Studying design and use of healthcare technologies in interaction: the social learning perspective in a Dutch quality improvement collaborative program

Designing technologies is a process that relies on multiple interactions between design and use contexts. These interactions are essential to the development and establishment of technologies. This article seeks to understand the attempts of healthcare organisations to integrate use contexts into the design of healthcare technologies following insights of the theoretical approaches of social learning and user representations. We present a multiple case study of three healthcare technologies involved in improving elderly care practice. These cases were part of a Dutch quality improvement collaborative program, which urged that development of these technologies was not “just” development, but should occur in close collaboration with other parts of the collaborative program, which were more focused on implementation. These cases illustrate different ways to develop technologies in interaction with use contexts and users. Despite the infrastructure of the collaborative program, interactions were not without problems. We conclude by arguing that interactions between design and use are not naturally occurring phenomena, but must be actively organised in order to create effect

Studying design and use of healthcare technologies in interaction: the social learning perspective in a Dutch quality improvement collaborative program

Designing technologies is a process that relies on multiple interactions between design and use contexts. These interactions are essential to the development and establishment of technologies. This article seeks to understand the attempts of healthcare organisations to integrate use contexts into the design of healthcare technologies following insights of the theoretical approaches of social learning and user representations. We present a multiple case study of three healthcare technologies involved in improving elderly care practice. These cases were part of a Dutch quality improvement collaborative program, which urged that development of these technologies was not “just” development, but should occur in close collaboration with other parts of the collaborative program, which were more focused on implementation. These cases illustrate different ways to develop technologies in interaction with use contexts and users. Despite the infrastructure of the collaborative program, interactions were not without problems. We conclude by arguing that interactions between design and use are not naturally occurring phenomena, but must be actively organised in order to create effect

Taming the snake in paradise: combining institutional design and leadership to enhance collaborative innovation

The growing expectations to public services and the pervasiveness of wicked problems in times characterized by growing fiscal constraints call for the enhancement of public innovation, and new research suggests that multi-actor collaboration in networks and partnerships is superior to hierarchical and market-based strategies when it comes to spurring such innovation. Collaborative innovation seems ideal as it builds on diversity to generate innovative public value outcomes, but there is a catch since diversity may clash with the need for constructing a common ground that allows participating actors to agree on a joint and innovative solution. The challenge for collaborative innovation – taming the snake in paradise – is to nurture the diversity of views, ideas and forms of knowledge while still establishing a common ground for joint learning. While we know a great deal about the dynamics of the mutually supportive processes of collaboration, learning and innovation, we have yet to understand the role of institutional design and leadership in spurring collaborative innovation and dealing with this tension. Building on extant research, the article draws suitable cases from the Collaborative Governance Data Bank and uses Qualitative Comparative Analysis to explore how multiple constellations of institutional design and leadership spur collaborative innovation. The main finding is that, even though certain institutional design features reduce the need for certain leadership roles, the exercise of hands-on leadership is more important for securing collaborative innovation outcomes than hands-off institutional design

Community space in complex learning communities : lessons learnt

Highly complex learning communities where diverse participants collaborate to achieve multiple aims through synergy have the potential to be highly creative and productive. However the diversity and multiple aims can also mean the advantages of a community - share understand, trust and direction - are difficult to achieve, resulting in few if any of the aims being realised. We review two case studies, where the learning community is trying to achieve multiple aims, in order to explore how virtual and physical space are employed to support collaborative learning and enhance synergistic potential. The analysis shows that high levels of diversity have influenced these spaces and trends towards differentiation and holistically designed hybrid, virtual and physical, collaboration space. The characteristics of theses cases are sufficiently general to lead us to draw insights for the building of collaborative space in multi-purpose complex learning communities. These are equably applicable to learning communities which share features such as heterogeneity, multiple locations or a mixture of spaces

A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

Computer-supported collaborative learning (CSCL) is often based on written argumentative discourse of learners, who discuss their perspectives on a problem with the goal to acquire knowledge. Lately, CSCL research focuses on the facilitation of specific processes of argumentative knowledge construction, e.g., with computer-supported collaboration scripts. In order to refine process-oriented instructional support, such as scripts, we need to measure the influence of scripts on specific processes of argumentative knowledge construction. In this article, we propose a multi-dimensional approach to analyze argumentative knowledge construction in CSCL from sampling and segmentation of the discourse corpora to the analysis of four process dimensions (participation, epistemic, argumentative, social mode)

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Neural Graph Collaborative Filtering

Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect. In this work, we propose to integrate the user-item interactions -- more specifically the bipartite graph structure -- into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in user-item graph, effectively injecting the collaborative signal into the embedding process in an explicit manner. We conduct extensive experiments on three public benchmarks, demonstrating significant improvements over several state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF. Codes are available at https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from the version published in ACM Digital Librar