This study investigated the impact of a scripted intervention aimed at externalizing four key regulation strategies—orientation, planning, monitoring, and evaluation—on regulation-related interactions and text quality during source-based writing in small university student groups. A total of 76 social sciences students, organized into 20 groups, completed 2 writing tasks: (1) writing a conclusion from a single source and (2) composing a synthesis text from multiple sources. In the first task, ten randomly selected groups received a script prompting discussion of the four regulation strategies, while the other ten did not. In the second task, the groups that received the script in the first task did not receive a script and vice versa. Analyses focused on the quantity and quality of regulation-related interactions and quality of the resulting texts. Results revealed that scripted groups engaged in significantly more orientation, planning, and evaluation interactions, with particularly high quality in orientation and evaluation during the first task. Monitoring was frequent and high quality across all groups, with no script-related differences. No significant effect of scripting was found on overall text quality. The findings highlight the script’s value in promoting regulation-related interactions, while also suggesting the need for improved scaffolding, particularly through more structured prompts for planning and monitoring. These findings contribute to the design of effective collaborative writing instruction.

There is widespread concern over the harms adolescents experience from digital media use. Yet, existing literature often overlooks mundane digital harms. This study administered a new instrument, the mundane Digital Harms scale (mDHS), to a sample of 865 adolescents to examine (RQ1) their mundane digital harm experiences, and how these associate with their (RQ2) time spent on different digital activities, (RQ3) use of digital disconnection strategies, and (RQ4) sociodemographic characteristics. Confirmatory factor analysis validated six distinct mundane digital harms: wasting time online, digital distraction, school-home blurring, negative social comparison, negative online interactions, and digital fatigue. We observed these harms to co-occur in adolescents' lives. Generally, however, adolescents did not agree with often experiencing them. Girls reported more than boys that they often feel bad after comparing themselves to others online. Younger adolescents reported having negative online interactions and struggling with school-home blurring more often, while older adolescents reported wasting time online and experiencing digital fatigue more often. Time spent on specific digital activities predicted specific harm experiences, albeit weakly. Adolescents who used more disconnection strategies reported higher levels of harms. These findings highlight the need for more nuanced interventions targeting specific harms and subpopulations effectively.

The focus of the present study is to investigate university students' views on the scripted computer-supported collaborative writing method. The study presented in this article was conducted at one Finnish university and one Belgian university. A total of 91 university students were randomly assigned to small groups of three to five members. All groups were instructed to follow a four-phase simultaneous sequential integrating construction script for their collaborative writing task. The inductive thematic analysis of the students' reflective essays indicated a variety of factors that either enabled or hindered computer-supported collaborative writing within the scripted learning context. Four main themes were captured: individual, group, script, and activity-based factors. Furthermore, our findings suggest that students specifically identified enabling factors as group-based, such as collaboration or effective communication between group members. Conversely, the data revealed that the hindering factors were especially script-based, such as students expressing confusion about the script or how to approach the collaborative writing task. These findings have practical implications for the design of scripted joint writing activities in computer-supported settings in higher education.

University students are frequently required to collaborate, often in the form of collaborative writing tasks. The process as well as the outcomes of the collaboration depend on choices made during the group formation phase. Studies on why students select partners for collaborative writing tasks are, however, lacking. Therefore, the present study aims to gain insights into (1) university students’ preferences with regard to teacher-assigned and self-selected group formation, (2) which motives they take into account when self-selecting a partner, and (3) the degree to which students select a partner similar to themselves. Sixteen dyads collaboratively wrote a research paper. Prior to the collaboration, 30 students individually completed questionnaires and 28 students were individually interviewed. The findings show that most students have mixed opinions regarding teacher-assigned or self-selected group formation (n = 18), while the others bar one prefer to self-select a partner (n = 9). Students’ main motive for self-selecting is familiarity, and, more in particular, prior collaboration experience with a specific partner. Other motives include friendship, ability, convenience, and attitude. Furthermore, students tend to select a partner with a similar attitude, ability, task approach, and perspective towards the content of the task. Predictability seems to be the most important driver for self-selection.

Synchronous Online Teaching (SOT) is becoming increasingly popular, but research shows many teachers are inadequately prepared, calling for greater investment in professional development (PD). Additionally, there is a significant gap in research systematically describing the design of evidence-based PD initiatives. Consequently, researchers and educators lack an understanding of the essential components of these interventions, which obstructs the replication, dissemination, and implementation of evidence-based PD programs for synchronous online teachers. This study presents the design of SOL-lab, an online PD program aimed at fostering teachers’ technical and social SOT competences. A detailed overview of the online PD design is offered, encompassing the underlying theoretical and empirical foundations, macro-level design principles, and micro-level instructional and learning activities. More particularly, three key design principles guided the online PD development process: focused observation of SOT, providing and receiving feedback, and stimulating the transfer of learning. By integrating authentic learning experiences and fostering reflective practice, our program aims to empower teachers with the necessary competences to thrive in SOT environments. An analysis of teachers’ perceptions indicates that they had a positive experience with the effective features of the PD program. This study offers valuable insights for the development of effective online PD initiatives for SOT.

Low-educated adults participate less in adult education than higher-educated adults. In this study, we analyze psychosocial barriers to learning while acknowledging that barriers for low-educated adults may be different from those of medium- and high-educated adults. An extended version of the Theory of Planned Behavior is used to study training intention. We add prior Learning Experiences as predictor to the model. A total of 563 adults filled in the questionnaire. Higher-educated adults show more Perceived Behavioral Control, Perceived Social Norms, and more positive Attitudes towards lifelong learning. Logistic regression demonstrated that Perceived Behavioral Control, Perceived Social Norms and Attitudes are related to training intention, but prior Learning Experiences are not. Mediation analyses showed that the relationship between Perceived Behavioral Control and Intention is mediated through Learning Experiences. The findings suggest that psychosocial barriers need to be taken into account when considering how to reach non-participating adults.

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass–fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.

This study concentrates on the effects of teacher feedback (FB) on students’ learning performance when students are tackling guiding questions (GQ) during the online session in a flipped classroom environment. Next to students’ performance, this research evaluates the sustainability in students’ self-efficacy beliefs and their appreciation of the feedback. Participants were second year college students (n = 90) taking the “Environmental Technology” course at Can Tho College (Vietnam). They were assigned randomly to one of two research conditions: (1) with extra feedback (WEF, n = 45) and (2) no extra feedback (NEF, n = 45) during the online phase of the flipped classroom design. In both conditions, students spent the same amount of time in the online environment as well as in the face-to-face environment. The findings indicate that students studying in the WEF condition achieve higher learning outcomes as compared to students in the NEF condition. With respect to student variables, we observe no significant differences between the two research conditions in terms of self-efficacy beliefs at various occasions. However, we explore significant differences between the two research conditions in terms of feedback appreciation during the posttest assessment.

Despite the attention devoted to situational and institutional barriers in studying participation in adult education, psychosocial barriers are often overlooked in research. However, low-educated, and non-participating adults are more likely to experience them. In this study, we examine low-educated, both participating and non-participating, adults' psychosocial views on learning. We interviewed 15 adults by using vignettes to elicit discussion on a delicate and difficult to interview theme and carried out a qualitative content analysis. Our findings demonstrate that adults point to situational barriers for not participating and, contrary to what we anticipated, have positive general attitudes towards learning. However, there are several prerequisites to having this attitude. For instance, learning has to be useful, a learning trigger is needed, spare hours should not be devoted, and there should be no exams or tests. As a result, our research demonstrates the complex nature of perceived 'situational' barriers, which frequently pertain to psychosocial stances.

This study delves into the perceptions of teachers regarding the collaboration between virtual and human teachers in online education. It is situated within the broader context of artificial intelligence (AI) in education, with a particular focus on Generative Artificial Intelligence (Gen AI) and its potential to transform online learning. This study aims to explore the possibilities of Virtual Humans (VH) as humanized Gen AI entities. More specifically, in this presentation, we study the perceptions of online teachers of d-teach online school regarding VH. This study employs a survey that combines the Agent Persona Instrument (API-R) and the Technology Acceptance Model (TAM) to measure the participants' perceptions with respect to using a VH as a digital co-teacher. The primary research objective is thus to understand how these stakeholders perceive VH roles and effectiveness in supporting the learning process. Follow up interviews are planned to deepen our insights on how real teachers think about virtual humans as a teacher. Although specific results are pending at this stage, the study promises to provide valuable insights into teachers' perspectives. Beyond the confines of the school, this research has the potential to benefit global education and training by enhancing the quality, efficiency, accessibility, and inclusivity of online education.

In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by analysing patterns in abstract syntax trees. This study investigates two primary questions: whether ECHO can predict feedback annotations to specific lines of student code based on previously added annotations by human reviewers (RQ1), and whether its training and prediction speeds are suitable for using ECHO for real-time feedback during live code reviews by human reviewers (RQ2). Our results, based on annotations from both automated linting tools and human reviewers, show that ECHO can accurately and quickly predict appropriate feedback annotations. Its efficiency in processing and its flexibility in adapting to feedback patterns can significantly reduce the time and effort required for manual feedback provisioning in educational settings.

In an authentic flight simulator, the instructor is traditionally located behind the learner and is thus unable to observe the pilot’s visual attention (i.e. gaze behaviour). The focus of this article is visual attention in relation to pilots’ professional learning in an Airbus A320 Full Flight Simulator. For this purpose, we measured and analysed pilots’ visual scanning behaviour during flight simulation-based training. Eye-tracking data were collected from the participants (N = 15 pilots in training) to objectively and non-intrusively study their visual attention behaviour. First, we derived and compared the visual scanning patterns. The descriptive statistics revealed the pilots’ visual scanning paths and whether they followed the expected flight protocol. Second, we developed a procedure to automate the analysis. Specifically, a Hidden Markov model (HMM) was used to automatically capture the actual phases of pilots’ visual scanning. The advantage of this technique is that it is not bound to manual assessment based on graphs or descriptive data. In addition, different scanning patterns can be revealed in authentic learning situations where gaze behaviour is not known in advance. Our results illustrate that HMM can provide a complementary approach to descriptive statistics. Implications for future research are discussed, including how artificial intelligence in education could benefit from the HMM approach.

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass-fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.

In the present study, we provide in-depth insight into the content and structure of a class-wide teacher-led educational intervention on argumentative writing.The computersupported intervention focuses on explicit writing instruction and collaborative writing, both proven to be effective instructional approaches in educational research.The intervention is systematically and analytically described by means of blueprint-based design principles for pedagogical interventions.Following this procedure, this writing program is described by defining design principles, instructional teaching activities, and student learning activities.

This paper has investigated the importance of explicit instruction and collaborative writing on (a) argumentative writing performance and (b) self-efficacy for writing of secondary school students. This intervention study additionally aimed to evaluate the effectiveness of alternating between individual and collaborative writing throughout the writing process (planning collaboratively, writing individually, revising collaboratively, and rewriting individually). A cluster randomized control trial (CRT) design was opted for. To investigate the effect of the intervention on secondary school students' writing performance and self-efficacy for writing, multilevel analyses were performed. It was found that the presence of explicit instruction in combination with collaborative writing is positively related to argumentative writing performance and self-efficacy for writing. Alternating between individual and collaborative writing was not significantly different from collaborating throughout all phases of the writing process. More in-depth research into the quality of collaboration is, however, needed to gain insight into the interaction processes and writing processes that take place during collaborative writing.

Collaborative source-based writing has been shown to be an often used and effective way for university students to acquire content knowledge, collaboration skills, and writing competencies.The outcomes of collaborative source-based writing processes depend on the interactions between the group members.However, these interactions remain underexplored.As a first step to uncover interactions, the development of a coding scheme to map interactions among collaborative source-based writing students is presented in this poster.

Students at university are often required to collaborate on tasks.However, collaboration processes are not always efficient and productive.Many problems can be attributed to group composition.Students are often free to choose a partner, but little is known about the underlying factors contributing to their decision-making process.In the current study, 30 students filled in questionnaires and were interviewed on their group formation process.

Evidence has emerged on the importance of emotions for students' problem-solving.Despite the convincing evidence on the role of emotions, there has not been much discussion on how the emergence of different kinds of emotions is related to IT students' problem-solving as well as the challenges of retention and dropout rates.We measure emotions in a multimodal way and study their role in the collaborative problem-solving (CPS) of IT students (N=50).We investigate how emotions are associated with IT students' CPS involving scripted and nonscripted conditions.Our findings will contribute on reconstructing theories on scripting, by focusing on missing elements, namely the role of emotions.

This study focuses on the problem solving skills in technology-rich environments of teachers. PIAAC (Programme for the International Assessment of Adult Competencies) data on adults’ (n = 11,294) competencies, is used to investigate how problem solving skills of teachers are associated with sociodemographic, work-related, and everyday-life related background factors. In addition, the problem solving skills in technology-rich environments of teachers are compared with those of other adults with a higher education degree. The main statistical analyses are conducted with logistic regression models under the design-based framework. Our findings illustrate that teachers’ strong or weak skills seem to be associated with sociodemographic factors and work-related factors. When comparing teachers with other professionals, for high problem solving skills numeracy skill use at home was important on top of the sociodemographic factors, while teachers’ weak skills seem to be associated with fewer ICT skill-use at work on top of the sociodemographic factors. Combining our results with earlier research that emphasises the importance of daily activities at work on the one hand, and the lack of room for teachers to actually work and learn together on the other hand, we argue that teachers may benefit from more opportunities to develop professionally at work.