Using Network Analysis to Understand Knowledge Mobilization in a Community-based Organization

Background Knowledge mobilization (KM) has been described as putting research in the hands of research users. Network analysis is an empirical approach that has potential for examining the complex process of knowledge mobilization within community-based organizations (CBOs). Yet, conducting a network analysis in a CBO presents challenges. Purpose The purpose of this paper is to demonstrate the value and feasibility of using network analysis as a method for understanding knowledge mobilization within a CBO by (1) presenting challenges and solutions to conducting a network analysis in a CBO, (2) examining the feasibility of our methodology, and (3) demonstrating the utility of this methodology through an example of a network analysis conducted in a CBO engaging in knowledge mobilization activities. Method The final method used by the partnership team to conduct our network analysis of a CBO is described. Results An example of network analysis results of a CBO engaging in knowledge mobilization is presented. In total, 81 participants completed the network survey. All of the feasibility benchmarks set by the CBO were met. Results of the network analysis are highlighted and discussed as a means of identifying (1) prominent and influential individuals in the knowledge mobilization process and (2) areas for improvement in future knowledge mobilization initiatives. Conclusion Findings demonstrate that network analysis can be feasibly used to provide a rich description of a CBO engaging in knowledge mobilization activities

Knowledge-based hierarchies: using organizations to understand the economy

We argue that incorporating the decision of how to organize the acquisition, use, and communication of knowledge into economic models is essential to understand a wide variety of economic phenomena. We survey the literature that has used knowledge-based hierarchies to study issues like the evolution of wage inequality, the growth and productivity of firms, economic development, the gains from international trade, as well as offshoring and the formation of international production teams, among many others. We also review the nascent empirical literature that has, so far, confirmed the importance of organizational decisions and many of its more salient implications

The interaction between humans and knowledge management systems : rethinking the future

In this workshop position paper, we propose a study to understand the importance of knowledge management systems among academics in Saudi higher education institutions, admitting that knowledge workers and Knowledge Management Systems are valuable organizational assets whose interaction should be improved. We intend to understand Saudi academics’ perception toward using the knowledge management system to share their teaching experiences. Based on the findings, we investigate the major research trends in knowledge management systems and give some recommendations for future research

Three things to do with knowledge ascriptions

Any good theory of knowledge ascriptions should explain and predict our judgments about their felicity. I argue that any such explanation must take into account a distinction between three ways of using knowledge ascriptions: to suggest acceptance of the embedded proposition, to explain or predict a subject's behavior or attitudes, or to understand the relation of knowledge as such. The contextual effects on our judgments about felicity systematically differ between these three types of uses. Using such a distinction is, in principle, open to both contextualist and pragmatic invariantist accounts of knowledge ascriptions. However, there are some implications pertaining to the use of the “method of cases” in the debate about knowledge ascriptions

Understanding Science Through Knowledge Organizers: An Introduction

We propose, in this paper, a teaching program based on a grammar of scientific language borrowed mostly from the area of knowledge representation in computer science and logic. The paper introduces an operationizable framework for understanding knowledge using knowledge representation (KR) methodology. We start with organizing concepts based on their cognitive function, followed by assigning valid and authentic semantic relations to the concepts. We propose that in science education, students can understand better if they organize their knowledge using the KR principles. The process, we claim, can help them to align their conceptual framework with that of experts which we assume is the goal of science education

Using Prior Knowledge and Student Engagement to Understand Student Performance in an Undergraduate Learning-to-Learn Course

This study examined prior knowledge and student engagement in student performance. Log data were used to explore the distribution of final grades (i.e., weak, good, excellent final grades) occurring in an elective under-graduate course. Previous research has established behavioral and agentic engagement factors contribute to academic achievement (Reeve, 2013). Hierarchical logistic regression using both prior knowledge and log data from the course revealed: (a) the weak-grades group demonstrated less behavioral engagement than the good-grades group, (b) the good-grades group demonstrated less agentic engagement than the excellent-grades group, and (c) models composed of both prior knowledge and engagement measures were more accurate than models composed of only engagement measures. Findings demonstrate students performing at different grade-levels may experience different challenges in their course engagement. This study informs our own instructional strategies and interventions to increase student success in the course and provides recommendations for other instructors to support student success

Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation

Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character's psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model's performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge

Is organic Farming ‘innovative’ enough for Europe?

The paper explores how organic agriculture fits into the framework of innovation systems that is becoming more widely accepted in supporting innovation also in agriculture which is faced with many societal challenges. It explores the need to better understand the role of different types of innovation and in particular the role of knowledge and how joint learning systems for sharing different types of knowledge can be developed using examples from SOLID and TP organics