The effect of performance support and training as performance interventions     

Frank Nguyen  James D. Klein 《Performance Improvement Quarterly》2008,21(1):95-114

For decades, training has been one of the most common interventions used by organizations to improve the performance of their employees and teach them new ideas and skills. But owing to the cost of developing and delivering training, organizations have adopted alternative ways to enable employee performance while reducing the cost and minimizing the time users spend away from the job. One alternative is electronic performance support systems (EPSS). The present study examined the effect of EPSS and training on user performance, time on task, and time in training. Results revealed that participants receiving only EPSS and those receiving training and EPSS performed significantly better on a tax preparation procedure than participants who received only training. Training‐only users also spent significantly more time completing the procedural task than their counterparts in other treatment groups, leading to a negative correlation between time on task and performance. The implications of these findings for the design and development of performance support and training interventions are discussed.  相似文献   

A Comparative Study of Electronic Performance Support Systems     

Frank Nguyen  James D. Klein  Howard Sullivan 《Performance Improvement Quarterly》2005,18(4):71-86

ABSTRACT Electronic performance support systems (EPSS) deliver relevant support information to users while they are performing tasks. The present study examined the effect of different types of EPSS on user performance, attitudes, system use and time on task. Employees at a manufacturing company were asked to complete a procedural software task and received support from either an intrinsic, extrinsic, external performance support system or no system at all. Results revealed significant differences on performance, attitudes and use between several treatment groups. The study suggests that providing any kind of EPSS to support task performance is better than having none at all. In addition, designers can improve user performance, attitudes and use by creating systems that integrate with the primary work interface.  相似文献   

Human and artificial intelligence collaboration for socially shared regulation in learning     

Sanna Järvelä  Andy Nguyen  Allyson Hadwin 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1057-1076

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.

  相似文献   

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

Andy Nguyen  Sanna Järvelä  Carolyn Rosé  Hanna Järvenoja  Jonna Malmberg 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):293-312

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.

  相似文献