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Abstract

Background: Continued use of digital learning platforms is often explained by perceived experience quality. Public application store reviews, however, offer no individual-level retention data, so it is unclear how far stated intentions can stand in for actual retention.


Objective: To determine how often educational application reviews state explicit continuance or discontinuance intentions, and how these relate to complaints about system quality, information quality, and technical burden.


Methods: GenAI-EdSent was analyzed as a cross-sectional review corpus. Multi-stage filtering of 480,831 catalog rows from 22 educational applications left 204,294 unique English reviews (2021 to 13 August 2025). Lexical rules separated explicit discontinuance, explicit continuance, and unstated intentions before DeLone–McLean and Expectation-Confirmation coding, without human annotation. Exact conditional odds ratios (ORs), 95% exact confidence intervals (CIs), Fisher's exact tests, and Benjamini–Hochberg false discovery rate (q) adjustment were used.


Results: Only 220 reviews (0.108%) stated an explicit intention: 180 discontinuance and 40 continuance. System quality issues appeared in 10/180 (5.6%) versus 1/40 (2.5%) reviews (OR = 2.29; 95% exact CI: 0.31–102.08; p = 0.694), information quality issues in 13/180 (7.2%) versus 1/40 (2.5%) (OR = 3.02; 95% CI: 0.43–132.28; p = 0.474), and technical burden in 2/180 versus 0/40, an unbounded OR (95% CI: 0.04–∞; p = 1.000; all q = 1.00). Multivariable regression was withheld because only 34 continuance comparators existed across three eligible applications.


Conclusion: Large review catalogs do not guarantee enough explicit intention outcomes for adjusted modeling. The estimates are imprecise crude associations, not measures of retention.

Keywords

Digital learning System quality Information quality Discontinuance intention User reviews GenAI-EdSent

Article Details

Copyright and licence. Authors retain copyright and unrestricted publishing rights in their article. By publishing in Enigma in Education, authors grant the journal the non-exclusive right of first publication. The Version of Record is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Licence (CC BY-NC-SA 4.0). Users may share and adapt the work for non-commercial purposes provided appropriate credit is given, a link to the licence is supplied, changes are indicated, and adaptations are distributed under the same licence.

How to Cite
Putri, H., & Munoz, M. (2026). System Quality, Information Quality, and Technical Bur-den in Relation to Discontinuance Intention in GenAI-EdSent Digital Learning Applications: An Exploratory Rule-Based Review Analysis Guided by the DeLone–McLean Framework. Enigma in Education, 4(2), 87-102. https://doi.org/10.61996/edu.v4i2.137

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