Peer and team assessment: Strategies and applications in engineering courses

Teamwork is one of the competencies that more often are referred has required to professional practice in Engineering. Working in teams in the learning process has been referred to be an effective way to promote the development of technical competencies while promoting the development of teamwork competencies. The students identify teamwork as motivating for their self-learning. In a teamwork environment student can deal with self-knowledge, critical analysis, knowledge of the others, individual and group performances, feedback, resilience, synergy, decision making, commitment, participation, self-esteem, leadership, and entrepreneurship. All these characteristics come from the understanding that a team is formed by individuals with different experiences, origins and individual profiles. But what are the criteria for peer or teamwork assessment? Which methods give fair rewards for different contributions to the team and its peers? There are several peer assessments studies where many experiences are described, but there are not many studies that compare the strategies of peer assessments between them. For example, when the criteria have different weights or when the scores given by the peers are anonymous or when the scores are decided by the group, and so on. The objective of this work is to describe several strategies of peer and team assessment, considering the categorization and organization carried out in order to assumptions and/or purposes of each strategy. Thus, a contribution will be made for increasing peer and teamwork assessment in Engineering courses.FCT - Fundação para a Ciência e a Tecnologia (UIDCEC003192019

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Data Mining refere-se ao processo de análise de dados e à aplicação de algoritmos que, mediante limitações de eficiência computacional aceitáveis, são capazes de produzir uma relação particular de padrões a partir de grandes massas de dados [Fayyad, 19964 A utilização desse processo em problemas do mundo real consiste na classificação dos dados, sejam eles categóricos ou contínuos. Problemas envolvendo dados categóricos são comumente denominados de problemas de classificação, enquanto que os dados contínuos são denominados de/problemas de regressão. Problemas do mundo real consistem geralmente de problemas de regressão. Dessa forma, cresce o interesse em utilizar o processo Data Mining para extrair padrões de problemas de regressão. Além da extração, esses padrões devem ser posteriormente analisados segundo algumas medidas de avaliação de conhecimento para determinar se o padrão é preciso, compreensível ou de interesse ao usuário. Para explorar esse processo de avaliação do conhecimento em problemas de regressão, são realizados, neste trabalho, experimentos com conjuntos de diferentes domínios e características utilizando o ambiente RREvaluation O RREvaluation tem a finalidade de apoiar os usuários do processo Data Mining na análise do conhecimento extraído de problemas de regressão. O ambiente RREvaluation aqui proposto permite a utilização de diversas formas de avaliação da precisão utilizando as medidas MSE, MAD e NMSE. A compreensibilidade através da identificação do número de condições da regra e da função matemática envolvida, assim como algumas medidas de interessabilidade como GanhoMAD, LC e Q.This process is used in real-world problems to classify data, whether it is categorical data or continuous data. Problems that involve categorical data are commonly called classification problems, while problems that involve continuous data are called regression problems. Realworld problems generally consist of regression problema Because of this, there is an increasing interest in the use of DM to extract patterns from regression problems. Along with their extraction, these patterns should also be analyzed according to some knowledge evaluation measurements to determine if the pattern is precise, comprehensible or of interest to the user. To explore this knowledge evaluation process in regression problems, experiments are executed on different domains with various characteristics using the RREvaluation environment. RREvaluation has as its main objective to suppoit the users of the DM process in the analysis of the knowledge extracted from regression problems. The proposed environment maltes it possible to use several forms of evaluating precision, using the MSE, MAD and NMSE measures. The comprehensibility can also be evaluated, by identifying the number of conditions in the rule and the mathematical function involved, as well as using some interestingness measures such as MADGain, LC and Q

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Data Mining refere-se ao processo de análise de dados e à aplicação de algoritmos que, mediante limitações de eficiência computacional aceitáveis, são capazes de produzir uma relação particular de padrões a partir de grandes massas de dados [Fayyad, 19964 A utilização desse processo em problemas do mundo real consiste na classificação dos dados, sejam eles categóricos ou contínuos. Problemas envolvendo dados categóricos são comumente denominados de problemas de classificação, enquanto que os dados contínuos são denominados de/problemas de regressão. Problemas do mundo real consistem geralmente de problemas de regressão. Dessa forma, cresce o interesse em utilizar o processo Data Mining para extrair padrões de problemas de regressão. Além da extração, esses padrões devem ser posteriormente analisados segundo algumas medidas de avaliação de conhecimento para determinar se o padrão é preciso, compreensível ou de interesse ao usuário. Para explorar esse processo de avaliação do conhecimento em problemas de regressão, são realizados, neste trabalho, experimentos com conjuntos de diferentes domínios e características utilizando o ambiente RREvaluation O RREvaluation tem a finalidade de apoiar os usuários do processo Data Mining na análise do conhecimento extraído de problemas de regressão. O ambiente RREvaluation aqui proposto permite a utilização de diversas formas de avaliação da precisão utilizando as medidas MSE, MAD e NMSE. A compreensibilidade através da identificação do número de condições da regra e da função matemática envolvida, assim como algumas medidas de interessabilidade como GanhoMAD, LC e Q.This process is used in real-world problems to classify data, whether it is categorical data or continuous data. Problems that involve categorical data are commonly called classification problems, while problems that involve continuous data are called regression problems. Realworld problems generally consist of regression problema Because of this, there is an increasing interest in the use of DM to extract patterns from regression problems. Along with their extraction, these patterns should also be analyzed according to some knowledge evaluation measurements to determine if the pattern is precise, comprehensible or of interest to the user. To explore this knowledge evaluation process in regression problems, experiments are executed on different domains with various characteristics using the RREvaluation environment. RREvaluation has as its main objective to suppoit the users of the DM process in the analysis of the knowledge extracted from regression problems. The proposed environment maltes it possible to use several forms of evaluating precision, using the MSE, MAD and NMSE measures. The comprehensibility can also be evaluated, by identifying the number of conditions in the rule and the mathematical function involved, as well as using some interestingness measures such as MADGain, LC and Q

Setbacks of the development of a concept inventory for Scrum: contributions from item response theory

Scrum is the more common framework for agile project management. Agile project management requires frequent feedbacks and delivered items in projects with dynamic requirements and changes. Training learners in Scrum permits building agility in solving problems and teamwork competencies. Measuring training effectiveness is essential to identify students' learning lacks or misconceptions to improve the training outcomes. To assess the development of competences, it is possible to use concept Inventories, which are an essential educational tool to observe students' learning gain between two moments, before and after training. Additionally, the Item Response Theory may be applied to concept inventory items to identify latent characteristics as guessing, difficulty, and discriminant values. Guessing is related to an arbitrary answer to one question and gets the correct answer with common learner knowledge. Difficulty characteristic is related to student knowledge level to one question. Discriminant characteristic considers that learners with high score get accurate answers to the questions. Thus, this work aims to present some of the main setbacks of developing a concept inventory for Scrum, supported by the Item Response Theory. In this way, other researchers may understand how to develop a concept inventory and some of the main obstacles they may have to overcome or avoid. The Item Response Theory offers some indexes and criteria values to each latent characteristic to improve the concept inventory questions. Therefore, this work focuses on the process of conceptualizing, building, applying, and improving a Scrum Concept Inventory in a training situation with engineering students.info:eu-repo/semantics/publishedVersio