This theoretical article examines the relationship between self-regulated learning and task-unrelated thoughts (TUTs) through the lens of metacognition. Grounded in Winne's COPES (conditions, operations, products, evaluations, and standards) model of self-regulated learning, we propose an interaction model emphasizing metacognitive monitoring and control. This model suggests the metacognitive cycle inherent to self-regulated learning can increase meta-awareness and mitigate prolonged experiences of TUTs. Learners can potentially redirect their focus by engaging in iterative cycles of metacognitive monitoring and control when thoughts inevitably drift toward TUTs. Foundational concepts explored include metacognition, meta-awareness, and the COPES facets. By synthesizing theoretical connections, processes are proposed through which learners’ self-regulatory capacities may influence TUT experiences via enhanced meta-awareness. This lays the groundwork to guide future inquiries on self-regulation dynamics underlying effective learning. Empirical research is recommended to investigate the viability of this theorized mechanism linking self-regulation processes to experiences of TUT and research agendas following from this theoretical framework are outlined.

Learners will inevitably think about something other than what they are trying to learn. The content of these thoughts has been largely studied in experimental settings, and resulting classifications often group them into task-unrelated thoughts, task-related interference, and external distractions. However, how these classifications pertain to learner’s off-task thoughts in naturalistic settings is still unknown. This study, situated within enabling pathway and undergraduate courses in a single Australian university, thematically analyzed students’ free-text thought reports when catching themselves thinking about something unrelated when watching educational videos. The resulting themes are Course Related, Previous Experiences, Judgements, Adaptation, Reactive Distractions, Embodied Experience, Current Concerns, Social Relationships, Multitasking, and Research Design. These themes were then mapped against the common classification of task-unrelated thoughts, task-related interference, and external distractions, showing that this categorization cannot capture the range of off-task thoughts occurring in a naturalistic setting. We argue that it is not for researchers to decide whether a thought is on- or off-task; our role is to enable learners to make this appraisal for themselves, as whether a learner adapts their learning process following off-task thoughts will depend on how they appraise these thoughts.

Video-based learning is a prevalent instructional method in higher education. Although video offers increased flexibility for learning, students face inherent challenges in maintaining attention and engagement. Mind wandering is a common challenge that can negatively impact learning outcomes. While prior research has examined the impact of mind wandering, less is known about how students respond when they become aware of mind wandering. This naturalistic case study explored how self-regulated learning relates to students’ awareness of mind wandering. It also examined the temporal focus of these thoughts and whether students choose to rewind videos after noticing they were mind wandering. A total of 53 university students from eight courses participated, reporting instances of mind wandering while watching short (< 30 min) instructional videos and completing the self-regulation for learning online (SRL-O) questionnaire. Students with stronger effort regulation and planning and time management skills, reported less mind wandering. Analysis of video navigation revealed only 15 instances where students rewound the video after reporting mind wandering, representing less than 5

As with other sectors over the last few years, Generative Artificial Intelligence (GenAI) is rapidly reshaping the education ecosystem introducing new epistemic, ethical, and organisational challenges. Education leaders face the need to shift established systems and processes to reimagine how more adaptive complexity-informed GenAI enabled systems operate with new governance, pedagogy, and strategic decision-making. In this context, traditional bureaucratic leadership structures are ill-suited for the pace, scale, and sociotechnical nature of the ongoing technological disruption. This paper addresses this gap by introducing a novel leadership framework called SPARK, designed to operationalise Complexity Leadership Theory (CLT) in the new emerging educational contexts. The framework is grounded in CLT and designed to enable system-level transformation in educational institutions. Drawing on Kuhn’s theory of paradigm shifts, Perez’s technoeconomic framework, and Uhl-Bien’s complexity leadership, the paper situates educational change within broader patterns of sociotechnical transformation and institutional misalignment.The SPARK framework (Systems, Problem, Analysis, Research, and Knowledge brokerage) provides a structured approach for educational leaders to navigate the tensions between institutional stability and the increasing demand for systemic innovation. SPARK combines distributed agency, adaptive capacity, and network-based leadership to guide the translation of high-potential innovations such as GenAI-enhanced pedagogy from isolated pilots to scalable, systemic practices. By embedding both technical infrastructure and social context within its design, SPARK provides effective guidelines for leaders to foster cultures of experimentation, trust and resilience. The paper argues that the systemic adoption of GenAI in education requires more radical approaches that go beyond mere technical implementations. It demands a new paradigm with reconfigured leadership models, professional identities and institutional logics. SPARK offers a pragmatic approach to assist leaders in responding to GenAI’s disruptive potential with intentional, sustainable, and equitable change.

While learning, students experience various kinds of thoughts. Some of these thoughts are fully on the task, but others might be about aspects of their private life or how they can apply what they are currently learning. These thoughts that are not fully on the task can be divided into task-unrelated thoughts (TUTs, also known as mind wandering) and task-related interference. Task-unrelated thoughts are independent of the task, while task-related interference thoughts may bear some relationship to the specific learning task. Prior research has largely focused on how often task-unrelated thoughts occur and how they relate to learning outcomes. There is a paucity of research on task-related interference leaving a gap in understanding of the prevalence of these thoughts and their influence on learning outcomes. This meta-analysis aimed to determine the frequency of task-related interference (TRI) and its correlation with learning outcomes. Results show that TRI occurs 20.54% of the time, yet no significant correlation with learning was observed. These findings suggest TRI should be accounted for alongside mind wandering, as the available evidence does not support a systematic relationship between TRI and learning performance, unlike for TUTs.

The increasing use of Generative AI (GenAI) in educational feedback has prompted growing interest in understanding how students interact with AI-generated feedback. However, most existing research examines student–AI feedback interactions through the lens of writing revision outcomes, self-reported perceptions, or conceptual theorisation, leaving the real-time behavioural processes largely underexplored. This study investigates the range of student behaviors that emerge during multi-turn conversational interactions with a GenAI tool implemented in a university-level machine learning course. Using inductive qualitative coding of 2,128 conversational messages between students and AI, we developed an empirically grounded behavioural coding scheme comprising 24 indicators organised across 8 dimensions: Feedback Orientation and Engagement, Cognitive Externalisation, Iterative Dialogue Management, Metacognitive Monitoring, Epistemic Positioning Toward AI, Self-Regulatory Persistence and Progression, Strategic AI Use Orientation, and Relational Framing Toward AI. Our findings reveal that students display a diverse range of behaviours that extends well beyond simple answer-seeking, ranging from minimal answer submission to extended self-explanation, and from managing dialogue depth to strategically using AI for forward learning plans. This coding scheme provides a systematic tool for researchers and practitioners to analyse and understand the complexity of student–AI feedback interactions in conversational settings.

Task-unrelated thought, commonly known as mind wandering, occurs frequently during learning and can negatively affect learning outcomes. In video-based learning environments, learners have the opportunity to mitigate the negative effect of task-unrelated thoughts by rewinding the video to review missed content. This study investigated whether rewinding following self-caught off-task thoughts (task-unrelated thoughts or task-related interference) affects learning outcomes. The study explores the types of off-task thoughts that co-occur with rewinding behaviour and how self-regulated learning aptitudes relate to awareness of off-task thoughts and learning outcomes. The study employed an experiment, with 222 participants watching a 15-minute video and reporting their off-task thoughts. Task-unrelated thoughts were more often followed by rewinding compared to other thought types. However, no significant difference in knowledge test scores was observed between participants who reviewed previous video segments after off-task thoughts and those who did not. To explore what underpins awareness of off-task thoughts and reactionary tactics such as rewinding, self-regulated learning aptitudes were examined. A piecewise structural equation model showed that higher scores on the Metacognition and Online Effort Regulation scales were associated with fewer reported off-task thoughts. These findings suggest a relationship between self-regulated learning aptitudes and meta-awareness that warrants further investigation to determine whether self-regulated learners experience fewer off-task thoughts or habitually regulate their learning in response to them.

The rise of generative artificial intelligence (GenAI) and accelerated globalization have necessitated a fundamental recalibration of higher education to prioritize domain-agnostic, 21st-century professional competencies. While institutional commitment to these skills is high, their systematic integration into the curriculum and evaluation remains fragmented, highlighting a critical gap between traditional academic success metrics and demonstrated workforce readiness. This special issue presents five complementary studies that investigate how the intersection of learning analytics (LA) and GenAI can bridge the gap between institutional rhetoric and demonstrated professional readiness. The contributions collectively advance a research agenda across four dimensions: 1) benchmarking large language models (LLMs) for curricular-competency alignment using reasoning-based prompting, 2) the iterative design of Socratic-style GenAI chatbots to scaffold self-regulated learning, 3) the application of psychometric modelling and Latent Profile Analysis to quantify 21st-century professional competencies, and 4) institutional governance and adoption of curriculum analytics. Collectively, these studies advocate for an epistemological shift toward processsensitive assessments that move beyond static, episodic indicators toward dynamic, longitudinal representations of learner capability. We conclude by outlining the sociotechnical infrastructure, including robust governance and interdisciplinary collaboration, required to responsibly transition these AI-driven innovations from research prototypes to sustainable enterprise infrastructure, ensuring that analytics serve the evolving needs of students, educators, and professional bodies.

Background Mind wandering is a common experience for students that negatively impacts learning outcomes. Research has attempted to mitigate the impact by including interpolated testing in the context of learning from videos. The results of these studies are mixed and indicate that interpolated testing may not have a practical effect on reducing mind wandering. Objectives In the present study, we aim to investigate whether writing self-explanations has a stronger effect than interpolated testing on reducing mind wandering and improving learning outcomes. We al-so explore whether self-regulated learning skills are related to meta-awareness of mind wander-ing. Methods 138 participants were recruited across three groups and presented with the same video to review. The first group was the control group. The participants in the second group answered interpolated tests, and the third group wrote self-explanations at pauses in the video. All participants complet-ed a knowledge test before and after watching the video to compare group learning outcomes. Additionally, participants completed questionnaires measuring their self-regulated learning. Self-caught thought reports were used in anticipation that participants expecting to write self-explanations would engage in metacognitive monitoring and thus become meta-aware. Results and Conclusions We found no significant difference between the groups regarding knowledge gain or meta-awareness. Interestingly, higher scores on metacognition and online task strategies subscales of self-regulated learning were associated with fewer thought reports. However, the number of thought reports written correlated positively with knowledge gain, indicating that meta-aware learners perform better on subsequent knowledge tests.

It is widely recognized that higher education (HE) graduates require a broad range of professional skills and abilities to succeed in their future careers. However, despite this acknowledgement, assessment practices in HE remain focused on content-based knowledge. This narrow emphasis limits the capacity to effectively and holistically evaluate a student's professional competency and readiness for employment. This issue is particularly acute for HE degrees that require graduates to demonstrate attainment of externally regulated professional standards. While the curricula are mapped to professional standards for accreditation purposes, demonstrating a student's attainment of these standards is not straightforward and has mostly been done through self-reported surveys. This study offers a novel curriculum analytics method for mapping assessment grades to the attainment of professional standards across a Teacher Education program. Specifically, we present an approach that uses psychometric modelling and learning analytics to identify distinct patterns in learners' acquisition of professional standards. This method does not alter current assessment practices in HE. Instead, the approach offers a scalable, automated means to infer a learner's attainment of documented professional standards, complementing current measures of academic success, such as GPA. The study underscores the advantages of complementing the current HE assessment practises with an outlined curriculum analytics approach, providing a holistic representation of a student's learning progress.

High-quality learning design is an important contributor to student success. Higher education institutions can leverage the combined potential of learning analytics (LA) and Generative Artificial Intelligence (GenAI) to enhance instructors’ learning design practices. However, the way LA and GenAI are being used in combination for learning design is not well understood. In this context, one theory that can provide valuable insights into instructors’ technology integration into learning design (LD) practice is Self-Determination Theory (SDT). Using SDT as an interpretative lens, this study illuminates the factors in instructors’ learning design practices when utilising LA and GenAI. For this purpose, we employed quantitative ethnography to explore how instructors utilise LA and GenAI for LD. Eleven focus groups were conducted with instructors from a large Australian university. Using thematic analysis, we created a codebook consisting of ten codes that were applied to our dataset and then analysed using Epistemic Network Analysis (ENA). Our results show that instructors’ LA-informed LD discussions primarily revolved around course, contextual, and creative problem-solving considerations, as well as student support. The predominant GenAI factors were assessment design, contextual consideration, creative problem solving and design for student self-determination. Based on our results, we discuss the integration of LA and GenAI in LD practice and decision-making, highlighting the practical implications of our findings.

Students often enter second year without the necessary self-regulated learning skills to be independent learners. This occurs at a time of diminishing institutional support services when compared to their first-year experience and can lead to a decline in motivation and academic performance. Prior research on the motivational beliefs and behaviours of second-year students remains largely unexplored within higher education literature. This paper addresses this gap by investigating the second-year experience and the motivational beliefs and behaviours of 26 second-year students at a major metropolitan Australian university, and the strategies they used to manage and sustain their motivation. The findings provide new insights into students' motivation contextualised in an emergency remote teaching context. Students' goals, including academic achievement and accountability to family, drive their motivation in the absence of social interactions with peers and help sustain them through the challenges they face at university. Students' use of positive scenarios to trigger motivational beliefs and behaviours is revealed in the study, which has not been reported elsewhere.

BackgroundThere is growing recognition in the education sector of the critical role empirical data plays in aiding strategic decision-making and supporting personalised learning. The call for increased and more nuanced data-driven decision-making has been primarily addressed by the institutional use of student learning dashboards and learner profiles in higher education; their application and evaluation in K-12 settings remain underexplored. Existing approaches often lack robust empirical evidence, and there is limited research addressing their use in real-world classroom environments.Objectives and MethodsThis study aims to bridge the knowledge gap by systematically reviewing both academic and non-peer-reviewed literature on decision-making systems in schools. The review focused on understanding the types of data used, the implementation status of dashboards and learner profiles, and the practical applications in K-2 education. The analysis included 19 academic papers as well as 10 pieces of non-peer-reviewed literature to examine the landscape of decision-making systems.ResultsThe analysis revealed that while academic literature highlights the underuse of diverse data types and a scarcity of real-world evaluations, non-peer-reviewed literature showcases promising holistic decision-making approaches employed by schools and government bodies globally. However, these practical applications often lack empirical validation.ConclusionsThis study provides a novel contribution by exploring how researchers can move beyond stand-alone studies focusing on academic performance to gauge the success of different decision-making systems. The study also recommends that practitioners invest in more research-backed systems to create better teaching and learning practices and enhance personalised learning. By highlighting the current gaps and offering forward-looking recommendations, our study clarifies the present landscape and serves as a foundation for future research to improve student learning outcomes.

Students engaged in learning experience both on and off-task thoughts. Prior research has focused on task-unrelated thoughts (TUT or mind wandering). However, off-task thoughts may also bear some relationship to the task (task-related interference, TRI). This meta-analysis aimed to determine the frequency of off-task thoughts (TRI + TUT) and the correlation of these with learning outcomes. The study included 28 samples using meta-analysis of single means for frequency determination and a mixed-effects model to examine correlations. The results show that TRI occurs 23% of the time and is not correlated with learning outcomes. TUTs occur 29% of the time and are negatively correlated with learning outcomes. Overall, based on a separate meta-analysis, off-task thoughts occur 51% of the time and are negatively correlated with learning outcomes. The results suggest that TRI should be accounted for when studying off-task thoughts, and unlike TUTs, are not considered detrimental to the learning process.

Curriculum mapping plays a critical role in education to ensure alignment between outcomes, content, graduate skills and assessment. Program requirements are typically informed by industry needs and embedded within the curricula and assessment tasks. Curriculum Analytics (CA) has introduced a level of automation to the process of curriculum mapping primarily through machine learning (ML) models. While such CA approaches have served to reduce workload pressures, they continue to face challenges in capturing the nuanced extent to which graduate skills are developed across a program. This study introduces a novel approach using Large Language Models (LLMs) to act as co-curriculum reviewers. Using data from an undergraduate program, we evaluate the effectiveness of LLMs in generating weighted mappings of graduate skills across assessments and compare them to those produced by ML based CA methods. The findings suggest that LLM-generated mappings more closely align with expert judgements (Krippendorff's alpha of 0.76) than those produced by ML based CA models (0.65). The results demonstrate the potential for LLM-driven approaches to curriculum mapping to enhance quality assurance processes linked to curricular alignment, accreditation, and personalized learning pathways.

Introduction: Learning Analytics (LA) has emerged as a potent tool in medical education, offering data-driven insights and personalized support to learners. This systematic review aims to provide a comprehensive overview of the current state of LA in medical education, exploring its applications, benefits, challenges, and future directions.Methods: The study was conducted as a systematic review of learning analytics (LA) in medical education. A comprehensive search was performed in June 2023 across the following databases: ProQuest, Scopus, ERIC, Web of Science, PubMed, and ScienceDirect, with no restrictions on publication dates. The search resulted in a total of 1095 records, which were screened after removing duplicates, leaving 552 titles for review. Following the exclusion of irrelevant articles, 12 studies were selected for synthesis.Results: Four key categories of LA applications emerged: curriculum evaluation, learner performance analysis, learner feedback and support, and learning outcome assessment. Thesynthesis of findings underscores LA potential to enhance learning experiences, identify at-risk learners, and improve formative assessment practices. However, ethical and privacy concernswarrant attention to bridge the gap between research and practice.Conclusion: This review suggests a collaborative and mindful approach to leveraging LA in medical education. Balancing data-driven insights with effective, ethical, and human-centricpedagogical practices is crucial. Addressing these concerns can ensure the integration of LA into medical education, fostering its transformative potential while upholding core values.

Learning Analytics (LA) aims to provide university instructors with meaningful data and insights that can be used to improve courses. However, instructors are often met with challenges that arise when wanting to use LA to inform their educational design decisions. For instance, there may be a misalignment between instructors’ needs and the data and insights LA systems provide. Further research is required to understand instructors’ expectations of LA and how it can support the diversity of educational designs. This case study addresses this gap by investigating the role of LA in instructors’ educational decision-making processes. The study employs self-determination theory's constructs to examine instructors’ existing practices when using LA to support their decision-making. The study reveals that LA enables instructors to make data-informed iterative educational design decisions, supporting their need for competence and relatedness. The emotional aspect of LA is an important consideration that can easily lead to demotivation and avoidance of LA. Support is needed to address instructors’ psychological needs so instructors can fully utilise LA to make effective educational design decisions. The findings inform a framework for considering how instructors’ data-informed educational decision-making can be understood. The implications of our findings and opportunities for the future are discussed.

Educational institutions worldwide are increasingly urged to recognize the significance of integrating creative thinking and other essential capabilities across different academic disciplines. Additionally, there is a growing focus on students' non-cognitive factors. However, relationships between creativity and non-cognitive factors, such as self-concept in subjects such as science, are not well understood. Given the global attempts to increase diversity in STEM subjects, this study explores the relationships between year 8 female students' non-cognitive factors in science and their creative thinking through a series of structural equation models (SEM). The findings identified a positive relationship between specific non-cognitive factors and creative thinking, which has significant implications for curriculum writers and teachers, who should consider these findings to potentially increase student creativity in STEM subjects. This is particularly pertinent in light of initiatives aimed toward fostering female students' engagement in STEM fields throughout their academic journey and into their future careers.

Second-year university students often experience a disconnection with their learning and may feel unmotivated, lack confidence, and are unprepared for the higher expectations and complex concepts of their courses. Their disconnection with their learning can be addressed through deepening the social connections between other second-year students, and instructors providing encouragement to seek help in their learning when they need it. There is scant research that examines the peer-interactions between second-years and how their interactions influence their help-seeking behaviours. This article focuses on the interactions and help-seeking behaviours of 26 students from a major metropolitan Australian university in 2021. Results show that peer interaction is highly valued by students but not easily facilitated, and the relationship between students and their instructor is foundational for future help-seeking behaviours. Implications for practice are also presented.