This study examines the effectiveness of the Teaching at the Right Level (TaRL) program when enriched by the judicious use of Information and Communication Technologies for Education (ICTE), from an explicit teaching perspective. The objective is to identify the factors that promote learning progress when remediation is differentiated according to students' actual skill levels. The experiment involved 1,152 students from public schools in Casablanca (Morocco), grouped by level of proficiency in Arabic, French, and mathematics, according to the TaRL approach. The data were analyzed using ADANCO (PLS-SEM) software to test seven hypotheses linking the students' final positioning to pedagogical, motivational, and technological variables. The results show that the strongest relationships concern techno-pedagogical motivation (β = 0.3803, p < 0.001) and initial skill level (β = 0.3684, p < 0.001). Digital autonomy and frequency of use have a moderate positive effect, while hypotheses related to preference alignment are not significant. These results confirm that the impact of ICT in education depends less on perceived personalization than on didactic consistency and explicit guidance. The study thus proposes an empirical model integrating TaRL, ICT, and explicit teaching, contributing to a more equitable and structured digital pedagogy.
educational equityexplicit teachingICT in educationmotivationTaRLThe author(s) declared that financial support was not received for this work and/or its publication.section-at-acceptanceDigital EducationIntroduction
In response to persistent concerns about learning inequalities, the issue of matching teaching practices to students' actual needs has become a central focus of education policy. The curricula of many education systems, particularly in the Global South, prescribe a linear progression based on age or grade level, regardless of students' actual levels of mastery. This rigid organization leaves a significant proportion of students deeply out of step with formal academic requirements, as highlighted by the results of national and international assessments, which reveal persistent gaps in basic skills, particularly in reading, writing, and mathematics (Binaoui et al., 2023). In response to this situation, alternative teaching approaches have emerged to re-examine the foundations of traditional teaching. Among these, explicit teaching is a structuring model based on planned and intentional practices aimed at making learning accessible to all students. This model is based on clear objectives, guided demonstration of skills, supported practice, and immediate feedback on errors, which promotes the controlled progression of each student toward the targeted skills (Mancenido et al., 2024; Peltier, 2024). Unlike inductive approaches, explicit teaching requires careful planning and rigorous monitoring of learners' responses and needs. This is precisely the approach taken by the Teaching at the Right Level (TaRL) program, developed by the NGO Pratham in India (Banerjee et al., 2016). This program breaks with the logic of homogeneous teaching to offer differentiation based on students' actual levels of acquisition (Mustafa et al., 2024). By focusing teaching on what the student is ready to learn, TaRL enables students in difficulty to re-engage in a progressive and contextualized learning path (Rodriguez-Segura, 2022). Numerous evaluations in Asia and sub-Saharan Africa have highlighted the effectiveness of this approach, particularly for students who are furthest from academic expectations (Ramaila, 2025). In Morocco, the program was trialed at the start of the 2022–2023 school year in more than 600 pilot schools, with very encouraging results: scores in mathematics quadrupled, in French tripled, and in Arabic doubled in just 2 months. However, the TaRL program is still in its early stages in Morocco and there is little documentation available. At the same time, the integration of Information and Communication Technologies for Education (ICTE) has been promoted for more than two decades by education policymakers as a lever for pedagogical transformation (Murphy and Arciuli, 2024). This article examines the pedagogical, motivational, and technological conditions under which the use of ICT can enhance the effectiveness of intensive remediation approaches based on the principles of Teaching at the Right Level (TaRL) and explicit teaching. More specifically, it seeks to answer the following question: To what extent and according to what factors does the thoughtful integration of educational technologies contribute to improving fundamental learning when it is linked to explicit and differentiated teaching practices? The goal is to identify synergies between technology, differentiation, and data-driven learning in order to better understand the mechanisms underlying measurable and equitable learning gains in various educational contexts. This contribution aims to fill an empirical gap in the literature on the combined effectiveness of ICT and structured pedagogical models, while providing a reference framework that can be transferred to other teaching environments. This study adopts the perspective of an initial empirical model, rather than a purely exploratory approach. It aims to test, under real implementation conditions, a theoretically grounded structural model linking explicit teaching, ICT integration, and the Teaching at the Right Level (TaRL) approach. The objective is not to exhaustively explain all determinants of learning outcomes, but to identify and validate key pedagogical, motivational, and technological predictors of measurable learning gains in a primary education context. This research makes two main contributions to the literature. First, it proposes an integrated empirical framework combining explicit teaching, ICT mediated learning, and TaRL based differentiation an articulation that remains underexplored in empirical educational research. Second, it provides a validated structural model (PLS-SEM) based on a large sample (N = 1,152), allowing the relative weight of pedagogical, motivational, and technological factors to be quantified in explaining students' final learning positioning.
Theoretical framework
The tension between equity and efficiency in contemporary education systems has led to a renewal of pedagogical approaches aimed at meeting the diverse needs of students. In this context, three theoretical axes structure the reflection: explicit teaching as a framework for learning, ICT as a potential vehicle for differentiation, and the TaRL approach as an organizational modality based on the student's actual level.
Explicit teaching as a didactic foundation
Explicit teaching is a structured pedagogical model (Temel Aslan and Ertaş Kiliç, 2022) based on intentional, sequenced, and guided learning. It relies on a rigorous didactic architecture: clearly formulated objectives, progressive demonstration of procedures, guided training tasks, differentiated independent practice, and immediate feedback on student performance (Sins et al., 2024). Far from being reduced to a frontal or directive pedagogy, this approach aims to make the act of learning visible by removing cognitive implicitities, structuring the learning process, and supporting students' active understanding (Falardeau et al., 2024). This model is based on principles derived from the cognitive sciences of learning, such as optimal cognitive load, the importance of spaced repetition, and the role of active engagement in knowledge construction (Sweller et al., 2019). It recognizes that not all students learn spontaneously through deduction or simple exposure to problem situations, particularly when they have fundamental gaps in their knowledge or have had a disrupted educational background. This is why explicit teaching is an effective response to differences in ability levels, securing learning through progressive and structured guidance (Vaughn and Fletcher, 2022). Several recent meta-analyses confirm its effectiveness, showing that a structured and intentional approach improves fundamental learning and student participation (Gunn et al., 2021). This research highlights that explicit teaching has a significant positive effect on academic performance, particularly in reading, comprehension, and mathematics, when it is based on frequent and observable teacher-student interactions. These effects are even more pronounced among students with difficulties or from low-educated backgrounds, for whom access to academic expectations is often hampered by a lack of familiarity with implicit academic codes (Mason and Otero, 2021). Explicit instruction appears to be a relevant response: several research reviews show that the implementation of clear and guided strategies significantly improves reading and comprehension performance (Hall et al., 2023). In this sense, explicit teaching provides students with a secure learning environment by making the stages of reasoning visible, increasing opportunities for active learning, and valuing individual progress (Frønes et al., 2020). Each student can thus progress from their actual level of competence, in a process of constant adjustment between the demands of the task and their available cognitive resources. This approach is particularly relevant in teaching contexts where diversity of levels within the same class is the norm rather than the exception. It is therefore consistent with the principles of educational equity by refusing to consider heterogeneity as an obstacle and, on the contrary, integrating it as a structuring parameter of pedagogical action (Bach et al., 2024). In this context, explicit teaching is not simply a method: it becomes a professional commitment to making knowledge accessible to all students, regardless of their pace, background, or starting level.
Information and communication technologies in education
Nowadays, Information and Communication Technologies for Education (ICTE) are profoundly reshaping learning environments, changing how knowledge is accessed, classroom interactions, and the types of support available. Over the past 20 years, there has been a surge in research aimed at identifying the benefits, limitations, and conditions for effectiveness (Wu, 2024). Similarly, social inclusion and education show that the benefits of the digital revolution remain unevenly distributed and that access to, use of, and appropriation of technologies are critical variables in vulnerable contexts (Ramírez-Correa et al., 2025). In other words, a digital tool is neither good nor bad by nature: it can only become a powerful lever for inclusion, motivation, or cognitive structuring (Machkour et al., 2025b) if it is aligned with the real needs of students (Machkour et al., 2025a). Thanks to technology, students can practice at their own pace, repeat exercises, correct their mistakes without stigma, and gradually develop metacognitive skills (Dahl-Leonard et al., 2024). From an explicit teaching perspective, ICT can also make the stages of learning visible by offering guided paths, multimodal instructions, or contextual aids that reinforce understanding. Teachers thus retain a central role, not as mere users of tools, but as designers of appropriate learning situations, attentive to the needs and reactions of each student.
TaRL: a teaching approach focused on actual skill level
In many classrooms, teachers are confronted with a well-known reality: not all students progress at the same pace, share the same knowledge, or have the same needs. However, official curricula often continue to offer a linear progression based on age or grade level, assuming a homogeneity that does not exist (Piper and Dubeck, 2024). Teaching at the Right Level (TaRL) addresses precisely this discrepancy: the approach organizes regular diagnostic assessments, groups students by actual skill levels, and offers sequences focused on the fundamentals (reading, writing, numeracy), with continuous adjustment based on progress. Recent reviews rank TaRL among the most cost-effective interventions for improving basic learning, including when combined with technological components (Angrist et al., 2023). The most recent empirical results confirm these effects in African and French-speaking contexts: in Côte d'Ivoire, a randomized controlled trial of a TaRL-inspired intervention showed significant gains in literacy and numeracy (measured by learning levels), with benefits particularly concentrated among the weakest students, pointing to the key role of targeting by actual level (Whitehead et al., 2025). Pedagogically, TaRL is consistent with the structured pedagogy framework: explicit objectives, modeling, guided practice, formative assessment, and frequent feedback. In short, TaRL is not just a remediation method: it is data-driven instructional engineering that takes students' actual levels seriously to guide teaching, maximize gains among those most in need, and improve learning equity at a controlled cost.
Crossroads of TaRL, ICT, and explicit teaching: foundations of research hypotheses
When the structuring principles of explicit teaching, the differentiation levers offered by educational technologies, and the logic of individualized adjustment promoted by the TaRL program are mobilized in a coordinated manner, it becomes possible to design a learning environment that is rigorous, adaptive, and inclusive. This coordination requires a detailed understanding of the professional skills needed to differentiate and make learning explicit (Meutstege et al., 2023). Each of these dimensions responds to a fundamental need in contemporary schools: explicit teaching ensures cognitive clarity and didactic progression in learning, TaRL breaks with the fictitious homogeneity of classes to anchor learning in real skill levels, and ICT introduces tools for mediation, reinforcement, feedback, and diversity in information processing modes, which are particularly beneficial for students with heterogeneous profiles (Contrino et al., 2024). However, such convergence cannot be taken for granted: it requires deliberate educational engineering, i.e., the ability to coordinate these resources in a way that closely aligns teaching objectives, teaching conditions, and the specific characteristics of students. Recent literature on differentiated instruction and hybrid approaches emphasizes that the effectiveness of these approaches depends on planning, teacher support, and consistency between grouping, explanation, and digital mediation (Bergtold and Shanoyan, 2024). The initial level of competence, autonomy in the use of digital tools, learning styles, intrinsic motivation, subject area, and school cycle are not independent variables: it is in their contextualized, carefully thought-out interaction that a truly transformative dynamic can emerge (Espada-Chavarria et al., 2023). Without this systemic consistency, the measures risk producing equipment or organizational effects without any real impact on learning pathways. It is therefore on this condition that of a thoughtful articulation between methodological framework, organizational modalities, and technological mediation that a more equitable and effective pedagogy can be built. With this in mind, eight hypotheses have been formulated: they aim to explore the mechanisms by which this triple interaction between guided explanation, differentiated grouping, and digital support promotes (or does not promote) significant progress in fundamental learning. Table 1 presents the theoretical hypotheses on the conditions for the effectiveness of the ICT supported TaRL system.
Research hypotheses arising from the intersection of TaRL, ICT, and explicit teaching.
Hypothesis
Articulation axis
Theoretical justification
Key references
H1 There is a positive relationship between the student's initial positioning and their final positioning at the end of the TaRLprogram.
Explicit instruction + TaRL
Prior learning is a significant predictor of academic progress, particularly in approaches based on actual achievement levels.
Simonsmeier et al., 2022; Brod, 2021
H2 Changes in positioning differ significantly depending on the subject (Arabic, French, Mathematics).
TaRL + ICT
The impact of teaching methods varies depending on the subject: cognitive processes differ depending on whether language or logical-mathematical skills are involved.
Ni et al., 2022; Wijaya et al., 2022
H3 Students with high digital autonomy scores achieve higher finalrankings.
ICT + Explicit teaching
Autonomy in the use of digital tools promotes learning regulation, particularly in semi-guided environments.
Chen, 2022
H4 Students with high intrinsic motivation toward ICT show greater gains inpositioning.
ICT + TaRL
Intrinsic motivation promotes cognitive engagement and perseverance, two essential drivers of ICT-assisted learning.
Yang et al., 2025
H5 There is a positive correlation between the frequency of use of digital tools and the finalranking.
ICT
Regular exposure to ICT improves fluency of use and access to content, provided that the use is qualitative.
Consoli et al., 2025
H6 Students whose tool is aligned with their preferred tool show better finalpositioning.
ICT + individualized approach (TaRL)
Aligning preferences with tools promotes motivation and a sense of competence, which supports learning.
Rusconi and Squillaci, 2023
H7 Students benefiting from dual alignment (tool used = preferred mode of comprehension AND expression) achieve greater gains inpositioning.
ICT + explicit teaching
Consistency between preferred input and output modalities optimizes cognitive load and promotes learning.
Çeken and Taşkin, 2022; Candido and Cattaneo, 2025
The research hypotheses presented highlight the complexity of the factors that may influence students' final positioning in basic learning. To further explore this idea, a model is proposed to structure these hypotheses by identifying the significant links between the pedagogical (explicit teaching), organizational (TaRL logic), and technological (use of ICT) dimensions (see Figure 1).
Visual representation of hypotheses.
Flowchart illustrating seven factors—initial positioning, discipline, autonomy in use of ICT, motivation for ICT, frequency of ICT use, tool/preference alignment, and double alignment comprehension and expression—leading to final positioning of students, which results in improved academic performance.
This model is based on the idea that the impact of ICT in education lies in its consistent alignment with learner profiles: initial level, digital autonomy, intrinsic motivation, cognitive preferences, and school cycle. This model thus provides an interpretative framework for analyzing the conditions for the effectiveness of an integrated approach, which is likely to enhance equity and progress in basic learning.
Study design
The study was conducted in several public schools in Casablanca (Morocco) over a 6-week period, after obtaining informed consent from the students, their parents, and the teachers involved. The selected schools were already committed to implementing the Teaching at the Right Level (TaRL) program, motivated by the positive results observed in other regions of the country. The experiment, enriched by the integration of interactive digital tools, followed a rigorous progression from the initial placement test to the final placement test, based on the TaRL model. At the end of the initial assessment, learners were divided according to their actual skill level: level A (advanced), level B (intermediate), and level C (beginner). Differentiated sequences were then deployed in French, Arabic, and mathematics, combining targeted remediation, digitally guided practice, and the structuring principles of explicit teaching. The assessments were administered at two separate times: at the beginning of the program, for the baseline measurement, and at the end of the 6 weeks, for the final assessment of progress. Quantitative and qualitative data collection took place in parallel with the implementation of the program, ensuring continuous monitoring of student progress.
Participants and context
A total of 1,152 students from public schools in Casablanca (Morocco) were selected. The sample covered primary school students aged 6–12. This choice was made because pioneering primary schools are generally older and more heavily involved in the TaRl program.
Data collection and analysis strategy
The data is collected in an Excel file completed by teachers for each student. This choice made it easy to collect data without having to install new software. Subsequently, structural equation modeling using Partial Least Squares (PLS-SEM) was performed using ADANCO software, allowing simultaneous evaluation of the relationships between latent variables, verification of the model's goodness of fit, calculation of the explained variance, and testing of the significance of the relationships using bootstrapping.
Operational definitions of key variables
For clarity, the main constructs used in the model were operationalized as follows. Techno-pedagogical motivation refers to students' perceived usefulness and engagement with digital tools when these are embedded in explicit and structured instructional sequences. Digital autonomy denotes students' ability to independently navigate and use digital learning tools within guided pedagogical tasks, rather than unsupervised exploration. Frequency of ICT use reflects regular exposure to digital tools in instructional contexts, and does not include recreational or non-pedagogical use.
Quality of the PLS-SEM measurement model
In accordance with the chosen specification, most constructs are measured by a single indicator (single-item constructs). In this case, convergent validity is trivial by construction (AVE = 1). For constructs with observed indicators, the factor loadings are all greater than 0.70, and AVEs between 0.55 and 0.72 indicate acceptable convergence. These elements support the adequacy of the measurement model prior to estimating structural relationships. The use of single-item constructs is justified by the nature of the variables under study, which correspond to clearly defined, concrete, and directly observable educational indicators (e.g., positioning level, discipline, frequency of ICT use). In such cases, single item measurement is considered appropriate in PLS-SEM, as the objective is not to estimate latent psychological traits but to model structural relationships between pedagogically interpretable variables. This choice supports model parsimony and facilitates practical interpretation in real educational settings.
Dependent variable
The final positioning (dependent variable) is moderately explained by the set of predictors (H1–H7), with an R2 = 0.433, indicating that 43.3% of the variance in final positioning is explained by the model. This value is consistent with the standards reported in PLS for complex educational models integrating individual factors (initial level, autonomy), motivational factors (motivation toward ICT in education), contextual factors (discipline), and techno-pedagogical design factors (alignments).
Results
The bootstrapping results (5,000 resamples) confirm the robustness of the standardized coefficients (β), with t-values greater than 1.96 for significant relationships at the 5% threshold. The complete model (see Figure 2) shows an explained variance R2 = 0.433 for the dependent variable Final Positioning, indicating that 43.3% of the final performance is explained by the seven predictive variables (H1–H7).
Structural analysis of relationships using the PLS-SEM model.
Path diagram illustrating seven factors labeled H1 through H7, each contributing to a central result variable with indicated path coefficients ranging from negative zero point one one eight to zero point three eight zero, with R squared equal to zero point four three three.
The results from the structural model estimated with ADANCO make it possible to evaluate the strength, direction, and statistical significance of the relationships between the latent variables in the model, while identifying the predictors that have the most significant influence on the final positioning of learners. Table 2 presents, for each of the hypotheses (H1–H7), the standardized coefficients (β), the t-values, and p-values obtained by the bootstrapping procedure (5,000 resamples), as well as the decision to confirm or reject.
Summary of hypotheses tested in the PLS-SEM structural model.
Hypothetical relationship
Coefficient (β)
t-value
p-value
Decision
Interpretation
Initial positioning → Final positioning
0.3684
16.09
< 0.001
Accepted
Strong and significant positive effect: initial achievements predict final success.
Discipline → Final positioning
−0.1178
−5.29
< 0.001
Accepted
Negative effect: variation depending on the discipline.
Digital autonomy → Final positioning
0.1272
4.84
< 0.001
Accepted
Moderate positive effect: digital autonomy supports progress.
ICT motivation → Final positioning
0.3803
16.08
< 0.001
Accepted
Strong positive effect: motivation is a major driver of learning.
Frequency of ICT use → Final positioning
0.0567
2.63
0.0086
Accepted partially
Small positive effect: regular exposure promotes gains.
Alignment tool used = preferred tool → Final positioning
−0.0373
−1.58
0.115
Rejected
Insignificant effect: stated preference does not guarantee effectiveness.
Double alignment tool used = preferred mode of comprehension AND expression → Final positioning
−0.0523
−2.24
0.025
Accepted
Minor negative effect: unguided double alignment can lead to cognitive overload.
The discipline variable was coded as a categorical predictor, with Arabic used as the reference category. The negative coefficient therefore indicates relative differences between disciplines rather than a negative pedagogical effect per se. This result reflects discipline-specific cognitive and instructional characteristics, rather than a lower effectiveness of the intervention in any given subject. Importantly, statistical significance should not be conflated with pedagogical value, which remains contingent on instructional design and learning objectives.
Discussion
The meaning and statistical significance of the relationships between the latent variables in the model confirm the robustness of the hypothetical relationships and provide empirical insights into the mechanisms of effectiveness of ICT integration within the Teaching at the Right Level (TaRL) program. The highly significant link between initial and final positioning (H1, β = 0.3684, p < 0.001) corroborates the fundamental principle of TaRL, namely that adjusting learning to the student's actual level maximizes progress. Studies conducted in similar contexts show that differentiation based on skill level significantly improves outcomes, particularly in explicit and structured approaches to remediation (Angrist et al., 2022; Adil et al., 2022). These results reaffirm that differentiated remediation strategies, supported by continuous assessment and explicit sequences, promote sustainable learning, particularly among students with the greatest difficulties. Furthermore, the strong and positive effect of techno-pedagogical motivation (H4, β = 0.3803, p < 0.001) highlights the decisive role of motivational and affective variables in hybrid learning environments. Students who perceive digital tools as useful and relevant show increased cognitive engagement, which translates into significantly improved performance. This result is consistent with TAM/UTAUT models and recent research showing that motivation and digital self-efficacy are powerful predictors of self-regulated learning and academic success (Kurtovic et al., 2019). The positive but moderate effects of digital autonomy (H3) and frequency of tool use (H5) confirm that technological competence and regular practice act as facilitators of learning, without replacing the guidance of the teacher. These results are consistent with studies showing that adaptive and structured use of digital environments enhances learner performance and satisfaction (Contrino et al., 2024). On the other hand, the hypotheses related to tool-preference alignment (H6, H7) reveal weak or even negative effects, suggesting that unsupervised personalization can be detrimental to performance. These findings are consistent with the conclusions of the literature (Hattie and O'Leary, 2025; Zhao et al., 2023), according to which simply matching learning preferences and teaching methods does not guarantee measurable gains unless accompanied by control of cognitive load and a balanced alternation of approaches. Several complementary explanations may account for the weak or negative effects observed for preference-alignment hypotheses (H6 and H7). First, excessive personalization may increase cognitive load, particularly when learners are exposed to multiple modalities without sufficient instructional guidance. Second, over-personalization can fragment instructional coherence, reducing the benefits of explicit sequencing and progressive scaffolding. Finally, alignment based solely on declared preferences may overlook learners' actual instructional needs, especially in remediation contexts where guidance and structure are critical. These results therefore suggest that personalization must remain pedagogically regulated to be effective. Overall, the modeling highlights a threefold dynamic: accurate diagnosis of actual level guides differentiation (TaRL), techno-pedagogical motivation supports engagement and perseverance, and explicit structuring of digital tasks ensures learning consolidation. From a pedagogical perspective, these findings suggest that the effectiveness of ICT within a TaRL framework depends less on technological personalization than on explicit instructional design. In classroom practice, digital tools appear most effective when they support guided practice, clear task sequencing, and immediate feedback, rather than autonomous or preference-driven use. This underscores the central role of the teacher as an instructional designer who orchestrates technology in alignment with learning objectives and students' actual levels. Beyond the interpretation of individual relationships, several strengths of this study should be emphasized. The large sample size (N = 1,152) provides strong statistical power and enhances the robustness of the structural estimates. Moreover, the theoretical framework is explicitly articulated, integrating cognitive, pedagogical, and technological dimensions in a coherent manner. Finally, the use of PLS-SEM allows for the simultaneous analysis of multiple interacting predictors, offering a systemic understanding of learning gains that goes beyond bivariate or isolated analyses.
Limitations of the study
Despite its contributions, this study presents several limitations that should be acknowledged when interpreting the results. First, several constructs were measured using single-item indicators. While this choice is methodologically acceptable in PLS-SEM when variables are concrete, unambiguous, and directly observable, it may increase sensitivity to measurement error and limits the assessment of construct validity in its classical psychometric sense. Consequently, the model prioritizes structural interpretability and ecological validity over psychometric depth, and the estimated relationships should be interpreted as associations between observable educational indicators rather than precise estimations of latent psychological traits.
Second, data collection relied on teacher-reported measures based on standardized TaRL grids. Although teachers were trained and provided with uniform data-entry procedures, this approach may introduce a degree of subjective evaluation bias.
Third, the study was conducted exclusively in public primary schools in Morocco, within a specific institutional and pedagogical context. As such, the results should be interpreted as context-dependent, and their direct generalization to other educational systems should be approached with caution. Nevertheless, the underlying pedagogical mechanisms identified explicit instruction, level-based grouping, and guided ICT integration are theoretically transferable and warrant replication in other educational contexts. While the empirical results are context-dependent, the underlying pedagogical mechanisms identified explicit instruction, level-based grouping, and guided ICT integration are theoretically transferable and may inform similar remediation programs in other low- and middle-income educational contexts.
These limitations do not undermine the validity of the findings but rather define the scope of their interpretation, calling for replication and extension in other educational contexts and with complementary measurement strategies.
Conclusion
This research demonstrated, through structural equation modeling (PLS-SEM) using ADANCO, that the thoughtful integration of Information and Communication Technologies for Education (ICTE) within the Teaching at the Right Level (TaRL) program is a measurable and statistically significant lever for learning. The results reveal that techno-pedagogical motivation and initial skill level are the two major determinants of learners' final positioning, confirming the relevance of the foundations of explicit teaching: accurate diagnosis, progressive guidance, and adaptive scaffolding. The analyses confirm that the effectiveness of the system is based on the synergy between three key dimensions: (1) pedagogical adjustment based on the student's actual level; (2) motivation and engagement supported by ICT; and (3) explicit structuring of learning. Conversely, the limited or negative effects observed for the preferential alignment hypotheses (H6, H7) call for caution when it comes to approaches focused solely on perceived personalization. Effectiveness does not lie in the simple matching of preferences and tools, but in the didactic and cognitive consistency of the system implemented. These findings do not question the relevance of personalization in digital learning per se, but rather highlight the limits of preference-based personalization when it is not embedded in explicit and well-structured pedagogical designs. On a theoretical level, this study helps to fill a gap in the literature on the relationship between TaRL, ICT, and explicit teaching by proposing an empirical model that has been validated and can be transferred to other educational contexts. On a practical level, it provides decision-makers and teachers with an operational framework for designing differentiated digital interventions aligned with the principles of equity, motivation, and measurable progress. Finally, future research incorporating contextual variables (teacher training, digital infrastructure, classroom climate, student self-regulation) could contribute to the development of an adaptive predictive model, serving to create a more inclusive and effective school system.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
Ethics statement
Ethical approval for this study was obtained from the Provincial Directorate of Education of Aïn Chock, under the authority of the Regional Academy of Education and Training (AREF) Casablanca-Settat (Authorization No. 2025/05). In accordance with national and institutional regulations, non-interventional educational research conducted in public institutions does not require the approval of a formal ethics committe such as an Institutional Review Board (IRB). Written informed consent was obtained from parents or legal guardians, and verbal consent was obtained from participating students. All participants were informed of the academic objectives of the research, and the confidentiality and anonymity of the data were strictly guaranteed. No personally identifiable data was collected.
Author contributions
MM: Writing – original draft, Software, Visualization, Data curation, Conceptualization, Resources, Funding acquisition, Project administration, Writing – review & editing, Investigation, Formal analysis, Methodology, Validation, Supervision. HG: Formal analysis, Resources, Conceptualization, Writing – original draft, Software. LL: Writing – original draft, Investigation, Validation. SF: Writing – original draft, Validation, Supervision. KM: Writing – original draft, Validation, Supervision.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2026.1742242/full#supplementary-material
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Edited by: Juhji Juhji, Universitas Islam Negeri Sultan Maulana Hasanuddin Banten, Indonesia
Reviewed by: Mona Novita, Nurul Jadid University (UNUJA), Indonesia
Aan Ansori, Sultan Maulana Hasanuddin Banten State Islamic University, Indonesia