The increasingly digital landscape of higher education has highlighted the importance of self-regulated learning in digital learning environments. To support this, academic goal setting is frequently used to enhance self-regulated learning in order to improve academic performance. Although many studies have explored the implementation of goal setting activities as behavioral modifiers, the implementation of goal setting across these studies is varied, and there is little consensus on the components which should be included and reported when studying goal setting activities. To provide an overview of the current state of the field, a systematic review was carried out examining studies which implemented academic goal setting activities within higher education over the last 14 years (2010–2024) to determine for whom, in what contexts, and how goal setting has been implemented. The results from the 60 included studies reveal a wide array of goal setting implementations covering many countries and academic disciplines. Overall, these implementations are highly heterogeneous, with large differences between studies in how goal setting is carried out. However, results also show a strong trend toward partial digitalization, with most studies using technology to deliver their goal setting activities, but very few adopting technologies for any further enhancements or support. Overall, the review reveals a focus on non-experimental studies exploring the content of student goals, with only a small selection testing the effect of goal setting in experimental studies. Based on these results we suggest future work focuses on testing the effect of goal setting, especially focusing on the interplay between the design of the activities and individual student needs, as well as further investigation of how emerging educational technologies can be used to scale and enhance goal setting activities.@en

Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI technology has been applied to learning analytics (LA), aiming at a more accurate, consistent and scalable understanding of learning to compensate for challenges that human intelligence faces. However, machine intelligence has been criticized for lacking contextual understanding and difficulties dealing with complex human emotions and social cues. In this work, we aim to understand learners' internal cognitive processes based on the external behavioural cues of learners in a digital reading context, using a hybrid intelligence (HI) approach, bridging human and machine intelligence. Based on the behavioural frameworks and the insights from human experts, we scope specific behavioural cues that are known to be relevant to learners' attention regulation, which is highly relevant for learners' cognitive processes. We utilize the public WEDAR dataset with 30 subjects' video data, behaviour annotation and pre–post tests on multiple choice and summarization tasks. We apply the explainable AI (XAI) approach to train the machine learning model so that human evaluators can also understand which behavioural features were essential for predicting the usage of the cognitive processes (ie, higher-order thinking skills [HOTS] and lower-order thinking skills [LOTS]) of learners, providing insights for the next-round feature engineering and intervention design. The result indicates that the dominant use of attention regulation behaviours is a reliable indicator of low use of LOTS with 79.33% prediction accuracy, while reading speed is a valuable indicator for predicting the overall usage of HOTS and LOTS, ranging from 60.66% to 78.66% accuracy, highly surpassing random guess of 33.33%. Our study demonstrates how various combinations of behavioural features supported by HI can inform learners' cognitive processes accurately and interpretably, integrating human and machine intelligence. Practitioner notes What is already known about this topic Human attention is a cognitive process that allows us to choose and concentrate on relevant information, which leads to successful learning. In affective computing, certain behavioural cues (eg, attention regulation behaviours) are used to indicate learners' attentional states during learning. What this paper adds Attention regulation behaviours during digital reading can work as predictors of different levels of cognitive processes (ie, the utilization of higher-order thinking skills [HOTS] and lower-order thinking skills [LOTS]), leveraged by computer vision and machine learning. By developing an explainable AI model, we can predict learners' cognitive processes, which often cannot be achieved by human observations, while understanding behavioural components that lead to such machine decisions is critical. It can provide valuable machine-driven insights into the relationship between humans' external and internal states in learning. Based on the frameworks spanning cognitive AI, psychology and education, expert knowledge can contribute to initial feature selection and engineering for the hybrid intelligence (HI) model development and next-round intervention design. Implications for practice and/or policy Human and machine intelligence form an iterative cycle to build a HI to understand and intervene in learners' cognitive processes in digital reading, balancing each other's strengths and weaknesses in decision-making. It can eventually inform automated feedback loops in widespread e-learning, a new education norm since the COVID-19 pandemic. Our framework also has the potential to be extended to other scenarios with digital reading, providing concrete examples of where human intelligence and machine intelligence can contribute to building a HI. It represents more systematic supports that apply to real-life practices.

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In the rapidly evolving domain of computer science education, fostering deep engagement and sustained motivation among students remains a challenge. This study introduces the Answers platform, a pioneering online learning environment developed at TU Delft. This research aims to reimagine the learning experience for Bachelor and Master of Science Students in computer science by integrating gamification elements grounded in Wenger's value creation framework. Our paper explores two critical research questions: the perception of learners towards gamified learning experiences and the impact of the Answers' system on value creation and motivation. We incorporated points, badges, and leaderboards in a semester-long intervention to enrich the learning landscape. The Value Creation Questionnaire (VCQ) results indicated that the platform effectively created potential and applied value, significantly enhancing students' learning practices and motivation. However, its impact on fostering social connections could have been more pronounced. The platform also moderately influenced students' ability to impact their world and shift perspectives. The Intrinsic Motivation Inventory (IMI) revealed that students generally enjoyed using the platform but felt it did not significantly enhance feelings of connectedness. This paper contributes to the body of knowledge by demonstrating the efficacy of gamification in computer science education and offering insights into the design of engaging online learning platforms. By bridging theoretical frameworks with practical application, the Answers platform exemplifies the potential of gamified environments to revolutionize educational practices in the digital age.

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In programming, the concept of variables is central to learning other concepts like loops, functions, and conditions, and the way variables are explained influences students' understanding. Prior work observed Massive Open Online Courses (MOOCs) on introductory programming to investigate how the topic is addressed in teaching materials. Our work aims to verify if their results generalize to other materials by analyzing 13 popular Scratch and Python programming books and investigating (1) which definitions and analogies are currently being used to explain the variables, (2) looking into the programming concepts that are introduced alongside variables, and (3) analyzing if and how variable naming practices are introduced. Our results support previous findings from MOOCs, suggesting that CS educators and developers of educational materials for introductory programming could pay more attention to how they explain variables and can be more deliberate and consistent when it concerns the teaching of naming practices. Additionally, we found specific analogies used to explain variables, and differences between programming languages in the order that variables are introduced. Our work can be used to update current educational materials and inform the development of new ones.@en

Computational Thinking (CT) is vital in today's digital era, especially in Engineering Education. While no official policy or teaching framework on CT education has been established in the Netherlands, a Western European country, there have been various initiatives for the integration of CT into the curriculum. Recognizing the crucial role of teachers in CT integration, we surveyed the perceptions and intentions of teachers in tertiary education in the Netherlands. Our survey encompassed two aspects: (1) teachers' perceptions of CT, and (2) their intentions to integrate CT into pedagogical activities. 38 teachers, mostly in Engineering Education, from across the Netherlands completed the questionnaire based on the UTAUT framework. Regarding CT perceptions, our investigation reveals that teachers possess an inadequate understanding of the relationship between CT and Computer Science, have limited training experiences in CT, and hold differing opinions on when and which constructs of CT should be integrated into different domains. Concerning teachers' intentions to integrate CT, the results exhibited a strong positive correlation between performance expectancy, attitude towards CT, and behavioral intention to implement CT in learning activities. To foster the integration of CT in tertiary education, our findings suggest the need for further development of higher education teacher training programs focused on CT and its relation to CS. Additionally, there is a call for further exploration of how to enhance teachers' performance expectancy and effort expectancy.@en

Despite extensive studies from the software engineering community on how naming practices influence programming behavior, the topic receives little attention in education. Prior work indicated little agreement on good naming because it depends on many factors. Students are told that "naming is important'' and "should be meaningful,'' yet its practical implementation is rarely discussed and feedback is lacking. The current work presents a dialogic teaching approach focused on teaching a critical reflection on naming practices through five activity types: (A) perceptions and experiences, (B) create names, (C) evaluate through ranking, (D) compare codes, and (E) locate a mistake. We developed, ran, and analyzed a one-hour workshop, that we present here and share our experiences, leading to recommendations for teachers. Our contribution is twofold: (1) we provide a set of (adaptable) activities and exercises for supporting deliberate naming practices, thereby assisting teachers interested in adopting naming practices into their curriculum; (2) we provide insights regarding the student perspective on naming practices, derived from the activities, revealing potential issues and opportunities in teaching the topic.

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Motivation. Variable naming practices are part of the software developer’s profession, influencing program comprehension and code quality. Yet, little is known about how variable naming practices are taught in beginner courses. Objective. This paper investigates naming beliefs, self-reported teaching practices, and observations regarding variable naming practices of teachers of introductory Python programming courses. Methods. We adopted an in-depth qualitative approach by interviewing ten teachers from secondary education and higher education and developed several themes in order to answer our research questions. Results. Among various opinions and practices, we found that teachers agree on using meaningful names, but have conflicting beliefs about what is meaningful. Moreover, the described teaching practices do not always match teacher’s views on meaningful names, and teachers rarely encourage students to use them. Instead, they express that naming practices should not be enforced and that students will develop them by example. Whereas some teachers report focusing solely on conventions, others deliberately dedicate time for students to engage with naming, create their own guidelines, provide continuous feedback, or include naming exercises on exams. Discussion. Naming practices do not seem to be deliberately taught, even though they influence program understanding and code quality. We also identified inconsistencies in teachers’ self-reported naming practices. As such, we encourage intentional conversations about naming practices in educational settings, specifically linking naming to code quality and readability. We see room for group and peer activities as a means to this end, as well as providing formative feedback dedicated to naming.

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Even though the field of Computer Science (CS) affects different aspects of society, several groups of society are underrepresented, including women and nonbinary people. Children might have different learning opportunities in CS due to their project preferences. Girls are likelier to work on stories and simple programs in Scratch, whereas boys tend to create games and more complex programs. We explore whether preferences and program implementation differ between genders within a story, game and visual adventure in Hedy, a gradual textual programming language. We analysed 14,233 programs within five Hedy levels created by 2,819 users who turned 10 to 14 in 2023. We found that boys, girls and nonbinary children worked most on the game adventure. Within the individual adventures, gender differences occur in all three adventures in the most elaborate Hedy level analysed. However, for some levels, no gender differences were found. Thus, programming assignments can be created in which children of different genders work on similar programs in terms of size and number of (unique) commands used.

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Evaluation of educational innovation at the course level is dominated by frameworks that are linear, built on simple models, lack grounding in theory, and are generally not flexible enough to provide actionable recommendations for improvements. Moreover, the guidelines provided by existing literature are difficult to transfer to other contexts as most of these are aimed at specific innovations only. Therefore, there is a need for a new approach that is flexible enough to enable adaptation to different contexts, and that draws on the strengths of existing evaluation traditions. This paper forms a starting point for a larger research initiative for developing a comprehensive innovation evaluation framework. The context of the paper is a project based within the four universities of technology in The Netherlands (4TU) dedicated to strengthening collaboration in engineering education and research needed to take on global societal problems. The potential reach of our framework has implications for the wider field. This paper describes the results of an exploratory literature review to draw on the strengths of various evaluation theories to develop a new approach to evaluating educational innovation in courses. Value, Methods, and Use are the main components identified for this approach. Secondly, we developed a workshop to pilot this approach. Lastly, the workshop was presented to educational researchers, engineering educators, and educational advisors. We share lessons learned from the workshop and conclude with descriptions of future research to refine the framework to prepare its application in real innovation initiatives.@en

Describing and analysing learner behaviour using sequential data and analysis is becoming more and more popular in Learning Analytics. Nevertheless, we found a variety of definitions of learning sequences, as well as choices regarding data aggregation and the methods implemented for analysis. Furthermore, sequences are used to study different educational settings and serve as a base for various interventions. In this literature review, the authors aim to generate an overview of these aspects to describe the current state of using sequence analysis in educational support and learning analytics. The 74 included articles were selected based on the criteria that they conduct empirical research on an educational environment using sequences of learning actions as the main focus of their analysis. The results enable us to highlight different learning tasks where sequences are analysed, identify data mapping strategies for different types of sequence actions, differentiate techniques based on purpose and scope, and identify educational interventions based on the outcomes of sequence analysis. @en

From an early age, girls may opt out of Computer Science (CS) for not fitting the CS stereotypes of being male, asocial and technology-oriented. These stereotypes might be strengthened by children's books on programming, but little is known about this. Therefore, this paper explores the gender, social interactions and interests of characters illustrated in ten popular extracurricular Scratch and Python children's books. We found more masculine than feminine characters in all but one book. Furthermore, nearly half of the characters are illustrated alone, and 15% are interacting with computers & robots. Over two-thirds of the characters fit at least one stereotypical trait. With this paper, we aim to create awareness of stereotypes in CS books among creators, publishers and buyers. Making and using more inclusive CS materials will help close the gender gap.

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Agent-based training systems can enhance people's social skills. The effective development of these systems needs a comprehensive architecture that outlines their components and relationships. Such an architecture can pinpoint improvement areas and future outlooks. This paper presents ARTES: a general architecture illustrating how components of agent-based social training systems work together. We studied existing systems and architectures for training and tutoring to design ARTES and identify its essential components and interaction characteristics. ARTES comprises two core components: the agent simulation of social situations, and educational elements to provide guided learning. We link ARTES's crucial components to four primary learning theories (behaviourism, cognitivism, social cognitive theory, and constructivism) to illustrate the role of agent simulation and tutoring elements in establishing desired learning outcomes. Furthermore, we map ARTES's components against eight architectures, 43 systems and three tools to indicate the components' relevance, completeness, generalisation, and deployment potential across contexts. In addition to ARTES, the paper also contributes by identifying future improvements and research directions, such as the agent's thinking, tutoring methods, knowledge transfer, and ethical implications. We believe ARTES can help bridge the gap between virtual human simulations and impactful educational learning, offering training system developers desirable features like understandability and adaptability.@en

Computational Thinking (CT) is considered a core 21st century digital skill. The aspect of assessment is crucial and knowing what, who, when, how, and where to assess is important for assessment design. In this study, we conducted an umbrella review to gain insights regarding CT assessment in higher education. In total, we analyzed 11 reviews, focusing on: (1) bibliographical and methodological characteristics of the reviews; (2) aspects relevant of assessment design, including a) assessed constructs, b) applied assessment methodologies, and c) assessment contexts. Our findings suggest an increased attention on this topic. However, hardly any reviews reasoned the selection of their review methodology, and most of the reviews did not thoroughly examine existing reviews. Regarding assessment design aspects, most reviews did not confine their scope to higher education; however, findings on interventions and educational settings show commonalities. We identified 120 unique assessed constructs and around 10 types of assessment methods. Though a combined use of distinct assessment methods is suggested in reviews, guidelines for appropriate assessment design are yet to be constructed. Based on the findings, we argue that it is necessary to explore different combinations of assessment design in various contexts to construct assessment guidelines.

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Background and Context. Increasing gender diversity in the field of Computer Science (CS) benefits the economy as well as gender equality. However, several obstacles - including underdeveloped CS interests, lack of programming experience, and a misfit with the stereotypes of computer scientists - prevent women from entering the field. Although these barriers develop from an early age, research focused on children is limited. Furthermore, limited work is done within European countries. Objectives. In this study, we research the interest children aged 7 to 14 have in a CS career. Additionally, we look into whether children with different characteristics have a different interest in CS. As such our research question: How does children's interest in a CS career differ based on their a) age, b) gender, c) computer interests, d) programming experience, and e) stereotypical beliefs? Method. We collected data from 200 children in a science museum located in the Netherlands. We gathered data on their gender, age, computer interests, programming experience, stereotypical beliefs and interest in becoming a programmer - as representative of a CS career. We used self-reported closed questions and reduced-length Child Implicit Association Tests. Findings. In general, the participating children are not interested in a CS career. We did find several characteristics related to a higher interest in a CS career: being a boy, having an interest in computers or video gaming, gaining programming experience at home or at an out-of-school activity, and the belief that programmers are social. Implications. To increase the participation of women in CS, we suggest motivating girls more to engage in computer and programming activities - especially out-of-school activities - while ensuring that these activities are gender inclusive.

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Increasing student numbers in higher education, particularly in engineering and computer science, make it difficult for motivated lecturers to continue engaging in active teaching methods such as Flipped Classrooms and Work-Based Learning. In these settings, digital Peer Assessment can be one approach to provide effective and scalable feedback. In Peer Assessment, students assess each other’s performance whilst gaining useful reflection and judgment skills at the same time. This umbrella review of 14 review papers on the use of (digital) Peer Assessment in education provides a comprehensive overview of design choices and their consequences open to educational practitioners wishing to implement digital Peer Assessment in their courses, the type of tooling available and the possible effects of these choices on the learning outcomes as well as potential pitfalls and challenges when implementing Peer Assessment. The paper will inform and assist educators in finding or developing a tool that fits their needs.@en

Women are underrepresented in Computer Science (CS). Closing the gender gap in CS benefits the economy, gender equality and society. However, girls have low CS interest while interest energizes learning and guides career trajectories. We explore the effects of the user interface of Hedy, a graduate language which teaches the syntax of Python, on the development of CS interest in girls.@en

Higher educational institutions have broadly adopted Collaborative Engineering Design (CED) activities to prepare students for complex problem-solving in multidisciplinary settings. These activities are non-linear and mediated by various social practices and tools. Therefore educators might struggle in facilitating the achievement of specific learning goals. Embodied cognition is an approach that explains non-linear behaviour through orgamism-environment interactions and might therefore provide educators with insights on how to prompt students towards desired actions in CED activities. According to embodied cognition, we learn through actions that emerge as a response to a problem (task) and environmental constraints. Educators can guide students’ behaviour by proposing tasks and adapting the environmental constraints of a learning situation, thus creating a field of promoted action. In this paper, we outline the progress of a design-based research in which insights from embodied cognition are implemented to promote desired student behaviour in CED activities. We report on the results of our problem-exploration phase. A systematic literature review and focus groups with students revealed that students are often hesitant to adopt new practices and tools that could potentially improve their collaborative design process. Next, we propose three theory-based design principles in which the task and environmental constraints are leveraged to foster the adoption of practices and tools and apply them to CED activities. Finally, we will share preliminary observations of the learning processes triggered by the designed activities and outline the directions for future research.@en

The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.@en

Different individual features of the learner data often work as essential indicators of learning and intervention needs. This work exploits the personas in the design thinking process as the theoretical basis to analyze and cluster learners’ learning behavior patterns as groups. To adapt to the learning practice, we develop data-driven personas by clustering learners’ features based on factual learning outcomes (i.e., knowledge gain, perceived learning experience, perceived social presence) based on unsupervised learning, a more accessible and objective intervention design strategy for e-reading practices. Using the Chi-square test, we quantitatively evaluate different clusters driven by various unsupervised learning methods on the multimodal SKEP dataset. Furthermore, for a more practical real-life application, we achieved automatic persona prediction based on the attention regulation behaviors of learners. The subject-independent evaluation results indicate the best classification accuracy of 70% for the four-level classification task, differentiating three personas of learners with needs and another without feedback needs. It also shows that time-based sampling on both independent and cumulative learner behaviors works as robust predictors of learner personas, achieving a stable accuracy range of 65%-70% throughout the e-reading with the SVM classifier. Our work inspires the design of a real-time feedback loop for e-learning based on conversational agents.@en