Affective states in computer-supported collaborative learning: Studying the past to drive the future
ARTICLE
Rachel Carlos Duque Reis, Seiji Isotani, Carla Lopes Rodriguez, Kamila Takayama Lyra, University of São Paulo, Brazil ; Patrícia Augustin Jaques, University of Vale do Rio dos Sinos, Brazil ; Ig Ibert Bittencourt, Federal University of Alagoas, Brazil
Computers & Education Volume 120, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd
Abstract
CSCL investigates ways of promoting students' collaboration through technology. Although affective states and socio-emotional factors have an important effect on learning, few studies have classified and analyzed the results obtained by the scientific community to show their real impacts on in CSCL settings. The lack of a comprehensive overview and understanding of previous findings hampers the survey on research gaps, challenges and trends. This work addresses a systematic mapping of the literature for the summarization of results and discussion on research directions for the use of affective states and socio-emotional factors to support the development of intelligent CSCL environments. Six digital libraries were queried and 58 studies were deeply analyzed and categorized. Over 90% of the studies considered emotion and personality traits in the CSCL environment; most studies used personality recognition by questionnaires rather than automatic sources of inference; most technological approaches employed instruments to collect, analyze and/or represent emotions; and most papers that reported empirical experiments focused on “emotional awareness” and “interaction among students in a CSCL environment”. We have identified three main challenges to be addressed by the community in the next decade for an adequate incorporation of affective states in CSCL environments: emotional awareness, orchestration of students' interaction and group formation.
Citation
Reis, R.C.D., Isotani, S., Rodriguez, C.L., Lyra, K.T., Jaques, P.A. & Bittencourt, I.I. (2018). Affective states in computer-supported collaborative learning: Studying the past to drive the future. Computers & Education, 120(1), 29-50. Elsevier Ltd. Retrieved March 28, 2024 from https://www.learntechlib.org/p/200481/.
This record was imported from Computers & Education on February 1, 2019. Computers & Education is a publication of Elsevier.
Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2018.01.015Keywords
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