Predicting Academic Performance Based on Students’ Blog and Microblog Posts

Dascălu, Mihai; Popescu, Elvira; Becheru, Alexandru; Crossley, Scott A.; Trăușan-Matu, Ștefan

doi:10.1007/978-3-319-45153-4_29

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Some institutions student performance can be observed by using internal assessment and co-curriculum. In the Indian context, an institution with the higher degree of reputation using the good academic record as its basic criteria for their admissions [1]. There are lots of definitions of student academic performance prediction should be given in the literature.…”

Section: Introductionsupporting

confidence: 43%

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Kumar¹,

Singh²,

Handa³

2017

IJEME

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One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student's performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

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Section: Introductionsupporting

confidence: 43%

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Kumar¹,

Singh²,

Handa³

2017

IJEME

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“…Forums participation [Dascalu et al 2016], [Hung et al 2016], [Gasevic et al 2016], [Neto and Castro 2015], [Cambruzzi et al 2015], [Hu et al 2014], [Romero et al 2013b], [Romero et al 2013a], [Zafra and Ventura 2012], [López et al 2012], [Jovanovic et al 2012], [Mogus et al 2012], [Zafra et al 2011], [Obadi et al 2010], [Carmona et al 2010], [Zafra and Ventura 2009], Assessment data/grades [Kostopoulos et al 2015], ], [Lykourentzou et al 2009b], [Hu et al 2014], [Gasevic et al 2016], [You 2016], [Kotsiantis 2012], [Moradi et al 2014], [Jovanovic et al 2012], [Romero et al 2013a], [Černezel et al 2014], [Pardos et al 2012], [Carmona et al 2010], [Hung et al 2016] Interaction logs [Joksimović et al 2015], [Xing et al 2015], [Kotsiantis et al 2010], [Zacharis 2015], [You 2016], [Zorrilla and Garcia-Saiz 2014], [Cambruzzi et al 2015], [Gamulin et al 2016], [Sharma and Mavani 2011a], [Sorour et al 2014], [Romero et al 2008], [Sharma and Mavani 2011b] Quizzes data [Kato and Ishikawa 2013],…”

Section: Table 2 Papers According To Attributes Used To Predict Students Performance Attributes Papersmentioning

confidence: 99%

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

Felix¹,

Ambrósio²,

Lima³

et al. 2018

Anais Do XXIX Simpósio Brasileiro De Informática Na Educação (SBIE 2018)

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Virtual learning environments facilitate online learning, generating and storing large amounts of data during the learning/teaching process. This stored data enables extraction of valuable information using data mining. In this article, we present a systematic mapping, containing 42 papers, where data mining techniques are applied to predict students performance using Moodle data. Results show that decision trees are the most used classification approach. Furthermore, students interactions in forums are the main Moodle attribute analyzed by researchers.This systematic mapping aims to identify what type of analysis is being used to predict students' performance on Moodle, using data mining. It is organized as follows:

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“…It is also highly explainable for explaining why the model predicts one student may fail in the examination. et al ( ), commentsLuo et al (2015; Dascalu et al (2016) as side information for providing explainability.…”

Section: Introductionmentioning

confidence: 99%

Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails

Niu¹,

Cao²,

Yu³

2021

Preprint

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As student failure rates continue to increase in higher education, predicting student performance in the following semester has become a significant demand. Personalized student performance prediction helps educators gain a comprehensive view of student status and effectively intervene in advance. However, existing works scarcely consider the explainability of student performance prediction, which educators are most concerned about. In this paper, we propose a novel Explainable Student performance prediction method with Personalized Attention (ESPA) by utilizing relationships in student profiles and prior knowledge of related courses. The designed Bidirectional Long Short-Term Memory (BiLSTM) architecture extracts the semantic information in the paths with specific patterns. As for leveraging similar paths' internal relations, a local and global-level attention mechanism is proposed to distinguish the influence of different students or courses for making predictions. Hence, valid reasoning on paths can be applied to predict the performance of students. The ESPA consistently outperforms the other state-of-the-art models for student performance prediction, and the results are intuitively explainable. This work can help educators better understand the different impacts of behavior on students' study.

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Predicting Academic Performance Based on Students’ Blog and Microblog Posts

Cited by 9 publications

References 13 publications

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails

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