Empathy, i.e., the ability to understand and feel what others are going through, is essential for value-based and user-centered software development. Empathy helps software engineers fully understand client needs, but also impacts how software engineers work with each other (e.g., within their team). However, junior and less experienced software engineers may not always understand what empathy means and why it matters in a technical domain like software development (and therefore do not pursue opportunities to develop it). We present a video-based training technique for empathy of software engineers. We also show preliminary findings of using the technique in a software engineering project course for second-year software engineering students. We report on student learning, engagement, as well as the perceptions of students on the training technique.

To learn effectively by watching videos, learners need to engage actively with video content. Writing comments on videos (also known as video annotation) is a common way of engagement in Active Video Watching (AVW). Reviewing comments on videos written by peers, as a form of social learning, has also been shown to increase learning. In this paper, we extended social learning in AVW by enabling learners to respond to comments written by their peers (rather than just reviewing peer comments) and replacing a categorical comment rating design with a numerable binary one. We investigated the impact of using such a form of comment reviewing on learning, engagement, and students' perceptions. The findings show that our intervention results in increased learning, engagement and student satisfaction compared to the situation when students could not respond to comments.

Supporting student engagement remains one of the key challenges in video-based learning. This challenge is addressed by active video watching (AVW), a learning approach that supports engagement through different interventions, such as note-taking in the form of comments that learners submit while watching videos. One platform to support AVW is AVW-Space. Previous studies on AVW-Space detail improvements in the system, such as the integration of Artificial Intelligence and Machine Learning (ML) models in the comment feature of the system. This study investigates two machine learning models used to automatically assess the quality of comments when learning communication skills via AVW. One model is generated based on a large set of comments created by students when engaging with videos about presentation skills. For this study, a new model is developed from comments that students submitted when engaging with videos about communication skills. Results show that the new model, which was created from data on communication skills, performed better when assessing comments for communication skills compared to the model generated from comments for another skill. This has been demonstrated by the higher value of inter-rater agreement with the comment quality assessment made by human coders.

Objectives. Communication skills are crucial for effective software development teams, but those skills are difficult to teach. The goal of our project is to evaluate the effectiveness of teaching face-to-face communication skills using AVW-Space, a platform for video-based learning that provides personalized nudges to support student's engagement during video watching. Participants. The participants in our study are second-year software engineering students. The study was conducted over three years, with students enrolled in a semester-long project course. Study Method. We performed a quasi-experimental study over three years to teach face-to-face communication using AVW-Space, a video-based learning platform. We present the instance of AVW-Space we developed to teach face-to-face communication. Participants watched and commented on 10 videos and later commented on the recording of their own team meeting. In 2020, the participants ( n = 50) did not receive nudges, and we use the data collected that year as control. In 2021 ( n = 49) and 2022 ( n = 48), nudges were provided adaptively to encourage students to write more and higher-quality comments. Findings. The findings from the study show the effectiveness of nudges. We found significant differences in engagement when nudges were provided. Furthermore, there is a causal effect of nudges on the interaction time, the total number of comments written, and the number of high-quality comments, as well as on learning. Finally, participants exposed to nudges reported higher perceived learning. Conclusions. Our research shows the effect of nudges on student engagement and learning while using the instance of AVW-Space for teaching face-to-face communication skills. Future work will explore other soft skills, as well as providing explanations for the decisions made by AVW-Space.