Association rule mining using genetic programming to provide feedback to instructors from multiple‐choice quiz data

Romero, Cristóbal; Zafra, Amelia; Luna, José María; Ventura, Sebastián

doi:10.1111/j.1468-0394.2012.00627.x

Cited by 64 publications

(37 citation statements)

References 16 publications

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“…Alguns trabalhos pretendem fornecer feedback para auxiliar a tomada de decisão por parte de professores e tutores [Romero et al, 2013] e [Feng et al, 2009]. Outra aplicação pode ser a de recomendações para estudantes, este conteúdo pode ser bem diversificado como por exemplo tarefas ou materiais de estudo.…”

Section: Trabalhos Relacionadosunclassified

Análise do Desempenho Estudantil na Educação a Distância Aplicando Técnicas de Mineração de Dados

Guércio

Marques

Ströele

et al. 2014

Workshops Do Congresso Brasileiro De Informática Na Educação

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Abstract. The availability of e-learning courses grows every year in Brazil and the use of e-learning systems supports increasingly this teaching modality. Due to the large amount of generated and stored data in e-learning systems, lots of information can be withdrawn from this large data set. Such information may assist educators and students in different learning aspects. This article aims to classify the students performance in earlier stages of discipline allowing decisions that might improve student performance. The experiments performed achieved an average accuracy of 73% in performance classification.Resumo. A disponibilização de cursos na modalidade a distância no Brasil cresce a cada ano e o uso de um Ambiente Virtual de Aprendizado (AVA) auxilia cada vez mais essa modalidade de ensino. Devido a grande quantidade de dados gerados e armazenados em um AVA muitas informações podem ser retiradas desse grande conjunto de dados. Tais informações podem auxiliar educadores e alunos em diferentes aspectos do aprendizado. O presente artigo tem como objetivo classificar o desempenho dos estudantes em estágios prematuros da disciplina possibilitando decisões que possam melhorar o desempenho dos estudantes. A acurácia média alcançada nos experimentos realizados foi de 73% na classificação do desempenho.

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Section: Trabalhos Relacionadosunclassified

Análise do Desempenho Estudantil na Educação a Distância Aplicando Técnicas de Mineração de Dados

Guércio

Marques

Ströele

et al. 2014

Workshops Do Congresso Brasileiro De Informática Na Educação

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“… Monitoring the progress of learning to detect in real time the undesirable behavior of students, such as the termination of training, low motivation, incorrect use of educational forums, abuse, fraud, etc., creating warnings to the parties concerned [13], provision feedback to the teachers in order to support decision-making on the improvement of student learning, the adoption of pre-emptive actions to remedy the situation [10];…”

Section: Data and Problems In Edmmentioning

confidence: 99%

“… Predicting student achievement, assessment of knowledge and learning outcomes [10], formation of recommendations to students based on their interests and activities in the learning process [14];  Individual approach, adapting training to each student, including course content, navigation on the course, the presentation of the material [15], [16], identification the groups of students according to their individual characteristics, personal characteristics, features of the training, etc. [17] [18];  Building a curriculum and educational content [19], [20], planning and scheduling of future courses, course planning, planning of resource allocation, organization of access to learning materials, planning consultations, curriculum development, etc.…”

Section: Data and Problems In Edmmentioning

confidence: 99%

“…Global use of the Internet has led to development of many new Web based educational system, such as e-learning systems, distance learning systems, online training systems, and so on, and the increasing use of artificial intelligence has led to the emergence of new intelligent and adaptive educational systems. The main types of currently used systems include: LMS (Learning management systems) [7], ITS (Intelligent tutoring systems) [8], AIHS (Adaptive intelligent hypermedia systems) [9], Test and quiz systems [10] and others. Each of them provides a variety of data sources that need to be processed in different ways depending on the nature of the available data and the specific problems and tasks that are solved by using Data mining techniques.…”

Section: Data and Problems In Edmmentioning

confidence: 99%

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Application of Data Mining and Process Mining approaches for improving e-Learning Processes

Grigorova¹,

Malysheva²,

Bobrovskiy³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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“…EA are search algorithms that generate solutions for optimization problems using techniques inspired by natural evolution. Evolutionary computation is usually used to discover AR in both EA [42,43] and Genetic Programming [44,45] due to they offer a set of advantages for knowledge extraction and specifically for rule induction processes [46].…”

Section: Mining Association Rules: a Reviewmentioning

confidence: 99%

Discovering gene association networks by multi-objective evolutionary quantitative association rules

Martínez-Ballesteros

Nepomuceno-Chamorro

Riquelme

2014

Journal of Computer and System Sciences

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In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. 1.IntroductionSince late 1990s, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. Microarray technology has revolutionized the biological research because it allows to study thousand of genes or even whole genomes [1].As molecular biology is rapidly evolving into a quantitative science, it increasingly relies on computational algorithms to make sense of high-throughput data. One of the main goals in Microarray analysis is the reconstruction of gene regulatory processes and a key task is the inference of regulatory interactions among genes from gene expression data [2].Our aimisto infer the relationships between genes from an organism in a particular biological process. This relationships can be modeled in several levels of abstraction, these levels range from the detailed gene regulatory processes (where a chain of intracellular reaction activates a regulatory molecule, transcription factors until a protein is synthesized) to the high models of abstraction named gene association networks. In the reconstruction of gene regulatory processes, building gene association networks has been proven to provide useful insights for such task, the reconstruction of gene regulatory processes. A gene association network can be defined as a graph in which nodes represent genes and edges represent the influence between them. Our goal in this work is the inference of gene association networks from Microarray datasets.There are several statistical methods to infer gene association networks from Microarray data. A microarray dataset is a bidimensional data structure where conditions are experiments or sources and the columns are gene expression values. In our problem the conditions will be the instances and the gene expression values will be the attributes or features. These methods range from relatively straightforward correlation-based methods to more sophisticated methods based on

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Association rule mining using genetic programming to provide feedback to instructors from multiple‐choice quiz data

Cited by 64 publications

References 16 publications

Análise do Desempenho Estudantil na Educação a Distância Aplicando Técnicas de Mineração de Dados

Análise do Desempenho Estudantil na Educação a Distância Aplicando Técnicas de Mineração de Dados

Application of Data Mining and Process Mining approaches for improving e-Learning Processes

Discovering gene association networks by multi-objective evolutionary quantitative association rules

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