NOVIX is an early‑stage extended reality (XR) prototype designed to explore how children understand interactions in non‑visual XR environments. For the initial co‑design phase, we developed the GhostXR activity, where children engage with a playful, invisible ‘ghost’ through movement, touch, proximity, and haptic/auditory cues. The system combines spatial audio, controller-based light and sound, and vibration cues. Our demo showcases a playable XR experience highlighting how children interpret non-visual agents in XR, how they express possible action–reaction loops, and how multisensory feedback supports their meaning‑making.

Generative artificial intelligence (GenAI) can produce natural language and other content to support a wide range of tasks. While much research examines its performance in educational assessment, less is known about how teachers and GenAI can complement each other in providing formative feedback. To address this gap, we present Learny, a GenAI tool that generates draft feedback for teachers to adapt, and report findings from a case study with 15 teachers. We analyzed how teachers edited GenAI feedback to provide empirical evidence of its use in K-12 formative assessment. Teachers generally found the drafts high quality and a useful starting point, but refined and personalized them, underscoring the value of human-AI synergy rather than substitution. We discuss implications for learning analytics (LA) and hybrid intelligence (HI), highlighting how teacher expertise and GenAI capabilities can be combined to enhance feedback.

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.

1. In this inaugural editorial as the new Editor-in-Chief of Behaviour & Information Technology (BIT), I aim to offer a succinct perspective on the journal and share my plans to support its continu...

Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education. In the commentary, it is acknowledged that GenAI’s capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment. Nevertheless, we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses. The identified avenues for further research include the development of new insights into the roles human experts can play, strong and continuous evidence, human-centric design of technology, necessary policy, and support and competence mechanisms. Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices.

1. Learning, Design and Technology (LDT) is an interdisciplinary field of research that concerns the phenomena surrounding the design, development, implementation, and evaluation of learning experi...

Multimodal data enables us to capture the cognitive and affective states of students to provide a holistic understanding of learning processes in a wide variety of contexts. With the use of sensing technology, we can capture learners' states in near real-time and support learning. Moreover, multimodal data allows us to obtain early predictions of learning performance, and support learning in a timely manner. In this contribution, we utilise the notion of ‘carry forward effect’, an inferential and predictive modelling approach that utilises multimodal data measurements detrimental to learning performance to provide timely feedback suggestions. Carry forward effect can provide a way to prioritise conflicting feedback suggestions in a multimodal data-based scaffolding tool. We showcase the empirical proof of the carry forward effect with the use of three different learning scenarios: game-based learning, individual debugging, and collaborative debugging.

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.

Open hardware and the need for ecologically valid measurements drive the Electroencephalography (EEG) democratization movement—EEG has been steadily transcending the boundaries of clinical research, making its way into interdisciplinary fields. In Human-Computer Interaction (HCI), EEG is used to measure cognitive workload and infer cognitive processes for building cognition-aware systems. We describe and evaluate our BCIglass prototype where EEG electrodes are embedded in the frame of a mainstream Head-Mounted Display (HMD) to create a skull-peripheral topology. We devised a lab study with 34 participants who completed seven established cognitive tasks. Then, we conducted a pilot field study with one participant to test BCIglass in everyday-life settings. Our findings demonstrate that BCIglass captures EEG activity in a manner comparable to a research-grade EEG-cap system. Our topology infers the cognitive task at hand, and the underlying cognitive process(es) by proxy, with an accuracy of ∼80% and only three electrodes at the skull periphery. Embedding EEG electrodes in lightweight HMDs represents a promising approach in the quest to achieve ubiquitous brain-computer interfacing in real-world settings.

Technology-enhanced embodied learning has gained traction in HCI, yet deeper insights into how children’s physical actions interplay with their cognitive and emotional states remain underexplored. This study investigates MOVES-NL, an embodied digital learning environment, as a medium for advancing understanding of the dynamic relationship between movement, engagement, and cognitive processes such as stress and learning. MOVES-NL combines movement and immediate formative feedback to foster arithmetic understanding of integers, offering a novel perspective on the integration between embodied interactions and conceptual development. Moving beyond traditional evaluations of learning impact or media comparisons, this work employs multimodal learning analytics (MMLA) integrating motion capture with physiological data to explore the nuanced dynamics of embodied learning. Through a mixed-methods approach integrating both qualitative and quantitative analyses, this study reveals how students’ physical movements relate to cognitive and emotional states, offering actionable insights to support engagement and learning processes. This research advances the understanding of how children’s physical movements relate to their cognitive processes and highlights key considerations for integrating embodied technologies into curricula to foster student engagement and deepen their conceptual understanding, adding value to ongoing conversations about the role of digital technology in children’s education and development.

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.

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.

Educational games have become an efficient and engaging way to enhance learning. Analytics have played a critical role in designing contemporary educational games, with most game design elements leveraging analytics produced during gameplay and learning. The presented study tackles the complex construct of engagement, which has been the central piece behind the success of educational games, by investigating the role of analytics-driven game elements on players’ engagement. To do so, we implemented a casual word game incorporating game design elements relevant to learning and conducted a within-subjects study where 39 participants played the game for two weeks. We found that the frequency of use of different game elements contributed to different dimensions of engagement. Our findings show that five of the eight game elements implemented in the word game engage players on an emotional, motivational, and cognitive level, thus emphasizing the importance of engagement as a multidimensional construct in designing educational casual games that offer highly engaging experiences.

Augmented reality (AR) provides numerous opportunities for digitally-aided language interventions for autistic children.Educators and researchers have reported a variety of outcomes and insights about the use of AR as an educational and pedagogical tool.The unique advantage of AR suggests that it supports attentional behaviour, fosters emotional and social skills, and improves communication abilities in children with autism.However, the efficacy of this technology for language learning in autistic children has not previously been systematically reviewed.The purpose of this review was to summarise findings on AR-enhanced language learning interventions for autistic children to help guide researchers in future studies.We conducted a systematic literature review, collecting 44 peer-reviewed studies covering different language domains.From there, further analysis identified 13 studies that focused on language acquisition and 31 that focused on language use.Our survey presents an overview of AR research for language learning in autistic children over the last 10 years, providing insights into aspects of language skills, the AR technologies used, research design and strategies, the underlying learning theories and models adopted in the studies, and whether the reported results were accompanied by some form of evaluation.Finally, this review proposes several recommendations for future research.

Multisensory Environments (MSEs) enable different forms of interactions to enhance children’s learning and play. While traditional MSEs are "built-in" to a specific space (e.g., a room or a part of a room), our movable solution, named MOVES, goes beyond these constraints by overcoming their "hardwired" nature and enabling children to play and learn through five different interaction modalities: feet, hand, card, wand, and voice. MOVES wooden structure (see Figure 1) hold several devices such as a PC, 2 projectors, a depth sensor, a LED strip, a RFID card reader, a tablet and a Wi-Fi access point to enable discrete immersivity, to offer children different ways to interact and teachers to be always in control of the experience. This paper focuses on presenting the MOVES technology, along with details about its rationale, design, and usage.

Allowing children to engage in technologically enabled embodied interaction activities has the potential to enhance learning and play. This work leverages the capabilities provided by multisensory environments (MSEs) to address the underlying research question: What are the benefits, challenges, and trade-offs between the various interaction modalities in the context of educational MSEs for children? To answer this question, we designed and deployed MOVES, a MSE-enabler that goes beyond the previous "hardwired" technologies and affords different interaction modalities. We conducted an in-situ field study with 175 children aged 6–10, who engaged with MOVES and the five interaction modalities. We captured children’s experiences (perceptions and actual use) through action logs, data collected from the various sensors (e.g., physiological data from wristbands, skeletal data from motion sensors), and pictorial-based self-reports. The results provide the differences between the various interaction modalities and design considerations aimed at facilitating children’s learning experiences within an MSE.

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.

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

Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students’ development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts but also to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students’ knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students’ proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

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.

In the context of learning systems, identifying causal relationships among information presented to the user, their behavior and cognitive effort required/exerted to understand and perform a task is key to building effective learning experiences, and to maintain engagement in learning processes. An unexplored question is whether our interaction with presented information affects our cognitive effort (and behaviour), or vice-versa. We investigate causal relationship between information presented and cognitive effort (and behaviour) in the context of two separate studies (N = 40, N = 98), and study the effect of instruction (active/passive task). We utilize screen-recordings and eye-tracking data to investigate the relationship among these variables. To investigate the causal relationships among the different measurements, we use Granger’s causality. Further, we propose a new method to combine two time-series from multiple participants for detecting causal relationships. Our results indicate that information presentation drives user focus size (behaviour), and that cognitive load (a measure of cognitive effort exerted) drives information presentation. This relationship is also moderated by instruction type and performance-level (high/low). We draw implications for design of educational material and learning technologies.

Abstract Embodied interaction describes the interplay between the brain and the body and its influence on the sharing, creation and manipulation of meaningful interactions with technology. Spatial skills entail the acquisition, organization, utilization and revision of knowledge about spatial environments. Embodied interaction is a rapidly growing topic in human–computer interaction with the potential to amplify human interaction and communication capacities, while spatial skills are regarded as key enablers for the successful management of cognitive tasks. This work provides a systematic review of empirical studies focused on embodied interaction and spatial skills. Thirty-six peer-reviewed articles were systematically collected and analysed according to their main elements. The results summarize and distil the developments concerning embodied interaction and spatial skills over the past decade. We identify embodied interaction capacities found in the literature review that help us to enhance and develop spatial skills. Lastly, we discuss implications for research and practice and highlight directions for future work.

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.

Authors of academic papers are generally required to nominate several keywords that characterize the paper, but are rarely offered guidance on how to select those keywords. We analyzed the keywords in the past 15 years of selected computing education publications: the 1274 papers published in the proceedings of ICER and ITiCSE, including the ITiCSE working group reports. As well as the keywords assigned by the authors, we mined the abstracts of these papers to extract a separate list of keywords. Our work has two goals: to frame the thematic landscape of the field, using keywords that communicate the work conducted; and to detect differences between the human judgement and interpretation of keywords and the machine 'intelligence' on handling those keywords, with respect to the clusters of thematic topics identified in each case. The analysis shows that the field is dominated by learning approaches (e.g., active learning, collaborative learning), aspects of programming (e.g., debugging, misconceptions), computational thinking, feedback, and assessment, while other areas that have attracted attention include academic integrity (e.g., plagiarism) and diversity (e.g., female students, underrepresented groups). It was observed that the keywords chosen by authors are often too general to provide information about the paper (e.g., 'concerns', 'course', 'fun', 'justice'). We elaborate on the findings and begin a discussion on how authors can improve the communication of their research and make access to it more transparent.

This study aims to identify the conceptual structure and the thematic progress in Learning Analytics (evolution) and to elaborate on backbone/emerging topics in the field (maturity) from 2011 to September 2019. To address this objective, this paper employs hierarchical clustering, strategic diagrams and network analysis to construct the intellectual map of the Learning Analytics community and to visualize the thematic landscape in this field, using co-word analysis. Overall, a total of 459 papers from the proceedings of the Learning Analytics and Knowledge (LAK) conference and 168 articles published in the Journal of Learning Analytics (JLA), and the respective 3092 author-assigned keywords and 4051 machine-extracted key-phrases, were included in the analyses. The results indicate that the community has significantly focused in areas like Massive Open Online Courses and visualizations; Learning Management Systems, assessment and self-regulated learning are also basic topics, yet topics like natural language processing and orchestration are emerging. The analysis highlights the shift of the research interest throughout the past decade, and the rise of new topics, comprising evidence that the field is expanding. Limitations of the approach and future work plans conclude the paper.

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. However, effort is not directly observable. Multimodal data can provide additional insights into the learning processes and may allow for effort estimation. This paper presents an approach for the classification of effort in an adaptive assessment context. Specifically, the behaviour of 32 students was captured during an adaptive self-assessment activity, using logs and physiological data (i.e., eye-tracking, EEG, wristband and facial expressions). We applied k-means to the multimodal data to cluster students' behavioural patterns. Next, we predicted students' effort to complete the upcoming task, based on the discovered behavioural patterns using a combination of Hidden Markov Models (HMMs) and the Viterbi algorithm. We also compared the results with other state-of-the-art classification algorithms (SVM, Random Forest). Our findings provide evidence that HMMs can encode the relationship between effort and behaviour (captured by the multimodal data) in a more efficient way than the other methods. Foremost, a practical implication of the approach is that the derived HMMs also pinpoint the moments to provide preventive/prescriptive feedback to the learners in real-time, by building-upon the relationship between behavioural patterns and the effort the learners are putting in.

Popular approaches in learner modeling explore response-time as observational data supplemental to response correctness, to enrich the predictive models of learner knowledge. It has been argued that the relationship between response-time and knowledge mastery is non-linear. Determining the degree of association (dependence structure) between those two observations is an open question. To address this objective, we propose an approach based on copulas, i.e., a statistical tool suitable for capturing dependence structure between two variables. All of the information about the dependence structures can be estimated by copula models separately, allowing for the construction of more flexible joint distributions than existing multivariate distributions. This paper puts into practice a two-step pipeline for building the analytical models. Specifically, we propose a flexible copula-based approach that describes the dependence structure between students' response-time and mastery, in learning and testing contexts, and apply the methodology on four datasets. The two datasets are coming from Intelligent Tutoring Systems and are shared via an online repository, and the other two were collected during the validation of an (adaptive) assessment system. The results reveal five generic patterns of associations across-datasets, for various types of activities, domains and learner characteristics (i.e., not across-contexts). We elaborate on those findings and on the implications of our approach for adaptive systems.

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.

The Interaction Design and Children (IDC) Community has a long history of innovating methods and techniques for the design and evaluation of technologies for children. Many innovations have been reported in the academic literature but the uptake of methods by industry has been slow and the community has hitherto failed to seriously consider how best to develop, present and promote their methods beyond academia. The aim of the workshop is to weave together IDC researchers and IDC key personnel coming from the industry, with genuine interest in industry-academia collaboration, into a community interested in building a coherent, high-impact collaboration channel. The goal of the workshop is to encourage a critical discussion and debate about how IDC methods can be further adopted, modified or even extended by the IDC related industry. This workshop is expected to reinforce IDC industry-academia collaboration with an ultimate goal to increase understanding and develop a community of interest that is going to co-develop ideas and novel design approaches that can bring IDC methods closer to the industrial practice

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.

Project-based learning has been introduced in many university courses as a dynamic classroom approach that motivates active exploration of real-world problems. It is also proven as one of the most effective ways for students to acquire practical skills and deeper knowledge. However, while learning with technologies in project-based blended environments, students are expected to know how to cope with real-world complex issues. Hence, students from two universities participated in an exploratory study with a focus in motivational and emotional challenges as part of collaborative learning. In particular, the study explored what regulation strategies students practiced as an answer to the challenges they encountered in customer-driven project-based learning activities. Nonetheless, the broad idea is to understand in what ways collaborative learning can be beneficial or debilitating for students' progress, and how technology can support or influence positive outcomes.

The success of technology enhanced learning can be increased by tailoring the content and the learning resources for every student; thus, optimizing the learning process. This study proposes a method for evaluating content difficulty and knowledge proficiency of users based on modified Elo-rating algorithm. The calculated ratings are used further in the teaching process as a recommendation of coding exercises that try to match the user's current knowledge. The proposed method was tested with a programming tutoring system in object-oriented programming course. The results showed positive findings regarding the effectiveness of the implemented Elo-rating algorithm in recommending coding exercises, as a proof-of-concept for developing adaptive and automatic assessment of programming assignments.

The interaction with the various learners in a Massive Open Online Course (MOOC) is often complex. Contemporary MOOC learning analytics relate with click-streams, keystrokes and other user-input variables. Such variables however, do not always capture learners' learning and behavior (e.g., passive video watching). In this paper, we present a study with 40 students who watched a MOOC lecture while their eye-movements were being recorded. We then proposed a method to define stimuli-based gaze variables that can be used for any kind of stimulus. The proposed stimuli-based gaze variables indicate students' attention (i.e., with-me-ness), at the perceptual (following teacher's deictic acts) and conceptual levels (following teacher discourse). In our experiment, we identified a significant mediation effect of the two levels of with-me-ness on the relation between students' motivation and their learning performance. Such variables enable common measurements for the different kind of stimuli present in distinct MOOCs. Our long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide students gaze-aware feedback to improve overall learning process.

Learning analytics are used to track learners' progress and empower educators and learners to make well-informed data-driven decisions. However, due to the distributed nature of the learning process, analytics need to be combined to offer broader insights into learner's behavior and experiences. Consequently, this paper presents an architecture of a learning ecosystem, that integrates and utilizes cross-platform analytics. The proposed cross-platform architecture has been put into practice via a Java programming course. After a series of studies, a proof of concept was derived that shows how cross-platform analytics amplify the relevant analytics for the learning process. Such analytics could improve educators' and learners' understanding of their own actions and the environments in which learning occurs.

It is widely recognized by public and private organizations, that the biggest challenge faced in light of the data revolution is finding people with the required set of skills to transform data into actionable insight. The growing interest on the role of the data scientist and the relating data analytics skills has seen an increasing amount of research on the importance of data analytics skills in the contemporary working environment. Yet, there is still limited understanding on the importance of data analytic skills, and even more, there is limited research on the discrepancies between the skills that are needed in the market and what graduates possess. To this end, this research uses a mixed-methods approach combining quantitative survey data from 113 IT executives, and qualitative interview data from 27 big data project managers to explore the significance, discrepancies, and aspects of data analytic skills. Our results show that data analytic skills significantly contribute firm performance, particularly for firms that are data-oriented. In addition, we find that the need for skills greatly exceeds those that graduates possess. Lastly, our analysis suggests that the data skills of the data scientist span multiple subject areas which are further discussed.

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.

A broad review of research on the teaching and learning of programming was conducted by Robins et al. in 2003. Since this work there have been several reviews of research concerned with the teaching and learning of programming, in particular introductory programming. However, these reviews have focused on highly specific aspects, such as student misconceptions, teaching approaches, program comprehension, potentially seminal papers, research methods applied, automated feedback for exercises, competency-enhancing games, and program visualisation. While these aspects encompass a wide range of issues, they do not cover the full scope of research into novice programming. Some notable areas that have not been reviewed are assessment, academic integrity, and novice student attitudes to programming. There does not appear to have been a comprehensive review of research into introductory programming since that of Robins et al. It is therefore timely to conduct and present such a review in order to gain an understanding of the research focuses, to highlight advances in knowledge since 2003, and to indicate possible future directions for research. The working group will conduct a systematic literature review based on the guidelines proposed by Kitchenham et al. This research project is well suited to an ITiCSE working group as the synthesis and discussion of the literature will benefit from input from a variety of researchers drawn from different backgrounds and countries.

The presented study tries to tackle an intriguing question of how user-generated data from current technologies can be used to reinforce learners' reflections, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize users' gaze to examine the role of a mirroring tool (i.e. Exercise View in Eclipse) in orchestrating basic behavioral regulation of participants engaged in a debugging task. The results demonstrated that students who processed the information presented in the Exercise View and acted upon it, improved their performance and achieved higher level of success than those who failed to do it. The findings shed a light how to capture what constitute relevant data within a particular context using gaze patterns, that could guide collection of essential learner-centred analytics for the purpose of designing usable and modular learning environments based on data-driven approaches.

This study focuses on compiling students' response-time allocated to answer correctly or wrongly, their self-regulation, as well as their satisfaction from content, in order to explain high or medium/low learning performance. To this end, it proposes a conceptual model in conjunction with research propositions. For the evaluation of the approach, an empirical study with 452 students was conducted. The fuzzy set qualitative comparative analysis (fsQCA) revealed five configurations driven by the admitted factors that explain students' high performance, as well as five additional patterns, interpreting students' medium/low performance. These findings advance our understanding of the relations between actual usage and latent behavioral factors, as well as their combined effect on students' test score. Limitations and potential implications of these findings are also discussed.

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.

Educators and researchers have been working to understand the reasons that may be contributing to high dropout rates, and low rates of participation, by females in the computer and information sciences discipline. Along the same lines, and propelled by the increased need for information technology (IT) professionals worldwide, we implemented a students' survey during the fall of 2015 in Norway's primary university for technological education. In this initiative we aim to identify reasons that may be contributing to high dropout rates, low rates of participation by females and aspects important for the efficient preparation of young people for careers in computer science and information technology. The results provide valuable insights and allow us to take appropriate measures for enhancing students' learning experience in the computer and information sciences.

The paper seeks to examine how existing communities in computing education thrive in Norway and manage to empower school pupils and tutors realising their role in the digital society, learn programming, and become familiar with Computer Science and Information Technology. Two semi-structured interviews were conducted with organisers and designers of activities that promote computing education and programming in Norway. Also, one focus group discussion was conducted with high school students that participated in a small number of learning activities on design thinking, programming and Internet of Things. The results were qualitatively analysed using the Grounded Theory in order to conclude how different aspects (cognitive, social, organisational, policy) are manifested and interweaved in these communities.

The special track “Motivating students with Mobiles, Ubiquitous applications and the Internet of Things for STEM (MUMI4STEM)”, within the “EDUCON20I7 IEEE Global Engineering Education Conference”, integrates two main areas of interest in STEM education: I) motivating students with mobile devices and 2) exploiting ubiquitous computing and the Internet of Things. Following the growing interest of the educational and research community towards fostering STEM education, this special session aims at promoting the discussion about the motivational aspects of mobile learning and the benefits of ubiquitous applications and the Internet of Things (IoT), with special focus on supporting STEM education.

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.

This study aims to identify the interrelations among performance expectancy, effort expectancy, enjoyment, and satisfaction in order to predict high intention to use a mobile application for educational services. To this end a mobile application was developed which includes important services for students in one place and it was tested through feedback from questionnaires. Building on complexity and configuration theory we present a conceptual model and employ fuzzy-set qualitative comparative analysis (fsQCA) to examine how performance expectancy, effort expectancy, enjoyment, and satisfaction combine in order to explain high and low intention to use mobile learning. The results indicate different configurations of the examined factors that explain user behavior, and verify the existence of asymmetric relations among them. The study is one of the first in the area evaluating a mobile learning application, and has both theoretical and practical implications towards the development, design and provision of mobile learning applications.

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.

Orchestrating holistic school improvement requires school leaders to effectively engage in the tasks of collecting and processing diverse educational data from the school ecosystem, and more importantly, to be able to 'translate' these analyses to specific remedying actions for targeted improvement. However, these processes can be cumbersome, especially given that existing 'School Analytics' methods aiming to support them have mainly focused on the former task, but have yet to explicitly address the latter. In this context, the paper presents and initially validates a novel School Analytics approach, which employs fuzzy-set Qualitative Comparative Analysis as the means to provide leaders with actionable insights on how to create the school conditions for fostering students' learning outcomes, focusing on 'digital skills' as a case study.

Social commerce has been gaining momentum over the last few years as a novel form of e-commerce, creating substantial changes for both businesses and consumers. However, little is known about how consumer behaviour is influenced by characteristics on social commerce platforms. The purpose of this research is to elucidate how user intentions to purchase and to spread word-of-mouth (WOM) are influenced by characteristics present on social commerce platforms. More specifically, we adopt a uses-and-gratifications perspective and examine the influence of socialising, personal recommendation agents, product selection, and information availability. Partial least squares structural equation modelling analysis is performed on a sample of 165 social commerce users. Outcomes of the analysis indicate that socialising and personal recommendation agents positively influence purchase and WOM intentions, while product selection is found to only enhance purchase intentions. Interestingly, our findings reveal that information availability has no significant effect on purchase and WOM intentions. Finally, we find that when purchase intentions are triggered, they will tend increase consumers’ intentions to WOM.

This study uses complexity theory to understand the causal patterns of factors that stimulate students’ intention to continue studies in computer science (CS). To this end, it identifies gains and barriers as essential factors in CS education, including motivation and learning performance, and proposes a conceptual model along with research propositions. To test its propositions, the study employs fuzzy-set qualitative comparative analysis on a data sample from 344 students. Findings indicate eight configurations of cognitive and noncognitive gains, barriers, motivation for studies, and learning performance that explain high intention to continue studies in CS. This research study contributes to the literature by (1) offering new insights into the relationships among the predictors of CS students’ intention to continue their studies and (2) advancing the theoretical foundation of how students’ gains, barriers, motivation, and learning performance combine to better explain high intentions to continue CS studies.

The Workshop of Making as a Pathway to Foster Joyful Engagement and Creativity in Learning (Make2Learn) aims to discuss the introduction of creative and joyful production of artifacts in the learning processes. A variety of environments have been developed by researchers to introduce making principles to children. Making principles enable them foster co-creativity and joy in learning processes and construct knowledge. By involving children in the design decisions they begin to develop technological fluency and the needed competences, in a joyful way. Make2Learn aims to bring together international researchers, educators, designers, and makers for the exploration of making principles towards the acquisition of 21st Century learning competences, by employing the state of the art aspects of learning technologies, new media, gaming, robotics, toys and applications. Make2Learn aims to develop a critical discussion about the well-established practices and technologies of the maker movement, and expected outcomes of putting them into practice under different spaces such as Hackerspaces, Makerspaces, TechShops, FabLabs etc. This will allow us to better understand and improve the value of Maker philosophy and the role of entertainment technologies to support teaching and learning.

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.

In this paper, we consider the key dimensions of learning analytics applications in adaptive learning. We then review recent publications on the topic and map them to the dimensions of the reference model. Twenty one peer-reviewed articles are identified and analyzed. The findings of the review suggest that interesting work has been carried out during the last years on the topic. Yet, there is a clear lack of studies on school education and in topics outside STEM. In addition, there is a lack of studies that do not focus solely on the (self) reflection of students or tutors. Finally, the majority of the studies included look at student performance, as an adaptation parameter. Yet, a new trend of taking into account more complex student behaviors, like collaboration, is emerging.

Research in the area of Computer Science (CS) education, has focused on identifying the reasons that students do not finish their studies in CS. Although there is increasing demand for CS professionals, there is not enough knowledge to explain the high dropout rates in CS education. This study aims to empirically examine how students' intention to complete their studies (retention) in CS is affected by variables playing a key role in higher education. By identifying which variables contribute to dropout in CS studies, we will be able to focus on how to improve aspects related with them in order to reduce dropout rates. To do so we identified the following variables: Year of studies, Gender, Age, Students' Effort, Absence from Classes, Expected Grade point average (GPA), and Current GPA, and tested their effect on retention, based on the responses collected from 241 CS student. Year of studies and Effort have positive effects on students' intention to finish their studies in CS. Interestingly, the expected GPA has a negative effect on students' intentions to finish their studies. The findings contribute to theory and practice, as they offer CS educators and policy makers insights that may aid towards increased student retention and reduced dropout rates.

The International Workshop of Smart Environments and Analytics on Video-Based Learning ([email protected]) aims to connect research efforts on Video-Based Learning with Smart Environments and Analytics to create synergies between these fields. The main objective is to build a research community around the intersection of these topical areas. In particular, [email protected] aims to develop a critical discussion about the next generation of video-based learning environments and their analytics, the form of these analytics and the way they can be analyzed in order to help us to better understand and improve the value of educational videos to support teaching and learning. [email protected] is based on the rationale that combining and analyzing learners' interactions with other available data obtained from learners, new avenues for research on video-based learning have emerged. This can have a significant impact in current educational trends such as Massive Open Online Courses (MOOCs) and Flipped Classroom.

As computing increasingly deals with our complex daily experiences, designers are challenged with new methods and ways of implementing complex interactions in limited user space and tools, but without hindering user experience and creativity. In this paper, we present the results of an empirical investigation regarding the effect of space and tool availability on user experience and creativity. The goal is to understand whether and how space and tool availability allow users to be more creative and improve their overall experience. To do so, we developed a connect-the-dots drawing multi-user application with a focus on having certain restrictions in size and specific tool availability. Based on this application, we conducted an empirical study with 38 users. For the evaluation, surveys, photos and observations were recorded and used in our analysis. The results showed that: (a) tool availability does not affect user creativity and experience and (b) space availability affects user creativity, collaboration and hedonic motivation (pleasure). Although our results are early, provide insights that tools' limitation does not hinder users' ability to be creative; and space availability is of great importance in creative activities.

During the last few years, the focus of computer science education (CSE) in primary and secondary schools (shortly K-12) have reached a significant turning point. This study reviews the published papers on the field of K-12 computing education in order to summarize the findings, guide future studies and give reflections for the major achievements in the area of CSE in K-12 schools. 47 peer-reviewed articles were collected from a systematic literature search and analyzed, based on a categorization of their main elements. Programming tools, educational context, and instructional methods are the main examined categories of this research. Results of this survey show the direction of CSE in schools research during the last years and summarized the benefits as well as the challenges. In particular, we analyzed the selected papers from the perspective of the various instructional methods aiming at introducing and enhancing learning, using several programming tools and educational context in K-12 CSE. Despite the challenges, the findings suggest that implementing computing lessons in K-12 education could be an enjoyable and effective learning experience. In addition, we suggest ways to facilitate deep learning and deal with various implications of the formal and informal education. Encouraging students to create their own projects or solve problems should be a significant part of the learning process.

Aiming to collect various concepts, approaches, and strategies for improving computer science education in K-12 schools, we edited this second special issue of the ACM TOCE journal. Our intention was to collect a set of case studies from different countries that would describe all relevant aspects of specific implementations of Computer Science Education in K-12 schools. By this, we want to deliver well-founded arguments and rich material to the critical discussion about the state and the goals of K-12 computer science education, and also provide visions for the future of this research area. In this editorial, we explain our intention and report some details about the genesis of these special issues. Following, we give a short summary of the Darmstadt Model, which was suggested to serve as a structuring principle of the case studies. The next part of the editorial presents a short description of the five extended case studies from India, Korea, NRW/Germany, Finland, and USA that are selected to be included in this second issue. In order to give some perspectives for the future, we propose a set of open research questions of the field, partly derived from the Darmstadt Model, partly stimulated by a look on large-scale investigations like PISA.

No abstract available.

Recent technical and infrastructural developments posit flipped classroom approaches ripe for exploration. Flipped classroom approaches have students use technology to access the lecture and other instructional resources outside the classroom in order to engage them in active learning during in-class time. Scholars and educators have reported a variety of positive outcomes of a flipped (or inverted) approach to instruction. Although, flipped classroom practices have been used in a number of education studies, the detailed framework and data obtained from students' interaction with the technology materials are typically not described. In this paper, we present a flipped classroom framework and the first captured results of such data. The framework incorporates basic e-learning tools and traditional learning practices, making it accessible to anyone wanting to implement a flipped classroom experience in his/her course. The framework is structured on open-source and easy-to-use tools, allowing for the incorporation of any additional specificities of a course. This work-in-progress can provide insights for other scholars and practitioners to further validate, examine, and extend the proposed approach. This approach can be used for those interested in incorporating flipped classroom in their teaching, since it is a flexible procedure that may be adapted to meet their needs.

Teachers have employed online video as an element of their instructional media portfolio, alongside with books, slides, notes, etc. In comparison to other instructional media, online video affords more opportunities for recording of student navigation on a video lecture. Video analytics might provide insights into student learning performance and inform the improvement of teaching tactics. Nevertheless, those analytics are not accessible to learning stakeholders, such as researchers and educators, mainly because online video platforms do not share broadly the interactions of the users with their systems. As a remedy, we have designed an open-access video analytics system and employed it in a video-assisted course. In this paper, we present a longitudinal study, which provides valuable insights through the lens of the collected video analytics. In particular, we collected and analyzed students' video navigation, learning performance, and attitudes, and we provide the lessons learned for further development and refinement of video-assisted courses and practices.

Computer Science (CS) teachers' training and profile is crucial to ensure students have access to quality Computer Science Education (CSE). The aim of this study is to examine the profile of CS teachers in Greece and map it using the technique of persona. This study examines a national sample of 1127 CS teachers who teach algorithms and programming in upper secondary education. The building of the persona is based on teachers' abilities and needs regarding the central aspects of their knowledge in respect to three key domains in the Technological, Pedagogical, and Content Knowledge (TPACK) framework. According to the results in the TPACK subscales, teachers' state that their Content Knowledge scales is sufficient and Pedagogical, Content Knowledge needs to be improved on. In addition, teachers feel that they need further training in how to incorporate technology in their teaching as well as how to teach algorithms; which are two areas that relate to Pedagogical Content Knowledge and TPACK By mapping the knowledge, abilities and needs of CS teachers, we will be able to recognize the challenges they face during teaching and consider strategies and policies for addressing these challenges.

Video learning analytics are not open to education stakeholders, such as researchers and teachers, because online video platforms do not share the interactions of the users with their systems. Nevertheless, video learning analytics are necessary to all researchers and teachers that need to understand and improve the effectiveness of the video lecture pedagogy. In this paper, we present an open video learning analytics service, which is freely accessible online. The video learning analytics service (named Social Skip) facilitates the analysis of video learning behavior by capturing learners' interactions with the video player (e.g., seek/scrub, play, pause). The service empowers any researcher or teacher to create a custom video-based experiment by selecting: 1) a video lecture from You Tube, 2) quiz questions from Google Drive, and 3) custom video player buttons. The open video analytics system has been validated through dozens of user studies, which produced thousands of video interactions. In this study, we present an indicative example, which highlights the usability and usefulness of the system. In addition to interaction frequencies, the system models the captured data as a learner activity time series. Further research should consider user modeling and personalization in order to dynamically respond to the interactivity of students with video lectures.

Video lectures are nowadays widely used by growing numbers of learners all over the world. Nevertheless, learners' interactions with the videos are not readily available, because online video platforms do not share them. In this paper, we present an open-source video learning analytics system, which is also available as a free service to researchers. Our system facilitates the analysis of video learning behavior by capturing learners' interactions with the video player (e.g, seek/scrub, play, pause). In an empirical user study, we captured hundreds of user interactions with the video player by analyzing the interactions as a learner activity time series. We found that learners employed the replaying activity to retrieve the video segments that contained the answers to the survey questions. The above findings indicate the potential of video analytics to represent learner behavior. Further research, should be able to elaborate on learner behavior by collecting large-scale data. In this way, the producers of online video pedagogy will be able to understand the use of this emerging medium and proceed with the appropriate amendments to the current video-based learning systems and practices.

There is a growing awareness of the importance of including computing education in the curriculum of secondary schools in countries like the United States of America, the United Kingdom, New Zealand, and South Korea. Consequently, we have seen serious efforts to introduce computing education to the core curriculum and/or to improve it. Recent reports (such as Wilson et al. 2010; Hubwieser et al. 2011) reveal that computing education faces problems regarding its lack of exposure as well as a lack of motivators for students to follow this line of study. Although students use computers for many tasks both at home and at school, many of them never quite understand what computer science is and how it relates to algorithmic thinking and problem solving. This panel will bring together leaders in computing education from Australia, Germany, Greece, Israel and Norway to describe the state of computing education in each of their countries. Issues raised will include how high school computer education is conducted in that country, how teachers are skilled /accredited, the challenges that are being faced today and how these challenges are being addressed. Panellists will suggest lessons other countries may find of value from their way of doing things. An important issue is how to recruit female students in to computer education at high school level and how to encourage them to continue in the discipline to university. The problem is exacerbated because computer education is still not included as a compulsory subject in the regular curriculum of high schools in all of these countries

In view of the recent developments in many countries, for example, in the USA and in the UK, it appears that computer science education (CSE) in primary or secondary schools (K-12) has reached a significant turning point, shifting its focus from ICT-oriented to rigorous computer science concepts. The goal of this special issue is to offer a publication platform for soundly based in-depth experiences that have been made around the world with concepts, approaches, or initiatives that aim at supporting this shift. For this purpose, the article format was kept as large as possible, enabling the authors to explain many facets of their concepts and experiences in detail. Regarding the structure of the articles, we had encouraged the authors to lean on the Darmstadt Model, a category system that was developed to support the development, improvement, and investigation of K-12 CSE across regional or national boundaries. This model could serve as a unifying framework that might provide a proper structure for a well-founded critical discussion about the future of K-12 CSE. Curriculum designers or policy stakeholders, who have to decide, which approach an upcoming national initiative should follow, could benefit from this discussion as well as researchers who are investigating K12 CSE in any regard. With this goal in mind, we have selected six extensive and two short case studies from the UK, New Zealand, USA/Israel, France, Sweden, Georgia (USA), Russia, and Italy that provide an in-depth analysis of K-12 CSE in their respective country or state.

Creative activities for children have drawn great interest in the last years. The advent of programming languages for children (i.e., Scratch) combined with accessible programmable hardware platforms (i.e., Arduino) makes it possible for children to engage in creative development of digital artifact, like robots and interactive installations. However, there are limited studies towards the design and improvement of these activities. The goal of this study is to provide validated knowledge about the trade-off between collaboration and control throughout creative programming activities. To this end, a group of researchers and artists designed and implemented two workshop programs of a total of 51 pupils, exploring their experiences with open source software. The workshops were based on Reggio Emilia philosophy of creative reuse and the open-source software Scratch. Qualitative and quantitative approaches of the research are based on data collected through interviews, surveys and observations. The results of this paper argue that: (a) collaboration among children improves the value of the workshops, (b) however big groups lead children to lose the control over their actions during the workshop, and (c) children's control significantly affects workshop's usefulness.

Healthcare games are becoming increasingly popular because of their potential to improve patients' wellbeing before, during, and after medical treatment. Even though young children (here referred to as toddlers) make up a growing group of gamers, there is a lack of research focusing on healthcare games for this group. Since toddlers often express unmotivated behavior towards receiving medical treatment, the potential of healthcare gaming applications for this group should be explored. The purpose of our study is to provide a set of research-derived design considerations for healthcare games and applications for toddlers. Our approach included an initial best practices collection through a workshop involving experts from pediatric healthcare and pedagogy, and an affinity diagramming categorization by a focus group with HCI and health researchers. This resulted in a robust set of best practices that was further used for establishing a connection with game components and transformation into design considerations. As an illustrating example we present a prototype of a healthcare game developed to improve nebulizer treatment for toddlers. The final result of this work is a set of key aspects to consider when designing healthcare games and applications for toddlers. The results should be useful for designers and researchers who work in the intersection between health and young user groups.

The advent of programming languages for children (i.e., Scratch) combined with accessible programmable hardware platforms (i.e., Arduino) makes it possible for teenagers to engage in creative development of digital enriched artifacts, like robots and interactive installations. But what are the important factors that characterize these development activities? And more specifically, what motivates children to participate in such software and hardware intensive activities? In this paper we present the results of an empirical investigation regarding the key aspects of a creative learning context. The goal is to understand what motivates children to participate in these development activities. In our empirical evaluation, a group of researchers and artists designed, implemented, and evaluated three workshop programs of 66 children total, with the final goal of exploring children's attitudes software and hardware-intensive activities. The workshops were based on the Reggio Emilia education principles, open source software Scratch and Arduino and were conducted in centers that use recycled materials for creative purposes. For the first phase of the evaluation, qualitative data was collected from 11 interviews and was analyzed using content analysis. For the second phase, we designed a survey grounded in motivational factors for technology. 37 survey responses were collected. For both evaluation phases, photos and observations were recorded and used to triangulate our data. The results showed that: (a) software and hardware intensive activities raise awareness of technology, intensify the experience, and invite students to explore boundaries and increase collaboration and the exchange of views and ideas, and (b) the activity's easiness and usefulness significantly affect children's intention to participate. These results have implications for those programming languages and hardware platforms for children, as well as for those setting up creative learning frameworks around such technology.

The International Workshop on Analytics on Video-based Learning (WAVe2013) aims to connect research efforts on Video-based Learning with Learning Analytics to create visionary ideas and foster synergies between the two fields. The main objective of WAVe is to build a research community around the topical area of Analytics on video-based learning. In particular, WAVe aims to develop a critical discussion about the next generation of analytics employed on video learning tools, the form of these analytics and the way they can be analyzed in order to help us to better understand and improve the value of video-based learning. WAVe is based on the rationale that combining and analyzing learners' interactions with other available data obtained from learners, new avenues for research on video-based learning have emerged.

The curricula for Computer Science Education (CSE) of many countries comprise both Programming and Information and Communication Technology (ICT); however these two areas have substantial differences, inter alia the attitudes and beliefs of the students regarding the intended learning content. In this study, variables from the Unified Theory of Acceptance and Use of Technology and Social Cognitive Theory were chosen as important factors in students' behavior and attitude towards CSE. This hybrid framework aims to measure the level of the selected key variables on CSE and identify potential differences among ICT and Programming courses. Responses from the total of 126 Greek students, (71 attending ICT courses and 55 attending Programming Courses) were used to measure the variables and to identify the differences between ICT and Programming students. The results revealed several differences in the measured variables. The overall outcomes are expected to contribute to the understanding of students' likelihood to pursue computing related careers and promote the acceptance of CSE.

This article investigates the uses and gratifications of the popular social networking site Facebook. In the exploratory stage, 70 users generated phrases to describe the manner they used Facebook. Interestingly, some users not only described the uses, but also mentioned how they perceive these uses. These phrases were coded into 14 items and clustered into four factors. The principal component analysis that was conducted in the third stage of the study, which was addressed to 222 Facebook users, verified the validity of the four factors: Social Connection, Social Network Surfing, Wasting Time and Using Applications. Previous user studies on Facebook have examined the immediate social effects of this popular social networking site, but they have not regarded emerging uses of the platform, such as gaming and applications, which do have a social component as a feature and not as a core principle. The ‘Wasting Time’ factor and the growth of ‘Using Applications’ factor indicate that Facebook has already become an integral part of daily computing routine, alongside with the rest of the entertainment desktop and web applications.

Educational video games have been employed by teachers in order to make educational software more attractive to students. However, limited research has been made on the design and assessment of the storytelling elements and the educational effectiveness of these games in sciences curricula. For this purpose, we used Scratch to develop a storytelling mathematics video game and then we measured its educational effect to a small group of twelve students. We found that the story-based math video game has captivated the interest of students and it has been beneficial in the improvement of their performance in an assessment test. Most notably, the improvement was higher for students who used to have poor performance in mathematics. In practice, educators should develop similar games for similar science topics (e.g., physics, chemistry, etc), while further research should consider the active involvement of students in the design of serious games.

In this study, we present results of a research that investigates the impact of attending programming courses at Lyceum (15-18 years old students), on students' confidence and behavioral intention towards algorithmic logic. In particular, we measured students' behavioral intention for programming and students' confidence regarding data structures, problem solving and programming commands (Conditional - Loop). Responses from 81 graduate students, whose curriculum included programming courses at Lyceum were used to examine the benefits of programming in secondary education. The results indicate that students with prior attendance of programming courses exhibit high levels of confidence and acceptance regarding structured logic.

In this study, we extended the Unified Theory of Acceptance and Use of Technology (UTAUT) to include key variables from Social Cognitive Theory (SCT) and Theory of Planed Behavior (TPB). We used this hybrid framework to clarify several issues regarding the adoption of the educational webcasts' and to investigate the effects of the key variables. Responses from 292 webcast based learners were used to examine the adoption of the educational webcast.