Personalized tutoring model through the application of Learning Analytics phases

Simanca, Fredys; González, Rubén; Rodriguez, Luis Genaro; Burgos, Daniel

doi:10.1109/tla.2020.9049456

Cited by 4 publications

(2 citation statements)

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“…Clusters C3 and C4 were characterized by high peaks in the event 'read external page from web content' (EV9) in the 10 periods, although cluster C3 had average values in the range [4][5][6][7][8][9] in the last 9 periods, while in cluster C2 these values remained below 3. Furthermore, in cluster C4, the rest of the events in all periods presented low values close to 0, while in cluster C3 in periods PER6, PER7, PER9, and PER10, some of the events took values between 1 and 1.5 (see values boxed in red in the tables of clusters C3 and C4 in Fig.…”

Section: A Som Clustering Results: Analysis Of Activity Patternsmentioning

confidence: 99%

“…Data science and artificial intelligence can help to scale the size of study samples and refine subsequent profiles and actions [4], [5], and analysing student behaviours and attitudes can improve the educational experience, either in face-to-face, blended or online settings. A deep analysis of what the student does (behaviour) and not only of what the student declares to do (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks

Delgado¹,

Morán

Jose

et al. 2021

IEEE Access

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An accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learning management systems store and categorise. With the COVID-19 pandemic still on-going, face-to-face learning settings have migrate into online and blended ones, meaning an increase of online students and teachers in need for a tailored and effective support to their needs. A novel unsupervised clustering technique based on the Self-Organizing Map (SOM) artificial neural network model is used in this research to analyse 1,709,189 records of online students enrolled from 2015 to 2019 at Universidad Internacional de La Rioja (UNIR), a fully online Higher Education institution. SOM performs a precise and diverse user clustering based on those records. Results highlight that specific clusters are linked to the intake average profile at the university, with a clear relation between user interaction and a higher performance. Further, results show that, out of a targeted desk research compared to the analysis in this paper, face-to-face and online settings are connected through the methodological approach beyond the technology-based environment, which presents a similar behaviour in both contexts.

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