Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 14

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Enhancing Student Engagement through Personalized Motivations

Staikopoulos, Athanasios, OKeeffe, Ian, Yousuf, Bilal, Conlan, Owen, Walsh, Eddie, Wade, Vincent·2015·2015 IEEE 15th International Conference on Advanced Learning Technologies

Abstract

A lack of appropriate engagement in online learning courses is a significant factor leading to poor performance and high dropout rates. This paper presents a framework that provides personalized motivations in the form of feedback, advices and reminders in order to enhance the engagement of students with online learning. It also describes the design, implementation and evaluation of the framework which has been used by over one hundred students. The paper focuses on how the framework by modelling and monitoring the level of engagement of students, it provides tailored motivations to stimulate and sustain their engagement. Finally, the paper compares the results from a previous student cohort (where no motivations were provided) to highlight how the framework has significantly increased the level of engagement of students.

Overview

Integrity signals

Registry

Crossref · 10.1109/icalt.2015.116

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 14 references

Research works printed but not linked: 2

  1. chapman, 1997, Models of engagement: Intrinsically motivated interaction with multimedia learning software, University of Waterloo, 430
  2. koller, 2012, MOOCs on the Move: How Coursera Is Disrupting the Traditional Classroom (text and video), Knowledge Wharton Podcast University of Pennsylvania

Cited by

Metadata

Authors
Staikopoulos, A., OKeeffe, I., Yousuf, B., Conlan, O., Walsh, E., & Wade, V.
Venue
2015 IEEE 15th International Conference on Advanced Learning Technologies
Publisher
IEEE
Published
2015-07
Pages
340-344
Cited by
14
References
14

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.