ASSISTments is an online adaptive tutoring system with the ability to provide assistance to students in the form of hints and scaffolding. ASSISTments has many features to help students improve their knowledge. Researchers run studies in order to discover ways for students to learn better but ASSISTments is missing one major aspect for researchers: student level personalization. It is easy to create an assignment for a particular class or school but it would take much longer to create an assignment for each student and it would be difficult for the teacher to look through many assignment reports. One of the strongest code blocks in coding is the if-then; allowing the program to branch off to another set of code under certain circumstances. ASSISTments needed an if-then system in order for students to branch off to other parts of the assignment under certain circumstances. With this, researchers would be able to personalize assignments to give more help to lower knowledge students or allow students to get a choice of what kind of tutoring they would like to receive. With this idea in mind, the basic if-then structure was implemented into ASSISTments using problem or problem set correctness as the condition statement. Once the if-then system was created opportunities opened to create additional experiments and run studies in ASSISTments. The basic if-then was limited in using correctness only for its condition statement. This meant that a new if-then system would need to be implemented to include custom condition statements that allowed the researcher to have the assignment branch on any condition using all the information recorded in the assignment. While work was being done on the if-then system, research was being done and two papers were written on partial credit in ASSISTments. Partial credit was found out to be as accurate as knowledge tracing in determining student performance on the next problem. Once a partial credit algorithm was found, a study using if-then was analyzed. It was found that there was no statistically significant difference between students who were given a choice on their feedback and students who received no choice.

Com a popularidade e o crescimento das Redes Sociais Online, o interesse pelo entendimento de como seus usuários interagem entre si também tem crescido, configurando assim um cenário rico no qual são criadas oportunidades para melhorar o design de interfaces, compreender a organização de movimentos sociais, o marketing viral e a distribuição de conteúdos multimídia, dentre outros. Na investigação dessas oportunidades de pesquisa, um modelo de representação da atividade dos usuários amplamente empregado é baseado na construção de um grafo. Embora as atividades dos usuários em uma Rede Social Online sejam variadas, assim como as possibilidades de interação entre usuários, a construção de um grafo normalmente considera uma interação específica, que é então analisada a partir da interpretação de medidas baseadas em grafos ou de medidas estatísticas. Técnicas de mineração de dados podem ser empregadas de forma alternativa e complementar ao modelo baseado em grafos, possibilitando a extração e a avaliação de padrões das atividades de usuários. Entretanto, tanto na análise baseada em grafos quanto na utilização de técnicas de mineração de dados, a literatura reporta trabalhos em que são implicitamente representados elementos associados com a atividade dos usuários como ações executadas, mídias compartilhadas, aplicações e tipos de dispositivos utilizados. Nesse cenário, existe uma demanda por um modelo descritivo que permita a representação explícita dos elementos associados com as atividades dos usuários representação essa que possa ser utilizada na extração e na avaliação das interações entre usuários. Para atender essa demanda, nesta tese é apresentada uma técnica e um método para representar, extrair e avaliar interações entre usuários de Redes Sociais Online. São também reportados resultados de experimentos da aplicação da técnica e do método a partir de dados obtidos de uma Rede Social Online / The interest in understanding user interactions is increasing with the popularity of Online Social Networks. This scenario leads to rich opportunities to improve the design of interfaces, to understand the organization of social movements, to model the dynamics of viral marketing and to characterize the distribution of multimedia content, among others. In the investigation of these research opportunities, a widely used representation of users activities is based on graphs as underlying data models. Graph data models usually consider a specific interaction, which is analyzed based on the interpretation of statistical measures and graph-based measures. On the other hand, user activities and possibilities of interaction in social networks are multifaceted, so that singleinteraction graph-based approaches might prove to be unfeasible to properly model the problem. In order to deal with these limitations, data mining techniques may be employed as an alternative to graph-based modeling approaches, by enabling extraction and evaluation of users activities as patterns. However, both in the use of the model graph-based as in the use of data mining techniques, the literature reports an implicit representation of elements associated with the users activities such as executed actions, shared media, used applications and devices. In this scenario, there is a demand for a descriptive model which allows explicit representation of the elements associated with users activities - this representation can be used in the extraction and evaluation of interactions among users. To meet this demand, this thesis presents a technique and a method to represent, to extract and to evaluate interactions among users of Online Social Networks. Results of experimentation of applying the technique and method based on data obtained from an Online Social Network are also reported

Com a popularidade e o crescimento das Redes Sociais Online, o interesse pelo entendimento de como seus usuários interagem entre si também tem crescido, configurando assim um cenário rico no qual são criadas oportunidades para melhorar o design de interfaces, compreender a organização de movimentos sociais, o marketing viral e a distribuição de conteúdos multimídia, dentre outros. Na investigação dessas oportunidades de pesquisa, um modelo de representação da atividade dos usuários amplamente empregado é baseado na construção de um grafo. Embora as atividades dos usuários em uma Rede Social Online sejam variadas, assim como as possibilidades de interação entre usuários, a construção de um grafo normalmente considera uma interação específica, que é então analisada a partir da interpretação de medidas baseadas em grafos ou de medidas estatísticas. Técnicas de mineração de dados podem ser empregadas de forma alternativa e complementar ao modelo baseado em grafos, possibilitando a extração e a avaliação de padrões das atividades de usuários. Entretanto, tanto na análise baseada em grafos quanto na utilização de técnicas de mineração de dados, a literatura reporta trabalhos em que são implicitamente representados elementos associados com a atividade dos usuários como ações executadas, mídias compartilhadas, aplicações e tipos de dispositivos utilizados. Nesse cenário, existe uma demanda por um modelo descritivo que permita a representação explícita dos elementos associados com as atividades dos usuários representação essa que possa ser utilizada na extração e na avaliação das interações entre usuários. Para atender essa demanda, nesta tese é apresentada uma técnica e um método para representar, extrair e avaliar interações entre usuários de Redes Sociais Online. São também reportados resultados de experimentos da aplicação da técnica e do método a partir de dados obtidos de uma Rede Social Online / The interest in understanding user interactions is increasing with the popularity of Online Social Networks. This scenario leads to rich opportunities to improve the design of interfaces, to understand the organization of social movements, to model the dynamics of viral marketing and to characterize the distribution of multimedia content, among others. In the investigation of these research opportunities, a widely used representation of users activities is based on graphs as underlying data models. Graph data models usually consider a specific interaction, which is analyzed based on the interpretation of statistical measures and graph-based measures. On the other hand, user activities and possibilities of interaction in social networks are multifaceted, so that singleinteraction graph-based approaches might prove to be unfeasible to properly model the problem. In order to deal with these limitations, data mining techniques may be employed as an alternative to graph-based modeling approaches, by enabling extraction and evaluation of users activities as patterns. However, both in the use of the model graph-based as in the use of data mining techniques, the literature reports an implicit representation of elements associated with the users activities such as executed actions, shared media, used applications and devices. In this scenario, there is a demand for a descriptive model which allows explicit representation of the elements associated with users activities - this representation can be used in the extraction and evaluation of interactions among users. To meet this demand, this thesis presents a technique and a method to represent, to extract and to evaluate interactions among users of Online Social Networks. Results of experimentation of applying the technique and method based on data obtained from an Online Social Network are also reported

With the advances in technology, the change of the healthcare environment, and the need for users, the use of computerized support systems or expert systems are able to cut down costs for unnecessary procedures, achieve higher levels of efficiency and productivity. Applied to the nursing department, it may provide good quality of care, decrease the time that nurses duplicate patient history, reduce nurses¡¦ burden and enhance the abilities to solve problems. The topic of this research mainly focused on the nursing department in the pediatric ward. I propose a framework for nursing knowledge management by using subjective data, objective data, assessment, and care plan (SOAP), which is used by the nursing staffs as a way of decision-making processes. The method is to collect subjective and objective data, read relevant clinical practice guidelines, make clinical judgments about patients¡¦ actual or potential problems and provide applicable nursing plans and interventions. The staffs review and make final decision to accept or reject these judgments, nursing plans and related interventions. If the staffs reject any judgment, nursing plan and intervention, the system should have inquiry-signs to ask physician and nursing staff. Then the staffs correct the inappropriateness. These clear and easy-to-follow processes help student nurses or beginning nurses cultivate their abilities to care and hope it can provide as a guide to nursing teaching and clinical patient care.