Modelling Learner's Personality Profile through Analysis of Annotation Digital Traces in Learning Environment

Omheni, Nizar; Kalboussi, Anis; Mazhoud, Omar; HadjKacem, Ahmed

doi:10.1109/icalt.2015.76

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…Analytics/student information This paper presented the soft learn activity reporter (SLAR) service which automatically generates textual short-term reports about learners' behavior in virtual learning environments. (Omheni, Kalboussi, Mazhoud, & Hadjkacem, 2015) Others Classification Analytics/student information This paper proposed modeling learner's personality by referring to their annotations. The experiments show the significant role of annotation activity to reflect certain learner' personality traits.…”

Section: Information Extractionmentioning

confidence: 99%

Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

141

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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Section: Information Extractionmentioning

confidence: 99%

Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

141

View full text Add to dashboard Cite

show abstract

“…In our sample we have the two sexes (44 women and 76 men). Furthermore, all the selected volunteers have frequently the habit of reading and annotation of their documents [22].…”

Section: Profile Constructor Modulementioning

confidence: 99%

Assistance of Student by Web Services based on Annotations

Mazhoud

Kalboussi

Kacem

2020

Proceedings of the 21st Annual Conference on Information Technology Education

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We focus in our research on the annotative activity of learner because annotation practice is very common and omnipresent. While reading, learner usually uses comments, highlights, circles sections and posts it to annotate the consulted resources. So, we propose an approach of recommendation of web services from the learner's annotative activity to assist him in his learning activates. This process of recommendation is based on two preparatory phases: the phase of modelling learner's personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of these two main modules (web service discovery module and profile constructor module) through empirical studies realized on groups of learners based on the Student's t-test showed significant results.

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“…In the same context, Omheni et al [35,36] try to extract the personality traits of the learner from his annotations with the aim of recommending a well-defined profile for each annotator. Therefore, Omheni et al [32] present an approach that can measure some personality traits (conscientiousness and neuroticism) with reasonable accuracy by reference to learner's digital annotation practices [33].…”

Section: G To Recommend Data Related To Annotationsmentioning

confidence: 99%

“…Researchers have mostly focused on providing more and more sophisticated interfaces with several annotation functionalities. These annotations tools help learners to organize and understand learning materials, and to add a personalized view [33].…”

Section: Introductionmentioning

confidence: 99%

Functionalities provided by Annotation Systems for Learners in Educational Context: An Overview

Kalboussi

Mazhoud

Kacem

2016

Int. J. Emerg. Technol. Learn.

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Abstract-During his activities, the learner usually uses comments, highlights and circles sections to tag consulted digital documents. Therefore, many systems have been developed to annotate different electronic resources, often adapted to specific users and for particular uses of documents. This great variety of annotation systems reveals a lack of a clear strategy of how to compare the developed annotation systems of the literature according to their functionalities. As a result, few works have tried to present comparative studies of these tools. The aim of this article is to provide a study of some annotation tools used by learners in educational practices. Therefore, we present a comparison of functionalities provided by forty annotation systems developed by industry and academia during the last decade. The study further reveals gaps in systems and opportunities for further research.

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Modelling Learner's Personality Profile through Analysis of Annotation Digital Traces in Learning Environment

Cited by 9 publications

References 4 publications

Text mining in education

Text mining in education

Assistance of Student by Web Services based on Annotations

Functionalities provided by Annotation Systems for Learners in Educational Context: An Overview

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