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.

Active Video Watching supports engagement through scalable interventions, such as notetaking in the form of comments. Machine Learning is used to categorize comments based on their quality to provide personalized feedback to students. In previous work on AVW-Space, an online portal for active video watching, a machine learning model was trained using data from several studies on presentation skills. In this paper, we explore the effectiveness in assessing the comment quality of this model in Face-to-Face Meeting Communication skills in comparison to a model trained specifically for this soft skill. We used Explainable Artificial Intelligence to identify and compare the important features of the models. Results show the need for comment quality assessment models to be specific to the soft skill in question and show major differences between their important features, highlighting the necessity to create a model specific to a particular soft skill.

Recent studies on Explainable Artificial Intelligence (XAI) in education show benefits for student learning. However, integrating XAI in AI-based education (AIED) systems requires understanding students' explanation needs. Some approaches to adding XAI to AIED systems include participatory design and co-design involving learners. This study presents a participatory approach to implement explanations in Active Video Watching (AVW). We designed explanations based on the requirements on timing and presentation of explanations and additional feedback from learners during the participatory activity. The implemented explanations support students who made low to medium-quality comments on video content by explaining how comment quality was determined. Furthermore, explanations included recommendations to improve future comments. We present the results of a pilot study on explanations in an AVW platform.

The use of videos in learning has increased over the past years. Along with the popularity of video-based learning is the surge in the interest in Artificial Intelligence in education. Previous studies explored the use of Artificial Intelligence technologies in Active Video Watching, a form of video-based learning. A particular case of Artificial Intelligence in Active Video Watching would be Active Video Watching (AVW)-Space, a video-based learning platform developed by the University of Canterbury. The use of AI in AVW-Space, for example, in assessing the quality of comments made by users, has resulted in an increase in student engagement and learning. Student feedback in recent surveys on the use of Active Video Watching showed an interest in explanations of how the system's AI makes decisions. A way to integrate explanations to the system is through Explainable Artificial Intelligence (XAI). Therefore, this research aims to provide additional insights into the use of XAI and explanations in education and professional training through active video watching. This research also aims to explore the potential of XAI as a way to increase user engagement and learning when using AI-supported features of active video watching systems. A second goal is to look at currently implemented AI / ML models used in active video watching and identify potential points of improvement in the AI / ML models used in active video watching.

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.

Designing explanations for Artificial Intelligence (AI) systems continues to be a challenge due to AI's black-box nature. Among the solutions developed to help in designing explanations in AI technologies is the Question-Driven Design Process for Explainable Artificial Intelligence (XAI) User Experience. In this paper, we report on our experiences using the question-driven design process for XAI in active video watching. We used Active Video Watching (AVW)-Space, an AVW platform developed at the University of Canterbury, as the context for AVW. In the question analysis process, we elicited questions from users on the AI features of the system. We conducted a survey to elicit questions from users on the AI features of the system. We conducted a survey to elicit three human raters categorized the user questions into the different XAI bank categories. Results show that most users tend to ask "how" and "why" questions about the AI-enabled features in the platform. The results of the question analysis will be used in mapping the determined question categories to potential XAI techniques. This can help in deciding the types of explanations to provide to users of AVW in future works on XAI in active video watching.

Video-based learning (VBL) provides self-paced and flexible learning. However, VBL is often a passive learning method. Active video watching (AVW) has been proposed as an approach to increase learner engagement. We investigate the motivation and perception of learners towards gamification to further increase engagement in AVW. Results from a survey in New Zealand and the Philippines show a positive perception towards integrating gamification into AVW, with learners preferring a combination of game elements rather than individual elements. Our findings provide foundations for a gamification intervention in AVW.

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.

We investigated the affective states (both individual and shared emotions) of students using a collaborative and educational game for English called Ibigkas! Our goal was two-fold: (1) To determine the incidence and persistence of affective states exhibited by the students when working individually and in groups, and (2) to adapt the Baker Rodrigo Ocumpaugh Monitoring Protocol for collaborative learning situations. Our findings for this study are as follows: (1) in single-player mode, students exhibited greater engaged concentration, pride, and frustration and less excitement, delight, and confusion compared to the multiplayer mode; (2) that individual emotions can be distinct from group emotions; (3) that negative emotions like frustration and blame/guilt were only felt at the individual level and were not observed as shared by all the members of the group; (4) affective states tended to persist more within an individualized game setting compared to the collaborative game setting where there was a greater number of opportunities to experience a wider range of emotions, hence the low chance of persistence; (5) students within an individualized setting spent more time solving the game rounds, had fewer incorrect answers, even as they experienced more frustration, and finally, (6) students within a collaborative setting had fewer errors when they had a higher incidence of excitement and had more errors when they appeared to be concentrating due to the presence of the “gaming the system” behavior.