Basic Creation of Name and Title Authorities     

Les Hawkins  Hien Nguyen  Heylicken Moreno 《期刊图书馆员》2016,70(1-4):343-355

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Key Problems in Organizing and Structuring University Research in Vietnam: The Lack of an Effective Research “Behaviour Formalization” System     

Huong Thi Lan Nguyen  Vincent Lynn Meek 《Minerva》2016,54(1):45-73

Structure and organization seems to be at the root of many of the questions raised about institutional behaviour; however, with respect to research on university capacity building, few studies have examined research organizational problems, particularly in developing countries. This study investigates academic reactions to the structure and organization of research at four leading Vietnamese universities. Through document analysis and semi-structured interviews with 55 participants, the study finds that the four case-study Vietnamese universities have accomplished a number of the more visible tasks of research management such as creating research and research management positions; deciding primary organizational units for research delivery; creating a research office; and creating research oversight committees. However, they seem to neglect the other less visible tasks of organizing and structuring research such as developing rules for research integrity; developing a mechanism for evaluating the quality of research outcomes; preparing researchers and research managers for the necessary skills and knowledge; and deciding vertical and horizontal decentralization. The study concludes that even though research has been formally structured and organized, the management of research has not yet been professionalized. The key problem in organizing and structuring research is the lack of an effective system for research behaviour formalization. A more effective system for better formalizing research behaviours should be developed so that Vietnamese universities can integrate more successfully into the global research.  相似文献   

Examining the Effects of Changes in Classroom Quality on Within-Child Changes in Achievement and Behavioral Outcomes     

Tyler W. Watts  Tutrang Nguyen  Robert C. Carr  Lynne Vernon-Feagans  Clancy Blair 《Child development》2021,92(4):e439-e456

This study examines whether changes in classroom quality predict within-child changes in achievement and behavioral problems in elementary school (ages spanning approximately 6–11 years old). Drawing on data from a longitudinal study of children in predominantly low-income, nonurban communities (n = 1,078), we relied on child fixed effects modeling, which controlled for stable factors that could bias the effects of classroom quality. In general, we found that changes in classroom quality had small and statistically nonsignificant effects on achievement and behavior. However, we found that moving into a high-quality classroom, particularly those rated as high in Classroom Organization, had positive effects on achievement and behavior for children with significant exposure to poverty in early life.  相似文献   

A systematic literature review of faculty development for teacher educators     

Tam T. Phuong  S. Catherine Cole  Jill Zarestky 《高等教育研究与发展》2018,37(2):373-389

Although ample literature exists regarding the effectiveness of faculty development (FD) activities, there is a gap in the literature synthesizing its outcomes. This review, using a predetermined review protocol, analyzed 22 publications on FD for teacher educators using Kirkpatrick’s training evaluation taxonomy. We found that North American universities provide the most empirical evidence about FD activities, focusing on technology integration in teaching and pedagogical skill improvement. Formal programs, such as serial workshops and developmental relationship, were reported with high frequency, compared to self-directed learning and organizational development activities. Empirical research was heavily based on self-reported, qualitative data and emphasized individual-level outcomes. There is a lack of information regarding the impact of FD activities for teacher educators on subsequent student learning or the downstream effect on school systems. Similarly, institutional involvement in FD remains largely unexplored. We conclude with robust recommendations for research and practice.  相似文献   

Demystifying COVID-19 publications: institutions,journals, concepts,and topics     

Haihua Chen  Jiangping Chen  Huyen Nguyen 《Journal of the Medical Library Association》2021,109(3):395

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Linking versus searching: A case study of performance support use     

Frank Nguyen  Matthew Hanzel 《Performance Improvement》2007,46(10):40-44

In highly competitive global organizations, the practice of hit‐and‐miss searching for information is a luxury seldom afforded. This article examines a case study of an organization that explored the use of different types of performance support interventions for four years. Results show that performance support systems that link to relevant support content were used significantly more than systems that required the performer to search for and locate information. A comparison of these findings with the existing literature and the implications of these results for performance technologists 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.

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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.

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