The convergence of digital technology and educational policy has catalyzed a global expansion of e-learning programmes, reshaping how pedagogical content is designed, delivered, and assessed. In Sub-Saharan Africa, where structural inequities in educational access have historically disadvantaged rural and low-income learners, e-learning initiatives have emerged as a strategic mechanism for bridging the educational divide. Rwanda, in particular, has pursued digital education as a cornerstone of its Vision 2050 long-term development framework, entrusting the Rwanda Basic Education Board (REB) with the mandate to design and govern a national e-learning programme targeting secondary and primary school teachers and students (MINECOFIN, 2020; Rwanda Basic Education Board, 2022).

Despite sustained investment in digital education infrastructure, the outcomes of REB's e-learning programme have been inconsistent. Recent evaluations indicate that only 40% of e-learning projects in comparable settings met their stated objectives within scheduled timelines, with more than 60% failing to deliver expected results within budget (World Bank, 2022). These failures are not attributable solely to technological deficiencies but reflect systemic inadequacies in the project management approaches governing e-learning implementation. Specifically, the dominance of rigid, plan-driven management methodologies that fail to accommodate the rapidly changing technological landscape, heterogeneous user capacities, and context-specific infrastructural constraints has been identified as a critical constraint on programme effectiveness (Daraojimba et al., 2024; Parsons, 2022; Eldahab et al., 2023).

Agile Project Management (APM), characterised by iterative development cycles, stakeholder collaboration, continuous feedback integration, and adaptive responsiveness, has been widely adopted in software and technology sectors to manage complexity and uncertainty (Beck et al., 2020; Schwaber & Sutherland, 2021). Within educational technology contexts, APM has demonstrated potential to enhance platform responsiveness, accelerate learning outcomes, and increase adoption rates (Gemino et al., 2020; Parsons, 2022). Among APM's constituent dimensions, adaptability occupies a theoretically central position: it is the mechanism through which a programme continuously reconfigures its tools, content, and delivery modalities in response to empirical feedback, technological evolution, and changing user needs (Boehm, 2024; Conforto et al., 2021). Without adaptability, iterative cycles and planning techniques remain procedurally compliant but substantively inert.

While the international literature has documented adaptability's role in EdTech outcomes broadly (Education Development Center, 2020; World Bank, 2021), no published study has empirically tested the specific, disaggregated relationships between adaptability's constituent sub-dimensions which are adaptive learning pathways, users' capacity building, and continuous iterative updates and the distinct outcome sub-dimensions of students' learning outcomes which are digital literacy improvement, and platform adoption, within a Rwandan national e-learning context. This constitutes a substantive gap in both the project management and educational technology literatures Gholipour M., (2024).

Statement of the Problem

Rwanda's national e-learning programme faces a paradox: significant public investment in digital infrastructure coexists with persistently modest programme outcomes. According to the National Institute of Statistics of Rwanda (2021), only 45% of schools were equipped with basic digital devices, and internet connectivity reached merely 35.56% of secondary schools. Over 60% of secondary and primary school teachers lack the digital literacy required for effective e-learning integration (Parsons, 2021). These structural challenges are compounded by the absence of an adaptive, user-responsive management approach capable of adjusting platform features, training provision, and content to the evolving needs of diverse user groups.

Current project management practices applied to REB's e-learning programme have been criticised for resource misallocation, limited responsiveness to user feedback, and failure to account for the heterogeneity of teacher digital competence across rural and urban settings (World Bank, 2022). There is therefore a compelling imperative to investigate whether and to what extent adaptability as an APM practice can address these deficiencies and generate measurable improvements in programme outcomes. This study responds directly to that imperative. 

Research Objective 

• To examine the role of adaptability, as an Agile Project Management dimension, on the outcomes of the e-learning programme implemented by the Rwanda Basic Education Board.

Research Question

• To what extent do adaptive learning pathways, users capacity building, and continuous iterative improvements of adaptability in APM predict students' learning outcomes, users' digital literacy improvement, and platform adoption within REB's e-learning programme? 

Research Hypotheses 

• H₁: Adaptive learning pathways have a significant positive effect on students' learning outcomes in REB's e-learning programme. 
• H₂: Users' capacity building has a significant positive effect on users' digital literacy improvement in REB's e-learning programme.
• H₃: Continuous updates and iterative improvements have a significant positive effect on platform adoption in REB's e-learning programme. 

Significance of the Study

This study makes four distinct contributions. Theoretically, it extends Agile Management Theory to national-scale e-learning governance in Sub-Saharan Africa, providing the first disaggregated empirical test of adaptability's sub-dimensional effects on e-learning outcomes in Rwanda. Methodologically, it pioneers a mixed-methods sequential design that integrates regression modelling with thematic qualitative analysis to produce both effect-size estimates and contextual explanations for those effects. Practically, it furnishes REB, the Ministry of Education (MINEDUC), and EdTech practitioners with evidence-based recommendations for restructuring adaptive management practices in digital education programmes. Comparatively, the findings provide a baseline against which future longitudinal studies can assess the durability and scalability of APM-grounded adaptability interventions in comparable East African educational contexts.

Agile Project Management in Educational Technology

Agile Project Management emerged from the software engineering domain, formalized through the Agile Manifesto (Beck et al., 2020), which codified a philosophy of adaptive delivery premised on iterative progress, continuous feedback, and stakeholder collaboration. The application of APM to educational contexts has grown significantly over the past decade as the dynamic and user-sensitive nature of e-learning platforms has revealed the limitations of traditional waterfall management approaches (Highsmith, 2020; Rigby et al., 2022). Parsons, (2022) demonstrated that flexible, iterative approaches improve learning outcomes in educational technology deployments, while Gemino et al. (2020), in the most comprehensive synthesis to date covering 477 cross-industry projects, found that hybrid agile approaches produced a 52% improvement in project success metrics relative to conventional methods.

In the Sub-Saharan African context specifically, APM has been applied; though not always systematically, to e-learning initiatives in Kenya, Uganda, Tanzania, and South Africa (Amutuhaire, 2023; Daraojimba et al., 2024; Simmons, 2024). Kenya's Digital Literacy Programme leveraged agile principles to continuously adapt its digital learning platform in response to teacher and student feedback, resulting in measurably higher adoption rates (UNICEF, 2021). Ghana's application of agile frameworks in online learning programme design similarly expanded digital education access through learner-centred iterations (Ansar, 2023). However, empirical evidence specifically isolating adaptability as a predictor of disaggregated e-learning outcomes remains absent from the literature.

Conceptualizing Adaptability in APM

Within APM theory, adaptability is not a peripheral or optional feature but a foundational operating principle that distinguishes agile from plan-driven management (Schwaber & Sutherland, 2021). Boehm, (2024) defines adaptability in project management as the sustained organisational capacity to reconfigure project deliverables, processes, and resource allocations in response to empirical feedback, environmental signals, and evolving stakeholder requirements. In the context of e-learning programme management, this translates into three operationally distinct but theoretically interdependent sub-dimensions.

Adaptive Learning Pathways

Adaptive learning pathways refer to the platform's technical and pedagogical capacity to modulate content delivery, pacing, interface complexity, and learning sequences in response to individual and aggregate user data. Du et al. (2024) demonstrated that adaptive pathways substantially improve both learner engagement and knowledge retention compared to static content delivery architectures. Beck et al. (2020) identified adaptive content modulation as a direct application of agile's responsiveness principle to the learning domain. In contexts characterised by heterogeneous learner digital literacy as in Rwanda's schools adaptive pathways are particularly critical because they allow the platform to meet users at their current proficiency level rather than imposing a uniform competency threshold (Conforto et al., 2021).

Empirically, the Education Development Center, (2020) found that African e-learning platforms featuring adaptive interfaces and tailored content achieved significantly higher rates of teacher engagement and student performance improvement relative to non-adaptive platforms. Rwanda's own deployment of affordable smartphones and offline-compatible platform versions in rural areas constitutes a practical instantiation of adaptive pathway design, enabling mobile learning where computer-based access is constrained (Parsons, 2022; RURA, 2023).

Users' Capacity Building

Users' capacity building encompasses the provision of structured, continuous training that equips teachers and students with the skills, confidence, and pedagogical competencies required to exploit evolving platform functionalities effectively. Conforto et al. (2021) identify capacity building as a mediating variable between platform sophistication and actual user adoption, arguing that even technically superior platforms will underperform if users lack the competence to leverage their features. This is particularly germane in Rwanda, where over 60% of secondary and primary school teachers lack foundational digital literacy (Parsons, 2021), rendering capacity building not merely supplementary but constitutive of programme effectiveness. Change Management Theory (Kotter, 2021; Cameron & Green, 2020) further strengthens the importance of ongoing training by emphasizing that behavioural change not merely technological provision is the proximate driver of adoption. Lewin's, (2023) refreezing concept, which describes the institutionalization of new practices as enduring norms, requires sustained training delivery across platform update cycles rather than one-off onboarding. The World Bank (2021) confirmed that regions where digital literacy programmes were delivered iteratively alongside platform updates demonstrated significantly greater skill retention and platform utilisation than those receiving training only at programme inception.

Continuous Updates and Iterative Improvements

Continuous updates and iterative improvements reflect the programme's commitment to deploying regular platform enhancements informed by systematic user feedback, technological developments, and educational research. This sub-dimension operationalises the Agile Manifesto's principle that responding to change over following a plan (Beck et al., 2020) is the defining characteristic of adaptive management. In e-learning contexts, continuous updates serve three functions: they maintain platform relevance as curriculum demands evolve; they signal to users that their feedback is valued and acted upon, strengthening engagement; and they incrementally expand platform functionality, progressively building the digital ecosystem available to learners and teachers (Du et al., 2024; Schwaber & Sutherland, 2021). Parsons, (2022) found that e-learning platforms characterised by regular, user-informed update cycles demonstrated superior long-term adoption rates compared to platforms with infrequent or administratively driven updates. The mechanism, as elucidated by Diffusion of Innovation Theory (Rogers, 2003), is that continuous visible improvement increases the platform's perceived relative advantage and trialability among hesitant adopters; the early and late majority, who require demonstrable evidence of improvement before committing to adoption.

E-Learning Programme Outcomes: A Multi-Dimensional Framework

This study conceptualises e-learning programme outcomes as a three-dimensional dependent construct, following the framework developed for REB's programme evaluation. Each outcome dimension corresponds theoretically to one or more adaptability sub-dimensions, as discussed below.

Students' Learning Outcomes

Students' learning outcomes refer to measurable improvements in academic performance, knowledge retention, conceptual understanding, and critical thinking attributable to e-learning platform use. REB's, (2020) performance data revealed that schools with digitally trained teachers scored an average of 15.3% points higher in national examinations, establishing a direct empirical link between e-learning effectiveness and student attainment. APM theory predicts adaptive learning pathways by personalizing content to learner needs; most directly enhancing learning outcomes through increased engagement and reduced cognitive load (Du et al., 2024; Parsons, 2022). 

Users' Digital Literacy Improvement

Digital literacy improvement encompasses gains in teachers' and students' capacity to access, evaluate, create, and communicate information using digital technologies. UNESCO (2021) estimated that over 60% of urban Rwandan schools successfully implemented e-learning, compared to only 30.5% of rural schools, with insufficient digital literacy identified as the primary causal factor for rural underperformance. APM theory, and specifically users' capacity building, predicts that structured, iterative training programmes will close this literacy gap progressively, aligning with the Diffusion of Innovation Theory's prediction that targeted competency development accelerates adoption across all user categories (Rogers, 2023; Greenhalgh et al., 2024).

Platform Adoption

Platform adoption refers to the extent to which teachers and students actively, regularly, and confidently integrate the e-learning platform into their teaching and learning practices. The Technology Acceptance Model (Davit, 2025) and Diffusion of Innovation Theory both predict that perceived ease of use and perceived usefulness are the primary adoption determinants; continuous updates that demonstrably improve both dimensions should therefore increase adoption rates. The World Bank, (2022) estimated that approximately 30% of e-learning projects in comparable settings failed due to inadequate platform adoption, underscoring its criticality as an outcome measure.

Theoretical Framework

Agile Management Theory

Agile Management Theory (Beck et al., 2020; Schwaber & Sutherland, 2021; Highsmith, 2020) provides the primary theoretical lens for this study. The theory posits that in complex, uncertain, and rapidly changing environments, precisely the conditions characterizing Rwanda's digital education landscape project success depends not on comprehensive upfront planning but on rapid empirical cycles of build-measure-learn (Ries, 2021). Adaptability is Agile Management Theory's central operational mechanism: it is the capacity through which each empirical cycle generates learning that reshapes the next cycle's direction (Boehm, 2024). Applied to REB's e-learning programme, Agile Management Theory predicts that a platform managed through structured adaptability practices updating pathways, building user capacity, and continuously improving features will generate progressively stronger learning outcomes, digital literacy gains, and adoption rates relative to a statically managed counterpart.

Diffusion of Innovation Theory

Rogers', (2003) Diffusion of Innovation Theory provides complementary explanatory power for the adoption dimension of programme outcomes. The theory identifies five attributes of innovations that determine their adoption rate: relative advantage, compatibility with existing values and practices, simplicity, trialability, and observability. Adaptability directly shapes four of these five attributes. Adaptive learning pathways increase compatibility by aligning content complexity to existing user competence. Continuous updates enhance relative advantage by demonstrably improving the platform's utility over time. Capacity building increases simplicity by reducing the cognitive demands of platform use. Observable improvements from iterative cycles increase the observability of benefits for hesitant adopters (Greenhalgh et al., 2024; Straub, 2020). This multi-attribute influence mechanism explains why adaptability should be particularly powerful in heterogeneous populations such as Rwanda's teacher workforce where different adopter categories are sensitive to different innovation attributes.

Change Management Theory

Change Management Theory, drawing on Lewin's, (2023) three-stage model and Kotter's (2021) eight-step framework, contextualizes the human organizational dimensions of adaptability. Both frameworks emphasize that technical innovation succeeds only when accompanied by structured human transition support. Lewin's unfreezing-changing-refreezing model maps directly onto adaptive platform management: unfreezing requires communicating the inadequacy of current practices (addressed through capacity building that reveals new possibilities); changing involves implementing adaptive features and pathways; and refreezing requires institutionalising new behaviours through sustained training and feedback mechanisms (Cameron & Green, 2020; Burnes, 2024). Kotter's emphasis on creating urgency, building guiding coalitions, and anchoring change in culture highlights the critical role of REB programme managers as change agents who must actively maintain the human infrastructure of adaptability, not merely the technical infrastructure. This theoretical lens explains why technically adaptive platforms may underperform if the organizational change management structures supporting user transition are absent or inadequate.

Research Gap

A systematic review of the literature reveals three intersecting gaps that this study addresses. First, while APM's adaptability dimension has been conceptually elaborated in the project management literature (Boehm, 2024; Schwaber & Sutherland, 2021), its empirical relationship to specific e-learning outcome sub-dimensions has not been quantitatively modelled in Sub-Saharan African national education contexts. Second, existing studies tend to treat e-learning outcomes as a unitary construct, obscuring the differential relationships between distinct adaptability practices and distinct outcome dimensions. Third, no published study has applied disaggregated regression modelling to the specific adaptability-outcomes nexus within Rwanda's educational system, making evidence-based policy recommendations for REB dependent on extrapolation from non-comparable contexts. This study fills all three gaps simultaneously.

Research Design

The study adopted a mixed-methods descriptive sequential design (Creswell & Plano Clark, 2017), in which qualitative data collection preceded and informed quantitative analysis, with both strands subsequently integrated in the interpretation phase. This design was selected because the research question requires both quantitative effect-size estimation to determine the strength and direction of adaptability's predictive relationships with outcome sub-dimensions and qualitative contextualization to explain why those relationships manifest as they do within REB's specific institutional context. The sequential structure also permits the qualitative findings to interrogate anomalies in the quantitative results, strengthening the interpretive validity of both strands (Tashakkori & Teddlie, 2010).

Study Setting and Population

The study was conducted between August 2024 and February 2025 in Gasabo District, Kigali Province, Rwanda. Two purposely selected schools using REB's e-learning platform participated: GS Kagugu, an urban school, and GS Kinyinya, a peri-urban institution. This purposive selection was designed to capture the rural–urban variability in digital infrastructure and teacher literacy that characterizes Rwanda's broader educational landscape, thereby maximizing the ecological validity of findings for national-level policy extrapolation. The study population comprised all 86 secondary and primary school teachers at the two schools, plus two REB programme managers directly responsible for the e-learning platform's design, development, and governance. Given the bounded, small-N nature of the population, a full census approach was adopted, eliminating sampling bias and maximizing statistical power. Table 1 presents the population structure.

Table 1: Study Population by Category.

Note. Source: Author's primary data (2025).

Data Collection Instruments

Quantitative Instrument: Teacher Questionnaire

The teacher questionnaire comprised 5 sections. Section A collected demographic information (gender, age, education, teaching experience). Section B measured iterative development cycle perceptions. Section C assessed rolling wave planning perceptions. Section D the focal section for this study measured adaptability across the three sub-dimensions using a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Section E measured e-learning programme outcomes across the three sub-dimensions. Item development for the adaptability and outcomes sections drew directly from the study's conceptual framework, ensuring content validity by mapping each item to a specific theoretical sub-construct. Expert review by two APM specialists and two e-learning practitioners at REB further validated item relevance, clarity, and coverage.

Qualitative Instrument: Programme Manager Interviews

Semi-structured interviews with the two REB programme managers were conducted using an interview guide of five open-ended questions exploring: (1) the role of APM adaptability in REB's programme outcomes; (2) the implementation of adaptive learning pathways; (3) the approach to user capacity building; (4) the management of continuous platform updates; and (5) challenges and enabling factors. Each interview lasted approximately 45 minutes and was audio-recorded with participant consent. Recordings were transcribed verbatim and subjected to thematic analysis.

Reliability and Validity

The internal consistency of the questionnaire was assessed using Cronbach's Alpha coefficient. The overall alpha was α = 0.859 (n = 9 items), exceeding the conventional threshold of α ≥ 0.70 (Nunnally, 1978), confirming high reliability. A pre-data-collection pilot study involving a small teacher cohort refined item wording and identified two ambiguous items that were subsequently revised. Construct validity was established through the item-to-construct mapping process described above. Criterion validity was assessed by comparing survey-derived perceptions of teacher digital literacy with REB's administrative records on training completion rates. Qualitative trustworthiness was ensured through member checking (sharing interview summaries with participants for confirmation) and investigator triangulation (comparing themes independently identified by the researcher and the supervisor) 

Data Analysis

Quantitative data were analyzed using IBM SPSS Statistics Version 26. The analysis proceeded in four sequential stages. First, descriptive statistics (means, medians, modes, standard deviations, and frequency distributions) were computed for all adaptability and outcome items. Second, Pearson Product-Moment Correlation coefficients were calculated to assess bivariate association strengths between each adaptability sub-dimension and each outcome sub-dimension, enabling hypothesis testing at the bivariate level. Third, simple linear regression was conducted with the composite adaptability scale as the sole predictor of the composite e-learning outcomes scale. Fourth, multiple regression analysis simultaneously modelled all three adaptability sub-dimensions as predictors of each outcome sub-dimension, permitting assessment of each sub-dimension's unique predictive contribution while controlling for the others. The regression model was:

Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + ε

where Y = E-Learning Programme Outcomes (or each sub-dimensional outcome); X₁ = Adaptive Learning Pathways; X₂ = Users' Capacity Building; X₃ = Continuous Updates and Iterative Improve-ments; β₀ = intercept; β₁, β₂, β₃ = unstandardized regression coefficients; ε = error term. Multicollinearity was assessed using Variance Inflation Factors (VIF) and Tolerance statistics. Effect sizes were interpreted using Cohen's (1988) benchmarks (f² ≥ 0.02 small, ≥ 0.15 medium, ≥ 0.35 large). Qualitative data were analyzed thematically following Braun and Clarke's (2006) six-phase framework: familiarization, initial code generation, theme development, theme review, definition, and reporting.

Respondent Profile

All 86 teachers returned completed questionnaires, yielding a 100% response rate. Both REB programme managers participated in the interviews. The gender distribution among teachers was near equal: 48.84% male and 51.16% female, reflecting Rwanda's broader commitment to gender equity in the teaching profession. The largest age cohort was 26–35 years (31%), followed by 36–45 years (25%), with only 6% aged above 55. Educational qualifications spanned secondary diploma (39.5%), A1 diploma (11.6%), bachelor's degree (44.2%), and master's degree (4.7%). Experience profiles showed that 44.2% had more than ten years of teaching experience, while 15.1% had less than one year. This profile indicates a predominantly experienced, bachelor's-qualified teaching workforce with substantial pedagogical expertise but potentially heterogeneous digital literacy, particularly among older and lower-qualified cohorts. 

Reliability of Measurement Instrument

Table 2: Reliability Statistics.

Note. Source: SPSS Output, 2025. The α = .859 exceeds the minimum acceptable threshold of .70, confirming high internal consistency.

Descriptive Statistics: Adaptability Sub-Dimensions

Table 3 presents descriptive statistics for the four items measuring the three adaptability sub-dimensions. Responses are interpreted on a five-point Likert scale, with mean values above 3.50 indicating agreement. All four items recorded means above 3.50, confirming a generally positive perception of adaptability practices across the teacher sample. However, meaningful variation in both the magnitude of endorsement and the degree of response dispersion (SD) warrants item-level interpretation.

Table 3: Descriptive Statistics for Adaptability Sub-Dimensions.

Note. Source: SPSS Output, 2025. Sub-dimension mapping: X₁ = Adaptive Learning Pathways (items X₁a, X₁b); X₂ = Users' Capacity Building (item X₂a); X₃ = Continuous Updates and Iterative Improvements (item X₃a). SD = Standard Deviation.

The item measuring adaptive learning pathways alignment with features recorded the highest mean (M = 4.02, SD = 0.88) and the highest endorsement rate (80.6%), indicating that teachers most strongly concur that adaptive pathways aligned with platform features enhance e-learning outcomes. The comparatively lower standard deviation (SD = 0.88) signals greater response consensus relative to other items, suggesting that adaptive pathway alignment is a uniformly valued experience across the teacher population. The item measuring platform adaptability significance and adoption recorded a slightly lower mean (M = 3.70, SD = 0.93), with 68.9% positive responses, indicating that while most teachers perceive platform adaptability as significant, a non-trivial minority (31.1%) remain neutral or dissatisfied a finding that points to uneven experience with adaptive features, likely correlated with digital literacy levels. 

The ability to use the platform without extensive support operationalizing users' capacity building through self-sufficiency recorded the lowest mean (M = 3.56) and the highest standard deviation (SD = 1.107), reflecting the greatest response heterogeneity across all adaptability items. The high dispersion is theoretically significant: it indicates that while some teachers have achieved platform self-sufficiency (consistent with effective capacity building), a substantial proportion remain dependent on external support, signaling gaps in the depth, continuity, or targeting of training provision. This item also recorded the lowest positive agreement rate (61.4%), directly reflecting the digital literacy challenges documented in the problem statement. Training sessions on new features (M = 3.77, SD = 0.99; 67.3% positive) indicate that training is perceived as moderately adequate by most teachers, but the 32.7% neutral and dissatisfied responses suggest that training cycles are insufficiently synchronized with platform update cycles.

Descriptive Statistics: E-Learning Programme Outcomes Sub-Dimensions

Table 4 presents descriptive statistics for the three e-learning programme outcome sub-dimensions: students' learning outcomes, users' digital literacy improvement, and platform adoption. Each sub-dimension was measured through multiple questionnaire items.

Table 4: Descriptive Statistics for E-Learning Programme Outcome Sub-Dimensions.

Note. Source: SPSS Output, 2025. Composite scores are mean averages of sub-dimension items. SD = Standard Deviation.

All three outcome sub-dimensions recorded positive endorsement rates above 70%, indicating that REB's e-learning platform is broadly perceived as effective. Students' learning outcomes (composite M = 3.91) and digital literacy improvement (composite M = 3.94) were rated similarly and most favourably, while platform adoption (composite M = 3.86) recorded the lowest mean, consistent with the known adoption barriers infrastructural constraints, digital literacy gaps, and inconsistent training documented in the problem statement. The relatively higher standard deviations for platform adoption (SD = 0.93) and adaptive platform use without support (SD = 1.107) reflect the heterogeneous adoption and self-sufficiency experiences that characterize a population spanning trained urban educators and under-supported rural teachers.

Correlation Analysis: Adaptability Sub-Dimensions and E-Learning Outcomes

Table 5 presents the Pearson correlation matrix examining bivariate relationships between each adaptability sub-dimension and each e-learning outcome sub-dimension. This analysis enables preliminary hypothesis testing and informs the subsequent regression modelling.

Table 5: Pearson Correlation Matrix: Adaptability Sub-Dimensions and E-Learning Programme Outcomes.

Note. N = 85. ** Correlation is significant at the p < .01 level (2-tailed). Bold values indicate the highest correlation for each adaptability–outcome pair, consistent with the study's theoretical hypotheses.

The correlation analysis reveals a consistent pattern of statistically significant positive relationships between all adaptability sub-dimensions and all outcome sub-dimensions, supporting the study's theoretical framework. Several findings merit specific discussion. Adaptive learning pathways demonstrated the strongest bivariate correlation with students' learning outcomes (r = .541, p < .01), consistent with Hypothesis H₁ and the theoretical prediction that personalized content modulation most directly enhances academic performance. The relationship between adaptive pathways and digital literacy improvement (r = .498) and platform adoption (r = .432) was also significant, though weaker, suggesting that adaptive pathway design primarily benefits learning performance while secondarily influencing literacy development and adoption behaviour. Users' capacity building showed its strongest correlation with digital literacy improvement (r = .571, p < .01), consistent with Hypothesis H₂ and theoretically expected given that capacity building directly targets the competency deficits that constrain digital literacy. The correlation with students' learning outcomes (r = .468) and platform adoption (r = .447) was also significant but lower, indicating that capacity building benefits literacy most directly while learning outcomes and adoption receive secondary benefits.

Continuous updates and iterative improvements produced the strongest correlation with platform adoption (r = .528, p < .01), consistent with Hypothesis H₃ and the Diffusion of Innovation Theory prediction that visible, continuous platform improvement increases the perceived relative adventage motivating adoption. The correlations with digital literacy (r = .489) and learning outcomes (r = .452) were also significant but secondary, reinforcing the theoretical specificity of update cycles as primarily an adoption-driving mechanism. The inter-construct correlations among the three adaptability sub-dimensions (.487 to .512) indicate meaningful but not excessive multicollinearity, which subsequent VIF analysis confirmed was not problematic. 

Regression Analysis: The Role of Adaptability on E-Learning Programme Outcomes

Regression analysis was conducted in two stages. First, a simple linear regression tested the overall predictive power of the composite adaptability scale on the composite e-learning outcomes scale. Second, multiple regression analysis examined each adaptability sub-dimension's unique predictive contribution to each outcome sub-dimension.

Simple Regression: Composite Adaptability Predicting Composite E-Learning Outcomes

Table 6: Model Summary Composite Adaptability Predicting E-Learning Programme Outcomes.

Note. Predictors: (Constant), Composite Adaptability (comprising adaptive learning pathways, users' capacity building, and continuous updates). Dependent variable: E-Learning Programme Outcomes. *** p < .001.

Table 7: ANOVA Composite Adaptability Predicting E-Learning Programme Outcomes.

Note. Dependent variable: E-Learning Programme Outcomes. *** p < .001. The regression sum of squares (19.649) substantially exceeds the residual (32.703), confirming that the model accounts for a meaningful proportion of outcome variance.

Table 8: Coefficient Estimates Composite Adaptability Predicting E-Learning Programme Outcomes.

Note. Dependent variable: E-Learning Programme Outcomes. * p < .05; ** p < .01; † p < .10 (marginal significance). VIF values below 2.0 confirm absence of harmful multicollinearity. β = standardized regression coefficient.

The simple regression model demonstrates that composite adaptability is a statistically significant predictor of composite e-learning programme outcomes (F(4, 76) = 11.416, p < .001), explaining 37.5% of the variance in outcomes (R² = 0.375). This constitutes a large effect size (f² = 0.60) by Cohen's, (1988) benchmarks, confirming that adaptability is not merely statistically significant but substantively meaningful as a programmatic driver of e-learning success. 

The adjusted R² of .342 indicates that the model retains strong predictive power after adjusting for model complexity. At the sub-dimensional level, continuous updates and iterative improvements emerged as the strongest individual predictor within the composite model (B = .291, β = .342, t = 2.920, p = .005), indicating that for each unit increase in the quality and regularity of platform updates, e-learning outcomes improve by .291 units. Adaptive learning pathways also produced a significant coefficient (B = .233, β = .259, t = 2.130, p = .036), confirming that pathway adaptability independently enhances outcomes. Users' capacity building approached but did not reach conventional significance (B = .177, β = .204, t = 1.811, p = .074), a finding that may reflect the inadequate training continuity identified in the qualitative data rather than a genuinely weaker theoretical relationship a distinction explored in Section 4.7 and the Discussion. VIF values between 1.412 and 1.523 confirm that multicollinearity does not distort these estimates.

Multiple Regression: Adaptability Sub-Dimensions Predicting Each Outcome Sub-Dimension

Table 9 presents the multiple regression results for all three outcome sub-dimensions simultaneously, providing the most granular test of the study's hypotheses. The sub-dimensional regression analysis produces several theoretically important and practically significant findings, each addressed below in relation to the study's hypotheses.

Table 9: Multiple Regression: Adaptability Sub-Dimensions Predicting Each E-Learning Outcome Sub-Dimension.

Note. N = 85. B = unstandardised regression coefficient; β = standardised regression coefficient. * p < .05; ** p < .01; *** p < .001; † marginal significance (p < .10). Highest β for each outcome sub-dimension shown in bold.

Hypothesis H₁ Adaptive learning pathways and students' learning outcomes: Adaptive learning pathways demonstrated the strongest and most statistically robust predictive effect on students' learning outcomes (B = .289, β = .312, p = .008), confirming H₁. This finding indicates that for each one-unit increase in adaptive pathway quality, students' learning outcomes improve by .289 units, controlling for the other two adaptability sub-dimensions. The sub-dimensional model explained 34.8% of the variance in students' learning outcomes (R² = .348, Adj. R² = .321, F = 9.872, p < .001), constituting a large effect (f² = 0.53). Adaptive pathways' unique dominance over students' learning outcomes compared to its weaker effects on the other two outcome dimensions is theoretically coherent: personalized content modulation directly addresses the learner-competency misalignments that suppress academic performance, whereas adoption and literacy are more proximally influenced by capacity building and update cycles respectively.

Hypothesis H₂ Users' capacity building and digital literacy improvement: Users' capacity building produced a significant positive effect on digital literacy improvement (B = .267, β = .297, p = .012), confirming H₂. This coefficient indicates that a one-unit improvement in training quality and continuity yields a .267-unit improvement in digital literacy, after controlling for adaptive pathways and updates. The sub-dimensional model for digital literacy explained 37.1% of variance (R² = .371, Adj. R² = .345, F = 10.947, p < .001). Capacity building's strongest effect on digital literacy rather than on learning outcomes or adoption is theoretically expected: digital literacy is the proximate outcome most directly targeted by training interventions. That adaptive learning pathways approached significance for digital literacy (B = .198, β = .218, p = .069) further suggests a mediation pathway in which adaptive pathways reinforce digital literacy development by exposing users to progressively more sophisticated platform features, consistent with Conforto et al. (2021) mediation argument.

Hypothesis H₃ Continuous updates and platform adoption: Continuous updates and iterative improvements demonstrated the strongest predictive effect on platform adoption (B = .312, β = .348, p = .003), confirming H₃. The adoption model explained 36.3% of variance (R² = .363, Adj. R² = .337, F = 10.502, p < .001). This finding is precisely consistent with Diffusion of Innovation Theory's prediction that visible, iterative improvements increase innovations' perceived relative advantage and observability, the attributes most directly motivating adoption among the early and late majority teacher categories in Rwanda's heterogeneous workforce. That users' capacity building also approached significance for adoption (B = .188, β = .210, p = .071) suggests a reinforcing pathway: training equips users to utilize new features, which in turn generates adoption behaviour a finding that validates Change Management Theory's emphasis on the human infrastructure of technological adoption. 

Qualitative Findings

Thematic analysis of programme manager interviews yielded four primary themes that contextualize, corroborate, and in some instances explain anomalies in the quantitative findings.

Theme 1: Adaptive Features as the Primary Platform Strength

Both managers independently identified the platform's adaptive pathway capabilities, specifically the modulation of content difficulty, the provision of offline-compatible learning modules for low-connectivity environments, and the periodic reconfiguration of the user interface as the programme's most commended feature by teachers. One manager stated that teacher satisfaction with the platform was highest when they could observe content aligning with their students' actual competency levels, directly corroborating the highest quantitative agreement score across all items (adaptive learning pathways: 80.6%). This convergence of quantitative and qualitative findings strengthens the internal validity of H₁'s confirmation.

Theme 2: Discontinuous Training as the Limiting Factor for Digital Literacy

Both managers acknowledged that training provision had been concentrated at programme inception and had not been systematically sustained across subsequent update cycles. One manager noted: “When we release updates, teachers often discover new features on their own. We need to have a structured process for communicating real-time changes and training people on them”. This qualitative disclosure directly explains the marginal statistical significance of users' capacity building (p = .074 in the composite model) despite its high theoretical importance: the effect is real but attenuated by the absence of training continuity. The relatively high standard deviation for platform autonomous use (SD = 1.107) is thus attributable not to theoretical irrelevance but to structural inconsistency in training delivery, a distinction with important policy implications.

Theme 3: Update Cycles Driving Adoption in Early-Majority Teachers

Programme managers reported that the most significant adoption increases occurred in the period following major platform updates communicated through school-based demonstrations. One manager described how a 2023 update that introduced data-use features directly relevant to teacher lesson planning produced a noticeable increase in daily active users in schools. This qualitative evidence is directly consistent with the quantitative finding that continuous updates most strongly predict platform adoption (β = .348, p = .003) and the Diffusion of Innovation Theory mechanism through which visible improvement converts hesitant adopters. Managers also noted, however, that update communication was inconsistent: some updates were not formally communicated at all, explaining why 29.4% of teachers remained neutral or dissatisfied with the update frequency item.

Theme 4: Feedback Institutionalisation as an Unrealised Potential

Both managers expressed awareness that the programme lacked a formalized feedback collection structure, instead relying on informal channels such as WhatsApp teacher groups and ad hoc school visits. One manager observed: “We get a lot of feedback informally, and we need a system for classifying it, prioritizing it, and feeding it back into the update cycle in a more structured way so that none of the good suggestions are lost.” This finding reveals a gap between the theoretical ideal of APM in which continuous feedback systematically informs iterative improvements and the current institutional reality, partially explaining why the composite model's R² of .375 does not approach the upper range documented in more mature APM deployments (Gemino et al., 2020). Addressing this gap represents the single highest-leverage structural intervention available to REB.