The interest in computing learning activities is continuously increasing, keeping pace with the evolution of educational technologies. This rise in engagement is evident within approaches grounded in constructivism and constructionism, which provide an ideal setting for the spectrum of skills, literacies, and competencies that global initiatives capitalise on in K–12 education. Due to recent advancements in technology and societal changes, we are addressing the need for an updated perspective focussed on designing and implementing computational activities. To achieve this, we have analysed forty-six peer-reviewed empirical studies from the last decade to summarise technology-supported learning strategies' current direction and future potential. The main findings describe programming software and IoT as the preferred digital tools, often presented in novel combinations, extensions, and contextualisations. Implementation primarily occurs within the domain of computer science but exhibits a high degree of interdisciplinarity. Furthermore, there is an increase in activities referring to real-life scenarios, encouraging learners to develop computational skills beyond the classroom and expand their digital awareness through interaction with emerging technologies. We leverage these trends and significant patterns by proposing a summary of focal points for future research and practice, with a focus on activity design.

Artificial Intelligence (AI) is playing a growing role in K-12 education.However, curricula often lack structure and proper assessment when paired with collaborative approaches like Design Thinking (DT).Here, behavioral and affective dynamics are overlooked, even though they are indicators of both performance and quality of the learning experience, warranting a more in-depth exploration through Multi-Modal Learning Analytics.Therefore, we engaged 63 students, divided into 29 groups (aged 11 to 15) in a DT workshop on AI, analyzing their performance across each stage of their experience, including their behavioral and affective (i.e., emotional) states, using data collected from physiological sensors, audio, and video recordings.Our results show that certain conditions (e.g., joint visual attention, boredom, and high stress) consistently predicted positive or negative performance across all stages of the workshop, while affective states such as confusion, frustration, high engagement, and low stress fluctuate with implications on the learning experience.

Gamification in education has drawn significant attention from researchers seeking to enhance engagement and improve learning outcomes, particularly in Science, Technology, Engineering, and Mathematics (STEM) subjects. This study investigates the role of gamification in enhancing children's motivation and engagement in informal science learning settings. A gamified web application was developed, and a case study was conducted with the participation of 53 fifth-grade students. A mixed-methods approach was used, incorporating questionnaires, log data, and observational insights. The results showed that gamification elements, such as points and leaderboards, significantly increased motivation by fostering a sense of achievement and introducing healthy competition. However, badges were less effective in increasing engagement. Despite this, students motivated by one game element were often motivated by others, and all gamified elements contributed to a generally positive perception of the application. The study highlights the promise of gamification in informal learning settings but also emphasizes the need for continuous evaluation to improve these techniques for diverse learner groups and avoid potential negative effects.

Design thinking (DT) is an approach used to address complex societal issues by engaging learners in hands-on problem-solving. Current educational guidelines seek to leverage the synergies between DT, digital literacy, and computational skills to enhance students' understanding and application in real-world matters, such as cyber-security and online awareness. However, tackling such learning experiences requires a shift in teaching approaches and the endorsement of educational technologies to foster digital agency. This empowers students to become proactive, informed participants in the digital future, starting from the classroom. To explore the feasibility of this approach from the students' perspectives, our study carried out a DT project with a block-based programming platform to emphasize the importance of cyber-security. Our study involved 113 students aged 10 to 12 across three K-12 schools. Of these, 52 agreed to participate in data collection via a semi-structured interview at the end of the project to share their experiences. We gained insights into students' perceptions regarding collaboration, technology interaction, and knowledge gained through thematic analysis of the interview data, considering both challenges and potential. For instance, we shed light on roadblocks arising from mismatched tool affordances and students' familiarity, as well as topic differentiation in elaboration based on participants' skills. At the same time, DT potential, when paired with digital tools, emerged to ease into everyday wicked problems in an engaging way and foster solution-oriented thinking.

Inquiry-based science learning is an educational strategy to enable students to actively engage in science learning concepts through inquiry activities such as experiments and observations. Gamification demonstrates a promising potential to engage children in learning contexts. In this regard, this paper presents Experiverse as an exemplar along with its associated six key design considerations to illustrate how to develop an application based on the concept of gamification and inquiry-based science learning for children. This paper reports on our experience evaluating Experiverse with 25 children (aged 9-13) in an informal setting based on data collected from log data, surveys and interviews to explore the feasibility of engaging children in science learning outside their classroom. Results indicated that children’s motivation (MO) significantly correlates with their enjoyment (PE) and perceived learning outcome (LOA) from using Experiverse. While children’s perceived learning outcome is significantly positively correlated with the number of view visits on Experiverse (EVV), the number of experiment view visits (EVV) is also significantly positively correlated with children’s perceived easiness of the app. Finally, this paper discusses the key findings of this study and points out the design implications for future research, like combining in-app experience and hands-on experimentation in real-life situations.

The underrepresentation of women in the software engineering field remains a challenge in both academia and industry. While enrollment numbers remain low, there is also limited research on how responsibilities and roles are distributed within student software development teams, particularly through a gendered lens. This study aims to address this gap by investigating the relationship between team gender composition, female leadership, and the distribution of technical roles among women in SE teams. We analyze data from 628 students—183 of whom were women—across 85 software development teams over seven years. Through predictive analysis, we test three hypotheses: (1) whether women in teams with fewer female members are less likely to assume highly specialized technical roles, (2) whether the presence of a woman in a leadership position increases the likelihood of other women taking on highly technical roles, and (3) whether the distribution of roles among women varies across different student cohorts. Our findings provide partial support for the first and third hypotheses. Women are generally less likely than men to occupy highly specialized technical roles; however, this likelihood is not significantly influenced by the gender ratio within teams. Similarly, while role diversification among women has increased over time—particularly in positions such as Scrum Master and Report Manager—this trend appears to be driven more by declining male participation in certain roles than by increased female engagement. Contrary to our second hypothesis, having a woman as team leader does not significantly impact the likelihood of women assuming highly technical roles. The insights from this study contribute to a deeper understanding of gender dynamics in SE education and highlight the importance of fostering inclusive environments that support diverse role exploration and long-term engagement for women in software engineering.

Researchers have emphasized the importance of Science, Technology, Engineering, and Mathematics (STEM) education and recognized it as a key aspect in today's global landscape. Exploring STEM learning opportunities in educational settings, our research leverages the need to apply different learning approaches that enhance children's engagement. This work goes beyond learning in the classrooms and focuses on the various science-related everyday life experiences. Examples of these experiences can be a museum visit, an independent exploration through reading, watching, or practical experimentation from children. Therefore, we suggest the Science Chaser which is a gamified web app that aims to integrate STEM activities into educational settings for children, while at the same time collecting data about their “science proficiency” level. More specifically, Science Chaser serves as a science companion and provides users with a variety of science activities and challenges through a user friendly, enjoyable, and gamified environment. The Science Chaser acts as the driving force leading users beyond the conventional use of a web app, enabling active exploration of new scientific paths while allowing them to create their individual science journey by reporting and monitoring their activities. Apart from offering an engaging experience for children, the data collected through Science Chaser offers evidence-based insights into the effectiveness of science activities.

Technology-enhanced learning environments in the fields of Science, Technology, Engineering, and Mathematics (STEM) are popular in both formal and informal educational settings. STEM learning in childhood provides students with hands-on experiences in research and problem-solving, affecting their future studies and career paths. The objective of this workshop is to bring together researchers, educators, designers, and practitioners to exchange insights on utilizing technology in creating engaging STEM learning experiences for children. The collaborative approach of the workshop is expected to facilitate a rich exchange of ideas and insights among attendees, contributing to the improvement of technology driven STEM learning for children.

Design thinking (DT) and computational activities foster children’s knowledge capital for 21st-century literacies. The analysis of these activities often overlooks affective and behavioural states despite their significance in providing insights into children’s learning processes. Typically, these states and their changes are self-reported, lacking real-time capturing. Moreover, inquiries via Multi-Modal Data (MMD) for more comprehensive views are underrepresented in the current literature. We, therefore, conducted a DT activity focusing on coding engaging 33 children (aged 10 to 12) and analysed measurements including learning gain (from knowledge tests) and behavioural and affective states (from physiological sensors, video and voice recordings). Our results show that engagement and confusion exhibit positive correlations between MMD measurements and learning gain, while stress, frustration and anger stand out as detrimental for it. By mapping transitions in states experienced by the children, we unravelled negative learning scenarios that should be limited, along with positive indicators of increased performance.

Nowadays, learning activities have become more interactive and collaborative than ever before. However, it remains unclear what makes the group perform differently in such a learning context. With the empowerment of multimodal data (MMD), we conducted a field study involving 12 groups of children who collaborated during two-day-long classroom activities. This paper reports on a quantitative analysis and temporal explanation concerning the relation between children's performance and their group-level MMD measurements during a collaborative coding session in a design thinking activity. We computed each group's performance based on the created artefacts and compared the groups with better performance than the others. The results demonstrate that high-performing groups show more joint engagement, joint visual attention, and joint emotional intensity of delight, while low-performing groups show significantly more joint emotional intensity of frustration. In addition, the evolution over the four temporal phases showed different patterns between high and low-performing groups. Finally, this paper discusses design and theoretical implications for educators, researchers and practitioners.

In the last decade, educational reforms introduced technologies and digital tools to a higher degree, serving as powerful catalysts in educational innovation for creating, managing, and delivering content in STEM education with a focus on computational and engineering subjects. However, in the small reality of the classroom, some discrepancies arise between policy objectives and everyday practice. The research community emphasises the importance of providing technology infusions based on educators' pedagogical beliefs as well as needs rooted in the authentic scholarly environment. Considering this perspective when orchestrating in-classroom technology can support educators in their daily experiences and enhance their digital competencies. To contribute to this aim, we propose a case study carried out via interviews with fellow primary and secondary teachers, tackling challenges in STEM (Science, Technology, Engineering, and Mathematics), including Computer Science (CS) and Computational thinking (CT) education delivery. Along with the teachers' perspectives, our study aims to obtain a detailed account of the classroom's internal dynamics as perceived by the teachers. The most prominent findings concern the disparity among educators' proficiency in technology endorsement, impacting the consistency in content delivery, the complexity of the assessment of students' learning process if it is digital-based, and the inclusivity and trustworthiness of online content. Finally, we suggest guidance for a further mindful in-school uptake of technology to support teachers in orchestrating content delivery with educational technologies and enhance their confidence in this aspect.

Environmental education (EE) plays a vital role in engaging young people in exploring environmental issues and developing their sense of responsibility to the environment. Although gamification appears to be a promising way to motivate and engage K-12 students, it is unclear how it should be implemented in EE and whether it holds promising results similar to other contexts, such as science and engineering education. This paper reports a systematic literature review analysing the 28 papers published in the last five years. The results show how gamification has been employed to support EE and in what contexts. More specifically, the findings of this review contribute to our knowledge in the following three aspects: (1) EE strategies for implementing gamified interventions, (2) gamification strategies and elements utilised in EE, and (3) reported outcomes of gamified EE intervention. Finally, the paper discusses the implications for future related research on developing gamified interventions for EE.

Embodied learning offers new opportunities to enhance learning effectively, and engage children with stimulating educational experiences. Multi Sensory Environments (MSEs) are spaces that allow for several interaction modalities that stimulate users’ senses and allow the collection of multimodal data. In educational contexts, they provide opportunities to support children’s learning in a playful manner. The use of MSEs is usually carried out with the collaboration of teachers; their perspectives and responsibilities are crucial for the children’s experience. The goal of our research is to uncover evidence-based challenges and opportunities, while considering teachers’ experiences. We conducted fourteen semi-structured interviews with teachers (n = 6) and researchers (n = 8) experienced using MSEs’, and analysed the identified challenges and considerations during a workshop with four Child-Computer Interaction (CCI) experts. We offer a series of implications for consideration when designing and/or using MSEs to support children’s learning.

Within the Child-Computer Interaction community fun and learning have been a prominent, recurring theme. However, despite the general interest, fun is often handled as a commonsense notion, without a commonly accepted definition, underlying theoretical framework and ways of measurements. Without these, both researching, designing for, implementing, and evaluating fun elements in learning activities are challenging, and can result in contradicting findings. This workshop aims to address these issues by bringing together participants from different disciplines with various backgrounds who has an interest and experience in the topic of fun in learning.

The role of fun in learning, and specifically in learning to code, is critical but not yet fully understood. Fun is typically measured by post session questionnaires, which are coarse-grained, evaluating activities that sometimes last an hour, a day or longer. Here we examine how fun impacts learning during a coding activity, combining continuous physiological response data from wristbands and facial expressions from facial camera videos, along with self-reported measures (i.e. knowledge test and reported fun). Data were collected from primary school students (N = 53) in a single-occasion, two-hours long coding workshop, with the BBC micro:bits. We found that a) sadness, anger and stress are negatively, and arousal is positively related to students’ relative learning gain (RLG), b) experienced fun is positively related to students' RLG and c) RLG and fun are related to certain physiological markers derived from the physiological response data.

The confluence of wearable technologies for sensing learners and the quantified-self provides a unique opportunity to understand learners’ experience in diverse learning contexts. We use data from learners using Empatica Wristbands and self-reported questionnaire. We compute stress, arousal, engagement and emotional regulation from physiological data; and perceived performance from the self-reported data. We use Fuzzy Set Qualitative Comparative Analysis (fsQCA) to find relations between the physiological measurements and the perceived learning performance. The results show how the presence or absence of arousal, engagement, emotional regulation, and stress, as well as their combinations, can be sufficient to explain high perceived learning performance

Identifying and supporting children’s play and problem solving behaviour is important for designing educational technologies. This can inform feedback mechanisms to scaffold learning (provide hints or progress information), and assist facilitators (teachers, parents) in supporting children. Traditionally, researchers manually code video to dissect children’s nuanced play and problem solving behaviour. Advancements in sensing technologies and their respective Multi-Modal Data (MMD), afford observation of invisible states (cognitive, affective, physiological), and provide opportunities to inspect internal processes experienced during learning and play. However, limited research combines traditional video annotations and MMD to understand children’s behaviour as they interact with educational technology. To address this concern, we collected data from webcam, wristband, eye-trackers, and Kinect, as 26 children, aged 10-12, played a Motion-Based Educational Games (MBEG). Results showed significant differences in children’s experience during play and problem solving episodes, and motivate design considerations aimed to facilitate children’s interactions with MBEG.

This paper investigates the relation between children’s joint gaze and emotions with the information flow of the screen from a causal point of view, in the context of collaborative coding. We employ Granger’s definition of causality to extend the knowledge we have about children’s collaborative activities from correlational methods. We organised a coding workshop with 50 children (10 dyads and 10 triads; 13-16 years old). While the children were coding collaboratively, their facial video and the screen were recorded. From the screen recording we computed the information flow; and from the facial video we computed children’s emotions (e.g., frustration and boredom) and estimated their gaze. The gaze estimation was used to compute the joint visual attention (JVA) of the team. Our results show that for high performing teams JVA drives the information flow; while for low performing teams we observe causal relation between emotions and information flow. In particular for the low performing teams, frustration and boredom drive the information flow and the information flow then drives children’s confusion. These results extend the understanding of the socio-cognitive processes underlying collaborative performance, which is primarily correlational in nature, with the causal relations between measurements. These novel results have the potential to guide the design of learning tools that scaffold children’s learning and collaboration.

Motion-Based Touchless Games (MBTG) are being investigated as a promising interaction paradigm in children's learning experiences. Within these games, children's digital persona (i.e, avatar), enables them to efficiently communicate their motion-based interactivity. However, the role of children's Avatar Self-Representation (ASR) in educational MBTG is rather under-explored. We present an in-situ within subjects study where 46 children, aged 8--12, played three MBTG with different ASRs. Each avatar had varying visual similarity and movement congruity (synchronisation of movement in digital and physical spaces) to the child. We automatically and continuously monitored children's experiences using sensing technology (eye-trackers, facial video, wristband data, and Kinect skeleton data). This allowed us to understand how children experience the different ASRs, by providing insights into their affective and behavioural processes. The results showed that ASRs have an effect on children's stress, arousal, fatigue, movement, visual inspection (focus) and cognitive load. By exploring the relationship between children's degree of self-representation and their affective and behavioural states, our findings help shape the design of future educational MBTG for children, and emphasises the need for additional studies to investigate how ASRs impacts children's behavioural, interaction, cognitive and learning processes.

Multi-Modal Data (MMD) can help educational games researchers understand the synergistic relationship between player's movement and their learning experiences, and consequently uncover insights that may lead to improved design of movement-based game technologies for learning. Predicting player performance fosters opportunities to cultivate heightened educational experiences and outcomes. However, predicting player's performance utilising player-generated MMD during their interactions with educational Motion-Based Touchless Games (MBTG) is challenging. To bridge this gap, we implemented an in-situ study where 26 users, age 11, played 2 maths MBTGs in a single 20-30 minute session. We collected player's MMD (i.e., gaze data from eye-tracking glasses, physiological data from wristbands, and skeleton data from Kinect) produced during game-play. To investigate the potential of MMD for predicting player's academic performance, we used machine learning techniques and MMD derived from player's game-play. This allowed us to identify the MMD features that drive rapid highly accurate predictions of players' academic performance in educational MBTGs. This might allow us to provide real-time proactive feedback to the player to support them through their educational gaming experience. Our analysis compared two data lengths corresponding to half and full duration of the player's question solving time. We showed that all combinations of extracted features associated with gaze, physiological, and skeleton data, predicted student performance more accurately than the majority baseline. Additionally, the most accurate prediction of player's performance derived from the combination of gaze and physiological data for both full and half data lengths. Our findings emphasise the significance of using MMD for real-time performance prediction in educational MBTG and offer implications for practice.

The assessment of learning during class activities mostly relies on standardized questionnaires to evaluate the efficacy of the learning design elements. However, standardized questionnaires pose additional strain on students, do not provide “temporal” information during the learning experience, require considerable effort and language competence, and sometimes are not appropriate. To overcome these challenges, we propose using wearable devices, which allow for continuous and unobtrusive monitoring of physiological parameters during learning. In this paper we set out to quantify how well we can infer students’ learning experience from wrist-worn devices capturing physiological data. We collected data from 31 students in 93 class sessions (3 class sessions per student), and our analysis shows that wrist data can predict the learning experience with 11% error. We also show that 6.25 min (SD = 3.1 min) of data are needed to achieve a reliable estimate (i.e., 13.8% error). Our work highlights the benefits and limitations of utilizing wearable devices to assess learning experiences. Our findings help shape the future of quantified-self technologies in learning by pointing out the substantial benefits of physiological sensing for self-monitoring, evaluation, and metacognitive reflection in learning.

This paper employs facial features to recognize emotions during a coding activity with 50 children. Extracting group-level emotional states via facial features, allows us to understand how emotions of a group affect collaboration. To do so, we captured joint emotional state using videos and collaborative experience using questionnaires, from collaborative coding sessions. We define groups' emotional state using a method inspired from dynamic systems, utilizing a measure called cross-recurrence. We also define a collaborative emotional profile using the different measurements from facial features of children. The results show that the emotional cross recurrence (coming from the videos) is positively related with the collaborative experience (coming from the surveys). We also show that the groups with better experience than the others showcase more positive and a consistent set of emotions during the coding activity. The results inform the design of an emotion-aware collaborative support system.

Computational thinking and coding has received considerable attention over the past several years. Considerable efforts worldwide suggest the need for more empirical studies providing evidence-based practices to introduce and engage children with coding activities. The main goal of this study is to examine which programming concepts students use when they want to develop a game, and what is the interrelation among these concepts. To achieve our goal, a field study was designed and data were collected from coding activities. In detail, during a two-week period, one-day workshops were organized almost every day on which 44 children participating in, with ages between 8-17. The workshops follow a constructionist approach and comprise of two parts. First the children interact with robots, and then develop a game using Scratch. The findings provide a deeper understanding on how children code by showing the use of specific programming concepts to develop their projects and their correlations. Hence, we improve our knowledge about children's competences in coding.

The Make2Learn workshop aims to explore the introduction in the learning processes of tools and methods for creative and joyful ideation, design and prototyping of Internet of Things (IoT) artifacts. Making IoT artefacts enable children to foster co-creativity and joy in learning processes and to construct knowledge; leading to STEM concepts. Making activities for IoT often have a broad perspective that includes not just digital fabrication, but also design thinking concepts such as problem elaboration, brainstorming, ideation and reflection. Make2learn aims to bring together a multidisciplinary group of experts for exploring how the intersection between the design, making, learning and IoT fields can accelerate the acquisition of 21st Century learning competences. During the workshop a set of tools and methods to engage children in ideation and prototyping of IoT artefacts will be demonstrated. Participants are also invited to bring and present their own tools. This will allow us to better understand and improve the value of Maker philosophy and the role of design and making IoT technologies to support teaching and learning.

Computational thinking and coding is gradually becoming an important part of K-12 education. Most parents, policy makers, teachers, and industrial stakeholders want their children to attain computational thinking and coding competences, since learning how to code is emerging as an important skill for the 21st century. Currently, educators are leveraging a variety of technological tools and programming environments, which can provide challenging and dynamic coding experiences. Despite the growing research on the design of coding experiences for children, it is still difficult to say how children of different ages learn to code, and to cite differences in their task-based behaviour. This study uses eye-tracking data from 44 children (here divided into "kids" [age 8-12] and "teens" [age 13-17]) to understand the learning process of coding in a deeper way, and the role of gaze in the learning gain and the different age groups. The results show that kids are more interested in the appearance of the characters, while teens exhibit more hypothesis-testing behaviour in relation to the code. In terms of collaboration, teens spent more time overall performing the task than did kids (higher similarity gaze). Our results suggest that eye-tracking data can successfully reveal how children of different ages learn to code.

During the last few years, the development of tools for learning programming in primary and secondary schools (shortly K-12) has reached a significant turning point. This study reviews the published papers on the field of tangible programming languages (TPLs) in K-12 schools in order to summarize the findings, guide future studies and give reflections for design and practice. From a systematic literature search 12 TPL peer-reviewed articles were collected and analyzed. Results of this short survey show that designers should emphasize on TPLs unambiguous manipulations, and consider clear mappings between tangible and virtual commands. Despite the challenges, the studies reviewed suggest that implementing programming lessons in K-12 education using TPL could be an enjoyable and effective learning experience.

Programming-based making activities are at the core of teaching strategies to engage young students in learning programming for developing computational thinking skills. Despite the initial evidences of enthusiastic participation in such activities, more systematic studies are needed to better understand drivers of students' intentions to participate in programming-based making activities. In this paper, we present an exploratory study which aim to address this issue by examining the interrelations among cognitive (i.e., perceived usefulness, perceived ease of use) and affective (i.e., enjoyment) characteristics for both boys and girls. To this end, we build on complexity theory and configuration theory, present a conceptual model, and employ fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of 105 young students, to identify such interrelations. The findings provide insights on how the examined factors may have a different influence for boys and girls, an outcome that can be used to re-design educational programs targeting maximizing engagement regardless gender.

With the proliferation of programming languages for children (i.e., Scratch, Alice, Kodu) combined with programmable hardware (i.e., Arduino, 3D printers, robots); efforts to provide evidence based best practices for introducing and engaging children in programming is urgently needed. In this work-in-progress we present the early results of an empirical investigation, regarding students' attitudes towards the creative learning context, and the differences between female and male students. The context is a workshop program, implemented by a group of computer science researchers and artists, with the purpose to introduce young students' programming through creative and meaningful experiences. Hundred and twenty-eight 15-years old students participated to the program. The empirical evaluation is implemented by a post-survey with 105 respondents, and organized around six attitudes: Satisfaction; Intention to use; Enjoyment; Easiness; Usefulness; and Learning Performance. Quantitative data analysis show that there is significant difference between the two genders and that the attitudes have significant relations between them.

Interactive ways of learning and creative activities in teaching methods of Computer Science (CS) have drawn great interest in the last years. The goal of this PhD project is to focus on the main research question: How could practices for computer science education become more attractive and effective for students and especially female students? Initially the project will define the existing teaching practices of CS being used inside and outside school environment. Next, empirical tests will evaluate the effectiveness of proposed learning practices based on learning activities of different types, e.g. collaborative games, supervised or unsupervised project work). Qualitative and quantitative methodology will investigate among other the learning success, growth in competences and students' understanding of computer science.