Evaluating the quality of learning design with learning analytics

Purpose This study aims to support quality assurance in education by exploring and clarifying the concept of learning design quality (LDQ) and how learning analytics (LA), specifically design analytics, can be used to enhance it. Design/methodology/approach The authors first provide structure to the concept of LDQ by conducting a rapid scoping review of relevant literature. Then the authors conduct an empirical evaluation of LDQ by analysing a large sample of 184 course learning designs (LDs), including 12,096 teaching and learning activities, developed in the innovative balanced design planning LD tool. Findings This study revealed two broad dimensions of LDQ which can be explored through LA: aspects that can be determined a priori (before implementation) and a posteriori (during/after implementation), with community feedback related to both. While a priori evaluation draws on design analytics and content checks, a posteriori evaluation relies on LA and academic analytics to assess implementation. Empirical findings from a priori evaluation suggest educators should pay particular attention to planning in accordance with contemporary pedagogies. Originality/value Besides providing the needed conceptual clarity regarding LDQ and using a large sample of LDs to demonstrate a priori evaluation, this study proposes quality checks in two cycles (during design and during/after implementation), which can be used to evaluate whether LD meets the criteria for a quality mark.

Authors

Institutions

Publication Details

Journal
Quality Assurance in Education
Published
2026-09-17
DOI
https://doi.org/10.1108/qae-03-2026-0091
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Evaluating the quality of learning design with learning analytics

Blaženka Divjak, Damir Horvat, Barbi Svetec
Quality Assurance in Education
Online Learning and Analytics
article

Evaluating the quality of learning design with learning analytics

Blaženka Divjak, Damir Horvat, Barbi Svetec
article en

Abstract

Purpose This study aims to support quality assurance in education by exploring and clarifying the concept of learning design quality (LDQ) and how learning analytics (LA), specifically design analytics, can be used to enhance it. Design/methodology/approach The authors first provide structure to the concept of LDQ by conducting a rapid scoping review of relevant literature. Then the authors conduct an empirical evaluation of LDQ by analysing a large sample of 184 course learning designs (LDs), including 12,096 teaching and learning activities, developed in the innovative balanced design planning LD tool. Findings This study revealed two broad dimensions of LDQ which can be explored through LA: aspects that can be determined a priori (before implementation) and a posteriori (during/after implementation), with community feedback related to both. While a priori evaluation draws on design analytics and content checks, a posteriori evaluation relies on LA and academic analytics to assess implementation. Empirical findings from a priori evaluation suggest educators should pay particular attention to planning in accordance with contemporary pedagogies. Originality/value Besides providing the needed conceptual clarity regarding LDQ and using a large sample of LDs to demonstrate a priori evaluation, this study proposes quality checks in two cycles (during design and during/after implementation), which can be used to evaluate whether LD meets the criteria for a quality mark.

Quality Assurance in Education
University North (HR), Opća Bolnica Varaždin (HR)
Quality Education
Openalex Percentile: Top 6%
Online Learning and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Evaluating the quality of learning design with learning analytics — Blaženka Divjak, Damir Horvat, et al. · Quality Assurance in Education (2026) | TGRS Research Map | TGRS