Quantification of improvement in triathlon swimming performance by textile speedsuits

To quantify swimwear-induced differences under triathlon-specific conditions, we compare the swimming performance, the metabolic cost, and the standardised passive drag of well-trained triathletes when wearing (1) five speedsuit models by different manufacturers from 2017, (2) usual swimming trunks/swimsuits (men/women), and (3) individually preferred competition trisuits. Because of the complexity of the underlying hydrodynamic and biomechanical effects, three separate experimental stages were realized, each with 6–12 well-trained short- and middle-distance triathletes (male and female, mean age 22?±?5 years) from the German national elite or junior elite level. All measurements were conducted on the basis of real athletes’ motion in the water to correctly account for all relevant effects, including skin and muscle vibrations. First, the athletes took part in a series of 100 m short-distance tests at maximal effort in a long-course pool to quantify swim-time differences in absolute terms. Second, the subjects completed multiple submaximal 400 m tests at 95% of their individual maximal speed in a swimming flume, with their swimwear-related differences in metabolic load being explored in terms of blood lactate and heart rate. Third, the passive drag of the triathletes was measured in the flume during a towing test under standardised conditions in velocity steps of 0.2 m/s within the triathlon-relevant range of 1.1–1.7 m/s. In all three test stages, the speedsuits exhibited performance advantages over trunks/swimsuits: in the 100 m maximal test, the mean swim time with speedsuits decreased by 0.99?±?0.30 s (????1.5%). During the 400 m submaximal flume test, the mean heart rate showed a reduction of 7?±?2 bpm (? ??4.0%), while the post-exercise blood lactate accumulation decreased by 1.0?±?0.2 mmol/L (? ??26.2%). Similarly, the passive drag in the towing test was lowered by 3.2?±?1.0 W (????6.9% as for normalised power and ??5.2% as for normalised force) for the speedsuits. Wearing speedsuits instead of usual trunks/swimsuits is shown to improve the swimming performance and to reduce the metabolic cost for well-trained triathletes under triathlon-specific test conditions. The reduction in passive drag of the passively towed athlete’s body due specific speedsuit surface textures seems to be only one reason for performance advantages: the effective reduction in muscular, soft tissue, and skin vibrations at the trunk and thighs during active propulsive motion of the swimmer seems to further contribute substantially.     

Correction to: Primary school students’ perceived social support in relation to study engagement

European Journal of Psychology of Education -     

Teachers’ enactment of policy in classrooms: making students accountable through inscriptions from the curriculum in classroom interactions

The purpose of this paper is to examine the complex relationships between educational policy and classroom practice. By employing a sociocultural perspective, we examine formulations inscribed in socio-material artifacts about what students should learn and how they should engage with knowledge. We explore how these formulations are mobilized in instructional work and the implications this activity has for student participation. To address this issue, we analyzed video data of how teachers invoke competence aims from the national curriculum in their instructional work in six classrooms. The analytical procedures were derived from interaction analysis. The analysis focuses on how such formulations explicitly mediate social interaction as it unfolds on a micro level. The findings show that competence aims gain different functions as they are mobilized in classroom practice; in other words, they serve different purposes in teachers’ instructional work and anticipate different modes of student participation. In this study, the competence aims were (a) invoked as a source of authority, (b) translated into instructions, and (c) mobilized to obtain social order in the classroom. More rarely, the competence aims were used in meta-level discussions, where they functioned to reach agreements on how to pursue work toward joint goals. We discuss the implications of these ways of invoking competence aims for student participation.

     

Infusing educational technologies in the heart of the university—A systematic literature review from an organisational perspective

Educational technologies have experienced unprecedented prominence on university agendas with many institutions motivated to keep the lessons learned from the COVID-19 sparked transition with regard to online teaching. In response to this renewed interest in ensuring the longevity of educational technologies in higher education, this systematic review analysed the various organisational factors—for example, leadership, infrastructure, strategy—considered essential in the literature for the successful implementation of educational technologies. Specifically, we reviewed 1614 papers published in five prominent educational technology journals in the last decade. From this sample, we identified 47 papers that discussed organisational factors. Drawing on these studies, we constructed an organisational framework, which outlines the different organisational factors, actors and processes involved in implementing educational technologies. The identified organisational factors are structured into three main categories: (1) Leadership and Strategy, (2) Infrastructure and Resources and (3) Recognition and Motivation. Our aim was to further the scholarly understanding of the organisational layer involved in digital change as well as provide concrete recommendations for practitioners.

Practitioner notes

What is already known about this topic

• Previous research has stressed the importance of taking organisational factors such as infrastructure, leadership, strategy and staff commitment into account when implementing educational technologies.
• However, review papers have failed to systematically organise these studies to create a comprehensive understanding of the organisational factors involved in implementing educational technologies and ensuring their longevity at an institution.
• There is currently a high level of interest in how educational technologies can be implemented in the higher education landscape, as many institutions are facing the question of what lessons they can learn from the crisis and how they can continue on their path of digitalisation.

What this paper adds

• This review paper addresses a gap in our scholarly understanding of the organisational layers involved in the implementation of educational technologies in higher education institutions (HEIs).
• This paper provides a framework on organisational factors, which influence the implementation of educational technologies in HEIs.
• This review paper demonstrates that bottom-up and opinion leadership, support structures tailored to the need and time of faculty as well as recognition and incentives have the largest impact on a sustainable implementation of educational technologies in HEIs.

Implications for practice and/or policy

• Universities should create structures that enable innovation and creativity by promoting bottom-up and opinion leadership as well as shared decision-making processes as they are important for the successful implementation of educational technologies in HEIs.
• Besides providing a reliable and suitable infrastructure, institutional support and resources in terms of technical advice and training tailored to specific needs, should be in place when planning the implementation of educational technologies in HEIs.
• The additional workload instructors face when implementing digital teaching should be recognised and incentivised as it strengthens instructor engagement which is crucial for the implementation of educational technologies in HEIs.

     

A systematic review of empirical studies using log data from open-ended learning environments to measure science and engineering practices

Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices.

Practitioner notes

What is already known about this topic

• Research has shown that technology-based, open-ended learning environments (OELEs) that collect users' interaction data are potentially useful tools for engaging students in practice-based STEM learning.
• More work is needed to identify generalizable principles of how to design OELE tasks to support student learning and how to analyse the log data to assess student performance.

What this paper adds

• We identified multiple barriers to the production of sufficiently generalizable and robust results to inform practice, with respect to: (1) the design characteristics of the OELE-based tasks, (2) the target competencies measured, (3) the approaches and techniques used to extract features from log files and (4) the models used to link features to the competencies.
• Based on this analysis, we can provide a series of specific recommendations to inform future research and facilitate the generalizability and interpretability of results:
  • Making the data available in open-access repositories, similar to the PISA tasks, for easy access and sharing.
  • Defining target practices more precisely to better align task design with target practices and to facilitate between-study comparisons.
  • More systematic evaluation of OELE and task designs to improve the psychometric properties of OELE-based measurement tasks and analysis processes.
  • Focusing more on internal and external validation of both feature generation processes and statistical models, for example with data from different samples or by systematically varying the analysis methods.

Implications for practice and/or policy

• Using the framework of evidence-centered assessment design, we have identified relevant criteria for organizing and evaluating the diverse body of empirical studies on the topic and that policy makers and practitioners can use for their own further examinations.
• This paper identifies promising research and development areas on the measurement and assessment of higher-order constructs with process data from OELE-based tasks that government agencies and foundations can support.
• Researchers, technologists and assessment designers might find useful the insights and recommendations for how OELEs can enhance science assessment through thoughtful integration of learning theories, task design and data mining techniques.

     

Exploring the development of executive functions in children in a digital world

European Journal of Psychology of Education - Children in many parts of the world spend increasing time using digital devices (tablets, mobile phones etc.). In the present study, the developmental...     

Human and artificial intelligence collaboration for socially shared regulation in learning

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human-AI collaboration by introducing a novel trigger concept and a hybrid human-AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human-AI collaboration in facilitating effective SSRL in teaching and learning.

Practitioner notes

What is already known about this topic

• For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor.
• Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning.
• It is questionable whether traditional theories of how people learn are useful in the age of AI.

What this paper adds

• Introduces a trigger concept and a hybrid Human-AI Shared Regulation in Learning (HASRL) model to offer insights into how the human-AI collaboration could occur to operationalize SSRL research.
• Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning.
• Provides clear suggestions for future human-AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills.

Implications for practice and/or policy

• Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning.
• Researchers and practitioners could benefit from methodological development incorporating human-AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.

     

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.

Practitioner notes

What is already known about this topic

• Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
• It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
• Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.

What this paper adds

• Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
• Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
• Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
• Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.

Implications for practice and/or policy

• Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
• This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.