Open AccessOnline Assessment of Electric Circuit based on Machine Learning During Covid-19 Pandemic Situation
Author(s) -
Kunanan Thapwiroch,
Apisada Kumlue,
Niradtapong Saoyong,
Praparat Taprasa,
Supachai Puengsungewan
Publication year - 2021
Publication title -
indonesian journal of teaching in science
Language(s) - English
Resource type - Journals
eISSN - 2776-6152
pISSN - 2776-6101
DOI - 10.17509/ijotis.v1i2.41188
Subject(s) - computer science , process (computing) , artificial intelligence , bachelor , cluster analysis , machine learning , feature extraction , set (abstract data type) , covid-19 , online learning , key (lock) , feature (linguistics) , data mining , multimedia , medicine , linguistics , philosophy , computer security , disease , archaeology , pathology , infectious disease (medical specialty) , history , programming language , operating system
Due to the Covid-19 pandemic crisis, educational institutions have to change their teaching styles because students cannot go to the school (on-site). Therefore, online learning management is required, but the problem of online learning is that the assessment is difficult and not easy to realize based on standard assessment. To achieve the online assessment, machine learning has been applied as a powerful algorithm to realize the novel online assessment for electric circuit course of bachelor students at the department of electrical technology education, King Mongkut’s University of Technology Thonburi, Thailand. To achieve the data collection process, speech to text algorithm has been applied. Next, feature extraction would be adopted as the main key to extracting the knowledge from the data from speech to text algorithm. The output of feature extraction is the dataset of the proposed system. Finally, the clustering algorithm would be applied to set up the learning process of the proposed method. The accuracy of the proposed method can reach 100% when the word feature is appropriate.