IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/42cbn_v1.html
   My bibliography  Save this paper

Triangular Learner Model

Author

Listed:
  • Nguyen, Loc PhD, PostDoc

Abstract

User model is description of users’ information and characteristics in abstract level. User model is very important to adaptive software which aims to support user as much as possible. The process to construct user model is called user modeling. Within learning context where users are learners, the research proposes a so-called Triangular Learner Model (TLM) which is composed of three essential learners’ properties such as knowledge, learning style, and learning history. TLM is the user model that supports built-in inference mechanism. So the strong point of TLM is to reason out new information from users, based on mathematical tools. This paper focuses on fundamental algorithms and mathematical tools to construct three basic components of TLM such as knowledge sub-model, learning style sub-model, and learning history sub-model. In general, the paper is a summary of results from research on TLM. Algorithms and formulas are described by the succinct way.

Suggested Citation

  • Nguyen, Loc PhD, PostDoc, 2022. "Triangular Learner Model," OSF Preprints 42cbn_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:42cbn_v1
    DOI: 10.31219/osf.io/42cbn_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/62baa66f2a61aa04311ce974/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/42cbn_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:42cbn_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.