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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.

This SIG will provide child-computer interaction researchers and practitioners, as well as other interested CHI attendees, an opportunity to discuss topics related to developing participatory methods to consider the ethics of emerging technologies for children. While the community has extensively debated on ethical issues, we have not had ample discussion of methods to study the ethical implications of emerging technologies. Consequently, we have been largely reactive and have not made significant contributions to public discussions on these topics, leaving these largely to experts from other fields. Our community is well-placed to contribute unique perspectives by leveraging its expertise in participatory methods, combining expert views with those of stakeholders, including children.

With the United Nations (UN) Sustainable Development Goals and national commitments requiring effective implementation of evidence-based innovations, we are witnessing a growing number of related interventions in educational environments based on digitalization and gamification. In particular, serious games and playful activities are being used to empower children’s awareness and reflection on issues related to climate change and sustainability. In this work, we discuss such approaches through the lens of our own experience with an intervention targeting school environments. Our study is based on a playful web application that focuses on sustainability awareness and energy-related aspects, the GAIA Challenge, which was used in over 25 schools in 3 countries, resulting in 3762 registered users. We present a longitudinal study on the use of GAIA Challenge focusing on children’s engagement, rate of completion of the content on offer, and overall reaction by the school communities involved in large-scale trials, complemented with insights from surveys answered by 723 students and 32 educators. Our results showed that up to 20% of the children completed all content available in the Challenge, when gameplay sessions were combined with strong community competition activities. The children also reported increased awareness of related sustainability issues. Our findings demonstrate that a simple playful experience can yield good results within educational environments, by taking into consideration typical school constraints, integrating it to the daily activities of schools and placing it within their strategy.

Educational Data Analytics (EDA) have been attributed with significant benefits for enhancing on-demand personalized educational support of individual learners as well as reflective course (re)design for achieving more authentic teaching, learning and assessment experiences integrated into real work-oriented tasks. This open access textbook is a tutorial for developing, practicing and self-assessing core competences on educational data analytics for digital teaching and learning. It combines theoretical knowledge on core issues related to collecting, analyzing, interpreting and using educational data, including ethics and privacy concerns. The textbook provides questions and teaching materials/ learning activities as quiz tests of multiple types of questions, added after each section, related to the topic studied or the video(s) referenced. These activities reproduce real-life contexts by using a suitable use case scenario (storytelling), encouraging learners to link theory with practice; self-assessed assignments enabling learners to apply their attained knowledge and acquired competences on EDL. By studying this book, you will know where to locate useful educational data in different sources and understand their limitations; know the basics for managing educational data to make them useful; understand relevant methods; and be able to use relevant tools; know the basics for organising, analysing, interpreting and presenting learner-generated data within their learning context, understand relevant learning analytics methods and be able to use relevant learning analytics tools; know the basics for analysing and interpreting educational data to facilitate educational decision making, including course and curricula design, understand relevant teaching analytics methods and be able to use relevant teaching analytics tools; understand issues related with educational data ethics and privacy. This book is intended for school leaders and teachers engaged in blended (using the flipped classroom model) and online (during COVID-19 crisis and beyond) teaching and learning; e-learning professionals (such as, instructional designers and e-tutors) of online and blended courses; instructional technologists; researchers as well as undergraduate and postgraduate university students studying education, educational technology and relevant fields.

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The goal on this chapter is to

The goals on this chapter are to

The goals on this chapter are to

Child–computer interaction (CCI) is a multidisciplinary field of research that concerns the phenomena surrounding the interaction between children and computational and communication technologies. During the last two decades, several endeavors and contributions have significantly furthered CCI research. The pace of children-relevant technological advances and establishment of CCI venues has increased CCI scholarship, and furthered our knowledge and understanding about the relationship between childhood and technology. This editorial article provides an introduction and overview of the special Issue on “review articles in child–computer​ interaction research”. The motivation of this special issue is to highlight review and survey papers that portray contemporary developments in the CCI literature. The contributions come from diverse contexts, cover a wide range of technological affordances, and address a variety of objectives and stakeholders (e.g., designers, learning scientists, policymakers, technologists). In this editorial, we present the background of CCI research, giving a brief overview of the contributions of the special issue, and conclude by highlighting potential emerging issues and challenges of the field.

Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 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

User interfaces (UI) are an inherent part of any technology with human end-users. The design of the UI depends heavily on the intended end-user and is therefore extremely important for research in both learning technology (where the learner is the end-user) and CCI (where the child is the end-user). Another important concept of learning technology and CCI research (and also in neighboring fields) is that of “artifact”. Artifacts correspond to novel designs (which may be prototype systems, interfaces, materials, or procedures) that have a certain set of qualities or components (such as functionalities and affordances) and that allow us to experiment (e.g., to isolate and test certain components). This chapter describes how researchers can design educational interfaces, visualizations, and other artifacts to support their experiments and enhance learners’ and children’s experience with technology.

As interdisciplinary research fields, child-computer interaction (CCI) and learning technologies have the advantage of enhancing their methods by borrowing from related fields. They represent a research stream that began by applying theories, methods, and tools from a variety of fields, such as the learning sciences, human-computer interaction, design, and the social sciences. Experiments and appropriate experimental methods have been employed extensively over many years to build scientific knowledge and to understand how to design technology to support human needs (e.g., learning, socializing, and wellbeing). This chapter discusses how experiments and experimental studies can be employed in the context of research on learning technology and/or CCI.

When learners interact with technologies and the learning context, a large amount of data is created. The collection, analysis, and utilization of those educational data has provided opportunities for learning technology (and CCI) research. In this chapter, we will discuss how learning systems produce and utilize educational data. In particular, we will discuss contemporary developments in the fields of learning analytics, educational data mining, and learner modelling; and how those advancements have impacted the design and functionalities of learning technologies.

Experiments and experimental studies are used to collect data and build scientific knowledge, and it is one of the primary methodologies for a wide range of disciplines including CCI and learning technology. In this chapter, we provide the basics of experimental research in CCI and learning technology. The goal is to support researchers to gain an understanding of the main methodological decisions and the ways in which experiments can answer RQs. Researchers will also be introduced in evaluating the circumstances that favour (or do not favour) the use of experiments, and how to make the necessary methodological decisions about the type and features of the experiment for CCI and learning technology research.

Learning technology research focuses on the design, development, and/or use of technologies that support learning, whereas CCI research focuses on the design, development, and/or use of technologies that support children’s lives (with a heavy emphasis on learning). Therefore, learning technology and CCI research can be described as research that focuses on the design, development, and/or use of technologies that support learning and/or children’s lives. In this chapter, we provide an introduction to CCI and learning technologies as interdisciplinary fields of research, provide good working definitions and discuss their commonalities, synergies, and complementarities.

This book is about the ways in which experiments can be employed in the context of research on learning technologies and child–computer interaction (CCI). It is directed at researchers, supporting them to employ experimental studies while increasing their quality and rigor. The book provides a complete and comprehensive description on how to design, implement, and report experiments, with a focus on and examples from CCI and learning technology research. The topics covered include an introduction to CCI and learning technologies as interdisciplinary fields of research, how to design educational interfaces and visualizations that support experimental studies, the advantages and disadvantages of a variety of experiments, methodological decisions in designing and conducting experiments (e.g. devising hypotheses and selecting measures), and the reporting of results. As well, a brief introduction on how contemporary advances in data science, artificial intelligence, and sensor data have impacted learning technology and CCI research is presented. The book details three important issues that a learning technology and CCI researcher needs to be aware of: the importance of the context, ethical considerations, and working with children. The motivation behind and emphasis of this book is helping prospective CCI and learning technology researchers (a) to evaluate the circumstances that favor (or do not favor) the use of experiments, (b) to make the necessary methodological decisions about the type and features of the experiment, (c) to design the necessary “artifacts” (e.g., prototype systems, interfaces, materials, and procedures), (d) to operationalize and conduct experimental procedures to minimize potential bias, and (e) to report the results of their studies for successful dissemination in top-tier venues (such as journals and conferences).

In this chapter, we present three topics that are of great importance to CCI and learning technology researchers. The first topic is concerned with the role of “context” in experimental studies, and CCI and learning technology in general. The second topic is concerned with the ethical considerations in experimentation in human-factors IT-related research. The third topic focuses on researchers conducting experimental studies with children, and the need to employ different methods, approaches, and techniques. Although those are the three topics I decided to include in this book, I also believe that additional topics can complement this list.

Thus far, the book provides materials to carry out the whole process of observation, rationalization, and validation, as well as the necessary supporting processes (e.g., artifact design, data analysis, and reporting). At the end of the process, everything is documented in a comprehensive report or a paper, and the respective prototypes, datasets, and practical information are kept on file. An important question, however, remains: In the context of CCI and learning technology research, what are the main reasons for reviewers rejecting a paper or asking for revisions? Considerable effort goes into preparing a paper contribution for a respectable venue (journal or a conference). Researchers do not want to see their effort go to waste, especially if it involves a potentially valuable contribution that could bring credit to authors and fresh insights to readers. Drawing on our own experience and on various guides on how to review papers in CCI, learning technology, and neighboring fields, in this chapter we provide a list of criteria and pitfalls that are common to CCI and learning technology venues.

In this chapter we present a summary of the book, and discuss how the book can support learning technology and CCI researchers. Moreover, we provide some concluding remarks and thoughts for the future of learning technology and CCI research.

This book is focusing on experimental studies in learning technology and CCI research. During the last years, the areas of data science and AI have influenced different aspects of human-factors IT-related research in general and learning technology and CCI research in particular. Therefore, although this book does not provide a deep discussion on how data science and AI have influenced contemporary learning technology and CCI research; in this chapter, we provide a brief presentation of the developments in data science and AI, and the role of those developments in learning technology and CCI research.

Conducting experimental studies in learning technology and CCI research entails an iterative process of observation, rationalization, and validation. Although data collection and data analysis procedures may vary widely in complexity, their selection is based on the research objectives, RQs or hypotheses. So the researchers need to carefully select them and make sure that the research design decisions of data collection and analysis, are adequate for the goals of the study. This chapter provides information on the various data collections and analyses that are usually employed in learning technology and CCI research. This chapter is intended to serve as a guide for CCI and learning technology researchers, and help them deciding what data they need to collect and how they should analyze them to address the goals of their study.

Educational research has used the information extracted from facial expressions to explain learning performance in various educational settings like collaborative learning. Leveraging this, we extracted the emotions based upon two different theoretical frameworks from videos with children aged 13–16 while collaborating to create games using Scratch. The two sets of emotions are based on the control value theory (happiness, sadness, anger, surprise) and the education-specific expressions (frustration, boredom, confusion, delight). We computed the groups’ objective performance, which was calculated based on their created artifacts. We divided them into high and low performance and compared them based on individual emotions’ duration and the transitions among the emotions. We also used the subjective indication of their perceived performance from a self-reported questionnaire, divided them into another performance category, and did a similar analysis with the objective performance. Results show that the objective performance is better explained by the education-specific emotions and the negative valance emotions from the control value theory-based emotions. On the other hand, subjective performance is better explained by the control value theory based on emotions. Based on the results, we suggest implications both for the instructors and students.

Designing and conducting high quality research is extremely important in CCI and learning technology research. However, the same high-quality needs to be followed during the reporting of the work. At the end of the day, this what the reviewers and readers will credit – therefore, reporting is of equal importance. This chapter provides information on how you should structure your article and the information that is usually required. This chapter is intended to serve as a template for CCI and learning technology researchers. Moreover, I have summarized some recommendations based on my experience, as well as on published guidelines and recommendations from neighboring fields.

There is a growing interest in the research and use of multimodal data in learning analytics. This paper presents a systematic literature review of multimodal learning analytics (MMLA) research to assess i) the available evidence of impact on learning outcomes in real-world contexts and ii) explore the extent to which ethical considerations are addressed. A few recent literature reviews argue for the promising value of multimodal data in learning analytics research. However, our understanding of the challenges associated with MMLA research from real-world teaching and learning environments is limited. To address this gap, this paper provides an overview of the evidence of impact and ethical considerations stemming from an analysis of the relevant MMLA research published in the last decade. The search of the literature resulted in 663 papers, of which 100 were included in the final synthesis. The results show that the evidence of real-world impact on learning outcomes is weak, and ethical aspects of MMLA work are rarely addressed. We discuss our results through the lenses of two theoretical frameworks for evidence of impact types and ethical dimensions of MMLA. We conclude that for MMLA to stay relevant and become part of mainstream education, future research should directly address the gaps identified in this review.

The power of big data and their applications are evident through the enormous attention they have received over the past few years, with the majority of the research focusing on solving technical and business problems. However, the challenge remains on how to harness the potential of big data in order to come up with innovative solutions on various societal problems. Big data have the potential to change the way that entrepreneurs as well as the other stakeholders of a social ecosystem take decisions , and develop new paths to create social innovation by taking data-driven decisions. Nonetheless, there is limited understanding on how social ecosystems need to change to embrace the advancement that big data entail. There is a need to institutionalize innovation through big data and social entre-preneurship, and examine how to successfully exploit big data towards achieving social good and sustainable change. We suggest building on the current state of the art, and go beyond it by merging and extending the findings with insights from the different stakeholders involved in the social innovation process. Further, we propose developing and testing a framework of best practices, that will be used as a roadmap by interested parties to successfully employ big data for social innovation, through the development of prototype applications which will clearly showcase the impact of big data on addressing societal challenges. This position paper concludes with research questions and challenges for future studies in the area.

Although the use of learning videos has been widely employed in the past years, recently the interest has been incrementally increased. Millions of learners watch videos from different platforms (eg, YouTube) on a diverse number of terminals (desktop, phone, tablet). Students access academic content via digital libraries, discussions with tutors by email and online courses from their homes. Additionally, massive online open courses (MOOCs) are becoming an increasingly important part of education (Martin, 2012).