Three-aspect taxonomy of user modelling research for personalization in learning environments¶
Research Method · All research methods
Evidence · 2 claims (2 for) · 1 study (1 review),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
The article organizes personalization research in learning environments into three main areas, derived from analysing 39 PALE workshop contributions: "1) modelling learners and their performance to provide engaging learning experiences, 2) designing adaptive support, and 3) building standards-based models to cope with interoperability and portability". The first area covers modelling affective states, confusion, self-regulation, reputation and open learner models; the second covers adaptive study plans, hints, recommendations and educator involvement; the third covers standards such as IMS LIP, IMS RDCEO and IMS AFA. The four special-issue papers are each situated within one of these aspects.
Accounts¶
- Three-aspect taxonomy of user modelling research for personalization in learning environments: The article organizes personalization research in learning environments into three main areas, derived from analysing 39 PALE workshop contributions: "1) modelling learners and their performance to provide engaging learning experiences, 2) designing adaptive support, and 3) building standards-based models to cope with interoperability and portability". The first area covers modelling affective states, confusion, self-regulation, reputation and open learner models; the second covers adaptive study plans, hints, recommendations and educator involvement; the third covers standards such as IMS LIP, IMS RDCEO and IMS AFA. The four special-issue papers are each situated within one of these aspects. (Olga C. Santos et al. (2016))
Claims¶
- The review reports that a log-file ensemble approach for perceived task-difficulty recognition outperforms classical ensemble methods [+M]
- The review reports that user studies on personality-adapted conversational agent feedback yielded two algorithms refined after a teacher study [+M]
Related Research Methods¶
-
Key Sources¶
- Olga C. Santos, Milos Kravcik, Jesus G. Boticario. (2016). Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings. Int J Artif Intell Educ 26:809–820. https://doi.org/10.1007/s40593-016-0114-z
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
three-aspect-taxonomy-of-user-modelling-research-for-personalization-in-learning-environme |
title |
Three-aspect taxonomy of user modelling research for personalization in learning environments |
description |
The article organizes personalization research in learning environments into three main areas, derived from analysing 39 PALE workshop contributions: "1) modelling learners and their performance to provide engaging learning experiences, 2) designing adaptive support, and 3) building standards-based |
status |
review |
generated |
by process:settle-candidates on 2026-10-28 |
trust |
unverified — no one has confirmed this page's content |
Frontmatter is the page's machine-readable record — see the Schema & Guide.