Open AccessLearning Management System Based on Assessment for Learning to Improve Computational Thinking
Author(s) -
Odhitya Desta Triswidrananta,
Agung Nugroho Pramudhita,
Indra Dharma Wijaya
Publication year - 2022
Publication title -
international journal of interactive mobile technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.316
H-Index - 16
ISSN - 1865-7923
DOI - 10.3991/ijim.v16i04.28979
Subject(s) - learning management , computer science , competence (human resources) , synchronous learning , process (computing) , educational technology , experiential learning , distance education , knowledge management , artificial intelligence , mathematics education , cooperative learning , multimedia , teaching method , psychology , social psychology , operating system
There was a change in the implementation of education policies in the emergency period of the spread of the covid-19 virus, the learning process was carried out from home online using various learning resources. This condition triggers the emergence of behavioural problems and student competence in Indonesia. One of the distance learning media that was carried out during the pandemic was using the Learning Management System (LMS). Not only that, a policy evaluation needs to be carried out to evaluate the distance learning system that has been implemented in universities. This is because technology-based distance learning requires a different approach in terms of planning, implementation and evaluation. To increase the effectiveness of learning can use the assessment for learning (AfL) model. The learning process is also expected to be in accordance with learning that directs to have a computational thinking approach which is much needed considering that problem-solving skills are needed in solving everyday problems. Based on the results of data analysis with the 4D development model (define, design, develop, and disseminate), a learning management system based on assessment for learning to improve computational thinking abilities got an average score of 85.2, which means it can be implemented well.