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Technology is gradually becoming an integral part of learning at all levels of educational. This includes the now pervasive presence of virtual learning environments (VLEs) and the inclusion of interactive devices used or worn by learners or that are present in the physical classroom environment. These new technology-rich educational ecosystems have greatly facilitated data capture about learners. Thus, several research areas, such as learning analytics (LA), educational data mining (EDM), and artificial intelligence in education (AIED), have grown exponentially during the last decade, with multiple venues supporting this research [1]. However, the inferences about learning that can be made by solely analyzing trace data from VLEs are limited, since logged data do not commonly provide a complete view of the learning experience [2]. Therefore, research communities are moving beyond the data obtained from VLEs and other online tools by incorporating data from external sources such as sensors, pervasive devices, and computer vision systems. Within the context of education, this subfield is often denominated as multimodal learning analytics (MMLA) [3]; nevertheless, the use of these data sources is also common in broader research areas, such as affective computing (e.g., [4]) and human-computer interaction (HCI) (e.g., [5]). The promise is to augment and improve the extent and quality of the analysis that can be performed with these new data sources [6]. Moreover, many new sensor-based tools, such as sensor-based games [7] or realistic laboratories [8,9], are being built to support the educational process. The challenge is embedding sensors and resulting data representations in authentic educational settings in pedagogically meaningful and ethical ways [10]. This Special Issue (SI) invited publications that include approaches to converting data captured using sensors (e.g., cameras, smartphones, microphones, or temperature sensors), wearables (e.g., smart wristbands, watches, or glasses), or other Internet of Things (IoT) devices (e.g., interactive whiteboards, eBooks, or tablets) into meaningful educational insights. Moreover, it invited papers on tools, architectures, or frameworks to manage the orchestration of these sensors and IoT devices to improve education. The submitted articles had to appropriately explain how the inclusiveness of sensor devices can augment the analyses performed to improve teaching, learning, or the educational context in which the sensing it occurs (e.g., in classrooms, VLEs, or other educational spaces). This SI has focused on empirical case studies that fulfill the aforementioned criteria and experimental architectures, methodologies, frameworks, or survey papers. (DIPF/Orig.).

Data about learning can support teachers in their decision-making processes as they design tasks aimed at improving student educational outcomes. However, to achieve systemic impact, a deeper understanding of teachers' perspectives on, and expectations for, data as evidence is required. It is critical to understand how teachers' actions align with emerging learning analytics technologies, including the practices of pre-service teachers who are developing their perspectives on data use in classroom in their initial teacher education programme. This may lead to an integration gap in which technology and data literacy align poorly with expectations of the role of data and enabling technologies. This paper describes two participatory workshops that provide examples of the value of human-centred approaches to understand teachers' perspectives on, and expectations for, data as evidence. These workshops focus on the design of pre-service teachers enrolled in teacher education programmes (N = 21) at two Australian universities. The approach points to the significance of (a) pre-service teachers' intentions to track their students' dispositions to learning and their ability to learn effectively, (b) the materiality of learning analytics as an enabling technology and (c) the alignment of learning analytics with learning design, including the human-centred, ethical and inclusive use of educational data in the teaching practice. [Author abstract]

Previous studies have demonstrated that students who are engaged in learning tasks and make errors before receiving instruction on how to complete them, achieve better learning outcomes than students who first receive instruction and then complete the learning activities with the aim of avoiding errors. Although simulation literature often refers to errors as learning opportunities, to date, there is limited understanding of how pedagogical approaches that promote learning from errors can guide the design of simulation-based learning in healthcare education. To (a) present the Learning from Errors conceptual model; and (b) provide an example of how educators can use this model. The Learning from Errors model is drawn from critical elements of two pedagogical approaches, productive failure and error management training and pedagogical features of high-quality healthcare simulations. We describe the Learning from Errors model, which emphasises the need for adopting pedagogical methods that explicitly use errors as learning opportunities and ultimately inform simulation design. We then illustrate the application of this model to a simulation example. The model includes the following elements: i) normalisation of errors, ii) challenging simulation scenarios, iii) self-directed learning, iv) collaborative teamwork and v) comparison with best practice. This discussion paper presents the Learning from Errors conceptual model, an evidence-based approach that can assist educators in the design of simulations that embrace errors as a catalyst for learning.

In healthcare education, it is important for nursing students to be able to reflect on their performance in high-fidelity clinical simulations in order to develop key skills. Learning Analytics (LA) offers opportunities for data-driven reflection by providing visual representations of educational experiences. While many LA tools rely on data visualisations to communicate insights, these are often difficult for students to interpret, limiting their effectiveness. Despite these challenges, there is limited research exploring alternative and potentially more accessible formats—such as data comics, a narrative visualisation technique that integrates data with the structure of traditional comic strips—to represent and communicate insights from learner data in a more engaging way. This study addresses that gap through a qualitative analysis of nursing students’ perceptions of data comics as reflective tools, focusing on: (i) support for student reflection, (ii) advantages and limitations, and (iii) concerns about their use in healthcare education. Third-year nursing students who participated in a simulation were interviewed and asked to reflect on personalised data comic prototypes generated from their multimodal data using a mix of human input and AI methods. The results indicated that while data comics present an engaging and accessible form of reflective visualisation, considerations need to be made regarding the designs to ensure that they are appropriate for the target audience and do not oversimplify the simulation experience. These findings indicate that data comics should not act as a replacement for conventional visualisations but rather serve as supplementary material to communicate contextual information or aid in interpretation of visualisations.

There is a growing interest in involving students and teachers in the design of human-centered Learning Analytics (LA) systems to align them with authentic learning needs. Yet, limited prior research has explored the implications of integrating both students’ and teachers’ perspectives within a structured co-design process. To address this shortcoming in the literature, we report on a study that examined how undergraduate nursing students and teachers co-designed an AI-powered LA system to support post-debriefing reflection on teamwork and communication in the context of healthcare simulation. This qualitative study, using a co-design approach, examined the design process of an LA system from conceptualization to post-use evaluation. The study addressed two key questions: i ) What tensions emerge from the contrasting perspectives of students and teachers in the co-design an AI-powered LA system? and ii ) How do students and teachers perceive their joint participation in the co-design process? Three key design tension themes emerged from the contrasting perspectives of students and teachers: teaching–learning goals tension , privacy–utility tension , and human-AI guidance preferences tension . The collaborative design process revealed mutual benefits: students valued teachers’ guidance in refining ideas and aligning system goals with learning objectives, while teachers, initially cautious about student involvement, came to see co-design as an opportunity to empower students and deepen their own understanding of responsible data use in practice. These findings contribute to the broader understanding of co-design dynamics in educational technology, underscoring the importance of balanced stakeholder involvement in developing practical, context-aware LA systems.

In Computer-Supported Collaborative Learning (CSCL) classrooms it may be challenging for teachers to keep awareness of certain aspects of the learning process of each small group or assess whether the enactment of the class script deviates from the original plan. Orchestration tools, aimed at supporting the management of the increasing uncertainty and complexity of CSCL classrooms, have been emerging in response. Similarly, learning analytics innovations hold the promise of empowering teachers by making certain aspects of the classroom visible and by providing information that can prompt actionable responses. However, the active role that data may play in teachers’ decision-making and orchestration processes is still not well understood. This paper investigates the perspectives of teachers who used a real-time analytics tool to support the orchestration of a CSCL classroom. A longitudinal study was conducted with a handheld dashboard deployed in a multi-display collaborative classroom during one full academic term. The dashboard showed real-time information about group participation and task progress; the current state of the CSCL script; and a set of text notifications informing teachers of potential students’ misconceptions automatically detected. The study involved four teachers conducting 72 classroom sessions during 10 weeks with a total of 150 students. The teachers’ perspectives discussed in this paper portray the promises and challenges of introducing new technologies aimed at enhancing orchestration and awareness in a CSCL classroom.

The original version of this article unfortunately contained duplicate images for Figs. 4 and 6. The correct images are hereby published.

The aim of this review was to identify the role of basic life support training interventions in international undergraduate nursing education, that support optimal acquisition and retention of knowledge, psychomotor skills and resuscitation self-efficacy. Twenty-four articles were identified and analysed using an integrative review approach. Studies were reviewed for quality using a Critical Appraisal Skills Programme checklist. Common objective and standardised methods of basic life support education practice were identified: instructor led, simulation experiences, self-directed learning, skills training combined with clinical practicum, and computer-based training. Evaluation of competency was collected primarily from multiple-choice questionnaires or researcher-designed checklists, with a lack of objective performance data noted. Importantly, current teaching approaches do not guarantee acquisition or retention of basic life support skills. Objective feedback from technologies supporting cardiopulmonary resuscitation training may be useful in acquisition and retention of psychomotor skills, and therefore requires further exploration. Development of robust, psychometrically sound instruments are needed to accurately and consistently measure nursing students' skills performance. •Traditional learning modes for CPR content does not support knowledge retention.•Studies report poor CPR skills by students, despite completion of BLS certification.•Emerging evidence supports CPR feedback devices to improve BLS skill acquisition.

The theory of Instrumental Genesis (IG) accounts for the mutual evolution of artefacts and their uses, for specific purposes in specific environments. IG has been used in Computer-Supported Collaborative Learning (CSCL) to explain how instruments are generated through the interactions of learners, teachers and artefacts in ‘downstream’ classroom activities. This paper addresses the neglected ‘upstream’ activities of CSCL design, where teachers, educational designers and educational technologists use CSCL design artefacts in specific design-for-learning situations. The paper shows how the IG approach can be used to follow artefacts and ideas back and forth on the CSCL design and implementation pathway. It demonstrates ways of tracing dynamic relations between artefacts and their uses across the whole complex of instrument-mediated activity implicated in learning and design. This has implications for understanding the communicability of design ideas and informing the iterative improvement of designs and designing for CSCL.

Teachers’ spatial behaviours in the classroom can strongly influence students’ engagement, motivation and other behaviours that shape their learning. However, classroom teaching behaviour is ephemeral, and has largely remained opaque to computational analysis. Inspired by the notion of Spatial Pedagogy, this paper presents a system called ‘Moodoo’ that automatically tracks and models how teachers make use of the classroom space by analysing indoor positioning traces. We illustrate the potential of the system through an authentic study with seven teachers enacting three distinct learning designs with more than 200 undergraduate students in the context of science education. The system automatically extracts spatial metrics (e.g. teacher-student ratios, frequency of visits to students’ personal spaces, presence in classroom spaces of interest, index of dispersion and entropy), mapping from the teachers’ low-level positioning data to higher-order spatial constructs. We illustrate how these spatial metrics can be used to generate a deeper understanding of how the pedagogical commitments embedded in the learning design, and personal teaching strategies, are reflected in the ways teachers use the learning space to provide support to students.