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Using Data Mining In Learning Management Systems Amidst Covid-19
- Source :
- Aksara, Vol 6, Iss 3, Pp 213-216 (2020)
- Publication Year :
- 2020
- Publisher :
- Magister Pendidikan Nonformal Pascasarjana Universitas Negeri Gorontalo, 2020.
-
Abstract
- The Second Semester of Academic Year 2019-2020 was temporarily suspended due to the widespread COVID-19 last March 16, 2020, forcing the President of the Republic of the Philippines, Hon. Rodrigo Roa Duterte imposed an Enhanced Community Quarantine in Luzon which is known as a lockdown closing all the border points of each town and provinces. One of the major problem encountered during the lockdown is the suspension of classes because as per IATF guidelines you need to stay home, the said Memorandum Order was posted in the official gazette, (Medialdea, 2020)The dataset on the features of the Learning Management Systems using Moodle is that Professors will be the one who will set the topics, quizzes, and exercises for his class even the assessment methods on the system. To prevent from slowing down the network, the Team of Seaversity the developer of the learning management systems headed by C/E Ephrem Dela Cernan conducts a ZOOM Training to all Faculty to be familiarized more on the Learning Management Systems of the Philippine Merchant Marine Academy. The Moodle Learning Management Systems is a user-friendly environment because of its features and users can easily adjust from the traditional face to face teaching going to e-Learning approach because of it’s all capabilities as a data mining methods such as statistics, association rule mining, pattern mining visualization, categorization, clustering, and text mining., (AlAjmi & Shakir, 2013)
- Subjects :
- Class (computer programming)
lcsh:LC8-6691
Academic year
Association rule learning
lcsh:Special aspects of education
Computer science
Memorandum
General Medicine
computer.software_genre
data mining, covid19, learning management system
Face-to-face
Categorization
Learning Management
Data mining
Cluster analysis
computer
Subjects
Details
- Language :
- English
- ISSN :
- 27217310 and 24078018
- Volume :
- 6
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Aksara
- Accession number :
- edsair.doi.dedup.....fd700cd3d5b7936d63f2fd5d7073190d