Social media use among teenagers has surged in recent years, heightening their exposure to risks such as online grooming—a process where adults build online relationships with minors for abusive purposes. To address this threat, it is essential to explore innovative, technological approaches for educating adolescents. Therefore, this study sees the development of a narrative-based mini game to help educate adolescents about online grooming. A pilot study with 25 adolescents and young adults evaluated the mini-game's effectiveness in assessing their understanding of grooming and detection skills through a questionnaire and their in-game behavior. The results demonstrate that the mini-game significantly improved participants' knowledge of grooming and their ability to detect grooming behaviors, as evidenced by enhanced performance throughout the game.

In the context of Social Media literacy, the success of educational interventions relies on designing motivational learning environments that seek students’ engagement. Despite existing achievements in social media education to include more appealing resources (e.g. gamification, authentic learning, interactive simulations), limited research has explored the factors that influence students’ engagement. This study aims to investigate the factors that influence engagement in an educational social media platform. Specifically, the factors that shape interest and enjoyment in Instareal, an educational tool that combines narrative scripts and collaborative learning flow patterns to educate teenagers about social media risks and challenges. To answer our research questions, we analyze the log data generated by high school students (N = 100) who tested a Social Media Literacy training with the support of Instareal. A factor analysis and a backward stepwise regression is performed over a dataset containing students’ traces of online activities and social interactions. The results show that factors such as the social participation of the students (comments, likes), curiosity (opening profiles, following others), and the quality of their answers influence interest and enjoyment within the platform. The results of this study offer new insights into measuring and enhancing student engagement when using educational social media environments.

Computer-supported collaborative learning (CSCL) seeks to improve students’ learning with the support of technology. Although CSCL facilitates an enriching environment for cooperation, CSCL activities might be affected by asymmetrical participation. Group awareness tools have proven to enhance learners’ interactions, balancing the levels of participation and increasing the learning outcomes. This study investigates the effect of a group awareness tool (GAT) on participation within synchronous CSCL activities in the context of extracurricular activities. We designed a quasi-experimental within-subject study (GAT exposure and control condition) and tested an ad hoc GAT in a web-based CSCL platform with high school students (N = 140). The measured variables included the effect of the GAT on students’ participation and the quality of discussion. A complementary analysis evaluated students’ perceptions of the tool. Results revealed that the implementation of a GAT encourages students to take part in CSCL discussions without explicitly hindering its quality. Even so, the balance of participation was not significantly affected. Students further reported that they found the GAT useful to regulate collaboration. The results from the study provide further knowledge of the functioning of group awareness tools in secondary education and share an insight into how students perceive them.

Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format. Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots using a prompt-based approach. We present a case study with a simple system that enables mixed-turn interactions and discuss the insights and preliminary guidelines obtained from initial tests. We examine ChatGPT's ability to pursue natural educational conversations, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles. Although the results are encouraging, challenges are posed by the highly structured form of responses by ChatGPT, as well as their variability, which can lead to an unexpected switch of the chatbot's role from a teacher to a therapist. We provide some initial guidelines to address these issues and to facilitate the development of effective educational chatbots.

Self-reports of affective states are increasingly being collected in educational settings. However, individual definitions and usage of emotion and mood terms are often subjective despite objective definitions becoming more widely accepted. We explore whether the variation among individual learners in how mood terms are defined presents an obstacle to using self-reported mood data for group comparison studies. Following a design-based research methodology, we ran two case studies to explore the use of ClassMood App in a multimodal learning study and the validity of the self-reported mood data it collected. During the first case, 24 primary school students experienced difficulty understanding the words used to describe the moods in ClassMood App. In the second case, involving 77 university students, we explored whether the misunderstanding of mood words persisted with older students. Participants were asked to rate their familiarity with and match definitions to a set of 8 mood words. We found that levels of familiarity with the mood words varied greatly and 17.9% of all definition matching attempts were incorrect. The results suggest that the variation in subjective definitions of mood terms is likely to affect the validity of the data collected by the ClassMood App for group comparison studies.