Movatterモバイル変換


[0]ホーム

URL:


loading
PapersPapers/2022PapersPapers/2022

Scitepress Logo

The Search is performed on all of the following fields:

Note: Please use complete words only.
  • Publication Title
  • Abstract
  • Publication Keywords
  • DOI
  • Proceeding Title
  • Proceeding Foreword
  • ISBN (Completed)
  • Insticc Ontology
  • Author Affiliation
  • Author Name
  • Editor Name
If you already have a Primoris Account you can use the same username/password here.
Research.Publish.Connect.

The Search is performed on all of the following fields:

Note: Please use complete words only.
  • Publication Title
  • Abstract
  • Publication Keywords
  • DOI
  • Proceeding Title
  • Proceeding Foreword
  • ISBN (Completed)
  • Insticc Ontology
  • Author Affiliation
  • Author Name
  • Editor Name
If you're looking for an exact phrase use quotation marks on text fields.

Paper

Paper Unlock

Authors:Manel BenSassi;Mona Laroussi andHenda BenGhezala

Affiliation:Riadi Laboratory, National School of Computer Science, Manouba University and Tunisia

Keyword(s):Learning Experience Design, Mobile Learning Scenario, Reliability, Predictive Analytics, Formal Method.

RelatedOntology Subjects/Areas/Topics:Usability and Ergonomics ;Web Information Systems and Technologies ;Web Interfaces and Applications

Abstract:In this paper, we present a predictive analytical framework for mobile and ubiquitous learning environment based on three main dimensions: learner, contextualized activity and space. The main objective of this proposal is to assist pedagogical designer in developing engaging and effective courses by focusing on the learner’s experience and his learning environment. To do that, a solid structure is essential to organize correlations between different activities and to assess their reliability with the learner’s context. The strengths of our proposal lie in the fact that, in a formal manner through friendly graphical interfaces, it allows pedagogical designers:(1) to specify, model, simulate, analyse and verify different types of context-aware and adaptive learning activities and their related contexts, (2) to assess the reliability of the indoor and outdoor learning spaces within pervasive environment through factual cases and to experiment various learning scenarios, (3) To simulateand to verify interactions and co-adaptability rules between learner, contextualized activity and space.(More)

In this paper, we present a predictive analytical framework for mobile and ubiquitous learning environment based on three main dimensions: learner, contextualized activity and space. The main objective of this proposal is to assist pedagogical designer in developing engaging and effective courses by focusing on the learner’s experience and his learning environment. To do that, a solid structure is essential to organize correlations between different activities and to assess their reliability with the learner’s context. The strengths of our proposal lie in the fact that, in a formal manner through friendly graphical interfaces, it allows pedagogical designers:(1) to specify, model, simulate, analyse and verify different types of context-aware and adaptive learning activities and their related contexts, (2) to assess the reliability of the indoor and outdoor learning spaces within pervasive environment through factual cases and to experiment various learning scenarios, (3) To simulate and to verify interactions and co-adaptability rules between learner, contextualized activity and space.

Full Text

Download
Please type the code

CC BY-NC-ND 4.0

Sign In

Guests can use SciTePress Digital Library without having a SciTePress account. However, guests have limited access to downloading full text versions of papers and no access to special options.
Guests can use SciTePress Digital Library without having a SciTePress account. However, guests have limited access to downloading full text versions of papers and no access to special options.
Guest:Register as new SciTePress user now for free.

Sign In

Download limit per month - 500 recent papers or 4000 papers more than 2 years old.
SciTePress user: please login.

PDF ImageMy Papers

PopUp Banner

Unable to see papers previously downloaded, because you haven't logged in as SciTePress Member.

If you are already a member please login.
You are not signed in, therefore limits apply to your IP address 153.126.140.213

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total
Popup Banner

PDF ButtonFull Text

Download
Please type the code

Paper citation in several formats:
BenSassi, M., Laroussi, M. and BenGhezala, H. (2018).Predictive Analytical Framework based on Formal Method to Enhance Mobile and Pervasive Learning Experience. InProceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-324-7; ISSN 2184-3252, SciTePress, pages 231-238. DOI: 10.5220/0006935802310238

@conference{webist18,
author={Manel BenSassi and Mona Laroussi and Henda BenGhezala},
title={Predictive Analytical Framework based on Formal Method to Enhance Mobile and Pervasive Learning Experience},
booktitle={Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST},
year={2018},
pages={231-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006935802310238},
isbn={978-989-758-324-7},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST
TI - Predictive Analytical Framework based on Formal Method to Enhance Mobile and Pervasive Learning Experience
SN - 978-989-758-324-7
IS - 2184-3252
AU - BenSassi, M.
AU - Laroussi, M.
AU - BenGhezala, H.
PY - 2018
SP - 231
EP - 238
DO - 10.5220/0006935802310238
PB - SciTePress

    - Science and Technology Publications, Lda.
    RESOURCES

    Proceedings

    Papers

    Authors

    Ontology

    CONTACTS

    Science and Technology Publications, Lda
    Avenida de S. Francisco Xavier, Lote 7 Cv. C,
    2900-616 Setúbal, Portugal.

    Phone: +351 265 520 185(National fixed network call)
    Fax: +351 265 520 186
    Email:info@scitepress.org

    EXTERNAL LINKS

    PRIMORIS

    INSTICC

    SCITEVENTS

    CROSSREF

    PROCEEDINGS SUBMITTED FOR INDEXATION BY:

    dblp

    Ei Compendex

    SCOPUS

    Semantic Scholar

    Google Scholar

    Microsoft Academic


    [8]
    ページ先頭

    ©2009-2025 Movatter.jp