Dimitrova VMitrovic, AntonijaPiotrkowicz ALau LWeerasinghe A2017-10-152017-10-152017Dimitrova V, Mitrovic A, Piotrkowicz A, Lau L, Weerasinghe A (2017). Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching. Bratislava, Slovakia: User Modeling, Adaptation and Personalization. 09/07/2017-12/07/2017. http://dl.acm.org/citation.cfm?id=3079628. 22-31.http://hdl.handle.net/10092/14529Videos can be a powerful medium for acquiring soft skills, where learning requires contextualisation in personal experience and ability to see different perspectives. However, to learn effectively while watching videos, students need to actively engage with video content. We implemented interactive notetaking during video watching in an active video watching system (AVW) as a means to encourage engagement. This paper proposes a systematic approach to utilise learning analytics for the introduction of adaptive intervention - a choice architecture for personalised nudges in the AVW to extend learning. A user study was conducted and used as an illustration. By characterising clusters derived from user profiles, we identify different styles of engagement, such as parochial learning, habitual video watching, and self-regulated learning (which is the target ideal behaviour). To find opportunities for interventions, interaction traces in the AVW were used to identify video intervals with high user interest and relevant behaviour patterns that indicate when nudges may be triggered. A prediction model was developed to identify comments that are likely to have high social value, and can be used as examples in nudges. A framework for interactive personalised nudges was then conceptualised for the case study.enThis work is licensed under a Creative Commons Attribution International 4.0 License.video-based learningsoft-skillspersonalised nudgesanalyticsConference Contributions - Published2017-07-12Fields of Research::39 - Education::3904 - Specialist studies in education::390405 - Educational technology and computingFields of Research::280000 Information, Computing and Communication Scienceshttps://doi.org/10.1145/3079628.3079683