Raul Wazlawick - Academia.edu (original) (raw)

Papers by Raul Wazlawick

Building a conceptual model is much more than placing concepts, associations, and attributes toge... more Building a conceptual model is much more than placing concepts, associations, and attributes together. Frequently the model does not work not for being wrong, but for being too complex to maintain. Analysis patterns consist of solutions already tested that can resolve families of recurrent problems. When adequately used these patterns may reduce significantly the complexity of an otherwise naive conceptual model. This chapter presents and sometimes reinterprets some patterns such as high cohesion, quantity, strategy, account, temporal, and others.

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

This Research Paper presents the evaluation of an instrument to identify the impact of motivation... more This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivation and engagement on student performance and retention, few studies have been found that address which factors are relevant in this process. The instrument is a questionnaire based on the compilation of several works of the literature containing 48 items divided into 6 groups: personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes/teachers. The questionnaire evaluation is based on a case study with 112 undergraduate students in Software Engineering. As a result, we found that the questionnaire can be considered reliable (Cronbach’s alpha = .8904). Considering the validity of constructs, we found an acceptable degree of correlation between the most pair of items in each group (averaging 63%). We also found that the item-total correlation coefficient was only not adequate for one factor group, indicating satisfactory correlation for all other items. Finally, we found that the number of factors is coherent, but there are several items from different groups strongly correlated, indicating the need for a reorganization.

This paper presents a textual programming language for conceptual modeling (based on UML classes/... more This paper presents a textual programming language for conceptual modeling (based on UML classes/associations and OCL constraints) and its compiler that can generate code in any target language or technology via extensible textual templates, both currently under initial stage of development. The language and compiler should allow the specification of information managed by ever-changing, increasingly distributed software systems. From a single source, automated code generation should keep implementations consistent with the specification across the different platforms and technologies. Furthermore, as the technology landscape evolves, the target templates may be extended to embrace new technologies. Unlike other approaches, such as MDA and MPS, the builtin tooling support, and the textual nature of this modeling language and its extensible templates, is expected to facilitate the integration of modeldriven software development into the workflow of software developers.

This Research to Practice Paper presents the results of the evaluation of pre-university factors ... more This Research to Practice Paper presents the results of the evaluation of pre-university factors that impact the initial motivation of undergraduate students in computing. Although there are studies in the literature that have investigated some previous factors, this paper replicates a previous work that aims to consolidate several pre-university factors and, as the main differential, uses the AMS (Academic Motivation Scale), a scale already consolidated in the literature to measure students' initial motivation, and evaluate the relation between motivation and candidate factors. We applied a questionnaire to 159 students from different computing programs in ten universities, which evaluates 20 factors divided into 4 groups: personal and demographic data, taste and knowledge of the program and area, computing experience, and school performance. To evaluate the correlation between factors and motivation, we used Spearman's coefficient, t-student test, and ANOVA to evaluate the correlation between factors and motivation. As main results, we found significant variation in the initial motivation according to following factors: taste for programming and technology, knowledge about the undergraduate program content, correct perception about computing professionals, knowledge and experience in computer programming, and general school performance.

Cohesion and coupling are regarded as fundamental features of the internal quality of object-orie... more Cohesion and coupling are regarded as fundamental features of the internal quality of object-oriented systems (OOS). Analyzing the relationships between cohesion and coupling metrics plays a significant role to develop efficient techniques for determining the external quality of an object-oriented system. Researchers have proposed several metrics to find cohesion and coupling in object-oriented systems. However, few of them have proposed an analysis of the relationship between cohesion and coupling. This paper empirically investigates the relationships among several cohesion and coupling metrics in object-oriented systems. This work attempts to find mutual relationships between those metrics by statistically analyzing the results of experiments. Three open-source Java systems were used for experimentation. The empirical study shows that cohesion and coupling metrics are inversely correlated.

Growing demand for hospital healthcare services has brought significant challenges for their mana... more Growing demand for hospital healthcare services has brought significant challenges for their managers. Variables with high uncertainty degree, such as the number of patients and the duration of their treatments, hinders the planning processes and make it difficult to properly comply with the established strategies. Controlling and identifying factors that affect the hospital management process depends on health database analysis. Therefore, it is important to consider the possibility of prospecting useful knowledge from the stored data. The objective of this research is to evaluate the hospital morbidity prediction through different data mining methods on ambulatory and hospital procedure records obtained from Brazilian public health databases. The research method consists of performing a predictive data mining by applying supervised learning algorithms on a regression problem. The highest Pearson correlation coefficient individually obtained in the three-month prediction time interval, through the data mining method that applied random forest associated with an attribute selection algorithm on the disease group of the ICD10 chapter XVI (Certain Conditions originating in the Perinatal Period), was 0.9682. Different results were achieved depending on the method applied, the group of diseases analyzed, and the proposed prediction time interval, which led to the conclusion that data mining on ambulatory and hospital records allowed the prediction of hospital morbidity. The hospital morbidity predictions obtained can minimize the undesired effect of the demand randomness for health services in the decision-making process.

Cadernos De Saude Publica, 2021

This short paper summarizes a work (Schoeffel et al., 2020) that presents and evaluates a method ... more This short paper summarizes a work (Schoeffel et al., 2020) that presents and evaluates a method to identify features that allow predicting at-risk students in introductory computing courses, based on four main components: preuniversity factors, initial motivation, motivation through the course, and professor perception. For each component was created questionnaires, which have been validated for their reliability and validity using statistical methods such as Cronbach's alpha coefficient, omega coefficient, intercorrelation, and factor analysis. The questionnaires were applied in two distinct moments: beginning the course and weekly through the course. The method, named EMMECS (Evaluation Method of Motivation and Engagement of Computing Students) was created to be easy and simple to apply, and it considers the student motivation longitudinally. It was applied with 245 students from different programs in four different universities in southern Brazil. We carried out several simulations of prediction, using ten different classification algorithms and different datasets. As a result, using support vector machine and AdaBoostM1 algorithms, we identified more than 90% of the failing students in the first week. Although this index has reduced slightly after that, the value of recall remained near or above 80% in the other weeks. The results show that the proposed method is effective compared with related works and it has as advantages its independence of programmatic content, specific assessments, grades, and interaction with learning systems. Another advantage of the EMMECS is that its application is simple and fast, it is possible to predict at-risk students since the first few weeks, and it allows replication independent of the course context or specific tools. Furthermore, the method allows the weekly prediction, with good results since the first few weeks. The main contribution of this work is to develop a method that makes it possible to identify in advance at-risk students in introductory computing courses. Each of the instruments allowed us to find factors related to the outcome and motivation of students. In several case studies, we found evidence of the relationship of the students' outcome to four aspects of initial motivation, fifteen educational factors, the professor's perception, and motivation through the course.

This article describes the instructional design and evaluation of a course about project manageme... more This article describes the instructional design and evaluation of a course about project management in a software engineering post-graduation program using different teaching approaches and with a focus on active learning. We use four different approaches: digital educational game, non-digital educational game, hands-on activity, and experiential activity. Each one of the activities is evaluated for the aspects of motivation, user experience, and learning, following the MEEGA evaluation model and Cidral’s experiential activity model. To verify the perceptions of students over time, we also assess graduated students that have concluded this course after two to four years, considering the aspects of motivation and learning. Results indicate a high level of approval for dynamic activities, regarding both motivation and learning. Activities with greater impact on motivation and learning are dynamics and educational games, group practical activities, and group theoretical activities. Among the factors that most influence students’ motivation, we highlight: active learning, teacher knowledge, the taste of the area, and teaching methods. We realized that there was no significant variation in the perception of the activities by students over time.

Anais dos Workshops do VII Congresso Brasileiro de Informática na Educação (CBIE 2018), Oct 28, 2018

Building a conceptual model is much more than placing concepts, associations, and attributes toge... more Building a conceptual model is much more than placing concepts, associations, and attributes together. Frequently the model does not work not for being wrong, but for being too complex to maintain. Analysis patterns consist of solutions already tested that can resolve families of recurrent problems. When adequately used these patterns may reduce significantly the complexity of an otherwise naive conceptual model. This chapter presents and sometimes reinterprets some patterns such as high cohesion, quantity, strategy, account, temporal, and others.

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

IEEE International Conference on Cloud Computing Technology and Science, May 17, 2016

This Research Paper presents the evaluation of an instrument to identify the impact of motivation... more This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivation and engagement on student performance and retention, few studies have been found that address which factors are relevant in this process. The instrument is a questionnaire based on the compilation of several works of the literature containing 48 items divided into 6 groups: personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes/teachers. The questionnaire evaluation is based on a case study with 112 undergraduate students in Software Engineering. As a result, we found that the questionnaire can be considered reliable (Cronbach’s alpha = .8904). Considering the validity of constructs, we found an acceptable degree of correlation between the most pair of items in each group (averaging 63%). We also found that the item-total correlation coefficient was only not adequate for one factor group, indicating satisfactory correlation for all other items. Finally, we found that the number of factors is coherent, but there are several items from different groups strongly correlated, indicating the need for a reorganization.

This paper presents a textual programming language for conceptual modeling (based on UML classes/... more This paper presents a textual programming language for conceptual modeling (based on UML classes/associations and OCL constraints) and its compiler that can generate code in any target language or technology via extensible textual templates, both currently under initial stage of development. The language and compiler should allow the specification of information managed by ever-changing, increasingly distributed software systems. From a single source, automated code generation should keep implementations consistent with the specification across the different platforms and technologies. Furthermore, as the technology landscape evolves, the target templates may be extended to embrace new technologies. Unlike other approaches, such as MDA and MPS, the builtin tooling support, and the textual nature of this modeling language and its extensible templates, is expected to facilitate the integration of modeldriven software development into the workflow of software developers.

This Research to Practice Paper presents the results of the evaluation of pre-university factors ... more This Research to Practice Paper presents the results of the evaluation of pre-university factors that impact the initial motivation of undergraduate students in computing. Although there are studies in the literature that have investigated some previous factors, this paper replicates a previous work that aims to consolidate several pre-university factors and, as the main differential, uses the AMS (Academic Motivation Scale), a scale already consolidated in the literature to measure students' initial motivation, and evaluate the relation between motivation and candidate factors. We applied a questionnaire to 159 students from different computing programs in ten universities, which evaluates 20 factors divided into 4 groups: personal and demographic data, taste and knowledge of the program and area, computing experience, and school performance. To evaluate the correlation between factors and motivation, we used Spearman's coefficient, t-student test, and ANOVA to evaluate the correlation between factors and motivation. As main results, we found significant variation in the initial motivation according to following factors: taste for programming and technology, knowledge about the undergraduate program content, correct perception about computing professionals, knowledge and experience in computer programming, and general school performance.

Cohesion and coupling are regarded as fundamental features of the internal quality of object-orie... more Cohesion and coupling are regarded as fundamental features of the internal quality of object-oriented systems (OOS). Analyzing the relationships between cohesion and coupling metrics plays a significant role to develop efficient techniques for determining the external quality of an object-oriented system. Researchers have proposed several metrics to find cohesion and coupling in object-oriented systems. However, few of them have proposed an analysis of the relationship between cohesion and coupling. This paper empirically investigates the relationships among several cohesion and coupling metrics in object-oriented systems. This work attempts to find mutual relationships between those metrics by statistically analyzing the results of experiments. Three open-source Java systems were used for experimentation. The empirical study shows that cohesion and coupling metrics are inversely correlated.

Growing demand for hospital healthcare services has brought significant challenges for their mana... more Growing demand for hospital healthcare services has brought significant challenges for their managers. Variables with high uncertainty degree, such as the number of patients and the duration of their treatments, hinders the planning processes and make it difficult to properly comply with the established strategies. Controlling and identifying factors that affect the hospital management process depends on health database analysis. Therefore, it is important to consider the possibility of prospecting useful knowledge from the stored data. The objective of this research is to evaluate the hospital morbidity prediction through different data mining methods on ambulatory and hospital procedure records obtained from Brazilian public health databases. The research method consists of performing a predictive data mining by applying supervised learning algorithms on a regression problem. The highest Pearson correlation coefficient individually obtained in the three-month prediction time interval, through the data mining method that applied random forest associated with an attribute selection algorithm on the disease group of the ICD10 chapter XVI (Certain Conditions originating in the Perinatal Period), was 0.9682. Different results were achieved depending on the method applied, the group of diseases analyzed, and the proposed prediction time interval, which led to the conclusion that data mining on ambulatory and hospital records allowed the prediction of hospital morbidity. The hospital morbidity predictions obtained can minimize the undesired effect of the demand randomness for health services in the decision-making process.

Cadernos De Saude Publica, 2021

This short paper summarizes a work (Schoeffel et al., 2020) that presents and evaluates a method ... more This short paper summarizes a work (Schoeffel et al., 2020) that presents and evaluates a method to identify features that allow predicting at-risk students in introductory computing courses, based on four main components: preuniversity factors, initial motivation, motivation through the course, and professor perception. For each component was created questionnaires, which have been validated for their reliability and validity using statistical methods such as Cronbach's alpha coefficient, omega coefficient, intercorrelation, and factor analysis. The questionnaires were applied in two distinct moments: beginning the course and weekly through the course. The method, named EMMECS (Evaluation Method of Motivation and Engagement of Computing Students) was created to be easy and simple to apply, and it considers the student motivation longitudinally. It was applied with 245 students from different programs in four different universities in southern Brazil. We carried out several simulations of prediction, using ten different classification algorithms and different datasets. As a result, using support vector machine and AdaBoostM1 algorithms, we identified more than 90% of the failing students in the first week. Although this index has reduced slightly after that, the value of recall remained near or above 80% in the other weeks. The results show that the proposed method is effective compared with related works and it has as advantages its independence of programmatic content, specific assessments, grades, and interaction with learning systems. Another advantage of the EMMECS is that its application is simple and fast, it is possible to predict at-risk students since the first few weeks, and it allows replication independent of the course context or specific tools. Furthermore, the method allows the weekly prediction, with good results since the first few weeks. The main contribution of this work is to develop a method that makes it possible to identify in advance at-risk students in introductory computing courses. Each of the instruments allowed us to find factors related to the outcome and motivation of students. In several case studies, we found evidence of the relationship of the students' outcome to four aspects of initial motivation, fifteen educational factors, the professor's perception, and motivation through the course.

This article describes the instructional design and evaluation of a course about project manageme... more This article describes the instructional design and evaluation of a course about project management in a software engineering post-graduation program using different teaching approaches and with a focus on active learning. We use four different approaches: digital educational game, non-digital educational game, hands-on activity, and experiential activity. Each one of the activities is evaluated for the aspects of motivation, user experience, and learning, following the MEEGA evaluation model and Cidral’s experiential activity model. To verify the perceptions of students over time, we also assess graduated students that have concluded this course after two to four years, considering the aspects of motivation and learning. Results indicate a high level of approval for dynamic activities, regarding both motivation and learning. Activities with greater impact on motivation and learning are dynamics and educational games, group practical activities, and group theoretical activities. Among the factors that most influence students’ motivation, we highlight: active learning, teacher knowledge, the taste of the area, and teaching methods. We realized that there was no significant variation in the perception of the activities by students over time.

Anais dos Workshops do VII Congresso Brasileiro de Informática na Educação (CBIE 2018), Oct 28, 2018