scholarly journals Towards a Reflexive Framework for Fostering Co—Learning and Improvement of Transdisciplinary Collaboration

2019 ◽  
Vol 11 (23) ◽  
pp. 6602 ◽  
Author(s):  
Marina Knickel ◽  
Karlheinz Knickel ◽  
Francesca Galli ◽  
Damian Maye ◽  
Johannes S. C. Wiskerke

Scholars in sustainability science as well as research funders increasingly recognize that a shift from disciplinary and interdisciplinary science to transdisciplinary (TD) research is required to address ever more complex sustainability challenges. Evidence shows that addressing real-world societal problems can be best achieved through collaborative research where diverse actors contribute different kinds of knowledge. While the potential benefits of TD research are widely recognized, its implementation remains a challenge. In this article, we develop a framework that supports reflection and co-learning. Our approach fosters monitoring of the collaboration processes, helps to assess the progress made and encourages continuous reflection and improvement of the research processes. The TD co-learning framework has four dimensions and 44 criteria. It is based on a substantial literature review and was tested in a Horizon 2020-funded research project ROBUST, which is applying experimental governance techniques to improve rural-urban relations in eleven European regions. The results demonstrate that the framework covers the key facets of TD collaboration and that all four broad dimensions matter. Each research-practice team reflected on how their collaboration is going and what needs to be improved. Indeed, the coordination team was able to see how well TD collaboration is functioning at a project level. We believe the framework will be valuable for actors involved in the planning and implementation of any type of multi-actor, interactive, innovation, transformation and action-oriented research project.

Societies ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 61
Author(s):  
Monica Cerdan Chiscano

Although librarians generally display an inclusive management style, barriers to students with disabilities remain widespread. Against this backdrop, a collaborative research project called Inclusive Library was launched in 2019 in Catalonia, Spain. This study empirically tests how involving students with disabilities in the experience design process can lead to new improvements in users’ library experience. A mix of qualitative techniques, namely focus groups, ethnographic techniques and post-experience surveys, were used to gain insights from the 20 libraries and 20 students with disabilities collaborating in the project. Based on the participants’ voices and follow-up experiences, the study makes several suggestions on how libraries can improve their accessibility. Results indicate that ensuring proper resource allocation for accessibility improves students with disabilities’ library experience. Recommendations for library managers are also provided.


1999 ◽  
Vol 17 (3) ◽  
pp. 296-307 ◽  
Author(s):  
Jacqueline S. Dowling ◽  
Mary Anne Bright

Leadership ◽  
2014 ◽  
Vol 12 (1) ◽  
pp. 53-85 ◽  
Author(s):  
Chantale Mailhot ◽  
Stéphanie Gagnon ◽  
Ann Langley ◽  
Louis-Félix Binette

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Juncai Li ◽  
Xiaofei Jiang

Molecular property prediction is an essential task in drug discovery. Most computational approaches with deep learning techniques either focus on designing novel molecular representation or combining with some advanced models together. However, researchers pay fewer attention to the potential benefits in massive unlabeled molecular data (e.g., ZINC). This task becomes increasingly challenging owing to the limitation of the scale of labeled data. Motivated by the recent advancements of pretrained models in natural language processing, the drug molecule can be naturally viewed as language to some extent. In this paper, we investigate how to develop the pretrained model BERT to extract useful molecular substructure information for molecular property prediction. We present a novel end-to-end deep learning framework, named Mol-BERT, that combines an effective molecular representation with pretrained BERT model tailored for molecular property prediction. Specifically, a large-scale prediction BERT model is pretrained to generate the embedding of molecular substructures, by using four million unlabeled drug SMILES (i.e., ZINC 15 and ChEMBL 27). Then, the pretrained BERT model can be fine-tuned on various molecular property prediction tasks. To examine the performance of our proposed Mol-BERT, we conduct several experiments on 4 widely used molecular datasets. In comparison to the traditional and state-of-the-art baselines, the results illustrate that our proposed Mol-BERT can outperform the current sequence-based methods and achieve at least 2% improvement on ROC-AUC score on Tox21, SIDER, and ClinTox dataset.


2015 ◽  
Vol 24 (2) ◽  
Author(s):  
Ken Chow ◽  
Samuel Kai Wah Chu ◽  
Nicole Tavares ◽  
Celina Wing Yi Lee

This study explored the impact of the role of teacher-researchers on in-service teachers’ professional development, as well as the reasons behind the lack of a teacher-as-researcher ethos in schools. In the study, teachers from four Hong Kong primary schools participated in a school-university collaborative research project that promotes collaborative inquiry project-based learning (IPjBL), in which they took the dual role of the teacher and researcher. Five focus group interviews were conducted with the teachers to collect in-depth qualitative data on their experiences. The impact of this experience on teacher professionalism was examined from four dimensions: knowledge enrichment, school culture, teaching practice and curriculum design. The study provides evidence for the benefits of teacher research and sheds light on how university-school collaboration could contribute to engaging teachers in action research in their everyday classroom.


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