Hur strategiarbete och processmätning bör gå till behandlas i befintliga teorier var för sig även om vikten av att sammankoppla dessa också beskrivs. Dock anses det saknas en enhetlig modell som förenar strategiarbete och processmätningar fullt ut och beskriver hur genomförandet praktiskt ska gå till. Med bakgrund i detta syftar examensarbetet Analytics for Management (AFM) till att utveckla en modell som beskriver hur mätetal (Key Performance Indicators, KPI:er) tas fram utifrån ett företags strategi och visualiseras på rätt sätt för att kunna beskriva enskilda processers kapacitet och förmåga. Detta resulterar i ett beslutsunderlag på ledningsnivå där strategiarbete och processmätning kopplas samman. Framtagandet av AFM-modellen inleddes med teoretiska studier inom ämnet vilket resulterade i en konceptuell modell. Därefter testades den konceptuella modellen genom fallstudier på tre företag med olika struktur och storlek. Genom att kombinera den konceptuella modellen med resultaten från fallstudierna färdigställdes sedan den slutgiltiga AFM-modellen. AFM-modellen består av tre faser där den första fasen involverar strategikonkretisering och målnedbrytning. Fasen innefattar att strategin bryts ner till strategiska mål och en strategikarta, formulering av kritiska framgångsfaktorer (KFF:er) och slutligen framtagning av taktiska mål. I den andra fasen sker KPI-framtagning genom att först generera preliminära KPI:er utifrån de taktiska målen och sedan kartlägga dem för att identifiera två typer av KPI:er. Dessa är utfallsmått (Key Performance Outcomes, KPO:er), som beskriver processprestandan och påverkansmått (Key Performance Drivers, KPD:er), som påverkar utfallen som KPO:er illustrerar. Genom att KPD:erna förbättras kan även KPO:erna förbättras och därför är det viktigt att styrning sker med hjälp av KPD:erna. Efter att KPI:er har kartlagts väljs vilka KPI:er som ska användas för mätning. I den sista fasen sker mätningsförberedelser genom att göra en datainsamlingsplan. Därefter sker mätningar och slutligen sammanställs mätdata för att visualisera KPI:erna på ett bra sätt. Hur KPI:er ska visualiseras beror på KPI-typ eftersom KPO:er och KPD:er ska användas för olika syften. KPO:erna ska visualiseras med enkla diagram i ett resultatkort som ledningen ska använda sig utav i ett första steg för att följa upp verksamheten. För att sedan finna orsaken till KPO:erna och kunna förbättra dem, ska ett styrkort användas. I styrkortet sammanställs KPD:erna med hjälp av styrdiagram, vilka visar variation över tid i processerna, för att tidigt upptäcka förändringar och därmed styra processer. AFM-modellen resulterar med hjälp av resultatkortet och styrkortet i ett beslutsunderlag där rätt KPI:er visualiseras på rätt sätt. Resultaten från fallstudieföretagen har visat att AFM-modellen har hög generaliserbarhet. Vidare kan AFM-modellen användas av olika typer av organisationer då strategiarbete och processmätning är aktuellt och viktigt för det dagliga arbetet oavsett bransch. Modellen ska genom beslutsunderlaget underlätta för organisationer att agera handlingskraftigt och arbeta mer proaktivt med hjälp av beslut baserat på data framtaget utifrån företagets strategi. AFM-modellen möjliggör därmed en enhetlig användning av mätetal i organisationen, där uppföljningsarbetet i hela verksamheten genomsyras av strategin. / In existing theory, strategic management and process measurements are commonly treated separately even though the importance of connecting them is also mentioned. However, a comprehensive model or theory that combines strategic management and process measurement fully and describes how the implementation should be done in practice has not been found in literature. Thereby, the aim of the thesis Analytics for Management (AFM) is to develop a model that describes how to develop the right Key Performance Indicators (KPIs) based on corporate strategy and visualize them correctly in order to describe process performance. This results in a decision-making tool that combines strategic management and process measurement to be used by management. The model was initially developed through theoretical studies that resulted in a conceptual model. Thereafter, the conceptual model was tested through case studies at three companies with different organizational structure and size. By combining the theories and the results from the field studies the AFM model was finalized. The AFM model consists of three phases, where the first phase involves strategy decomposition and goal formulation. This is done by a stepwise decomposition of the strategy through strategic objectives and a strategy map, formulation of critical success factors finally resulting in tactical objectives. In the second phase, KPIs are developed by generating preliminary KPIs based on the tactical goals and then mapping them. KPI mapping is used to distinguish between different types of KPIs resulting in identification of Key Performance Outcomes (KPO) that describe the process performance and Key Performance Drivers (KPD) affecting the outcome that the KPOs represent. The KPDs drive the results of the organization, and should therefore be used for monitoring and controlling the business. When the KPI mapping has been completed the KPIs that will be used for measurement are chosen. In the final phase, measurement preparations are done through a data collection plan. Thereafter measurements are performed and compiled in order to visualize KPIs correctly. As the KPOs and KPDs should be used for different purposes they should also be visualized differently. KPOs should be visualized through simple charts in a scorecard that the management should use as a first step for monitoring. Then, in order to find the cause of the KPOs and be able to improve them, a controlcard should be used. The KPDs are compiled in the controlcard through control charts, which show variation in processes, and enables early detection of changes and process control. The AFM model thereby, through the use of a scorecard and a controlcard, results in a decision-making tool where the right KPIs are visualized correctly. The results from the field studies and the different characteristics of the companies have proven a high level of generalizability of the model. Furthermore, the AFM model addresses highly important and pressing issues involving strategic management and process measurement, which all types of companies need to consider in daily operations. The AFM model aims to make it easier for organizations to act energetically and proactive through the decision-making tool. In conclusion, the AFM model enables a uniform use of metrics aligned with the strategy, in order to monitor and control process performance.

With the increasing amount of collected data, large-scale high-dimensional data analysis is becoming essential in many areas. These data can be analyzed either by using fully computational methods or by leveraging human capabilities via interactive visualization. However, each method has its drawbacks. While a fully computational method can deal with large amounts of data, it lacks depth in its understanding of the data, which is critical to the analysis. With the interactive visualization method, the user can give a deeper insight on the data but suffers when large amounts of data need to be analyzed. Even with an apparent need for these two approaches to be integrated, little progress has been made. As ways to tackle this problem, computational methods have to be re-designed both theoretically and algorithmically, and the visual analytics system has to expose these computational methods to users so that they can choose the proper algorithms and settings. To achieve an appropriate integration between computational methods and visual analytics, the thesis focuses on essential computational methods for visualization, such as dimension reduction and clustering, and it presents fundamental development of computational methods as well as visual analytic systems involving newly developed methods. The contributions of the thesis include (1) the two-stage dimension reduction framework that better handles significant information loss in visualization of high-dimensional data, (2) efficient parametric updating of computational methods for fast and smooth user interactions, and (3) an iteration-wise integration framework of computational methods in real-time visual analytics. The latter parts of the thesis focus on the development of visual analytics systems involving the presented computational methods, such as (1) Testbed: an interactive visual testbed system for various dimension reduction and clustering methods, (2) iVisClassifier: an interactive visual classification system using supervised dimension reduction, and (3) VisIRR: an interactive visual information retrieval and recommender system for large-scale document data.

The aim of this compilation thesis is to understand how technology for visual storytelling can be shaped and used in relation to social science education in primary school, but also how social dimensions, technical and other matters create emerging learning conditions in such an educational setting. The visual storytelling technology introduced and used in the study is ‘the Statistics eXplorer platform, a geovisual analytics. The choice of theoretical perspectives to inform and guide the study is a socio-cultural view of human action, but also actor network theory is used to take account also of activities of technology and other matters. The study builds on three empirical materials that generate data from 16 social science teachers, and 126 students from five social science classrooms, in three Swedish primary schools. It contains field notes from the introduction of the technology; focusgroup interviews with teachers; think-aloud interviews with students and two kinds of video recordings from the classrooms (with an ordinary video camera and with software that capture activities at the computer screen, students’ activities and the audio as well). The analysis shows that the visual storytelling technology is shaped in relevant ways for social science teachers. The analysis also illustrates that the visual educational material are usable for primary school students in their social science education. They illustrate further how teachers, students, technology, information, tasks, data types, etc. together and in in close relation create highly complex learning conditions. The technology can therefore be seen as appropriate for the educational practice, but the complexity together with students’ apprehension of how to announce knowledge distribute severe problem spaces in the learning activities. The technology can therefore be assumed as a catalyst for educational change, but to achieve its potentials, reflections on didactic design and knowledge formation is requested to support the quality of students’ knowledge in relation to visual analysis. / Syftet i denna avhandling är, att förstå hur teknik för visual storytelling kan vara utformad och användas i relation till samhällsorienterande undervisning i grundskolan (årskurs 4 – 6), men också hur sociala dimensioner, tekniska och andra faktorer skapar villkor för lärande i ett sådant undervisningssammanhang. I studien introduceras datavisualiseringsteknik för visual storytelling: ‘the Statistics eXplorer platform’, ett geovisual analytics. Den teoretiska referensramen har sin grund i ett social konstruktionistiskt synsätt Ett socio-kulturellt perspektiv används för att analysera social aktivitet, men även aktörnätverks teori används för att analysera både sociala och materiella aktörer. Avhandlingen bygger på tre empiriska material som genereras med hjälp av 16 lärare i samhällsorienterande ämnen, och 126 elever tillhörande fem olika klassrum i tre olika svenska grundskolor. Materialet innehåller: fältanteckningar ifrån introduktion av tekniken, fokusgrupps-intervjuer med lärare, ‘tänka högt’-intervjuer med elever och två sorters videoinspelningar ifrån klassrum (dels med vanlig videokamera och dels med mjukvara som spelar in aktiviteter på datorskärmen och elevernas aktiviteter vid datorn, liksom ljudet). Analysen visar hur lärare, elever, teknik, information, uppgifter, data-typer, etc. tillsammans, i nära samarbete i de studerade klassrummen, skapar mycket komplexa villkor för lärande. De läraktiviteter som uppstår i klassrummen där teknik för visuell analys inkluderas, erbjuder elever support att: hantera stora datamängder, bli delaktiga i olika läraktiviteter och uppnå olika utbildningsmål, men även andra sorters elevrelaterade mål. Därför kan tekniken sägas vara relevant för denna sorts undervisning. Vidare visar analysen hur komplexiteten tillsammans med elevernas uppfattningar av hur kunskap skall visas, skapar påtagliga ‘problem spaces’ i läraktiviteterna. Lärandevillkoren kan därför förstås som en klassrumspraktik som inte fullt ut överensstämmer med den introducerade teknikens erbjudanden för visuell analys. Därför efterfrågas en förändrad syn på didaktisk design och elevers kunskapsformering, vilket blir betydelsefullt för kunskapens kvalitet i förhållande till visuell analys.

For many years universities communicated generic graduate attributes (e.g. global citizenship) their students have acquired after studying. Graduate attributes are skills and competencies that are relevant for both employability and other aspects of life (Barrie, 2004). Over the past years and due to the Bologna Process, the focus on competencies has also found its way into universities' curricula. As a consequence, curricula were adapted in order to convey students both in-depth knowledge of a particular area as well as generic competences (Bologna Working Group on Qualifications Framework, 2005, Appendix 8). For example, students with a Master's degree should be able to “communicate their conclusions, and the knowledge and rationale underpinning these, to specialist and non-specialist audiences clearly and unambiguously” (p. 196). This shift has been supported by the demand of the labour market for students that have achieved social and personal competencies, in addition to in-depth knowledge (Heidenreich, 2011). On course level, this placed emphasis on collaborative learning, which had led to “greater autonomy for the learner, but also to greater emphasis on active learning, with creation, communication and participation” (Downes, 2005). The shift to collaborative learning has been supported by existing learning theories and models (Brown et al., 1989; Lave and Wenger, 1991; Vygotsky, 1978), which could explain the educational advantages. For example, collaborative learning has proved to promote critical thinking and communications skills (Johnson and Johnson, 1994; Laal and Ghodsi, 2012). As Haythornthwaite (2006) advocates: “collaborative learning holds the promise of active construction of knowledge, enhanced problem articulation, and benefits exploring and sharing information and knowledge gained from peer-to-peer communication” (p. 10). The term collaboration defies clear definition (Dillenbourg, 1999). In this article, cooperation is seen as the division of labour in tasks, which allows group members to work independently, whereas collaboration needs continuous synchronisation and coordination of labour (Dillenbourg et al., 1996; Haythornthwaite, 2006). Therefore, cooperation allows students to subdivide task assignments, work relatively independent, and to piece the results together to one final product. In contrast, collaboration is seen as a synchronous and coordinated effort of all students to accomplish their task assignment resulting in a final product where “no single hand is visible” (Haythornthwaite, 2006, p. 12). Due to the debate about digital natives (Prensky, 2001) and “students' heavy use of technology” in private life (Luo, 2010, p. 32), teachers have started to explore possible applications of modern technology in teaching and learning. Especially wikis have become popular and gained reasonable attention in higher education. Wikis have been used to support collaborative learning (e.g. Cress and Kimmerle, 2008), collaborative writing (e.g. Naismith et al., 2011), and student engagement (e.g. Neumann and Hood, 2009). A wiki is a “freely expandable collection of interlinked Web ‘pages’, a hypertext system for storing and modifying information - a database, where each page is easily editable by any user” (Leuf and Cunningham, 2001, p. 14; italics in original). Thereby, wikis enable the collaborative construction of knowledge (Alexander, 2006). With the intention to take advantage of the benefits connected with collaborative learning, this doctoral thesis focuses on the facilitation of collaboration in wikis to leverage collaborative learning. The doctoral thesis was founded on a constructivist understanding of reality. The research is associated with three different research areas: adoption of IT, computer-supported collaborative learning, and learning analytics. After reviewing existing literature, three focal points were identified that correspond to the research gaps in these research areas: factors influencing students' use of wikis, assessment of collaborative learning, and monitoring of collaboration. The aims of this doctoral thesis were (1) to investigate students' intentions to adopt and barriers to use wikis in higher education, (2) to develop and evaluate a method for assessing computer-supported collaborative learning, and (3) to map educational objectives onto learning-related data in order to establish indicators for collaboration. Based on the research aims, four studies were carried out. Each study raised unique research questions that has been addressed by different methods. Thereby, this doctoral thesis presents findings covering the complete process of the use of wikis to support collaboration and thus provides a holistic view on the use of wikis in higher education.

Les travaux pratiques représentent une composante incontournable de l'apprentissage. Toutefois, leur mise en œuvre au sein de laboratoires physiques requiert des infrastructures souvent coûteuses pour les institutions de formation qui peuvent ainsi difficilement faire face à la forte augmentation du nombre d'étudiants. Dans ce contexte, les laboratoires virtuels et distants (VRL) représentent une alternative pour assurer le passage à l'échelle des activités pratiques à moindre coût. De nombreux travaux de recherche ont émergé au cours de la dernière décennie en se focalisant sur les problématiques techniques et technologiques induites par ces nouveaux usages, telles que la fédération, la standardisation, ou la mutualisation des ressources de laboratoires. Cependant, les récentes revues de littérature du domaine mettent en exergue la nécessité de se préoccuper davantage des facettes pédagogiques liées à ces environnements informatiques innovants dédiés à l'apprentissage pratique. Dans cet objectif, nos travaux exploitent les traces issues des activités réalisées par les apprenants lors de sessions d'apprentissage pratique pour mettre en œuvre les théories socio-constructivistes qui sont au cœur de l'apprentissage exploratoire, et ainsi favoriser l'engagement et le processus de réflexion des étudiants. À partir de la littérature traitant des relations sociales entre apprenants, nous identifions dans un premier temps un ensemble de critères pour la conception de systèmes d'apprentissage pratique engageants. En s'appuyant sur une architecture de cloud computing, nous avons ensuite réalisé Lab4CE, un environnement web pour l'enseignement de l'Informatique capable de masquer la complexité des tâches de gestion des laboratoires, mais surtout d'exposer des capacités éducatives avancées. En effet, Lab4CE repose sur les Learning Analytics pour supporter différentes formes d'apprentissage telles que la collaboration, la coopération ou l'entraide entre pairs, mais également pour fournir des outils d'awareness et de réflexion visant à promouvoir l'apprentissage en profondeur pendant et après les activités pratiques. Plusieurs expérimentations en contexte d'apprentissage réel et présentiel montrent une évaluation positive de Lab4CE par les apprenants en terme d'utilisabilité, qu'ils s'appuient de manière significative sur nos outils d'awareness et de réflexion, mais que des artefacts supplémentaires sont nécessaires pour accroître leur engagement spontané dans des interactions sociales d'apprentissage. De plus, ces expérimentations soulignent l'existence d'une corrélation significative entre l'engagement des étudiants dans la plateforme et les stratégies d'apprentissage qu'ils mettent en œuvre d'une part, et leur performance académique d'autre part. Ces premiers résultats nous permettent d'affirmer que les théories socio-constructivistes sont un levier à l'engagement et à la réflexion dans les VRL. Ils nous invitent à confronter notre approche à d'autres modalités d'apprentissage, mais aussi à intégrer de nouvelles sources d'informations pour approfondir nos analyses du comportement et ainsi renforcer nos contributions à une meilleure prise en compte de l'apprentissage pratique dans les EIAH. / Practical activities, used in exploratory learning, represent a major component of education: they make learners acquire not only knowledge, but also skills and attitude, and they help them bridging the gap between theories and the real world within they are applied. However, the physical laboratories hosting these activities rely on expensive infrastructures that make very difficult for institutions to cope with the high increase of the students' population. Within this context, virtual and remote laboratories (VRL) bring an affordable alternative to provide practical activities at scale. Numerous research works have come up for the last decade; they mainly focused on technological issues such as the federation of remote laboratories, their standardization, or the pooling of the resources they provide. Nevertheless, the recent literature reviews highlight the need to pay more attention to the educational facets of these innovative learning environments. With that purpose in mind, our works make use of the learners' traces collected through their practical learning sessions to sustain socio-constructivist theories, on which practical activities rely on, and thus to engage students in their learning tasks and further their reflection. Starting from the study of scientific research, we identify as a first step a set of criteria required to design practical learning systems that support social interactions between learners. We then developed Lab4CE, a web-based environment for Computer Science education. This environment relies on a cloud computing architecture to provide learners with their own virtual resources, and hides the complexity of the inherent management tasks while offering advanced educational capabilities. Indeed, Lab4CE builds on learning analytics to enable different forms of learning such as collaboration, cooperation, or peer assistance, but also to supply learners as well as teachers awareness and reflection tools that aim at promoting deep learning during and after practical activities. We carried out several experimentations in authentic and hands-on learning contexts. They stressed the fact that learners evaluate positively the usability of Lab4CE, and they significantly rely on our awareness and reflection tools. However, extra artifacts are required to increase their spontaneous engagement in social learning interactions. Moreover, theses experimentations suggested a significant correlation between, on the one hand, student's activity in the environment and the learning strategies they apply and, on the other hand, their academic performance. These first results allow us to assess that socio-constructivist theories leverage engagement and reflection within VRL. They also invite us to put our approach into practice in other learning settings, but also to extend the sources of information to deal with our behavioral analyses in depth, and thus to enhance our contributions regarding the adoption of practical learning within technological environments.

Submitted by Lara Oliveira (lara@ufersa.edu.br) on 2017-04-10T19:19:25Z No. of bitstreams: 1 RafaelCS_DISSERT.pdf: 3240038 bytes, checksum: a153143f6eb3e6da1d442bbd9089c122 (MD5) / Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2017-04-13T14:41:47Z (GMT) No. of bitstreams: 1 RafaelCS_DISSERT.pdf: 3240038 bytes, checksum: a153143f6eb3e6da1d442bbd9089c122 (MD5) / Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2017-04-13T15:04:19Z (GMT) No. of bitstreams: 1 RafaelCS_DISSERT.pdf: 3240038 bytes, checksum: a153143f6eb3e6da1d442bbd9089c122 (MD5) / Made available in DSpace on 2017-04-13T15:04:46Z (GMT). No. of bitstreams: 1 RafaelCS_DISSERT.pdf: 3240038 bytes, checksum: a153143f6eb3e6da1d442bbd9089c122 (MD5) Previous issue date: 2016-09-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The growing evolution of technology in conjunction with its computing resources has provided new expectations in several areas of research such as industry, health and education. Such integration of technology in these environments combined with its widespread use has led to an increase in the volume of stored data. Therefore, researchers then realized the possibility of analyzing this large volume information in order to extract knowledge, so that this information can, for example, help to support on making decision. When applied in the education area, the collection, measurement and analysis of educational data to identify factors that may impact positively or negatively the teaching process are called Learning Analytics. Given this perspective, this work presents an assessment tool of behavioral actions of tutors teaching mode disciplines distance, so that through this be possible to evaluate the behavior of tutors and classes, as well as identify tutor behaviors that may or may not be related to the behavior of the class. With the information resulting from this work, one can better understand the impact of the behavior of tutors in the teaching mode groups at a distance, and enables pedagogical interventions guided in concise information in order to alleviate the problems faced by this type of education / A crescente evolução da tecnologia, em conjunto com seus recursos computacionais, tem propiciado novas expectativas em várias áreas de pesquisa, tais como na indústria, saúde e educação. A inserção da tecnologia nesses ambientes, combinada com sua larga utilização, tem gerado um aumento no volume dos dados armazenados. Diante disso, pesquisadores perceberam a possibilidade de analisar esse grande volume de informações a fim de extrair conhecimento, de modo que essas informações possam, por exemplo, auxiliar no suporte à tomada de decisão. Quando aplicadas no âmbito educacional, a coleta, a medição e a análise de dados educacionais, a fim de identificar fatores que possam impactar positivamente ou negativamente o processo de ensino, são denominadas Learning Analytics. Diante dessa perspectiva, o presente trabalho apresenta uma ferramenta de avaliação das ações comportamentais dos tutores de disciplinas da modalidade de ensino a distância, de modo que, por meio desta, seja possível avaliar os comportamentos de tutores e turmas, bem como identificar quais os comportamentos do tutor que podem ou não estarem relacionados com os comportamentos da turma. Com as informações resultantes deste trabalho, pode-se compreender melhor o impacto dos comportamentos dos tutores nas turmas da modalidade de ensino a distância, além de possibilitar intervenções pedagógicas pautadas em informações objetivas, a fim de atenuar os problemas enfrentados por essa modalidade de ensino / 2017-04-10

Arsenic is a naturally occurring toxic metal and its presence in food composites could be a potential risk to the health of both humans and animals. Arseniccontaminated groundwater is often used for food and animal consumption, irrigation of soils, which could potentially lead to arsenic entering the human food chain. Its side effects include multiple organ damage, cancers, heart disease, diabetes mellitus, hypertension, lung disease and peripheral vascular disease. Research investigations, epidemiologic surveys and total diet studies (market baskets) provide datasets, information and knowledge on arsenic content in foods. The determination of the concentration of arsenic in rice varieties is an active area of research. With the increasing capability to measure the concentration of arsenic in foods, there are volumes of varied and continuously generated datasets on arsenic in food groups. Visual analytics, which integrates techniques from information visualization and computational data analysis via interactive visual interfaces, presents an approach to enable data on arsenic concentrations to be visually represented. The goal of this doctoral research in Environmental Science is to address the need to provide visual analytical decision support tools on arsenic content in various foods with special emphasis on rice. The hypothesis of this doctoral thesis research is that software enabled visual representation and user interaction facilitated by visual interfaces will help discover hidden relationships between arsenic content and food categories. The specific objectives investigated were: (1) Provide insightful visual analytic views of compiled data on arsenic in food categories; (2) Categorize table ready foods by arsenic content; (3) Compare arsenic content in rice product categories and (4) Identify informative sentences on arsenic concentrations in rice. The overall research method is secondary data analyses using visual analytics techniques implemented through Tableau Software. Several datasets were utilized to conduct visual analytical representations of data on arsenic concentrations in foods. These consisted of (i) arsenic concentrations in 459 crop samples; (ii) arsenic concentrations in 328 table ready foods from multi-year total diet studies; (iii) estimates of daily inorganic arsenic intake for 49 food groups from multicountry total diet studies; (iv) arsenic content in rice product categories for 193 samples of rice and rice products; (v) 758 sentences extracted from PubMed abstracts on arsenic in rice. Several key insights were made in this doctoral research. The concentration of inorganic arsenic in instant rice was lower than those of other rice types. The concentration of Dimethylarsinic Acid (DMA) in wild rice, an aquatic grass, was notably lower than rice varieties (e.g. 0.0099 ppm versus 0.182 for a long grain white rice). The categorization of 328 table ready foods into 12 categories enhances the communication on arsenic concentrations. Outlier concentration of arsenic in rice were observed in views constructed for integrating data from four total diet studies. The 193 rice samples were grouped into two groups using a cut-off level of 3 mcg of inorganic arsenic per serving. The visual analytics views constructed allow users to specify cut-off levels desired. A total of 86 sentences from 53 PubMed abstracts were identified as informative for arsenic concentrations. The sentences enabled literature curation for arsenic concentration and additional supporting information such as location of the research. An informative sentence provided global “normal” range of 0.08 to 0.20 mg/kg for arsenic in rice. A visual analytics resource developed was a dashboard that facilitates the interaction with text and a connection to the knowledge base of the PubMed literature database. The research reported provides a foundation for additional investigations on visual analytics of data on arsenic concentrations in foods. Considering the massive and complex data associated with contaminants in foods, the development of visual analytics tools are needed to facilitate diverse human cognitive tasks. Visual analytics tools can provide integrated automated analysis; interaction with data; and data visualization critically needed to enhance decision making. Stakeholders that would benefit include consumers; food and health safety personnel; farmers; and food producers. Arsenic content of baby foods warrants attention because of the early life exposures that could have life time adverse health consequences. The action of microorganisms in the soil is associated with availability of arsenic species for uptake by plants. Genomic data on microbial communities presents wealth of data to identify mitigation strategies for arsenic uptake by plants. Arsenic metabolism pathways encoded in microbial genomes warrants further research. Visual analytics tasks could facilitate the discovery of biological processes for mitigating arsenic uptake from soil. The increasing availability of central resources on data from total diet studies and research investigations presents a need for personnel with diverse levels of skills in data management and analysis. Training workshops and courses on the foundations and applications of visual analytics can contribute to global workforce development in food safety and environmental health. Research investigations could determine learning gains accomplished through hardware and software for visual analytics. Finally, there is need to develop and evaluate informatics tools that have visual analytics capabilities in the domain of contaminants in foods. / Environmental Sciences / P. Phil. (Environmental Science)

Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-06-04T13:02:42Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Filipe Dwan.pdf: 3617202 bytes, checksum: 21261ba9c1db7a40af29004bd0bb6f52 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-06-04T13:02:58Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Filipe Dwan.pdf: 3617202 bytes, checksum: 21261ba9c1db7a40af29004bd0bb6f52 (MD5) / Made available in DSpace on 2018-06-04T13:02:58Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Filipe Dwan.pdf: 3617202 bytes, checksum: 21261ba9c1db7a40af29004bd0bb6f52 (MD5) Previous issue date: 2018-03-29 / CS1 (first year programming) classes are known to have a high dropout and non-pass rate. Thus, there have been many studies attempting to predict and alleviate CS1 student performance. Knowing about student performance in advance can be useful for many reasons. For example, teachers can apply specific actions to help learners who are struggling, as well as provide more challenging activities to high-achievers. Initial studies used static factors, such as: high school grades, age, gender. However, student behavior is dynamic and, as such, a data-driven approach has been gaining more attention, since many universities are using web-based environments to support CS1 classes. Thereby, many researchers have started extracting student behavior by cleaning data collected from these environments and using them as features in machine learning (ML) models. Recently, the research community has proposed many predictive methods available, even though many of these studies would need to be replicated, to check if they are context-sensitive. Thus, we have collected a set of successful features correlated with the student grade used in related studies, compiling the best ML attributes, as well as adding new features, and applying them on a database representing 486 CS1 students. The set of features was used in ML pipelines which were optimized with two approaches: hyperparameter-tuning with random search and genetic programming. As a result, we achieved an accuracy of 74.44%, using data from the first two weeks to predict student final grade, which outperforms a state-of-the-art research applied to the same dataset. It is also worth noting that from the eighth week of class, the method achieved accuracy between 85% and 90.62%. / Em média, um terço dos alunos no mundo reprova em disciplinas de introdução à programação de computadores (IPC). Assim, muitos estudos vêm sendo conduzidos a fim de inferir o desempenho de estudantes de turmas de IPC. Inicialmente, pesquisadores investigavam a relação das notas dos alunos com fatores estáticos como: notas no ensino médio, gênero, idade e outros. Entretanto, o comportamento dos estudantes é dinâmico e, dessa forma, abordagens orientadas aos dados vêm ganhando atenção, uma vez que muitas universidades utilizam ambientes web para turmas de programação como juízes online. Com efeito, muitos pesquisadores vêm extraindo e tratando os dados dos estudantes a partir desses ambientes e usando-os como atributos de algoritmos de aprendizagem de máquina para a construção de modelos preditivos. No entanto, a comunidade científica sugere que tais estudos sejam reproduzidos a fim de investigar se eles são generalizáveis a outras bases de dados educacionais. Neste sentido, neste trabalho apresentou-se um método que emprega um conjunto de atributos correlacionados com as notas dos estudantes, sendo alguns baseados em trabalhos relacionados e outros propostos nesta pesquisa, a fim de realizar a predição do desempenho dos alunos nas avaliações intermediárias e nas médias finais. Tal método foi aplicado a uma base de dados com 486 alunos de IPC. O conjunto de atributos chamado de perfil de programação foi empregado em algoritmos de aprendizagem de máquina e otimizado utilizando duas abordagens: a) ajuste de hiperparâmetros com random search e b) construção do pipeline de aprendizagem de máquina utilizando algoritmos evolutivos. Como resultado, atingiu-se 74,44% de acurácia na tarefa de identificar se os alunos iriam ser reprovados ou aprovados usando os dados das duas semanas de aula em uma base de dados balanceada. Esse resultado foi estatisticamente superior ao baseline. Destaca-se ainda que a partir da oitava semana de aula, o método atingiu acurácias entre 85% e 90,62%.

This study investigates the refinement of an evaluation framework for e-Government websites. The aim of the research was to determine how an existing evaluation framework, which recommends the use of multiple usability techniques, could be used to obtain usability data that would indicate how to improve e-Government websites and satisfy users' needs. The framework describes how common techniques, such as heuristic testing and user testing, can be used with the emerging discipline of web analytics to provide a comprehensive and detailed view of users' interactions on e-Government websites. The original framework was refined in the light of the findings and the refined framework should facilitate the improvement of e-Government websites depending on users' demands and interactions. The work involved implementing the original multi-dimensional framework in e-Government websites in Saudi Arabia. A case study method was used over two implementations. In the former implementation, the evaluation methods consisted of heuristic evaluation followed by usability testing then web analytic tools. However, in the later implementation, refinements to the evaluation framework were proposed and the order of methods was amended: web analytics was used first, followed by heuristic evaluation then usability testing. The framework recommends specific usability methods for evaluating specific issues. The conclusions of this study illustrate the potential benefits of using a multidimensional evaluation framework for e-Government websites and it was found that each usability method had its own particular benefits and limitations. The research concludes by illustrating the potential usefulness of the designed evaluation framework in raising awareness of usability methods for evaluating e-Government websites in Saudi Arabia.

Idag skapas och lagras enorma mängder data, samtidigt som endast en liten del av datan analyseras och används. Big data är ett begrepp som cirkulerat i flera år, men på senare år har det fått allt större innebörd. Allt fler företag börjar få upp ögonen för big data, samtidigt som få verkligen vet hur det ska användas. Vissa frågar sig till och med: finns det någon affärsnytta? Med fokus på detaljhandelsbranschen undersöker vi huruvida det finns en affärsnytta med big data, och framförallt vilka utmaningar och möjligheter som finns kopplade till det. Begrepp som business intelligence och analytics diskuteras i sambandet. I denna kvalitativa studie har tre experter från olika företag; IBM, Knowit och Exor, intervjuats. Resultatet från intervjuerna har kopplats till den teori som tagits fram ur litteratur kring ämnet, och jämförts i analysen. Samtliga identifierade utmaningar och möjligheter har listats, och bland slutsatser ses att de etiska och mänskliga faktorerna har stor betydelse, och att affärsnyttan kan vara beroende av ett företags storlek och marknad. Uppsatsen är skriven på svenska. / Enormous amounts of data is created and stored today, all the while only a small amount of data is being analysed and used. Big data is a term that has circulated for years, however in recent years its meaning have been increased. More enterprises are starting to open their eyes for big data, while few understand how to actually use it. Some even ask themselves: is there a business benefit? With a focus on the retail industry, we examine whether there is a business benefit with using big data, and above all what challenges and opportunities are connected to it. Terms such as business intelligence and analytics are discussed in the relationship to big data. In this qualitative study, three experts from different enterprises; IBM, Knowit and Exor, have been interviewed. The results from the interviews has been connected to the theory from the literature around the subject, and has been compared in the analysis. All identified challenges and opportunities have been listed, and among the conclusions can the ethical and human factors be seen to have a major importance, and that the business benefits can be dependent of an enterprises’ size and market. The essay is written in Swedish.