Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 283

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments

Mamun, Md Abdullah Al, Lawrie, Gwendolyn, Wright, Tony·2020·Computers & Education

Abstract

Abstract Technology is ubiquitous in the modern world; to harness its educational potential in the quest to introduce environments that are flexible and differentiate for individual student learning needs, the strategic use of the complex array of tools is required. Engagement with this challenge has the potential to lead to the provision of interfaces that allow students to access these resources and become independent learners. It is therefore important to identify and evaluate the features of such interfaces to calibrate and respond to individual student needs. In this context, the quality of differentiated support for learning, referred to as scaffolding, is established as paramount to the design and structure of online environments. In this study, the instructional design referred to as predict, observe, explain and evaluate (POEE), informed by constructivist theories of learning, to implement multiple scaffolding strategies is described. The POEE scaffolding strategy was applied in the creation of two inquiry learning modules. Student engagement with these inquiry modules in a self-directed online environment was explored to identify critical elements of the scaffolding. The findings of this study, based on students' interactions and engagement with the learning modules, enabled the conceptualisation of a multimodal scaffolding strategy for self-directed inquiry. We propose that the recommendations from the implementation of these scaffolded learning modules can represent exemplars illustrative of an enriched instructional design paradigm to support students’ independent study in blended environments.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2019.103695

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Cited by

Metadata

Authors
Mamun, M. A. A., Lawrie, G., & Wright, T.
Venue
Computers & Education
Publisher
Elsevier BV
Published
2020-01
Volume
144
Pages
103695
ISSN
0360-1315 (ISSN-L)
OpenAlex
W2973117219
Cited by
283
References
10
FWCI
52.15
Type
journal article
Language
en
Open access
closed

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.