Finding Patterns of Students' Behavior in Synthetic Social Networks

Obadi, Gamila; Dradilova, P.; Martinovič, Jan; Slaninová, Kateřina; Snáel, Václav

doi:10.1109/asonam.2010.63

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…Partitioning Methods [Gamulin et al 2016], [Moradi et al 2014], [Sorour et al 2014], [Romero et al 2013b], [Jovanovic et al 2012], [Mogus et al 2012], [López et al 2012], [Pardos et al 2012], [Kotsiantis et al 2010], [Obadi et al 2010], [Minaei-Bidgoli et al 2003] Association…”

Section: Q02 Has the Research Developed Some Tool Or Presented Only Analysis Results?mentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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Section: Q02 Has the Research Developed Some Tool Or Presented Only Analysis Results?mentioning

confidence: 99%

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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“…In a recent work, Obadi, et al [17] used Tolerance Rough Set clustering algorithm to cluster records published by the Digital Bibliography & Library Project (DBLP). Obadi, et al [17] conducted a comparative study evaluating the Tolerance Rough Set-based approach against other frequently used algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Obadi, et al [17] conducted a comparative study evaluating the Tolerance Rough Set-based approach against other frequently used algorithms. Kumar, et al used the Tolerance Rough Set method to successfully classify web usage data available through the UCI Machine Learning Archive [12].…”

Section: Related Workmentioning

confidence: 99%

Automatic approaches to clustering occupational description data for prediction of probability of workplace exposure to beryllium

Slutsky

et al. 2011

2011 IEEE International Conference on Granular Computing

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We investigated automatic approaches for clustering data that describes occupations related to hazardous airborne exposure (beryllium). The regulatory compliance data from Occupational Safety and Health Administration includes records containing short free text job descriptions and associated numerical exposure levels. Researchers in public health domain need to map job descriptions to Standard Occupational Classification (SOC) nomenclature for estimating occupational health risks. Previous manual process was time-consuming and did not advance so far to linkage to SOC. We investigated alternative automatic approaches for clustering job descriptions. The clustering results are the first essential step towards discovery of corresponding SOC terms. Our study indicated that the Tolerance Rough Set with Jaccard similarity was a better combination overall. The utility of the algorithm was further verified by applying logistic regression and validating that the predictive power of the automatically generated classifications, in terms of association of "job" with probability of exposure to beryllium above certain threshold, closely approached that of the manually assembled classification of the same 12,148 records.

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“…Social network analysis (SNA) of students social networks on learning platforms helps in understanding various phenomenon, such as the group learning behavior of students, the correlation of students' position in social networks and their academic performance, information diffusion among students, the extent of the homophily in the classrooms, and if any student is isolated and needs help [1]. We can thus extract information straight from the social networking and communication pattern data from the collaborative online learning platform [2]. The analysis of such data can improve our understanding of teaching and learning patterns, such as how to increase collaboration among students, what topic to re-teach some topics if not clear to students, identify help-seeking students and provide special assistance and so on.…”

Section: Introductionmentioning

confidence: 99%

A Network Science Perspective to Personalized Learning

Gera¹,

Saxena²,

Bartolf³

et al. 2021

Preprint

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The modern educational ecosystem is not one-size fits all. Scholars are accustomed to personalization in their everyday life and expect the same from education systems. Additionally, the COVID-19 pandemic placed us all in an acute teaching and learning laboratory experimentation which now creates expectations of self-paced learning and interactions with focused educational materials. Consequently, we examine how learning objectives can be achieved through a learning platform that offers content choices and multiple modalities of engagement to support self-paced learning, and propose an approach to personalized education based on network science. This framework brings the attention to learning experiences, rather than teaching experiences, by providing the learner engagement and content choices supported by a network of knowledge, based on and driven by individual skills and goals. We conclude with a discussion of a prototype of such a learning platform, called CHUNK Learning.

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Finding Patterns of Students' Behavior in Synthetic Social Networks

Cited by 5 publications

References 7 publications

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

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

Automatic approaches to clustering occupational description data for prediction of probability of workplace exposure to beryllium

A Network Science Perspective to Personalized Learning

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