癌症的病毒疗法     

DirkM．Nettelbeck  DavidT．Curiel  王伟  周松 《科学中国》2003,(12):52-59

一种称为病毒治疗的新技术，采用“以毒攻毒”，用病毒去攻克癌症。  相似文献   

Structural Conditions for Collaboration and Learning in Innovation Networks: Using an Innovation System Performance Lens to Analyse Agricultural Knowledge Systems     

Frans Hermans  Laurens Klerkx  Dirk Roep 《The Journal of Agricultural Education and Extension》2015,21(1):35-54

ABSTRACT

Purpose: We investigate how the structural conditions of eight different European agricultural innovation systems can facilitate or hinder collaboration and social learning in multidisciplinary innovation networks.

Methodology: We have adapted the Innovation System Failure Matrix to investigate the main barriers and enablers eight countries (England, France, Germany, Hungary, Italy, Latvia, The Netherlands and Switzerland).

Findings: Results show some of the recent trends the AKS actors in these countries have experienced and how these have affected their potential to act as collaborators in multidisciplinary innovation networks. Lack of funds, combined with horizontal and vertical fragmentation and the lack of proper evaluation criteria for collaborative innovation networks are among the most important threats we found.

Practical Implications: This study shows that each national AIS has some unique features. This means that the implementation of policies promoting collaboration and social learning (e.g. the European Innovation Partnerships and Operational Groups) should depend on a critical reflection of the existing structural elements of the AIS in each country and whether there is a need for inclusion of new actors, or whether certain innovations for collective goods should be promoted.

Originality: The paper contributes to the ongoing discussion in the scientific literature on the advantages and disadvantages of privatization of extension and advisory services and the shift from thinking in terms of the traditional Agricultural Knowledge System towards a broader Agricultural Innovation System.  相似文献   

Learning and Innovation Networks for Sustainable Agriculture: Processes of Co-evolution,Joint Reflection and Facilitation     

Heidrun Moschitz  Dirk Roep  Gianluca Brunori  Talis Tisenkopfs 《The Journal of Agricultural Education and Extension》2015,21(1):1-11

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Performance of student software development teams: the influence of personality and identifying as team members     

Conal Monaghan  Boris Bizumic  Katherine Reynolds  Michael Smithson  Lynette Johns-Boast  Dirk van Rooy 《European Journal of Engineering Education》2015,40(1):52-67

One prominent approach in the exploration of the variations in project team performance has been to study two components of the aggregate personalities of the team members: conscientiousness and agreeableness. A second line of research, known as self-categorisation theory, argues that identifying as team members and the team's performance norms should substantially influence the team's performance. This paper explores the influence of both these perspectives in university software engineering project teams. Eighty students worked to complete a piece of software in small project teams during 2007 or 2008. To reduce limitations in statistical analysis, Monte Carlo simulation techniques were employed to extrapolate from the results of the original sample to a larger simulated sample (2043 cases, within 319 teams). The results emphasise the importance of taking into account personality (particularly conscientiousness), and both team identification and the team's norm of performance, in order to cultivate higher levels of performance in student software engineering project teams.  相似文献   

The FEFM virus     

Dirk Cjelli 《Performance Improvement》1994,33(10):12-13

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Comparing video and virtual reality as tools for fostering interest and self-efficacy in classroom management: Results of a pre-registered experiment     

Yizhen Huang  Eric Richter  Thilo Kleickmann  Dirk Richter 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(2):467-488

Video is a widely used medium in teacher training for situating student teachers in classroom scenarios. Although the emerging technology of virtual reality (VR) provides similar, and arguably more powerful, capabilities for immersing teachers in lifelike situations, its benefits and risks relative to video formats have received little attention in the research to date. The current study used a randomized pretest–posttest experimental design to examine the influence of a video- versus VR-based task on changing situational interest and self-efficacy in classroom management. Results from 49 student teachers revealed that the VR simulation led to higher increments in self-reported triggered interest and self-efficacy in classroom management, but also invoked higher extraneous cognitive load than a video viewing task. We discussed the implications of these results for pre-service teacher education and the design of VR environments for professional training purposes.

Practitioner notes

What is already known about this topic

• Video is a popular teacher training medium given its ability to display classroom situations.
• Virtual reality (VR) also immerses users in lifelike situations and has gained popularity in recent years.
• Situational interest and self-efficacy in classroom management is vital for student teachers' professional development.

What this paper adds

• VR outperforms video in promoting student teachers' triggered interest in classroom management.
• Student teachers felt more efficacious in classroom management after participating in VR.
• VR also invoked higher extraneous cognitive load than the video.

Implications for practice and/or policy

• VR provides an authentic teacher training environment for classroom management.
• The design of the VR training environment needs to ensure a low extraneous cognitive load.

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Learning theories for artificial intelligence promoting learning processes     

David Gibson  Vitomir Kovanovic  Dirk Ifenthaler  Sara Dexter  Shihui Feng 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1125-1146

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

Practitioner notes

What is already known about this topic

• Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
• Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
• Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.

What this paper adds

• A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
• A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
• The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
• Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.

Implications for practice and policy

• Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
• Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

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Adaptive or adapted to: Sequence and reflexive thematic analysis to understand learners' self-regulated learning in an adaptive learning analytics dashboard     

Eunsung Park  Dirk Ifenthaler  Roy B. Clariana 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):98-125

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