DynGRAR: A dynamic approach to mining gradual relational association rules

Miholca, Diana-Lucia; Czibula, Gabriela

doi:10.1016/j.procs.2019.09.155

Cited by 2 publications

(2 citation statements)

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“…Future work will be carried out in order to enlarge the data sets used in the experiments, to extend this study on data collected from grown-up education, as well as to include other unsupervised learning models in the present analysis e.g. autoencoders, t-Distributed Stochastic Neighbor Embedding (van der Maaten & Hinton, 2008) or gradual relational association rules (Miholca & Czibula, 2019). In addition, the aim is to analyse the importance of progressive evaluation, like practical exams during the semesters or the assignments for each lecture/seminar.…”

Section: Discussionmentioning

confidence: 99%

Using Self-Organizing Maps for Comparing Students’ Academic Performance in Online and Traditional Learning Environments

ONEȚ-MARIAN¹,

Czibula²,

MAIER³

2021

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Nowadays, there is an increasing development of online learning and digitalisation in the field of education. In the current pandemic context, when teaching and evaluation activities, including lectures, assignments and examinations, are moved in online environments, there is an interest in analysing and understanding how online learning methodologies impact the students' learning outcomes as well as their evaluation process. This paper proposes the employment of a selforganizing map model trained using unsupervised learning for uncovering hidden patterns in academic data, with the aim of analysing the impact of online and traditional learning methods on the students' performance. By using real academic data sets collected from Babes-Bolyai University, from both online (academic year 2020-2021) and traditional learning environments (first semester of the academic year 2019-2020), the present analysis highlights that there are no significant differences between the students' academic performances in these environments. The unsupervised-learning based analysis is reinforced by the results obtained by applying logistic regression for predicting the students' performance.

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Section: Discussionmentioning

confidence: 99%

Using Self-Organizing Maps for Comparing Students’ Academic Performance in Online and Traditional Learning Environments

ONEȚ-MARIAN¹,

Czibula²,

MAIER³

2021

STUD INFORM CONTROL

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“…In addition, we aim to investigate if extending the feature set with grades received by students at other courses preceding the analysed course in the curricula would help increase the accuracy of the students' performance analysis. We further intend to use data collected from gymnasium and high schools, as well as to use other types of AEs and to include other unsupervised learning models in our analysis, such as gradual relational association rule mining [53].…”

Section: Discussionmentioning

confidence: 99%

Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance

2021

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Understanding students’ learning processes and education-related phenomena by extracting knowledge from educational data sets represents a continuous interest in the educational data mining domain. Due to an accelerated expansion of online learning and digitalisation in education, there is a growing interest in understanding the impact of online learning on the academic performance of students. In this study, we comparatively investigate traditional and synchronous online learning methods to assess students’ performance through the use of deep autoencoders. Experiments performed on real data sets collected in both online and traditional learning environments showed that autoencoders are able to detect hidden patterns in academic data sets unsupervised; these patterns are valuable for the prediction of students’ performance. The obtained results emphasized that, for the considered case studies, traditional evaluations are a little more accurate than online evaluations. Still, after applying a one-tailed paired Wilcoxon signed-rank test, no statistically significant difference between the traditional and online evaluations was observed.

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DynGRAR: A dynamic approach to mining gradual relational association rules

Cited by 2 publications

References 20 publications

Using Self-Organizing Maps for Comparing Students’ Academic Performance in Online and Traditional Learning Environments

Using Self-Organizing Maps for Comparing Students’ Academic Performance in Online and Traditional Learning Environments

Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance

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