27 research outputs found
Processing and Understanding Moodle Log Data and Their Temporal Dimension
No full textThe increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students’ online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that help to characterize what actions students take, when, and where (in which course and in which part of the course). Temporal analysis has been shown to be relevant in learning analytics (LA) research, and capturing time-on-task as a proxy to model learning behaviour, predict performance, and prevent drop-out has been the subject of several studies. In Moodle, one of the most used learning management systems, while most events are logged at their beginning, other events are recorded at their end. The duration of an event is usually calculated as the difference between two consecutive records assuming that a log records the action’s starting time. Therefore, when an event is logged at its end, the difference between the starting and the ending event identifies their sum, not the duration of the first. Moreover, in the pursuit of a better user experience, increasingly more online learning platforms’ functions are shifted to the client, with the unintended effect of reducing significant logs and conceivably misinterpreting student behaviour. The purpose of this study is to present Moodle’s logging system to illustrate where the temporal dimension of Moodle log data can be difficult to interpret and how this knowledge can be used to improve data processing. Starting from the correct extraction of Moodle logs, we focus on factors to consider when preparing data for temporal dimensional analysis. Considering the significance of the correct interpretation of log data to the LA community, we intend to initiate a discussion on this domain understanding to prevent the loss of data-related knowledge
Time-on-Task Estimation by data-driven Outlier Detection based on Learning Activities
No full textTemporal analysis has been demonstrated to be relevant in Learning Analytics research, and capturing time-on-task, i.e., the amount of time spent by students in quality learning, as a proxy to model learning behaviour, predict performance, and avoid drop-out has been the focus of a number of investigations. Nonetheless, most studies do not provide enough information on how their data were prepared for their findings to be easily replicated, even though data pre-processing decisions have an impact on the analysis' outcomes and can lead to inaccurate predictions. One of the key aspects in the preparation of learning data for temporal analysis is the detection of anomalous values of temporal duration of students' activities. Most of the works in the literature address this problem without taking into account the fact that different activities can have very different typical execution times. In this paper, we propose a methodology for estimating time-on-task that starts with a well-defined data consolidation and then applies an outlier detection strategy to the data based on a distinct study of each learning activity and its peculiarities. Our real-world data experiments show that the proposed methodology outperforms the current state of the art, providing more accurate time estimations for students' learning tasks
Task Definition in Big Sets of Heterogeneously Structured Moodle LMS Courses
No full textAnalysing Learning Management System (LMS) log data gives insight into student learning behaviour that can help to predict performance, and as a consequence to avoid drop-out. This contribution provides an application and an adaptation of Rotelli and Monreale’s methodology [RM22] for defining tasks in a set of 10,532 online courses collected from seven universities. Unlike [RM22], we access the log data directly from the Moodle database. Even though our data set is much bigger and more heterogeneous than the one described in [RM22], we could adapt the data selection and filtering, as well as the components’ redefinition and alignment and employ their methodology to define tasks. This work is a contribution to make log data preprocessing open, replicable and more transparent
Instructional design e content development per i MOOC SoBigData.
No full textL'elaborato presenta e discute un'esperienza di tirocinio svoltasi presso il Consiglio Nazionale delle Ricerche (CNR) di Pisa e relativa alla riprogettazione didattica e all'implementazione nella forma di MOOC di due dei corsi del Master SoBigData dell'Università di Pisa. L'ambiente di apprendimento è stato implementato impiegando il Learning Management System (LMS) open source Moodle, prestando particolare attenzione ai temi della User Experience & Interface (UIX) e dell'accessibilità.
This dissertation presents and discusses an internship experience conducted at the National Research Council (CNR) in Pisa, focusing on the redesign and implementation of two courses from the SoBigData Master's program at the University of Pisa, in the form of MOOCs. The learning environment was implemented using the open-source Learning Management System (LMS) Moodle, giving special attention to the issues of User Experience & Interface (UIX) and accessibility
A Data Science Perspective on Online Student Temporal Learning Patterns and Dynamics
No full textLearning Analytics research has demonstrated that temporal analysis is relevant since the greater timing flexibility in online environments may affect learning differently. Therefore, capturing time-on-task as a proxy to model learning behaviour, predict performance, and avoid drop-out has been the focus of a number of investigations. However, although data pre-processing decisions influence the outcomes and can lead to inaccurate predictions, the majority of studies do not provide enough information on how their data were prepared for their findings to be replicated. Moodle logging system and in particular its temporal dimension can be difficult to interpret. To illustrate how its knowledge can be used to improve data processing, we first propose an in-depth analysis of Moodle logging system, then, starting from the correct extraction of Moodle logs, we focus on factors to consider when preparing data for temporal analysis. One of the most important aspects of preparing learning data is the detection of anomalous duration values of student activities. The majority of works on this topic fail to account that distinct activities can have vastly different typical execution times.
Thus, we propose a methodology for estimating time-on-task that applies an outlier detection strategy based on a distinct examination of each learning activity and its peculiarities. Prepared data will then be used to uncover the various temporal habits that each student employs when learning online. Typically, when modelling trends, a chosen configuration is set to capture various habits, and a cluster analysis is undertaken. However, the selection of variables to be observed and the algorithm used to conduct the analysis reflect the researcher’s thoughts and ideas. To explore how students behave over time, we present alternative ways of modelling student temporal behaviour. We accompany our theoretical results with implementations and experiments on six courses. Our results reveal that the generated clusters may or may not differ based on the selected profile and unveil different student learning patterns. The temporal learning behaviour of some students is unique, while some others are always similar regardless of the perspective adopted to model the profile
Un'esperienza di project work multidisciplinare in cloud
No full textMoodle è una piattaforma di apprendimento a distanza che fornisce un sistema integrato, sicuro e affidabile per creare ambienti di apprendimento personalizzati. Con oltre 90 milioni di utenti di 229 paesi, Moodle è la piattaforma e-learning più diffusa e utilizzata al mondo.
L’interfaccia utente in lingua italiana è ufficialmente supportata e costantemente aggiornata nelle stringhe e nella documentazione contestuale. Per contro, la documentazione generale, a supporto delle varie versioni e funzionalità e consultabile esclusivamente online, è disponibile solo in poche lingue e in modo disomogeneo e non strutturato. Forte di oltre 2000 pagine web , è infatti completa solo in inglese, un ostacolo purtroppo significativo ancora per molti.
Il proliferare delle guide in italiano ne è una diretta conseguenza. Nate principalmente a supporto dei principianti di lingua italiana, spesso illustrano solo marginalmente le funzionalità di Moodle, impedendo il pieno sfruttamento della piattaforma e di tutte potenzialità messe a disposizione di educatori, amministratori di sistema e studenti.
Le richieste di aiuto da parte dei Moodler italiani e i tentativi più volte iniziati e mai conclusi di traduzioni di vecchie versioni, come testimoniato nel forum della community, hanno nutrito il desiderio di dar vita a un progetto di traduzione sicuramente ambizioso, ma sentito come ormai necessario di fronte all’utilizzo sempre più massivo di Moodle in Italia.
La presente trattazione illustra le fasi di realizzazione, le difficoltà incontrate, le soluzioni adottate e le riflessioni critiche relative alla traduzione in italiano della documentazione ufficiale di Moodle 3.5.
Partito nell’ottobre 2016 e durato quasi tre anni, il progetto è stato reso possibile grazie al lavoro congiunto di 12 tirocinanti (studenti magistrali del CdL in Linguistica e Traduzione), coordinati dal relatore e supportati dalla candidata (tesista magistrale del CdL in Informatica Umanistica) che, già in possesso di una laurea in lingue e forte dell’esperienza tecnologica maturata sul campo, ha supervisionato i lavori consentendo di affrontare la natura poli-specialistica della documentazione da tradurre.
Il progetto si è fin dal principio scontrato con difficoltà di natura linguistica: i contenuti della documentazione spaziano dalla pedagogia all’amministrazione di sistema, l’interfaccia utente impone il rispetto dei termini già tradotti la cui ricerca è stata resa più difficile da un layout in continuo mutamento per il susseguirsi delle versioni. Inoltre, la vastità dei documenti da tradurre ha reso necessario il passaggio in corso d'opera dalla documentazione della versione 3.1 alla 3.5 aggiungendo ulteriori difficoltà.
Le complessità organizzative non sono mancate. Coordinare 12 tirocinanti, impegnati in corsi ed esami, in sedi diverse o in Erasmus, ha rappresentato una sfida ulteriore. Nel tentativo di guidare e controllare i lavori, garantendo altresì libertà di azione e nei tempi, è stata predisposta una struttura tecnologica e organizzativa, affinata col tempo e al crescere del numero dei tirocinanti coinvolti, che ha consentito di costruire un gruppo di lavoro solido e collaborativo, in cui le differenti conoscenze e competenze pregresse si sono integrate trasversalmente, realizzando ciò che in tanti avevano iniziato, ma mai nessuno era riuscito a portare a termine
Making Sense of Moodle Log Data
Get PDFResearch is constantly engaged in finding more productive and powerful ways
to support quality learning and teaching. However, although researchers and
data scientists try to analyse educational data most transparently and
responsibly, the risk of training machine learning algorithms on biased
datasets is always around the corner and may lead to misinterpretations of
student behaviour. This may happen in case of partial understanding of how
learning log data is generated. Moreover, the pursuit of an ever friendlier
user experience moves more and more Learning Management Systems functionality
from the server to the client, but it tends to reduce significant logs as a
side effect. This paper tries to focus on these issues showing some examples of
learning log data extracted from Moodle and some possible misinterpretations
that they hide with the aim to open the debate on data understanding and data
knowledge loss.Comment: The paper has been split in two parts and totally renewed with
improved solution
Progettazione e sviluppo di una libreria di risorse online per il progetto Shelfie
No full textLa presente trattazione illustra il lavoro progettuale svolto nell’ambito del progetto europeo Shelfie per la diffusione della cultura digitale nelle scuole, che ha visto lo sviluppo di un’applicazione web per la creazione di una libreria interattiva. La trattazione descrive in primo luogo lo scenario delineatosi negli ultimi anni nell’ambito dello sviluppo di siti web e di applicazioni software. Viene quindi illustrata la realizzazione della libreria interattiva accessibile all’interno del sito web WordPress di Shelfie, che utilizza REST API per accedere ai dati memorizzati nel database WordPress, ma viene sviluppata separatamente come un’applicazione web utilizzabile in altri contesti. In questo modo è stato possibile trovare una soluzione di coesione tra un Content Management System quale WordPress e un Framework di sviluppo di interfacce grafiche quale Vue.js. La libreria rappresenta il punto di riferimento per l’accesso a risorse e linee guida per lo sviluppo di competenze digitali di studenti e insegnanti di scuole provenienti da vari paesi. A spingermi nella realizzazione di questa tesi sono state molteplici motivazioni: in primis la mia collaborazione con Shelfie, resa possibile grazie allo svolgimento del tirocinio formativo presso il Laboratorio di Cultura Digitale dell’Università di Pisa. Sono stati poi determinanti la volontà di indagare le differenti componenti che sussistono nella comunicazione client-server in un contesto applicativo web, la voglia di esplorare le possibilità applicative di un framework di sviluppo e il desiderio di proporre una personale soluzione che rispondesse alla necessità di affrontare una problematica reale. L’obiettivo della tesi è quello di mostrare le fasi che hanno portato alla realizzazione del sito web e della libreria di Shelfie. Inoltre, poiché le possibilità e le tecnologie in ambito di sviluppo web e di applicazioni sono ad oggi sempre più numerose, verranno trattate le varie soluzioni che mi hanno portato a identificare la scelta più appropriata per la realizzazione del mio lavoro. Il primo capitolo introdurrà il progetto Shelfie, dall’organizzazione con i partner agli obiettivi, fino a illustrare le necessità tecniche e implementative; nel secondo capitolo verranno discussi gli elementi che entrano in gioco nello sviluppo web e di interfacce grafiche, con una particolare attenzione al caso di studio, ripercorrendo la costruzione del sito di Shelfie durante il tirocinio. Infine, nel terzo capitolo verrà affrontato lo sviluppo della libreria vera e propria, corredato di analisi e riflessioni, seguite poi dalle conclusioni finali.
This discussion illustrates the project work carried out within the Shelfie European project for the dissemination of digital culture in schools, which saw the development of a web application for the creation of an interactive library. The discussion describes in the first place the scenario that has emerged in recent years in the context of the development of websites and software applications. It then illustrates the creation of the interactive library accessible within the Shelfie WordPress website, which uses REST API to access the data stored in the WordPress database, but is developed separately as a web application that can be used in other contexts. In this way it was possible to find a cohesion solution between a Content Management System such as WordPress and a development framework for graphical interfaces such as Vue.js. The library represents the reference point for accessing resources and guidelines for the development of digital skills of students and teachers of schools from various countries. There were multiple motivations to push me into the realization of this thesis: first of all my collaboration with Shelfie, made possible thanks to the internship at the Digital Culture Laboratory of the University of Pisa. The desire to investigate the different components that exist in client-server communication in a web application context, the desire to explore the applicative possibilities of a development framework and the desire to propose a personal solution that would respond to the need to address a real problem. The goal of the thesis is to show the steps that led to the creation of the Shelfie website and library. Furthermore, since the possibilities and technologies in the field of web development and applications are increasingly numerous today, the various solutions that have led me to identify the most appropriate choice for the realization of my work will be discussed. The first chapter will introduce the Shelfie project, from the organization with the partners to the objectives, up to illustrating the technical and implementation needs; in the second chapter the elements that come into play in web development and graphical interfaces will be discussed, with particular attention to the case study, retracing the construction of the Shelfie site during the internship. Finally, the third chapter will deal with the development of the actual library, accompanied by analyzes and reflections, followed by the final conclusions
Session-based Time-Windows Identification in Virtual Learning Environment
No full textDue 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
Modeling Trust Judgments in Educational Videos: An Exploratory Predictive Approach Integrating Navigation Behavior, Attitude, and Disposition to Critical Thinking
No full textThis study investigates how navigation behaviors, attitude, and critical thinking disposition predict trust attribution in educational videos among middle and high school students. Grounded in the hypothesis that epistemic judgments are shaped by a multidimensional interplay of cognitive, affective, and dispositional factors, we analyzed data collected from a video-based learning platform integrating selected videos and reflective questioning on controversial socio-scientific issues. Partial correlation analyses revealed significant associations between students’ interaction patterns with the videos (e.g., seeking, pausing), attitude, and critical thinking disposition. Notably, a positive attitude about video content was negatively associated with critical thinking disposition, suggesting that students’ positive perception about the topic may attenuate epistemic vigilance. Predictive modeling confirmed that including attitudinal and dispositional variables significantly improves the classification accuracy of normative trust judgments compared to models based on navigation traces alone. The Critical Thinking Disposition indicator (CTDI) proved to be a robust predictor of evaluative success
