Analysis of classifiers in a predictive model of academic success or failure for institutional and trace data

Silveira, Pedro David Netto; Cury, Davidson; Menezes, Crediné Silva de; Santos, Otávio Lube dos

doi:10.1109/fie43999.2019.9028618

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…Considering the factors used to carry out the prediction tasks in OULAD, it can be observed slight differences in the most used factors with respect to the previous general work. Thus, a 39% of studies use the number of accesses to resources (clickstreams) [26,[29][30][31][32][33][34][35], while a 25% of studies combine this information with demographic data from the students [27,28,32,[36][37][38][39]. Focusing solely on assignment information, only one study [40] uses exclusively this factor.…”

Section: Predicting Student Success In Distance Higher Educationmentioning

confidence: 99%

“…Regarding the purpose of the different works, under the main task of predicting student performance, it can be found that the majority of studies pretend to predict whether the student will pass or fail a course [3,27,[30][31][32][37][38][39][40][41][42][43][44][45]. Other approaches focus on the dropout rate [26,29,32,33], while others follow an early prediction study [33,35,36,46].…”

Section: Predicting Student Success In Distance Higher Educationmentioning

confidence: 99%

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Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

2021

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Studies on the prediction of student success in distance learning have explored mainly demographics factors and student interactions with the virtual learning environments. However, it is remarkable that a very limited number of studies use information about the assignments submitted by students as influential factor to predict their academic achievement. This paper aims to explore the real importance of assignment information for solving students’ performance prediction in distance learning and evaluate the beneficial effect of including this information. We investigate and compare this factor and its potential from two information representation approaches: the traditional representation based on single instances and a more flexible representation based on Multiple Instance Learning (MIL), focus on handle weakly labeled data. A comparative study is carried out using the Open University Learning Analytics dataset, one of the most important public datasets in education provided by one of the greatest online universities of United Kingdom. The study includes a wide set of different types of machine learning algorithms addressed from the two data representation commented, showing that algorithms using only information about assignments with a representation based on MIL can outperform more than 20% the accuracy with respect to a representation based on single instance learning. Thus, it is concluded that applying an appropriate representation that eliminates the sparseness of data allows to show the relevance of a factor, such as the assignments submitted, not widely used to date to predict students’ academic performance. Moreover, a comparison with previous works on the same dataset and problem shows that predictive models based on MIL using only assignments information obtain competitive results compared to previous studies that include other factors to predict students performance.

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Section: Predicting Student Success In Distance Higher Educationmentioning

confidence: 99%

Section: Predicting Student Success In Distance Higher Educationmentioning

confidence: 99%

Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

2021

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“…Marbouti et al [33] also employed LR to evaluate student performance in advance of the course with attributes of their attendances and assessment behavior. Silveira et al [2] compared LR, SVM, Naive Bayes and J48 in predicting academic success/failure based on the institutional data and trace data generated by a VLE, and the algorithm J48 presented the best classification accuracy and had the best execution time (excluding Naive Bayes). These machine learning methods show promising results in predicting students' performance with fix-length data.…”

Section: Student Performance Predictionmentioning

confidence: 99%

“…Firstly, VLEs provide convenience for participants to enroll courses by breaking time and distance limitations. Moreover, online learning platforms based on the Internet are able to record a type of data, including data from a user's VLEs and other learning systems, which is called trace data [2] and profoundly help to provide personalized educational service after necessary analysis. However, online learning emerges in serious situations with a high dropout rate and heavy academic failure.…”

Section: Introductionmentioning

confidence: 99%

Online At-Risk Student Identification using RNN-GRU Joint Neural Networks

Chen

et al. 2020

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Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.

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“…Para isso, podemos usar dados de rastreio, que são dados gerados a partir da utilização de ambientes virtuais de aprendizagem ou de respostas a questionários e dados institucionais que socioeconômicos. Esse conjunto de dadosé altamente recomendado para execução de técnicas de mineração de dados educacionais, tanto para predição, quanto para realização de agrupamentos [Silveira et al 2019b].…”

Section: Uma Opção De Suporte Computacional Para Ap3cunclassified

Uma Experiência de Construção Cooperativa de Conhecimento na Cultura Digital

Silveira¹,

Menezes²,

Cury³

2019

Anais Dos Workshops Do VIII Congresso Brasileiro De Informática Na Educação (CBIE 2019)

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We are experiencing a change in the educational paradigm leveraged by advances in digital technologies, notably the internet. Barriers to communication have been reduced and thus individuals can easily interact by establishing networks for cooperative knowledge building. Even so, there are still some difficulties in the traditional school induced largely by technological limitations, which we intend to mitigate with the fostering of new proposals for learning ecosystems. In this paper, we present a pedagogical architecture developed in order to help the cooperative construction of knowledge having as theoretical support the conception of learning in the context of ecosystems.Resumo. Estamos vivenciando uma mudança no paradigma educacional alavancada pelos avanços da tecnologias digitais, notadamente a internet. As barreiras para comunicação foram reduzidas e com isso indivíduos podem interagir com facilidade estabelecendo redes para construção cooperativa de conhecimento. Mesmo assim ainda existem algumas dificuldades na escola tradicional induzidas em grande parte pelas limitações tecnológicas, que intentamos atenuar com a fomentação de novos propostas para ecossistemas de aprendizagem. Neste artigo, apresentemos uma arquitetura pedagógica desenvolvida com o intuito de auxiliar a construção cooperativa do conhecimento tendo como suporte teórico a concepção da aprendizagem no contexto de ecossistemas.

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Analysis of classifiers in a predictive model of academic success or failure for institutional and trace data

Cited by 9 publications

References 18 publications

Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

Online At-Risk Student Identification using RNN-GRU Joint Neural Networks

Uma Experiência de Construção Cooperativa de Conhecimento na Cultura Digital

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