A New Way of Working

本专栏由英国大使馆文化处提供稿件,旨在让读者从中学习地道英文,体悟英伦百味。本期介绍了一种新的工作模式——在家办公。越来越多的英国人在家办公,既可省去路上耗费的时间,又有利于提高工作效率、节约能源,一举多得。     

Categories in conflict: Combating the application of an intuitive conception of inheritance with category construction

The idea that characteristics acquired by an organism during its lifetime can be inherited by offspring and result in evolution is a substantial impediment to student understanding of evolution. In the current study, we performed a preliminary examination of how acquiring physical changes in a question prompt may differentially cue intuitive and scientific justifications of inheritance and evolution and how this varies based on how student learned the concept. Middle school students in a suburban northeastern district (N = 314) either learned about evolutionary change with a category construction task (with different levels of feedback support) or completed a worksheet. Three days later students responded to two free response scenarios (one where a physical change is acquired). Responses were coded based on student justifications for either science accuracy or intuitive nature. Specific reasons were coded by justification type with high inter-rater agreement (k > 0.93). Results showed that students were more likely to apply intuitive reasoning when a physical change was acquired (50%) than if the change was behavioral in nature (16%). Additionally, students who completed the category construction task provided significantly more scientifically accurate justifications about inheritance (M = 1.12) than control students (M = 0.47), and significantly less intuitive justifications (M = 0.67) than control (M = 1.13). Finally, category construction produced the most scientific reasoning when feedback was provided. Taken together, these results suggest that intuitive reasoning is differentially applied based on physical organismal changes, intuitive reasoning is less frequent when learning via category construction, and the category construction task is more effective for this population with the inclusion of feedback.     

Cultural dimensions of decisions about educational participation among 14‐ to 19‐year‐olds: the parts that Tomlinson doesn't reach

The continued increase in levels of educational participation and patterns of attainment among young people from less advantaged social backgrounds suggests that class cultures can no longer be seen as providing significant barriers to educational progression. Indeed, it can be argued that this increase in educational participation has been facilitated by a processes of cultural convergence in which the benefits derived from qualifications are more or less universally acknowledged. At the same time, it is clear that some young people are put in positions at school that effectively promote processes of cultural resistance as survival strategies.

This paper provides an overview of some of the ways in which class cultures impact on patterns of educational participation. It draws on three recent studies in an attempt to differentiate barriers to educational progress that can be tackled through school reforms from those that originate or are constantly reinforced in the community. In the light of these observations of the cultural dimensions of the educational decisions of 14‐ to 19‐year‐olds, a brief assessment is made of the likely impact of the Tomlinson proposals.     

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.