Police Knowledge Exchange: Full Report 2018

[Executive Summary] This report was commissioned to explore the enablers and barriers to sharing within and between police forces and between police forces and partners, including the public. This was completed from an interdisciplinary review of international literature covering sharing, knowledge exchange, learning and organisational learning. The literature broke down into four main factors; who, why, what and how. An introduction to the literature is presented with ‘Who’ is sharing which considers both personal identity and different institutional issues. The ‘Why’ literature covers issues of cultural and community motivators and barriers. The ‘What’ segment reviews concepts of data, information and knowledge and related legislative issues. Finally, the ‘how’ section spans face to face sharing approaches to technologies that produce both enablers and barriers. A series of 42 in-depth interviews and focus groups were completed and combined with 47 survey responses . The aim of the interviews, focus groups and survey was to show perceptions and beliefs around knowledge sharing from a small sample across policing in order to complement the findings from the literature review. The survey was adapted from a standardised questionnaire (Biggs, 1987). The Biggs questionnaire focused on what motivated students to learn and how they approached their learning. Our adapted survey looked at what motivated police to share, and how they approached sharing. The responses showed a trend, across the police, towards a motivation for sharing to develop a deeper understanding of issues. However, the approaches and the strategies they used to share with others, which were primarily driven by achieving and surface approaches (to get promoted and get the job done). According to Biggs (1987) this could leave them discontented as they never progress to a deeper understanding of issues. Scaffolding sharing within the police through processes that are clearly defined, effective and valued could help to overcome these issues. Within the interviews and focus group findings a similar structured approach to sharing was adopted. Within the ‘who’ section some key aspects around personal relationships, reciprocity and reputation were identified. The ‘why’ the police share was one of the largest discussion points. Not only was there a deep motivation to solve key policing issues there was an approach of reciprocity. Police sharing was deeply motivated to support ‘good practice’ in the prevention and detection of crime. However, a sharing barrier was identified in the parity of value given to different types of knowledge for example between professional judgement and research evidence knowledge. Sharing was achieved when there were reciprocal benefits, in particular with personal networks or face to face sharing which was noted as ‘safe’. Again, this was inhibited by misunderstandings around the ‘risks’ of sharing, frequently attributed to data protection legislation; producing cautious reactions and as an avoidance tactic to save time and effort sharing. However, a divide was noted between technical users and those who avoided any online systems for sharing; often due to poorly designed systems and a lack of confidence in how to use systems. The police culture was identified as being risk-adverse, and competitive due to multiple factors, a lack of supported time to share, Her Majesty’s Inspectorate of Constabulary (HMIC) reviews and promotion criteria. The result was perceived to be a poor cultural ability to learn from mistakes and a likelihood to repeat errors. A set of strategic recommendations are given and include the use of a sharing authorised professional practice for HMIC reviews, sharing networks and training. A further set of operational recommendations are given such as; sharing impact cases for evidence based practice, data sharing officers and evaluating mechanisms for sharing. This full report is supported by the Police Knowledge Exchange Summary Report 2018 which gives an overview of the findings and recommendations

A SEMIOTIC ANALYSIS OF LINGUISTIC AND CONCEPTUAL DEVELOPMENT IN MATHEMATICS FOR ENGLISH LANGUAGE LEARNERS

This study explores how an elementary mathematics teacher supported English language leaners’ (ELLs’) academic language and concept development in the context of current high- stakes school reform. The conceptual frameworks informing this study include Halliday’s theory of systemic functional linguistics (e.g., Halliday & Matthiessen, 2014) and Vygotsky’s sociocultural theory of concept development (Vygotsky, 1986). Specifically, this study analyzes the interplay between academic and everyday language and how this interplay can facilitate the development of what Vygotsky referred to as “real” or complete concepts as students shift from “spontaneous” to more “scientific” understanding of phenomenon (Vygotsky, 1986, p.173). This year-long qualitative study combines case study methods with discourse analysis using SFL tools. Participants included an English-as-Second-Language teacher and her 14 ELL students. At the time of the study these students had varying degrees of English proficiency and were enrolled in a mix-aged classroom in an urban elementary school in Massachusetts. In SFL terms, the findings from this investigation indicate that the teacher used language in a structured way to interweave everyday language connected with familiar or “Given” information with academic language regarding “New” information. In addition, the data suggest that student talk, over time, mirrored the way the teacher used language to “bind” everyday language representing spontaneous concepts with academic language representing mathematical concepts. Moreover, mathematics classroom discourse in this context often related multiple semiotic resources as “Token” to their meanings as “Value.” Drawing Halliday and Matthiessen’ (2014) concept of “decoding” and “encoding” activities associated with Token-Value relationships, students were guided in verbalizing mathematical reasoning that promoted both spontaneous and scientific concept development. In addition, the participant teacher made linguistic choices differently depending on the multisemiotic resources she used during instruction. The findings of this study suggest that teachers’ use of language plays a pivotal role in developing students’ language and mathematical conceptual knowledge simultaneously. Drawing teachers’ attention to the role discourse plays in classroom interactions and students’ disciplinary literacy development is especially consequential given the discourses of high-stakes testing, standardization, and accountability systems in K-12 schools in the United States

Designing Digital Work

Combining theory, methodology and tools, this open access book illustrates how to guide innovation in today’s digitized business environment. Highlighting the importance of human knowledge and experience in implementing business processes, the authors take a conceptual perspective to explore the challenges and issues currently facing organizations. Subsequent chapters put these concepts into practice, discussing instruments that can be used to support the articulation and alignment of knowledge within work processes. A timely and comprehensive set of tools and case studies, this book is essential reading for those researching innovation and digitization, organization and business strategy

Pedagogic approaches to using technology for learning: literature review

This literature review is intended to address and support teaching qualifications and CPD through identifying new and emerging pedagogies; "determining what constitutes effective use of technology in teaching and learning; looking at new developments in teacher training qualifications to ensure that they are at the cutting edge of learning theory and classroom practice and making suggestions as to how teachers can continually update their skills." - Page 4

Designing Asynchronous Online Discussion Environments: Recent Progress and Possible Future Directions

Asynchronous online discussion environments are important platforms to support learning. Research suggests, however, threaded forums, one of the most popular asynchronous discussion environments, do not often foster productive online discussions naturally. This paper explores how certain properties of threaded forums have affected or constrained the quality of discussions, and argues that developing alternative discussion environments is highly needed to offer better support for asynchronous online communication. Using the Productive Discussion Model developed by Gao, Wang & Sun (2009), we analyzed current work on four types of asynchronous discussion environments that have been developed and researched: constrained environments, visualized environments, anchored environments and combined environments. The paper has implications for developing future asynchronous discussion environments. More specifically, future work should aim at (a) exploring new environments that support varied goals of learning; (b) integrating emerging technologies to address the constraints of current environments; (c) designing multi-functional environments to facilitate complex learning, and (d) developing appropriate instructional activities and strategies for these environments

Understanding and Supporting Vocabulary Learners via Machine Learning on Behavioral and Linguistic Data

This dissertation presents various machine learning applications for predicting different cognitive states of students while they are using a vocabulary tutoring system, DSCoVAR. We conduct four studies, each of which includes a comprehensive analysis of behavioral and linguistic data and provides data-driven evidence for designing personalized features for the system. The first study presents how behavioral and linguistic interactions from the vocabulary tutoring system can be used to predict students' off-task states. The study identifies which predictive features from interaction signals are more important and examines different types of off-task behaviors. The second study investigates how to automatically evaluate students' partial word knowledge from open-ended responses to definition questions. We present a technique that augments modern word-embedding techniques with a classic semantic differential scaling method from cognitive psychology. We then use this interpretable semantic scale method for predicting students' short- and long-term learning. The third and fourth studies show how to develop a model that can generate more efficient training curricula for both human and machine vocabulary learners. The third study illustrates a deep-learning model to score sentences for a contextual vocabulary learning curriculum. We use pre-trained language models, such as ELMo or BERT, and an additional attention layer to capture how the context words are less or more important with respect to the meaning of the target word. The fourth study examines how the contextual informativeness model, originally designed to develop curricula for human vocabulary learning, can also be used for developing curricula for various word embedding models. We identify sentences predicted as low informative for human learners are also less helpful for machine learning algorithms. Having a rich understanding of user behaviors, responses, and learning stimuli is imperative to develop an intelligent online system. Our studies demonstrate interpretable methods with cross-disciplinary approaches to understand various cognitive states of students during learning. The analysis results provide data-driven evidence for designing personalized features that can maximize learning outcomes. Datasets we collected from the studies will be shared publicly to promote future studies related to online tutoring systems. And these findings can also be applied to represent different user states observed in other online systems. In the future, we believe our findings can help to implement a more personalized vocabulary learning system, to develop a system that uses non-English texts or different types of inputs, and to investigate how the machine learning outputs interact with students.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162999/1/sjnam_1.pd

Designing ubiquitous computing for reflection and learning in diabetes management

This dissertation proposes principles for the design of ubiquitous health monitoring applications that support reflection and learning in context of diabetes management. Due to the high individual differences between diabetes cases, each affected individual must find the optimal combination of lifestyle alterations and medication through reflective analysis of personal diseases history. This dissertation advocates using technology to enable individuals' proactive engagement in monitoring of their health. In particular, it proposes promoting individuals' engagement in reflection by exploiting breakdowns in individuals' routines or understanding; supporting continuity in thinking that leads to a systematic refinement of ideas; and supporting articulation of thoughts and understanding that helps to transform insights into knowledge. The empirical evidence for these principles was gathered thought the deployment studies of three ubiquitous computing applications that help individuals with diabetes in management of their diseases. These deployment studies demonstrated that technology for reflection helps individuals achieve their personal disease management goals, such as diet goals. In addition, they showed that using technology helps individuals embrace a proactive attitude towards their health indicated by their adoption of the internal locus of control.Ph.D.Committee Chair: Elizabeth D. Mynatt; Committee Member: Abowd, Gregory; Committee Member: Bruckman, Amy; Committee Member: Dourish, Paul; Committee Member: Nersessian, Nanc