LUSTIGOVÁ, Zdena and Pavel BROM. HOW TO OBSERVE ONLINE LEARNER? THE NEW WAYS OF EDUCATIONAL DATA MINING. Online. In Ugur Demiray. Proceedings of 5th International Conference on New Trends in Education iconte 2014. Antalya, Turkey, 2014, p. NESTRÁNKOVÁNO.
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Basic information
Original name HOW TO OBSERVE ONLINE LEARNER? THE NEW WAYS OF EDUCATIONAL DATA MINING
Authors LUSTIGOVÁ, Zdena and Pavel BROM.
Edition Antalya, Turkey, Proceedings of 5th International Conference on New Trends in Education iconte 2014, p. NESTRÁNKOVÁNO, 2014.
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50300 5.3 Education
Country of publisher Turkey
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
Organization unit University College Prague – University of International Relations and Institute of Hospitality Management and Economics, Ltd.
Keywords in English Educational data mining; remote laboratory; log files analysis; science education
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Zdena Lustigová, CSc., učo 12440. Changed: 27/2/2014 14:45.
Abstract
Data mining techniques in education and learning are still in their infancy. Even academic references in this area are limited, although some education-related publications are already beginning to pay attention to this new area and its research potential. Unluckily, the authors are mostly focused on developing data mining and text mining techniques and usually somewhat neglect educational content and intent. The present article describes findings within a medium-scale (N = 56) study, using log files from open remote laboratory at Charles University in Prague, Faculty of Mathematics and Physics, to observe secondary school students’ behaviour during their work in virtual environment. The simple data mining and text mining techniques were used to reveal individual user’s behavioural patterns, to detect disengagement, and to compare learning outcomes and student preferences. The results will be used mainly to improve remote laboratory systems’ adaptability to students’ requirements and capacities.
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