A visual recommender tool in a collaborative learning experience. (1st March 2016)
- Record Type:
- Journal Article
- Title:
- A visual recommender tool in a collaborative learning experience. (1st March 2016)
- Main Title:
- A visual recommender tool in a collaborative learning experience
- Authors:
- Anaya, Antonio R.
Luque, Manuel
Peinado, Manuel - Abstract:
- Highlights: We propose a tool that visually guides the recommendation process to increase collaboration. It analyzes interactions and an influence diagram which warns about the collaboration circumstances. A visual explanation decision tree shows the collaboration circumstances which are understandable. Our tool provokes self-reflection and provokes sense making about collaboration. Abstract: Collaborative learning incorporates a social component in distance education to minimize the disadvantages of studying in solitude. Frequent analysis of student interactions is required for assessing collaboration. Collaboration analytics arose as a discipline to study student interactions and to promote active participation in e-learning environments. Unfortunately, researchers have been more focused on finding methods to solve collaboration problems than on explaining the results to tutors and students. Yet if students do not understand the results of collaboration analysis methods, they will rarely follow their advice. In this paper we propose a tool that analyzes student interactions and visually explains the collaboration circumstances to provoke the self-reflection and promote the sensemaking about collaboration. The tool presents a visual explanatory decision tree that graphically highlights student collaboration circumstances and helps to understand the reasoning followed by the tool when prescribing a recommendation. An assessment of the tool has demonstrated: (1) the studentsHighlights: We propose a tool that visually guides the recommendation process to increase collaboration. It analyzes interactions and an influence diagram which warns about the collaboration circumstances. A visual explanation decision tree shows the collaboration circumstances which are understandable. Our tool provokes self-reflection and provokes sense making about collaboration. Abstract: Collaborative learning incorporates a social component in distance education to minimize the disadvantages of studying in solitude. Frequent analysis of student interactions is required for assessing collaboration. Collaboration analytics arose as a discipline to study student interactions and to promote active participation in e-learning environments. Unfortunately, researchers have been more focused on finding methods to solve collaboration problems than on explaining the results to tutors and students. Yet if students do not understand the results of collaboration analysis methods, they will rarely follow their advice. In this paper we propose a tool that analyzes student interactions and visually explains the collaboration circumstances to provoke the self-reflection and promote the sensemaking about collaboration. The tool presents a visual explanatory decision tree that graphically highlights student collaboration circumstances and helps to understand the reasoning followed by the tool when prescribing a recommendation. An assessment of the tool has demonstrated: (1) the students collaboration circumstances showed by the tool are easy to understand and (2) the students could realize the possible actions to improve the collaboration process. … (more)
- Is Part Of:
- Expert systems with applications. Volume 45(2016)
- Journal:
- Expert systems with applications
- Issue:
- Volume 45(2016)
- Issue Display:
- Volume 45, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 45
- Issue:
- 2016
- Issue Sort Value:
- 2016-0045-2016-0000
- Page Start:
- 248
- Page End:
- 259
- Publication Date:
- 2016-03-01
- Subjects:
- Recommender tool -- Data mining -- Influence diagram -- Collaborative learning
Expert systems (Computer science) -- Periodicals
Systèmes experts (Informatique) -- Périodiques
Electronic journals
006.33 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09574174 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.eswa.2015.01.071 ↗
- Languages:
- English
- ISSNs:
- 0957-4174
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.004220
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 7475.xml