subject predicate object context
72634 Creator c9d52ef4a11ad1277436a89c20d0a5d4
72634 Creator 17452a4d6573661e9aff8220d6982acc
72634 Date 2014-12-02
72634 Is Part Of repository
72634 Is Part Of p4126c1218b67ed02137c530131543764
72634 abstract Emotions play a critical role in the learning and teaching process because learners’ feelings impact motivation, self-regulation and academic achievement. In this literature review of 100+ studies, we identify approximately 100 different emotions that may have a positive, negative or neutral impact on learners’ attitudes, behaviour and cognition. In this review, we explore seven methods of data gathering approaches to measure and understand emotions (i.e., content analysis, natural language processing, behavioural indicators, quantitative instruments, qualitative approaches, well-being word clouds, and intelligent tutoring systems). With increased affordances of technologies to continuously measure emotions (e.g., facial and voice expressions with tablets and smart phones), it might become feasible to monitor learners’ emotions on a real-time basis in the near future.
72634 authorList authors
72634 issue 2
72634 status peerReviewed
72634 uri http://data.open.ac.uk/oro/document/1234614
72634 uri http://data.open.ac.uk/oro/document/1234615
72634 uri http://data.open.ac.uk/oro/document/1234616
72634 uri http://data.open.ac.uk/oro/document/1234617
72634 uri http://data.open.ac.uk/oro/document/1234618
72634 uri http://data.open.ac.uk/oro/document/1234619
72634 uri http://data.open.ac.uk/oro/document/1247076
72634 type Article
72634 label Rienties, Bart and Alden, Bethany (2014). Emotions used in Learning Analytics: a state-of-the-art review. LACE project.
72634 Publisher ext-e6694303da93fd2edc75cc8fa3e99ebe
72634 Title Emotions used in Learning Analytics: a state-of-the-art review
72634 in dataset oro