Method as a journey: a narrative dialogic partnership illuminating decision-making in qualitative educational research

Qualitative education research is an inherently complex landscape, presenting the qualitative researcher with constant ethical and reasoned decision-making. Presented as a narrative dialogic, this paper traces and juxtaposes the method stories of two qualitative researchers who focused their work around education phenomena, but in different contexts and in different ways. One researcher’s narrative study of phenomena via interviews is juxtaposed with the other’s phenomenographical investigation utilizing Facebook status updates. Seeing researcher thinking side-by-side provides a comparison that teases out the ways in which researchers might consciously grapple with thinking around method. These twin method stories offer ways for other qualitative researchers to think about their own decision-making as they address the challenges of balancing subjectivity and rigour, authenticity of reported data, and protection of participants. This dialogic partnership points to the malleability of method. It also encourages further exploration of the complexities of qualitative education research in which the researcher is often insider–outsider or outsider–insider.     

Validity of an ultra-wideband local positioning system to measure locomotion in indoor sports

The validity of an Ultra-wideband (UWB) positioning system was investigated during linear and change-of-direction (COD) running drills. Six recreationally-active men performed ten repetitions of four activities (walking, jogging, maximal acceleration, and 45º COD) on an indoor court. Activities were repeated twice, in the centre of the court and on the side. Participants wore a receiver tag (Clearsky T6, Catapult Sports) and two reflective markers placed on the tag to allow for comparisons with the criterion system (Vicon). Distance, mean and peak velocity, acceleration, and deceleration were assessed. Validity was assessed via percentage least-square means difference (Clearsky-Vicon) with 90% confidence interval and magnitude-based inference; typical error was expressed as within-subject standard deviation. The mean differences for distance, mean/peak speed, and mean/peak accelerations in the linear drills were in the range of 0.2–12%, with typical errors between 1.2 and 9.3%. Mean and peak deceleration had larger differences and errors between systems. In the COD drill, moderate-to-large differences were detected for the activity performed in the centre of the court, increasing to large/very large on the side. When filtered and smoothed following a similar process, the UWB-based positioning system had acceptable validity, compared to Vicon, to assess movements representative of indoor sports.     

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.