Educational data mining and learning analytics: An updated survey

Romero, Cristóbal; Ventura, Sebastián

doi:10.1002/widm.1355

Cited by 447 publications

(308 citation statements)

References 48 publications

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“…Predicting students' learning outcomes is considered one of the major tasks of the EDM field [22]. This is demonstrated by a great number of significant studies which put emphasis on the development and implementation of data mining methods and machine algorithms for resolving a plethora of predictive problems [23].…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning from Deep Neural Networks for Predicting Student Performance

et al. 2020

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Transferring knowledge from one domain to another has gained a lot of attention among scientists in recent years. Transfer learning is a machine learning approach aiming to exploit the knowledge retrieved from one problem for improving the predictive performance of a learning model for a different but related problem. This is particularly the case when there is a lack of data regarding a problem, but there is plenty of data about another related one. To this end, the present study intends to investigate the effectiveness of transfer learning from deep neural networks for the task of students’ performance prediction in higher education. Since building predictive models in the Educational Data Mining field through transfer learning methods has been poorly studied so far, we consider this study as an important step in this direction. Therefore, a plethora of experiments were conducted based on data originating from five compulsory courses of two undergraduate programs. The experimental results demonstrate that the prognosis of students at risk of failure can be achieved with satisfactory accuracy in most cases, provided that datasets of students who have attended other related courses are available.

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Section: Related Workmentioning

confidence: 99%

Transfer Learning from Deep Neural Networks for Predicting Student Performance

et al. 2020

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“…Different kinds of data can be collected in order to resolve educational problems depending on the type of the educational environment even traditional classroom education or computer-based education with the information system used, such as LMS, ITS, and MOOC [13]. Data preprocessing is considered a hard and complicated task.…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, various tasks and applications in the field of EDM have been studied and their purposes have been categorized [12]. Besides, [13] has described the most popular EDM methods. However, these proposed taxonomies are not exhaustive; they do not cover all the possible tasks.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

et al. 2020

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Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members' to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members' devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes' assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students' learning experiences.

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“…The literature in educational data mining (EDM) has reported several researches for analyzing educational data [Romero and Ventura 2020]. [Valente and Berry 2017] explored the relationship between students' performances and the manner of their admittance concerning the results of the Brazilian National Survey of Student Performance (ENADE).…”

Section: Introductionmentioning

confidence: 99%

Visual analysis to compare academic performances of quota and non-quota students from computer-related programs

Pio¹,

Sodré²,

Borges³

2020

Anais Do Workshop Sobre Educação Em Computação (WEI 2020)

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The implementation of affirmative actions in public universities is a topic of debate within the Brazilian society, specially regarding the academic performance of students that have been admitted through the quota system. This paper describes a visual analysis process to explore and compare the academic performances of quota and non-quota students from computer-related programs in a public Brazilian university. The results revealed that both failure and dropout rates for quota students are slightly higher than non-quota students in the first terms, but tends to present similar rates at the final terms.

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Educational data mining and learning analytics: An updated survey

Cited by 447 publications

References 48 publications

Transfer Learning from Deep Neural Networks for Predicting Student Performance

Transfer Learning from Deep Neural Networks for Predicting Student Performance

A Machine Learning-Based Recommender System for Improving Students Learning Experiences

Visual analysis to compare academic performances of quota and non-quota students from computer-related programs

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