A Predictive Model of Academic Failure or Success for Institutional and Trace Data

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

doi:10.1109/laclo49268.2019.00037

Cited by 1 publication

(4 citation statements)

References 9 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%

“…With regard to the algorithms used, it can be appreciated that different supervised learning techniques have been used, mainly trees-based methods [28,30,31,33,36,40,44,46], although Bayesian methods have also been applied [38], as well as support vector machines [27], generative methods based on sequences [26,32] and neural networks or deep learning [3,34,35,39,[41][42][43]45].…”

Section: Predicting Student Success In Distance Higher Educationmentioning

confidence: 99%

“…In our work, for these courses and algorithms, the best results reached show an average accuracy of 92'1%. Focusing on previous works focus on predicting student success or failure in a course but not based on assignments data, the best results are achieved by [30]. This work applies J48 over VLE activity data obtaining an average accuracy over all the courses 90%.…”

Section: Comparative Analysis With Previous Workmentioning

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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