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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-06262022-230328


Tipo di tesi
Tesi di laurea magistrale
Autore
MASLENNIKOVA, ALEKSANDRA
URN
etd-06262022-230328
Titolo
Session-based Time-Windows Identification in Virtual Learning Environment
Dipartimento
FILOLOGIA, LETTERATURA E LINGUISTICA
Corso di studi
INFORMATICA UMANISTICA
Relatori
relatore Prof.ssa Monreale, Anna
correlatore Dott.ssa Rotelli, Daniela
controrelatore Prof. Fiorentino, Giuseppe
Parole chiave
  • Session Identification
  • Session Timeout Threshold
  • Time-off-task
  • Time-on-task
  • Time-windows identification
Data inizio appello
11/07/2022
Consultabilità
Non consultabile
Data di rilascio
11/07/2092
Riassunto
Due to the flexibility of online learning courses, students organise and manage their own learning time by deciding when, what, and how to study. Each individual has distinctive learning habits that identify their behaviours and set them apart from others. To explore how students behave over time, this work seeks to identify adequate time-windows that could be used to investigate the temporal behaviour of students in online learning environments. We first propose a novel perspective to identify various types of sessions based on individual requirements. Most of the works in the literature address this problem by setting and arbitrary session timeout threshold. We propose an algorithm that helps us in determining the most suitable threshold for the session. Then, based on the identified sessions, we determine time-windows using data-driven methods. To this end, we created a visual tool that assists data scientists and researchers in determining the optimal settings for the session identification and locating suitable time-windows.
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