Discovering patterns of achievement in Hong Kong students: An application of the Rasch measurement model

Researchers have warned of the need to identify accurately students who are underachieving in Hong Kong, particularly among the gifted group. When comparing the relative effectiveness of three methods for estimating the proportion of underachievement, the absolute split method, using an arbitrary upper and lower limits for estimates of both performance and ability, is more useful for identifying gifted underachievers than the simple difference method (where standardized performance scores are subtracted from standardized ability scores) or the regression method. In contrast, the latter two methods are more useful for identifying underachievers at all levels of ability. All three methods, however, depend on measurements that are invariant, unidimensional and additive. With the advent of modern measurement theory using Rasch measurement models, it is now possible to satisfy these requirements. In this study, a sample of Primary 5 students in Hong Kong (n = 957) were asked to complete a test of mathematical achievement and the Ravens Progressive Matrices test in order to estimate the proportion of students who are underachieving at all levels of ability. Measurement scales were created using Rasch models for partial credit and dichotomous responses for each variable, respectively, and students placed on each scale according to their responses. Because the results are based on measurement scales that are invariant between persons, the identification of underachievement in these students across all levels of ability can be regarded as objective rather than sample dependent.     

The communication of laboratory investigations by university entrants

The purpose of the study reported here was to analyse the ways in which unversity entrant science students carry out and communicate experimental activities and to identify a model to explain characteristic communication practices. The study was prompted by a need to inform the development of an introductory laboratory course. The students studied shared an educational background characterised by a lack of experience with laboratory work and scientific writing. Seven groups of three students were studied. The investigative strategies of these groups were observed. Laboratory reports were used to identify the ways in which students communicated these strategies. Data are presented that show a discrepancy between the strategies used and those reported. The results suggest that: (i) students' perceptions of the purpose of a laboratory task influence their decisions on what to report; (ii) understandings of laboratory procedures greatly influence their decision on what to report and on how much detail to include in a report and; (iii) knowledge of discourse rules contributes to effective reporting. It is concluded that students' communication of an investigation results from the differential operation of various perceptual filters that determine both the procedural and discourse elements of their reports. It is recommended that the communication of science should be taught explicitly and alongside the procedures and concepts of science. © 2000 John Wiley & Sons, Inc. J Res Sci Teach 37: 839–853, 2000     

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.     

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.