Analysis of student behavior in virtual learning environment depending on student assessments

Benko, Ľubomír; Reichel, Jaroslav; Munk, Michal

doi:10.1109/iceta.2015.7558496

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Skalka et al noted that other special activities, like programming assignments, can also be considered to predict the final score in the course effectively [12]. The assignments have also been analyzed in [24,25]. Both studies confirmed that assignments could be successfully used for prediction students' learning outcomes.…”

Section: Related Workmentioning

confidence: 86%

Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Kabathova

Drlík

2021

Applied Sciences

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Early and precisely predicting the students’ dropout based on available educational data belongs to the widespread research topic of the learning analytics research field. Despite the amount of already realized research, the progress is not significant and persists on all educational data levels. Even though various features have already been researched, there is still an open question, which features can be considered appropriate for different machine learning classifiers applied to the typical scarce set of educational data at the e-learning course level. Therefore, the main goal of the research is to emphasize the importance of the data understanding, data gathering phase, stress the limitations of the available datasets of educational data, compare the performance of several machine learning classifiers, and show that also a limited set of features, which are available for teachers in the e-learning course, can predict student’s dropout with sufficient accuracy if the performance metrics are thoroughly considered. The data collected from four academic years were analyzed. The features selected in this study proved to be applicable in predicting course completers and non-completers. The prediction accuracy varied between 77 and 93% on unseen data from the next academic year. In addition to the frequently used performance metrics, the comparison of machine learning classifiers homogeneity was analyzed to overcome the impact of the limited size of the dataset on obtained high values of performance metrics. The results showed that several machine learning algorithms could be successfully applied to a scarce dataset of educational data. Simultaneously, classification performance metrics should be thoroughly considered before deciding to deploy the best performance classification model to predict potential dropout cases and design beneficial intervention mechanisms.

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Section: Related Workmentioning

confidence: 86%

Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Kabathova

Drlík

2021

Applied Sciences

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“…Using quartiles, nine pages were classified as auxiliary pages, and 49 pages were identified as content pages. As the results of previous studies [ 33 , 37 ] showed, the ratio estimated from the sitemap offered the best results for using the Reference Length method as session identification. Based on that, the aim of this experiment was to find a solution to estimate the ratio of auxiliary pages from the log file without using the sitemap.…”

Section: Methodsmentioning

confidence: 75%

“…Both log files were preprocessed using standard web usage data preprocessing techniques, as in [ 33 , 34 , 37 ]. The preprocessed log files were imported into the database separately.…”

Section: Methodsmentioning

confidence: 99%

Using Entropy in Web Usage Data Preprocessing

Munk

Benko

2018

Entropy

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The paper is focused on an examination of the use of entropy in the field of web usage mining. Entropy creates an alternative possibility of determining the ratio of auxiliary pages in the session identification using the Reference Length method. The experiment was conducted on two different web portals. The first log file was obtained from a course of virtual learning environment web portal. The second log file was received from the web portal with anonymous access. A comparison of the results of entropy estimation of the ratio of auxiliary pages and a sitemap estimation of the ratio of auxiliary pages showed that in the case of sitemap abundance, entropy could be a full-valued substitution for the estimate of the ratio of auxiliary pages.

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“…An interval of 45 min was chosen because normally one lesson took 1.5 hours. In this case, if the student did not attend the course at least twice during one lesson, we could assume that they did not use the e-course during that lesson [31][32][33]. Based on these times, the sessions were removed that contained only one entry, as this was not a session but only one login per class.…”

Section: Observing Student Movement In Lmsmentioning

confidence: 99%

Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets

Balogh

Kuchárik

2019

Applied Sciences

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This paper deals with the possibility of predicting student’s grades based on their usage of Learning Management System (LMS) Moodle. It is important to know what materials would be best suited in LMS as study materials and what materials could be improved or removed based on the student’s usage of the materials and the final grade. In order to do this, the correlations between access to materials and the final grade were observed. These correlations could also be used to predict the grades of the student. Therefore, a model with Petri nets was created that based on the highest correlation would be able to predict what grade the student would get based on his usage of LMS. Obviously, it would not be possible to predict every result with certainty, however, more precise predictions could be obtained with higher correlations.

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Analysis of student behavior in virtual learning environment depending on student assessments

Cited by 5 publications

References 17 publications

Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Using Entropy in Web Usage Data Preprocessing

Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets

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