Digital learning environments combine instructional resources, assessment, and communication, but their educational value also depends on platform quality, institutional support, and pedagogical design. Research on engagement in online learning indicates that access to technology alone does not ensure productive participation [4]. Learning management systems (LMSs) bring course content, assignments, assessment, and communication into a common environment [3]. Studies of e-learning system success distinguish system quality from perceived usefulness and educational outcomes [25], [26]. Usability problems may disrupt access to materials or assignment completion, but these disruptions do not, by themselves, establish effects on learning outcomes.

Google Classroom has been examined through acceptance models, perceptions of usefulness and ease of use, and studies of pedagogical integration [5], [7], [8], [9]. A structured usability evaluation has assessed interaction quality against explicit usability criteria [10]. More recently, a study of 77,454 Google Play reviews used sentiment analysis and association-rule mining to identify recurring terms associated with user complaints [11]. These approaches provide different evidence: surveys measure responses to predefined constructs, usability tests examine selected tasks, and review mining identifies patterns in voluntarily posted text. The present study instead examines how Nielsen’s categories classify a smaller corpus of Google Play reviews ordered by recency.

Usability concerns users’ effectiveness, efficiency, and satisfaction when pursuing specified goals in a particular context. This definition follows ISO 9241-11 [14]; Nielsen’s framework additionally distinguishes learnability, efficiency, memorability, errors, and satisfaction [15]. Learnability concerns initial understanding of an interface, efficiency concerns the effort required once it is understood, and memorability concerns how readily users can resume using an interface after a period of non-use. Nielsen’s errors attribute addresses errors and recovery, while satisfaction reflects subjective appraisal. In this study, reports of crashes, failed uploads, and authentication problems are treated as user-reported operational failures within the errors category; they are not independently verified system faults or direct measures of error rates.

For learning platforms, interface usability is distinct from instructional effectiveness and learning gains. E-learning evaluation research has long distinguished functional interaction from broader educational experience [27]. Mobile-app usability research likewise identifies the influence of device characteristics, usage context, and interaction requirements [16]. A failed assignment upload, praise for intuitive navigation, and a claim that the platform supports study activities reflect different experiences. The analysis therefore treats perceived usefulness as a favorable appraisal only when warranted by the review wording; it does not equate that appraisal with evidence of improved learning.

App-store reviews combine user-written text with metadata such as star ratings and publication dates. Unlike responses to researcher-designed tasks, these comments reflect what users choose to post publicly [19]. Research has mined such feedback for bug reports, requirements, feature requests, and sentiment [18], [20]. However, reviews are often brief, ambiguous, specific to an application, and dependent on when they were posted [22]. These comments can identify concerns worth investigating, but neither theme counts nor rating distributions capture the experiences of users who did not post reviews.

Reviews of educational applications often combine judgments about technical performance with perceptions of learning usefulness. Studies of mobile-learning app feedback describe concerns involving content, compatibility, support, and functionality [24], while educational-app research has also classified review content using software-quality frameworks [28]. For Google Classroom specifically, Pawestri and Khasanah used sentiment and keyword-based analysis of Google Play reviews to identify recurring problem terms [11]. Their approach differs from the present study’s assignment of one primary Nielsen dimension per review. Methodological research nonetheless cautions that either approach depends on collection settings, coding choices, and the content of the retrieved reviews [18], [21], [22]. Retaining an Other category prevents unsupported assignments to specific usability dimensions.

The analysis combines deductive framework categories with inductively identified themes. Nielsen’s five dimensions define the primary classification, while open codes describe the specific concern or appraisal expressed in each review. This separation follows content-analysis guidance on coding units, categories, and decision rules [29], [30]. It allows descriptive comparisons across the corpus, but the resulting frequencies should not be interpreted as standardized usability scores. The Other category quantifies reviews that could not be assigned to predefined categories; it does not describe their content.

The study used framework-guided qualitative content analysis with descriptive frequency summaries. Each Google Play review was the unit of analysis, and the principal concern conveyed in its text informed its primary classification. Nielsen’s five attributes—learnability, efficiency, memorability, errors, and satisfaction—provided deductive categories [15]; open codes retained the specific issues or appraisals expressed by users. A review was assigned to Other when its principal content did not support a defensible classification within the five dimensions [29], [30]. The study was descriptive and did not estimate population prevalence, infer causal effects, or administer a standardized usability assessment.

Google Classroom reviews were collected from Google Play using the Python google-play-scraper package (application identifier: com.google.android.apps.classroom). The retrieval script shown in Figure 1 specified lang='id', country='id', sort=Sort.NEWEST, and count=800. These settings identify the requested language, storefront, and sorting order; they do not verify each reviewer’s nationality, educational role, or place of use. The analysis used review text, star ratings, and review publication dates. Because the review stream and application versions change over time, the 800 collected reviews constitute a time-dependent, nonprobability corpus rather than a representative sample of Google Classroom users [22].

The retained analytic corpus comprised 800 reviews, matching the count specified in Figure 1. No review-level screening log was retained, so the extent of any duplicate removal or exclusion of incomplete or irrelevant reviews cannot be established. Neither the extraction timestamp nor the installed scraper version was preserved. Accordingly, the 800 available reviews serve as the denominator for all corpus-level results, and missing collection metadata limit reproducibility and cannot be reliably reconstructed [18], [22].

Coding began with open codes and proceeded to framework classification. Each review first received a code reflecting its main reported experience; related codes were grouped into themes such as Ease of Use, Fast and Practical, Consistent Interface, Bugs/Glitches, Failed Assignment Uploads, Login Errors, Crashes/Application Exits, Notification Issues, Positive Satisfaction, and Supports Learning. Each review was subsequently assigned one primary category—learnability, efficiency, memorability, errors, satisfaction, or Other—to produce mutually exclusive frequency counts. Classification was based on the dominant content: learning to use the interface (learnability), effort or speed of task completion (efficiency), recalling how to use the interface after previous use (memorability), reported failures or recovery difficulties (errors), or an overall user appraisal (satisfaction). A favorable comment about support for learning was classified under Satisfaction only when it expressed a positive experience, not an observed learning gain. Reviews without a sufficiently clear fit remained in Other.

A single primary category makes the counts mutually exclusive but may obscure secondary concerns when a comment describes more than one issue. The reported counts therefore represent dominant classifications rather than the total number of mentions of each topic. Likewise, infrequent coding of learnability or memorability reflects what reviewers chose to report, not evidence that these attributes are satisfactory or unimportant.

Descriptive counts were compiled for star ratings, primary categories, and themes. The five rating groups and six primary categories each sum to 800 reviews; the error subthemes sum to 241 reviews and the satisfaction subthemes to 158. Corpus percentages use 800 as the denominator, whereas percentages restricted to reviews classified within Nielsen’s dimensions use 427. Values are rounded independently to two decimal places, which may produce minor differences in displayed totals. The retained materials do not document independent second coding or intercoder agreement, so no reliability coefficient is reported. Because review-level links between final codes and star ratings were not preserved, category-by-rating analysis was not possible. Indonesian-language comments are summarized in English without reproducing reviewer identifiers. These reporting choices support cautious interpretation but cannot substitute for an auditable coding record.

Across the full corpus, 241 reviews (30.13%) were classified as errors and 158 (19.75%) as satisfaction; learnability (12; 1.50%), efficiency (10; 1.25%), and memorability (6; 0.75%) were much less frequent. Among the 427 reviews mapped to Nielsen’s dimensions, errors comprised 56.44% and satisfaction 37.00%, whereas the other three categories together comprised 6.56%. These proportions describe the dominant issues in the collected comments, not the relative quality or importance of the underlying usability attributes.

4.2.1 Learnability

Twelve reviews (1.50% of the full corpus) were classified under learnability, all within the Ease of Use theme (Table 3). These comments described the interface or its menus as easy to understand. These reports indicate that the reviewers found the interface easy to understand; they do not measure how quickly new users learn the system or establish its learnability across the wider user population.

4.2.2 Efficiency

Ten reviews (1.25% of the corpus) were classified under efficiency, all within Fast and Practical (Table 4). Reviewers described Google Classroom as convenient for routine tasks, including accessing course materials and submitting assignments. These comments indicate perceived convenience for the tasks described; they do not provide task-time measurements or establish overall system efficiency.

4.2.3 Memorability

Six reviews (0.75% of the corpus) were classified under memorability, all in the Consistent Interface theme (Table 5). One review described a familiar interface that was easy to remember, consistent with Nielsen’s memorability attribute [15]. However, the available reviews do not document controlled return-to-use tasks, and six comments cannot characterize memorability among Google Classroom users generally.

4.2.4 Errors

Errors were the largest Nielsen category, comprising 241 reviews (30.13% of the full corpus; 56.44% of mapped reviews). As Table 6 shows, Bugs/Glitches accounted for 96 reports (39.83% of error-classified reviews), followed by Failed Assignment Uploads (58; 24.07%), Login Errors (42; 17.43%), Crashes/Application Exits (31; 12.86%), and Notification Issues (14; 5.81%). Failed uploads and login difficulties together accounted for 41.49% of error-classified reviews. These are reports of disrupted interactions, not independently verified software defects or estimates of failure rates across installations.

Reported examples included unsuccessful assignment submission, difficulty signing in after an update, and unexpected application closure. These incidents concern activities that students and instructors must complete to participate in online learning. Under ISO 9241-11, obstacles to goal completion can affect effectiveness and satisfaction [14]. The reports therefore identify workflows that warrant technical investigation, although the reviews alone cannot establish why the failures occurred.

4.2.5 Satisfaction

Satisfaction comprised 158 reviews (19.75% of the corpus; 37.00% of mapped reviews). Table 7 separates Positive Satisfaction (102 reviews; 64.56% of this category) from Supports Learning (56; 35.44%). Comments in the latter theme described Google Classroom as helpful or as making learning activities easier; they were included because they expressed favorable appraisals. They do not provide evidence of measurable learning gains. Neither theme constitutes a standardized satisfaction score.

Within the 427 reviews assigned to Nielsen’s framework, operational problems formed the most frequent primary category. The coded error themes concerned unsuccessful uploads, login difficulties, application crashes, notifications, and other reported faults. This pattern is consistent with app-review research showing that public feedback often includes reports of software problems and requirements relevant to maintenance [18], [19], [20]. A separate Google Classroom review-mining study also identified assignment- and upload-related terms in negative reviews [11]. Because app-review results depend on collection conditions and analytic procedures [22], that study provides contextual evidence rather than a direct replication of the present frequencies. User reports do not establish technical causes or failure rates across installations. For learning platforms, these reports identify workflows whose disruption could affect time-sensitive educational activities, consistent with research that treats system quality as distinct from learning outcomes [25], [26].

The dominance of operational complaints should not be read as evidence that Google Classroom is educationally ineffective. A platform may be perceived as useful for learning while presenting serious difficulties during particular interactions. Reviews of mobile-learning applications similarly encompass usability, compatibility, support, and perceived value [24]. The findings identify recurrent problems described by reviewers; determining their technical causes or effects on learning would require additional evidence, such as diagnostic logs, controlled usability tests, or outcome measures.

Positive feedback was also common: 36.00% of the corpus comprised five-star reviews, and 37.00% of framework-mapped reviews were coded under satisfaction. These aggregate measures describe different aspects of the corpus and should not be treated as contradictory. Previous Google Classroom research has emphasized perceived usefulness, ease of use, and factors shaping acceptance [5], [8], [9]. In this dataset, satisfaction-related comments also described perceived usefulness for study activities. However, the absence of a preserved review-level link between ratings and codes prevents assessment of whether error reports corresponded to low ratings or favorable appraisals to high ratings.

Star ratings provide an overall evaluation but reveal little about what users valued or found difficult. Ratings may influence educational-app selection [31], yet they are not direct measures of usability, acceptance, or learning outcomes. Previous app-review research also distinguishes textual sentiment and technical concerns from numeric ratings [22], [24]. Here, ratings describe the distribution of expressed evaluations, whereas the coding identifies the topics that appeared in review text. No association between the two was tested.

Learnability, efficiency, and memorability accounted for only 28 reviews (3.50% of the corpus; 6.56% of mapped reviews). Their low frequency indicates limited spontaneous mention, not as evidence that these qualities were unimportant or performed well. Assessing learnability or memorability often requires observation of first use or return after non-use, while efficiency can be assessed through task time and effort [15]. Structured usability methods can examine these attributes more directly [16], [17]. By contrast, failed logins or assignment submissions may readily prompt public complaints.

The pattern of topics therefore reflects, in part, what reviewers choose to report. Few mentions of memorability cannot establish strong memorability, just as repeated upload complaints cannot be used to calculate an overall software failure rate. Review mining and structured Google Classroom usability studies [10] address different kinds of evidence. Combining them could clarify both interaction attributes seldom articulated in public reviews and problems reported under everyday conditions.

Other was the largest primary classification: 373 reviews (46.63%) could not be assigned confidently to the five Nielsen dimensions. This result indicates limited coverage of the selected coding scheme within the observed feedback, not an intrinsic failure of Nielsen’s framework. Public reviews may discuss software and educational concerns that do not correspond neatly to a predefined usability taxonomy [18], [20], [22], while framework-based app studies also identify multiple quality domains [28]. Retaining Other avoids assigning a specific usability dimension when the review text does not justify one.

No subcategories were preserved for the 373 reviews classified as Other, so the analysis cannot determine which concerns predominate within that group. The size of Other indicates limited classification coverage but does not reveal its internal themes. A future study could retain the raw time-stamped corpus and code these reviews inductively alongside the deductive framework [18], [21]. Such an approach would allow topic-level interpretation of material that falls outside the initial categories without inventing classifications retrospectively.

For developers and institutions, the findings identify essential learning workflows associated with user complaints: authentication, assignment uploads, application stability, and notifications. Institutions may need alternative submission or communication arrangements when the platform becomes unavailable. The review themes suggest five priorities for investigation: 1) examining application crashes and recovery; 2) investigating login and authentication failures; 3) testing assignment-upload and resubmission procedures; 4) evaluating the timeliness and reliability of notifications; and 5) comparing reported difficulties across app versions, devices, account states, and network conditions.

The first four priorities follow the types of failures described in the reviews; the fifth requires evidence not present in the dataset. No application logs, device diagnostics, network measurements, or reliable review-to-version links were retained. These are investigative priorities, not verified diagnoses of software defects. Combining review analysis with operational analytics and controlled usability tests would help identify the causes and consequences of recurrent problems [19], [20], [21].

This study classifies Google Classroom reviews using a usability framework and quantifies the coding scheme’s coverage. Previous work has examined acceptance and structured usability [5], [8], [9], [10], while a recent review-mining study analyzed sentiment and recurring problem terms [11]. The present analysis instead reports which concerns were assigned to Nielsen’s five dimensions and the proportion that could not be classified within the framework. Of 800 reviews, 427 (53.38%) were classified within those dimensions and 373 (46.63%) as Other; errors dominated the mapped reviews. This makes the coding scheme’s coverage explicit but does not validate the framework, characterize the uncategorized reviews, or establish the prevalence of operational problems among Google Classroom users. The contribution is a descriptive account of reported usability concerns and the coverage of a predefined coding scheme within this corpus.

Six limitations constrain these findings. First, the 800 reviews were voluntarily submitted and retrieved using Indonesian language and country settings; the corpus is not a probability sample, and reviewer nationality and educational roles cannot be determined from the retrieval settings. Second, the extraction timestamp, scraper version, and record-level screening decisions are unavailable, restricting reproducibility. Third, the available materials do not document independent double coding or interrater agreement, limiting assessment of classification reliability. Fourth, assigning only one category per review can omit secondary concerns. Fifth, the lack of a review-level matrix linking codes and ratings prevents analysis of their association. Sixth, no preserved subcategories describe the 373 reviews assigned to Other. Future studies should archive collection metadata and coding decisions, assess coding agreement, retain review-level links, permit multiple codes where justified, and examine unmapped content inductively across collection periods [18], [22], [30].

Not applicable. The study reports no identifiable individual participants or private personal information.

Not applicable. The study did not involve a clinical trial or intervention requiring prospective registration.

The authors report that this manuscript has not previously been disseminated. It has not been published as a preprint, conference paper or abstract, thesis or dissertation, working paper, repository version, protocol, report, or translated publication. No substantially related manuscript using the same dataset or analysis has been published or submitted elsewhere.