Technol. Learn.

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Remote Virtual Laboratory Innovation on MIAP Engineering Teaching Model for Electrical Measurement Course

Chumchuen

Akatimagool

2023

Lecture Notes in Networks and Systems

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Enhancing Learning Activities Using Innovation Based Problem-Solving Process in Promoting Practical Skills of Electrical Technology

Chumchuen,

Akatimagool

2024

Towards a Hybrid, Flexible and Socially Engaged Higher Education

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Professional Teaching Practice Through MIAP based Integrated Learning Activities for Electrical Engineering Education

Cited by 11 publications

References 4 publications

Internship Effect Prediction for Physical Education Majors Based on Artificial Neural Network

Internship Effect Prediction for Physical Education Majors Based on Artificial Neural Network

Remote Virtual Laboratory Innovation on MIAP Engineering Teaching Model for Electrical Measurement Course

Enhancing Learning Activities Using Innovation Based Problem-Solving Process in Promoting Practical Skills of Electrical Technology

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