Abstract. Although videos are a highly popular digital medium for learning, video watching can be a passive activity and results in limited learning. This calls for interactive means to support engagement and active video watching. However, there is limited insight into what engagement challenges have to be overcome and what intelligent features are needed. This paper presents an empirical way to elicit requirements for innovative functionality to support constructive video-based learning. We present two user studies with an active video watching system instantiated for soft skill learning (pitch presentations). Based on the studies, we identify whether learning is happening and what kind of interaction contributes to learning, what difficulties participants face and how these can be overcome with additional intelligent support. Our findings show that participants who engaged in constructive learning have improved their conceptual understanding of presentation skills, while those who exhibited more passive ways of learning have not improved as much as constructive learners. Analysis of participants' profiles and experiences led to requirements for intelligent support with active video watching. Based on this, we propose intelligent nudging in the form of signposting and prompts to further promote constructive learning.

Videos 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.

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