Student engagement with resources as observable signifiers of success in practice based learning

Practice-based learning activities with a focus on Science, Technology, Art, Math and Engineering (STEAM) are providing new opportunities for teaching these subjects. However, we lack widely accepted ways of assessing and monitoring these practices to inform educators and learners and enable the provision of effective support. Here, we report the results from a study with 15 teenage students taking part in a 2-day Hack. We present results from analysis of video data recording collaborative working between groups of students. The analysis of the video data is completed using the ERICAP analytical framework (Luckin et al., 2017) based on ecology of resources and interactive, constructive, active and passive engagement concepts. The results illustrate the differences between students' engagement with resources which might be utilized as signifiers of student success in similar learning environments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysing collaborative problem-solving from students' physical interactions

Collaborative problem-solving (CPS) is a fundamental skill for success in modern societies, and part of the most common constructivist teaching approaches. However, its effective implementation and evaluation are challenging for educators. Current inquiries on the identification of the observable features and processes of CPS are progressing at a pace in digital learning environments. However, still, most learning and teaching occurs in physical environments. In my current research, I investigate differences in student behaviours when groups of students are solving problems collaboratively in face-to-face, practice-based learning (PBL) environments in high school and universities. My data is often based on students’ hand position and head direction, which can be automated deploying existing learning analytics systems. Using nonverbal indexes of students’ physical interactivity in PBL, I try to interpret the key parameters of CPS including synchrony, equality, individual accountability, and intra-individual variability. The ultimate aim of my research is to be able to continuously evaluate and support students’ collaborative learning during their engagement with constructivist pedagogies

Learning Sciences Beyond Cognition: Exploring Student Interactions in Collaborative Problem Solving

Composed of insightful essays from top figures in their respective fields, the book also shows how a thorough understanding of this critical discipline all but ensures better decision making when it comes to education

Integrating Physiological Indicators with a Competency Model for Enhanced Collaborative Problem Solving in Small Groups

Improving the collaboration process has long been a subject of inquiry. Yet, evaluating collaboration quality is a significant challenge for researchers and practitioners. Recently, the generalized competency model of collaborative problem solving (CPS) has been suggested, encompassing facets, sub-facets, and indicators (verbal and nonverbal) that directly align with CPS skills. Here we discuss the integration of physiological data to potentially further improve the detection of cognitive and affective aspects of CPS. This paper aims to bridge the gap between physiological data features or characteristics and collaboration quality. More specifically, we present our attempts to integrate physiological data with verbal and nonverbal indicators of a generalized competence model of CPS in small groups comprising four individuals. Moreover, this integration can be further developed into interventions such as reflective exercises or real-time feedback provided by AI agents, with the goal of enhancing collaborative skills

Solved! Making the case for collaborative problem-solving

This report argues that the ability to solve problems with others is a crucial skill for our young people in the workplace of the future but the current education system does little to support it. Key findings Collaborative problem-solving (CPS) is an increasingly important skill to teach young people in order to prepare them for the future. Despite strong evidence for its impact, CPS is rarely taught in schools but if structured well it can reinforce knowledge and improve attainment. Significant barriers exist for teachers implementing this practice, from behaviour management to curriculum coverage, to task-design. For CPS to gain ground, a concerted shift is needed including teacher training, better resources and system level support. This report is part of Nesta’s ongoing commitment to equipping young people with the skills they need to succeed. It makes a series of recommendations on how organisations and policymakers can help support and embrace the implementation of CPS. Nesta is following this up with a series of small-scale pilots of aligned programmes in order to evaluate impact and explore how CPS can be implemented in a range of practical settings. Policy recommendations Stimulate production of quality collaborative problem-solving (CPS) resources and training, from primary education onwards. Fund existing, aligned programmes to scale and evaluate impact. Educate and involve the out-of-school learning sector and volunteer educators. Develop smarter collaborative problem-solving assessment methods. Help higher education organisations and MOOCs to track what works

The role of learning theory in multimodal learning analytics

This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge

What the research says about the use of different technologies to enhance learning

Educational technology is growing fast, with schools, colleges and universities more than ever looking for the best ways to use technology to support learning. At the same time, there is an increasing appetite for learning and teaching practices to be backed up by evidence. Few resources are able to offer guidance that has been vigorously tested by research. Now, 'Enhancing Learning and Teaching with Technology' brings together researchers, technologists and educators to explore and show how technology can be designed and used for learning and teaching to best effect. It addresses what the research says about: - how and why learning happens and how different technologies can enhance it - engaging a variety of learners through technology and helping them benefit from it - how technology can support teaching. This book is an accessible introduction to learning and teaching with technology for teachers and other educational professionals, regardless of their experience with using technology for education

The significance of context for the emergence and implementation of research evidence: the case of collaborative problem-solving

One of the fundamental purposes of educational research is to provide evidence to facilitate effective practice. However, the evidence itself does not have much value for practitioners unless key information about the context from which the evidence was generated is also provided. In this paper, we use the word ‘context’ to refer to factors that are relevant for learning, including the interactions that learners experience with multiple people, artefacts, and environments. Unfortunately, in many educational research studies, either these factors do not get the required attention or information about them is presented in an incoherent structure. The resultant lack of information leads to two significant drawbacks. First, it creates confusion among practitioners who want to apply research evidence in their practice. Second, it leads to research studies that on the face of it are similar, but that in reality have resulted from evidence that has been collected in significantly different contexts being included under the same categories in reviews, meta-reviews, and best-evidence syntheses. In this paper, we draw on the concept of ‘relatability’ of evidence and present taxonomy for collaborative problem-solving (CPS) that can be used to provide the valuable information against which research evidence can be indexed. By addressing the need for more detailed information about the contextual factors from which the evidence is generated to bridge the gap between research and practice in CPS research, we aim to exemplify the approach that is needed in educational research more generally

Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative Learning

Using learning analytics to investigate and support collaborative learning has been explored for many years. Recently, automated approaches with various artificial intelligence approaches have provided promising results for modelling and predicting student engagement and performance in collaborative learning tasks. However, due to the lack of transparency and interpretability caused by the use of "black box" approaches in learning analytics design and implementation, guidance for teaching and learning practice may become a challenge. On the one hand, the black box created by machine learning algorithms and models prevents users from obtaining educationally meaningful learning and teaching suggestions. On the other hand, focusing on group and cohort level analysis only can make it difficult to provide specific support for individual students working in collaborative groups. This paper proposes a transparent approach to automatically detect student's individual engagement in the process of collaboration. The results show that the proposed approach can reflect student's individual engagement and can be used as an indicator to distinguish students with different collaborative learning challenges (cognitive, behavioural and emotional) and learning outcomes. The potential of the proposed collaboration analytics approach for scaffolding collaborative learning practice in face-to-face contexts is discussed and future research suggestions are provided.Comment: 12 pages, 5 figure