Evaluation of SOVAT: An OLAP-GIS decision support system for community health assessment data analysis

Background. Data analysis in community health assessment (CHA) involves the collection, integration, and analysis of large numerical and spatial data sets in order to identify health priorities. Geographic Information Systems (GIS) enable for management and analysis using spatial data, but have limitations in performing analysis of numerical data because of its traditional database architecture. On-Line Analytical Processing (OLAP) is a multidimensional datawarehouse designed to facilitate querying of large numerical data. Coupling the spatial capabilities of GIS with the numerical analysis of OLAP, might enhance CHA data analysis. OLAP-GIS systems have been developed by university researchers and corporations, yet their potential for CHA data analysis is not well understood. To evaluate the potential of an OLAP-GIS decision support system for CHA problem solving, we compared OLAP-GIS to the standard information technology (IT) currently used by many public health professionals. Methods. SOVAT, an OLAP-GIS decision support system developed at the University of Pittsburgh, was compared against current IT for data analysis for CHA. For this study, current IT was considered the combined use of SPSS and GIS ("SPSS-GIS"). Graduate students, researchers, and faculty in the health sciences at the University of Pittsburgh were recruited. Each round consisted of: an instructional video of the system being evaluated, two practice tasks, five assessment tasks, and one post-study questionnaire. Objective and subjective measurement included: task completion time, success in answering the tasks, and system satisfaction. Results. Thirteen individuals participated. Inferential statistics were analyzed using linear mixed model analysis. SOVAT was statistically significant (α = .01) from SPSS-GIS for satisfaction and time (p < .002). Descriptive results indicated that participants had greater success in answering the tasks when using SOVAT as compared to SPSS-GIS. Conclusion. Using SOVAT, tasks were completed more efficiently, with a higher rate of success, and with greater satisfaction, than the combined use of SPSS and GIS. The results from this study indicate a potential for OLAP-GIS decision support systems as a valuable tool for CHA data analysis. © 2008 Scotch et al; licensee BioMed Central Ltd

Public Participation GIS for sustainable urban mobility planning: methods, applications and challenges

Sustainable mobility planning is a new approach to planning, and as such it requires new methods of public participation, data collection and data aggregation. In the article we present an overview of Public Participation GIS (PPGIS) methods with potential use in sustainable urban mobility planning. We present the methods using examples from two recent case studies conducted in Polish cities of Poznań and Łodź. Sustainable urban mobility planning is a cyclical process, and each stage has different data and participatory requirements. Consequently, we situate the PPGIS methods in appropriate stages of planning, based on potential benefits they may bring into the planning process. We discuss key issues related to participant recruitment and provide guidelines for planners interested in implementing methods presented in the paper. The article outlines future research directions stressing the need for systematic case study evaluation

Sequential Voting Promotes Collective Discovery in Social Recommendation Systems

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.Comment: To be published in the 10th International AAAI Conference on Web and Social Media (ICWSM) 201

Evaluating the impact of electronic voting systems on university mathematics teaching and learning

This thesis presents an evaluation of the impact of the use of Electronic Voting Systems (EVS) on mathematics teaching and learning, based on the research question: What are the views of academic staff on the impact of EVS use on their mathematics teaching; and how has EVS use influenced student engagement and learning approach to mathematics? To answer the question, a descriptive survey of academic staff, and semi-structured interviews with students were conducted; data from these studies were supplemented by classroom observations of EVS use, relevant documentary evidence, and preliminary studies conducted. Survey data was analysed via quantitative techniques; while the annotated interview transcripts were analysed via thematic analysis, and the application of an integrated theoretical framework. The validity, reliability and replicability of both studies were also established. The findings show that feedback is viewed as the single, most beneficial impact of EVS use, as it enables instructors, through formative assessment, to identify student misconceptions, which then helps instructors to focus on the identified problem areas. EVS has also positively impacted student emotion, behaviour, and cognition. EVS use helps focus student attention, enhances participation and interactivity, and enables students to cognitively engage with learning material. The adoption of an integrated theoretical framework helps to characterise, and to reveal qualitative differences in student learning approaches. Also, the use of specific EVS question types tends to induce specific learning approaches in students. Implications of the findings include the need for EVS-using instructors to have clearly defined pedagogical objectives and well-designed questions, and for learners to re-adapt their mathematical ideas in response to EVS feedback. Findings also show the need to incorporate instructional measures that would promote both procedural and conceptual learning approaches in students, and to perhaps rethink the role of calculator usage and guesswork in student approaches to learning. The requirements for technologies that may replace EVS, the need to align assessment with instructional practices, and for instructors to undergo further EVS training and/or form mathematics-specific support group(s) are also highlighted