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Educational data mining e learning analytics na melhoria do ensino online

Faria, Susana Maria Sousa Martins Leite de
Fonte: Universidade Aberta de Portugal Publicador: Universidade Aberta de Portugal
Tipo: Dissertação de Mestrado
Publicado em //2014 POR
Relevância na Pesquisa
65.95%
Dissertação de Mestrado em Estatística, Matemática e Computação apresentada à Universidade Aberta; Educação é um dos temas mais importantes e discutidos em todo o mundo. Sendo um processo de aquisição de conhecimento e/ou aptidões, tem sofrido grandes alterações ao longo dos tempos. Na última década, os avanços das tecnologias de informação e computação têm permitido às pessoas interagirem e aprenderem de uma nova forma. Com as inovações tecnológicas, as escolas e universidades estão a alterar a forma como transmitem e partilham conhecimentos. Ao passo que, até ao ensino secundário, as Escolas disponibilizam uma plataforma Moodle, onde os professores divulgam e partilham alguns documentos e tarefas que servem de apoio às suas práticas letivas; já no ensino superior as alterações são mais significativas. As Universidades chegam mesmo a alterar a metodologia dos seus cursos. Para além do ensino tradicional optam por outras modalidades de ensino: b-learning (ensino simultaneamente presencial e à distância) e/ou e-learning (ensino à distância). Os modelos de ensino/aprendizagem assentes em ambientes online permitem aos alunos terem acesso ao conhecimento a qualquer hora e em qualquer lugar. No entanto...

Caracterização de alunos em ambientes de ensino online: estendendo o uso da DAMICORE para minerar dados educacionais; Characterization of students in online learning environments: extending the use of DAMICORE to educational data mining

Moro, Luis Fernando de Souza
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 04/05/2015 PT
Relevância na Pesquisa
96.23%
Com a popularização do uso de recursos tecnológicos na educação, uma enorme quantidade de dados, relacionados às interações entre alunos e esses recursos, é armazenada. Analisar esses dados, visando caracterizar os alunos, é tarefa muito importante, uma vez que os resultados dessa análise podem auxiliar professores no processo de ensino e aprendizagem. Entretanto, devido ao fato de as ferramentas utilizadas para essa caracterização serem complexas e pouco intuitivas, os profissionais da área de ensino acabam por não utilizá-las, inviabilizando a implementação de tais ferramentas em ambientes educacionais. Dentro desse contexto, a dissertação de mestrado aqui apresentada teve como objetivo analisar os dados provenientes de um sistema tutor inteligente, o MathTutor, que disponibiliza exercícios específicos de matemática, para identificar padrões de comportamento dos alunos que interagiram com esse sistema durante um determinado período. Essa análise foi realizada por meio de um processo de Mineração de Dados Educacionais (EDM), utilizando a ferramenta DAMICORE, com o intuito de possibilitar que fossem geradas, de forma rápida e eficaz, informações úteis à caracterização dos alunos. Durante a realização dessa análise...

Mineração de dados educacionais para a construção de alertas em ambientes virtuais de aprendizagem como apoio à prática docente; Educational data mining to support teacher in learning management systems

Kampff, Adriana Justin Cerveira; Reategui, Eliseo Berni; Lima, Jose Valdeni de
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Conferência ou Objeto de Conferência Formato: application/pdf
POR
Relevância na Pesquisa
55.81%

Towards Evaluating and Modelling the Impacts of Mobile-Based Augmented Reality Applications on Learning and Engagement

Poitras, Eric; Kee, Kevin; Lajoie, Susanne P; Cataldo, Dana
Fonte: Brock University Publicador: Brock University
Tipo: Parte de Livro
EN
Relevância na Pesquisa
55.81%
Mobile augmented reality applications are increasingly utilized as a medium for enhancing learning and engagement in history education. Although these digital devices facilitate learning through immersive and appealing experiences, their design should be driven by theories of learning and instruction. We provide an overview of an evidence-based approach to optimize the development of mobile augmented reality applications that teaches students about history. Our research aims to evaluate and model the impacts of design parameters towards learning and engagement. The research program is interdisciplinary in that we apply techniques derived from design-based experiments and educational data mining. We outline the methodological and analytical techniques as well as discuss the implications of the anticipated findings.

A case study: Data mining applied to student enrollment

Vialardi, César; Chue, Jorge; Barrientos, Alfredo; Victoria, Daniel; Estrella, Jhonny; Peche, Juan Pablo; Ortigosa, Álvaro
Fonte: Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. Publicador: Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr.
Tipo: conferenceObject; conferencePoster
ENG
Relevância na Pesquisa
55.9%
This is an electronic version of the poster presented at the International Conference on Educational Data Mining (EDM 2010) held in Pittsburgh, PA, USA on 2010; One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students’ past performance. The objective is to improve students’ performance in general, by reducing the rate of misguided enrollment decisions.

Recomendaciones pedagógicas para el uso de la plataforma Moodle apoyadas por herramientas de Minería de Datos; Pedagogicals recommendations for the use of Moodle platform supported by Data Mining tools

Parra Cicero, Priscila Guadalupe
Fonte: Universidade de Cantabria Publicador: Universidade de Cantabria
Tipo: Dissertação de Mestrado
SPA
Relevância na Pesquisa
66.03%
RESUMEN: En la actualidad el mundo académico y empresarial se encuentran en una fuerte competencia entre organizaciones que se rige principalmente por tener a las personas más cualificadas. Por ello, todas ellas apuestan por la capacitación o la forma más allegada de aprendizaje. Es importante tomar en cuenta que en la actualidad se vive un proceso de crisis económica global, que afecta a dichas organizaciones y, por ello, es más común la búsqueda de herramientas que faciliten la llegada de conocimientos a los usuarios de una manera más rápida, eficaz y económica. Es ahí donde entran herramientas como Moodle (Learning Managment System – LMS) que es un Sistema de Gestión del Aprendizaje que permite desarrollar un espacio de aprendizaje internet/intranet donde los usuarios puedan aprender aquello que la organización desea de una manera agradable, flexible, pero sobre todo, buscando la eficiencia y la eficacia en el Proceso de Enseñanza Aprendizaje (PEA). Pero la tarea de las organizaciones no termina ahí, es importante implantar un sistema de Evaluación Continua que permita verificar el correcto aprendizaje de los contenidos y hacer de manera rápida cualquier cambio en los mismos. El problema surge cuando dentro de dicho LMS se maneja una cantidad de información tan grande que para las organizaciones representa muchas horas de análisis si se hace uno a uno. Una solución que permite un análisis mejor y más rápido de dichos datos es la Minería de Datos (su término en inglés Data Mining es igualmente usado)...

Recommendation in higher education using data mining techniques

Vialardi, César; Bravo Agapito, Javier; Shafti, Leila Shila; Ortigosa, Álvaro
Fonte: Barnes, T., Desmarais, M., Romero, C., & Ventura, S. Publicador: Barnes, T., Desmarais, M., Romero, C., & Ventura, S.
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
55.91%
This is an electronic version of the paper presented at the International Conference on Educational Data Mining (EDM'09), held in Cordoba (Spain) on 2009; One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to take decisions on their acedemic itineraries. More specifically, it provides support for the student to better choose how many and which courses to enrol on, having as basis the experience of previous students with similar academic achievements. For this purpose, we have analyzed real data corresponding to seven years of student enrolment at the School of System Engineering at Universidad de Lima. Based on this analysis, a recommendation system was developed.

Metodologia de mineração de dados para ambientes educacionais online; Data mining methodology for online educational environments

Geraldo Ramos Falci Júnior
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 21/12/2010 PT
Relevância na Pesquisa
66%
Educação a distância populariza-se como meio prático de ensino com a expansão de recursos computacionais e da Internet. Apesar disto, ela traz dificuldades ao educador para compreender as necessidades de suas classes. A análise do uso desses Sistemas de Gerência de Aprendizado a distância por meio de técnicas de mineração de dados é uma forma de obter informações relevantes que permitam ao educador observar essas necessidades e modificar seus cursos de acordo. O objetivo deste trabalho é elaborar uma metodologia de trabalho que permita abordar problemas dessa natureza de forma objetiva e flexível, facilitando identificar potenciais problemas na análise e pontos de retorno adequados para correção e retomada do processo. Um conjunto de etapas é elaborado para compor esta metodologia e em seguida colocado à prova com um conjunto de dados reais obtidos através da instância do TIDIA-Ae utilizada pela UNICAMP como auxiliar às aulas presenciais. Os resultados mostram a eficácia do método proposto e permitiram a observação de diversos problemas devido à maneira de utilização do sistema por alunos e professores; Computer-based distance education is becoming popular as computational resources and the Internet expand. Nevertheless...

Recomendações personalizadas de alunos em sistemas de hipermédia adaptativa educacional usando Data Mining

Matos, Clarisse Celeste Cravo
Fonte: Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto. Publicador: Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto.
Tipo: Dissertação de Mestrado
Publicado em //2013 POR
Relevância na Pesquisa
66.25%
O aumento de tecnologias disponíveis na Web favoreceu o aparecimento de diversas formas de informação, recursos e serviços. Este aumento aliado à constante necessidade de formação e evolução das pessoas, quer a nível pessoal como profissional, incentivou o desenvolvimento área de sistemas de hipermédia adaptativa educacional - SHAE. Estes sistemas têm a capacidade de adaptar o ensino consoante o modelo do aluno, características pessoais, necessidades, entre outros aspetos. Os SHAE permitiram introduzir mudanças relativamente à forma de ensino, passando do ensino tradicional que se restringia apenas ao uso de livros escolares até à utilização de ferramentas informáticas que através do acesso à internet disponibilizam material didático, privilegiando o ensino individualizado. Os SHAE geram grande volume de dados, informação contida no modelo do aluno e todos os dados relativos ao processo de aprendizagem de cada aluno. Facilmente estes dados são ignorados e não se procede a uma análise cuidada que permita melhorar o conhecimento do comportamento dos alunos durante o processo de ensino, alterando a forma de aprendizagem de acordo com o aluno e favorecendo a melhoria dos resultados obtidos. O objetivo deste trabalho foi selecionar e aplicar algumas técnicas de Data Mining a um SHAE...

Data Mining as a Torch Bearer in Education Sector

Pandey, Umesh Kumar; Bhardwaj, Brijesh Kumar; pal, Saurabh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/01/2012
Relevância na Pesquisa
56.03%
Every data has a lot of hidden information. The processing method of data decides what type of information data produce. In India education sector has a lot of data that can produce valuable information. This information can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Information and communication technology puts its leg into the education sector to capture and compile low cost information. Now a day a new research community, educational data mining (EDM), is growing which is intersection of data mining and pedagogy. In this paper we present roadmap of research done in EDM in various segment of education sector.; Comment: 11 pages; Technical Journal of LBSIMDS, 2011

Data Mining on Educational Domain

Shirwaikar, Prof Rudresh; Rajadhyax, Nikhil
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/07/2012
Relevância na Pesquisa
56.05%
Educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings, and using those methods to better understand students and the settings which they learn in. Data mining enables organizations to use their current reporting capabilities to uncover and understand hidden patterns in vast databases. As a result of this insight, institutions are able to allocate resources and staff more effectively. In this paper, we present a real-world experiment conducted in Shree Rayeshwar Institute of Engineering and Information Technology (SRIEIT) in Goa, India. Here we found the relevant subjects in an undergraduate syllabus and the strength of their relationship. We have also focused on classification of students into different categories such as good, average, poor depending on their marks scored by them by obtaining a decision tree which will predict the performance of the students and accordingly help the weaker section of students to improve in their academics. We have also found clusters of students for helping in analyzing student's performance and also improvising the subject teaching in that particular subject.; Comment: 6 pages; http://www.ijascse.in/publications2012. arXiv admin note: text overlap with arXiv:1201.3417 by other authors

Mining Educational Data to Analyze Students' Performance

Baradwaj, Brijesh Kumar; Pal, Saurabh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/01/2012
Relevância na Pesquisa
56.13%
The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.; Comment: 7 pages. arXiv admin note: substantial text overlap with arXiv:1002.1144 by other authors without attribution

Data Mining: A prediction for performance improvement using classification

Bhardwaj, Brijesh Kumar; Pal, Saurabh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/01/2012
Relevância na Pesquisa
56.06%
Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.; Comment: 5 pages. arXiv admin note: substantial text overlap with arXiv:1002.1144 by other authors without attribution

A CHAID Based Performance Prediction Model in Educational Data Mining

Ramaswami, M.; Bhaskaran, R.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/02/2010
Relevância na Pesquisa
66.07%
The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO). A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student records, which were used for CHAID prediction model construction. A set of prediction rules were extracted from CHIAD prediction model and the efficiency of the generated CHIAD prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory.; Comment: International Journal of Computer Science Issues...

Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

Yadav, Surjeet Kumar; Pal, Saurabh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/03/2012
Relevância na Pesquisa
56.04%
Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student's performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student's data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.; Comment: 6 pages...

Educational data mining using jmp

Hussain, Sadiq; Hazarika, G. C.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 08/11/2014
Relevância na Pesquisa
65.98%
Educational Data Mining is a growing trend in case of higher education. The quality of the Educational Institute may be enhanced through discovering hidden knowledge from the student databases/ data warehouses. Present paper is designed to carry out a comparative study with the TDC (Three Year Degree) Course students of different colleges affiliated to Dibrugarh University. The study is conducted with major subject wise, gender wise and category/caste wise. The experimental results may be visualized with Scatterplot3D, Bubble Plot, Fit Y by X, Run Chart, Control Chart etc. of the SAS JMP Software.

Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue

Thakar, Pooja
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/09/2015
Relevância na Pesquisa
66.06%
In this era of computerization, education has also revamped itself and is not limited to old lecture method. The regular quest is on to find out new ways to make it more effective and efficient for students. Nowadays, lots of data is collected in educational databases, but it remains unutilized. In order to get required benefits from such a big data, powerful tools are required. Data mining is an emerging powerful tool for analysis and prediction. It is successfully applied in the area of fraud detection, advertising, marketing, loan assessment and prediction. But, it is in nascent stage in the field of education. Considerable amount of work is done in this direction, but still there are many untouched areas. Moreover, there is no unified approach among these researches. This paper presents a comprehensive survey, a travelogue (2002-2014) towards educational data mining and its scope in future.; Comment: 9 pages

A Study on Feature Selection Techniques in Educational Data Mining

Ramaswami, M.; Bhaskaran, R.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/12/2009
Relevância na Pesquisa
66.09%
Educational data mining (EDM) is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the behaviors of students in the learning process. In this EDM, feature selection is to be made for the generation of subset of candidate variables. As the feature selection influences the predictive accuracy of any performance model, it is essential to study elaborately the effectiveness of student performance model in connection with feature selection techniques. In this connection, the present study is devoted not only to investigate the most relevant subset features with minimum cardinality for achieving high predictive performance by adopting various filtered feature selection techniques in data mining but also to evaluate the goodness of subsets with different cardinalities and the quality of six filtered feature selection algorithms in terms of F-measure value and Receiver Operating Characteristics (ROC) value, generated by the NaiveBayes algorithm as base-line classifier method. The comparative study carried out by us on six filter feature section algorithms reveals the best method, as well as optimal dimensionality of the feature subset. Benchmarking of filter feature selection method is subsequently carried out by deploying different classifier models. The result of the present study effectively supports the well known fact of increase in the predictive accuracy with the existence of minimum number of features. The expected outcomes show a reduction in computational time and constructional cost in both training and classification phases of the student performance model.

Data Mining Applications: A comparative Study for Predicting Student's performance

Yadav, Surjeet Kumar; Bharadwaj, Brijesh; Pal, Saurabh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
56.11%
Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.; Comment: 7 pages, 2 figures. arXiv admin note: text overlap with arXiv:1201.3417 and arXiv:1201.3418

Scientific Data Mining in Astronomy

Borne, Kirk
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/11/2009
Relevância na Pesquisa
56.02%
We describe the application of data mining algorithms to research problems in astronomy. We posit that data mining has always been fundamental to astronomical research, since data mining is the basis of evidence-based discovery, including classification, clustering, and novelty discovery. These algorithms represent a major set of computational tools for discovery in large databases, which will be increasingly essential in the era of data-intensive astronomy. Historical examples of data mining in astronomy are reviewed, followed by a discussion of one of the largest data-producing projects anticipated for the coming decade: the Large Synoptic Survey Telescope (LSST). To facilitate data-driven discoveries in astronomy, we envision a new data-oriented research paradigm for astronomy and astrophysics -- astroinformatics. Astroinformatics is described as both a research approach and an educational imperative for modern data-intensive astronomy. An important application area for large time-domain sky surveys (such as LSST) is the rapid identification, characterization, and classification of real-time sky events (including moving objects, photometrically variable objects, and the appearance of transients). We describe one possible implementation of a classification broker for such events...