Development and evaluation of a mobile assisted pedagogical model to enhance academic enthusiasm learning and collaboration in EFL contexts

Abstract

Mobile phone technology has gained increasing attention in education; however, its systematic pedagogical application in high school English as a foreign language (EFL) instruction remains underexplored. This study designed, validated, and evaluated a mobile-assisted pedagogical model aimed at enhancing academic enthusiasm, collaborative learning, and English language achievement among high school learners. A sequential exploratory mixed-methods design was adopted. In the qualitative phase, model development was informed by a systematic literature review and expert consultation. In the quantitative phase, the model underwent an expert-based content evaluation, and its instructional effects were subsequently examined using a quasi-experimental pretest–posttest design. The sample comprised 450 10th-grade female students, randomly assigned to experimental (n = 225) and control (n = 225) groups. Instruments included a curriculum-aligned language proficiency test, a collaborative learning questionnaire, and an academic enthusiasm scale. Data were analyzed using descriptive statistics and ANCOVA. Results indicated that the experimental group significantly outperformed the control group in academic enthusiasm, collaborative learning, and English learning outcomes. The findings highlight the potential of mobile-assisted pedagogy to foster motivation, structured interaction, and sustained language learning. The study contributes a validated instructional framework grounded in sociocultural, self-determination, and cognitive load theories. Practical implications include integrating mobile-based activities into lesson planning, providing teacher training for technology-enhanced pedagogy, and ensuring equitable access to digital tools. Future research should examine long-term effects, gender comparisons, and the applicability of the model across diverse cultural and curricular contexts.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-46662-z.

Introduction

Although mobile phones are now common in secondary classrooms, research on mobile-assisted language learning rarely explains how specific instructional features such as task structure, scaffolding, and feedback affect learning, motivation, or collaboration in practical EFL teaching^1,2. Unlike earlier studies that mainly examined whether mobile learning is effective, the present research explicitly moves beyond effectiveness claims by developing and testing a theory-mapped, design-focused mobile pedagogy model for high school EFL contexts. This study treats mobile phone use as a design problem. We ask which theory-informed features should be built into mobile-supported instruction to improve learning, collaboration, and academic enthusiasm. To address this gap, we propose a mobile phone–based pedagogical model and evaluate it through a pretest–posttest quasi-experimental comparison. The purpose is to provide design-level guidance that can be replicated under typical school constraints.

To examine the impact of mobile learning, this study focuses on three key constructs in EFL contexts:

1. English learning outcomes, referring to measurable performance in language skills, including comprehension, vocabulary, and practical use of English^3–5.

2. Collaborative learning, reflecting learners’ active participation, teamwork, and peer interaction during learning activities^6–8.

3. Academic enthusiasm refers to motivation, interest, and persistence in learning tasks.

4. It has been discussed in relation to students’ enthusiasm in academic activities^9,10.

Although mobile-assisted learning shows promise, studies frequently examine constructs in isolation or focus on university contexts. High school EFL learners remain underrepresented, and few studies test integrated, theory-guided models that combine achievement, collaboration, and motivation-related outcomes¹¹. Moreover, structured mobile phone-based instructional models that systematically incorporate students’ perspectives, teachers’ input, and educational best practices are scarce in this context.

These outcomes align with the three frameworks used to design the model. Specifically, academic enthusiasm aligns with the autonomy and competence dimensions of Self-Determination Theory; collaborative learning reflects relatedness and sociocultural interaction in Vygotsky’s framework; and learning achievement is linked to cognitive efficiency under Cognitive Load Theory. Therefore, this study aims to develop and evaluate a mobile phone-based pedagogical model designed to enhance English learning outcomes, collaborative learning, and academic enthusiasm among 10th-grade female EFL learners in Zanjan City. By providing evidence-based guidance, the study seeks to inform educational practices and policies, optimizing mobile technology for engaging, motivating, and providing effective learning experiences.

Research questions

1. Does the mobile phone-based pedagogical model have a significant positive effect on students’ English learning outcomes?

2. Does the model significantly enhance students’ collaborative learning?

3. Does the model increase students’ academic enthusiasm?

Literature review

Pedagogical design is most useful for school-based mobile learning when it specifies actionable instructional features rather than providing extended historical overviews. For classroom implementation, design guidance is needed on how to structure interaction, motivation support, and content presentation so that mobile tools function as part of instruction rather than as an add-on¹².The present study, therefore, draws on three complementary theoretical perspectives to specify the model’s design features and to derive testable expectations regarding learning outcomes, collaboration, and academic enthusiasm.

Instructional design and learning theories

To maintain conceptual focus, the following theoretical discussion is intentionally presented only to justify the model’s design features rather than to provide a comprehensive review of each framework. This study uses three complementary frameworks to justify specific, implementable mobile phone–based design features in EFL instruction. Sociocultural Theory (SCT) explains how learning develops through mediated interaction and scaffolding during purposeful peer activity¹³. Self-Determination Theory (SDT) explains how instructional environments support motivation by meeting learners’ needs for autonomy, competence, and relatedness¹⁴. Cognitive Load Theory (CLT) explains how instructional effectiveness depends on managing working-memory demands by reducing extraneous load and sequencing tasks to support processing¹⁵. Together, these theories support a design logic in which mobile activities are structured to (a) organize peer interaction and teacher mediation (SCT), (b) support motivation and sustained engagement (SDT), and (c) present language content in manageable, guided segments (CLT). Building on these perspectives, the present study integrates Sociocultural Theory, Self-Determination Theory, and Cognitive Load Theory to inform the design of a mobile-assisted pedagogical model for high school EFL learners (Table 1).

Table 1.

Mapping of theory to mobile phone pedagogy design features and outcomes.

Theory	Design features used in the intervention (mobile phone pedagogy)	Primary outcome(s) targeted
Sociocultural Theory (SCT)	Structured peer tasks; guided discussion prompts; teacher scaffolding and feedback during interaction; shared problem-solving and reflection	Collaborative learning; learning outcomes
Self-Determination Theory (SDT)	Autonomy-supportive task choices; clear success criteria; competence feedback; opportunities for peer relatedness; supportive communication norms	Academic enthusiasm; persistence in learning tasks
Cognitive Load Theory (CLT)	Segmented micro-tasks; clear instructions; reduced extraneous multimedia/navigation; guided examples; gradual increase in task complexity	Learning outcomes; reduced cognitive overload

SCT Sociocultural Theory, SDT Self-Determination Theory, CLT Cognitive Load Theory.

Mobile learning: potential and challenges

Mobile-assisted language learning research generally reports potential advantages such as flexible access and multimedia support^2,16; however, findings are mixed across contexts and implementations^3,17,18. Positive outcomes are more likely when mobile activities are designed with clear pedagogical structure, curriculum alignment, and scaffolding, whereas novelty effects, weak task design, and limited guidance can reduce or shorten motivational gains^2,18. Accordingly, the key gap is not whether mobile phones can support EFL learning, but which design features produce reliable improvements in achievement, collaboration, and sustained engagement-related outcomes in school settings. This study addresses that gap by evaluating a theory-mapped pedagogical model that specifies designable instructional components rather than relying on general claims about technology benefits. To clarify how this design logic translates into measurable learning improvements, the following subsections review three key outcome areas targeted by the model—English learning outcomes, collaborative learning, and academic enthusiasm.

English learning outcomes in EFL contexts

EFL learning outcomes across reading, writing, speaking, listening, vocabulary, and grammar depend on thoughtful instructional design; without it, interventions risk emphasizing memorization rather than communicative competence. Sustainable improvement requires scaffolding, feedback, and curriculum alignment^3,18. Therefore, the proposed model incorporates CLT-informed task segmentation and guided practice, together with feedback and curriculum alignment, to test whether theory-guided mobile pedagogy improves English learning outcomes.

Collaborative learning in EFL contexts

Collaboration supports learner participation, peer interaction, and language practice through discussion- and project-based activities^6,19. Yet challenges persist, including unequal participation, differences in digital literacy, and weaker social cues compared with face-to-face interaction²⁰. Effective mobile-facilitated collaboration therefore, depends on structured tasks and teacher guidance. Accordingly, the model includes SCT-informed collaborative task structures and teacher mediation routines to test whether mobile phone–supported instruction strengthens collaborative learning.

Academic enthusiasm and motivation

Academic enthusiasm refers to motivation, persistence, and interest in learning activities. Mobile learning can enhance academic enthusiasm through interactivity, gamification, and flexibility²¹. Enthusiastic learners engage more deeply and persist longer in challenging tasks. However, academic enthusiasm varies with socioeconomic background, teacher support, and digital competence²². Poorly structured interventions may yield short-lived motivation gains¹⁸. Sustained academic enthusiasm requires goal-oriented pedagogical strategies integrated with mobile tools. Consistent with SDT, the model embeds autonomy-supportive choices and competence feedback within mobile activities to test whether this design increases academic enthusiasm beyond short-lived novelty effects.

Critical synthesis and research gaps

Although mobile-assisted learning shows promise, studies frequently examine constructs in isolation or focus on university contexts. High school EFL learners remain underrepresented, and few studies test integrated, theory-guided models that combine achievement, collaboration, and motivation-related outcomes^11,23. High school-level EFL learners remain underrepresented, and few studies present integrated, validated models combining theory, pedagogy, and empirical evidence. Moreover, cultural and gender-specific contexts are often overlooked, even though they shape learner enthusiasm and outcomes.

Addressing these gaps, the present study develops and evaluates a mobile phone–based pedagogical model designed to improve English outcomes, collaborative learning, and academic enthusiasm simultaneously. Grounded in sociocultural, SDT, and CLT perspectives, this study aims to provide a validated framework that can guide teachers and policymakers in adopting mobile-assisted pedagogy for meaningful, equitable, and sustainable language learning.

Based on this synthesis, we propose the following hypotheses:

Hypothesis 1

Students receiving mobile-assisted instruction will report significantly higher academic enthusiasm than those receiving traditional instruction.

Hypothesis 2

Students in the experimental group will achieve significantly higher English learning outcomes than those in the control group.

Hypothesis 3

Students in the experimental group will demonstrate significantly higher collaborative learning than those in the control group.

Method

Research design

A sequential exploratory mixed-methods design was used. The model was first developed through a literature review and expert input, and subsequently tested in a quasi-experimental school study. In the quantitative phase, the model underwent an expert-based content evaluation, and its instíructional effects were examined through a quasi-experimental, school-based intervention study.

Participants and sampling

The target population comprised approximately 14,000 10th-grade female EFL students in Zanjan City, Iran. Zanjan City is administratively divided into two educational districts. From a total of 23 eligible schools across the city, 12 schools in District 2 were invited to participate, and six schools voluntarily agreed to take part in the study. Because individual-level randomization was impractical within existing class structures, participating schools were recruited by institutional consent, forming a convenience sample. A two-stage cluster sampling approach was then used: schools were selected first, and intact classes were randomly chosen from each. To prevent cross-group contamination, experimental and control conditions were implemented in separate schools, ensuring no overlap in teachers, scheduling, or instructional activities. This approach minimized potential bias while preserving ecological validity.

Each selected class consisted of approximately 30 to 35 students. The required sample size was determined using Morgan’s table, which indicated a minimum of 384 students for a population exceeding 10,000. To account for the clustered nature of the data (students nested within intact classes), a design effect of 1.2 was applied, resulting in an adjusted target sample size of approximately 460 students. Accordingly, 460 students were initially enrolled; after 10 withdrawals, the final sample consisted of 450 students.

In total, seven intact classes (n = 225) were assigned to the experimental group, and seven intact classes (n = 225) were assigned to the control group. Inclusion criteria included ownership of an Android smartphone, which was required solely for implementation purposes because the instructional application was Android-based, and voluntary participation. This requirement was applied uniformly to all participants across both study groups. All research procedures were performed in accordance with the relevant institutional guidelines and regulations. Ethical approval was obtained from the appropriate authority, and written informed consent was received from all participants (and their parents, where applicable) prior to data collection.

Method of data collection

Qualitative stage. The mobile-assisted educational design model was developed through a systematic literature review (SLR) and expert consultation. Following standard SLR procedures, studies published in English between 2000 and 2025 were screened, categorized, and synthesized. From 12 books and 36 articles, 24 were analyzed in depth to identify core components. Four instructional technology specialists provided feedback to refine the framework and clarify interrelationships.

Quantitative stage. An expert-based content evaluation was conducted involving 30 instructional technology specialists (18 Ph.D. students, 12 Ph.D. holders) who assessed the proposed pedagogical model using a seven-item questionnaire. The purpose of this phase was to examine experts’ perceived adequacy, relevance, and clarity of the model, and their feedback was used to finalize the instructional design. The effectiveness of the model was subsequently examined through a quasi-experimental pretest–posttest design involving 450 10th-grade female students, assigned to experimental (n = 225) and control (n = 225) groups. In the Iranian educational context, many secondary schools are gender-segregated, and differences in instructional and socio-cultural settings may act as confounding factors. Therefore, to maintain contextual homogeneity and reduce ambiguity arising from gender-based contextual differences, the present intervention study focused on 10th-grade female students. The intervention targeted the third lesson of the 10th-grade English textbook. For the experimental group, materials were adapted into mobile-based resources (audio files and collaborative activities) delivered via Telegram. Over four weeks, students engaged in structured online practice, peer interaction, and teacher-facilitated discussions, while the control group received traditional in-person instruction. The control group received standard in-person English instruction following the official curriculum. Sessions included teacher-led lessons, textbook exercises, oral practice, and written assignments, without mobile or online components, consistent with standard EFL teaching practices in Iranian high schools. Before the intervention, teachers completed standardized training and followed uniform lesson plans. Fidelity was maintained through weekly observations using a checklist and teacher implementation logs reviewed by the research team.

Pre- and post-tests measured academic enthusiasm, collaborative learning, and language learning outcomes. Telegram use followed national educational technology regulations. No personal identifiers were shared; participation took place in moderated groups, and all communications were limited to academic content. Data privacy and platform ethics were reviewed and approved by the institutional ethics committee. The following table summarizes the meeting schedule: (Table 2: Meeting Schedule).

Table 2.

Enriched education intervention program through mobile phone.

Meetings	Content	Education activity	Learning activity	Method
The first week: Three one-hour sessions	New words	Pre-training activity, teaching the correct pronunciation of words, making sentences with words, and Correct usage in the sentence	Active listening, pronunciation of words, use in a sentence, sentence making, Group discussion and collaboration in written and spoken form, report of collaborative activities in a group	Collaborative and question and answer
The second week: Three one-hour sessions	Reading	Pre-training activity, teaching text reading skills, and questions from previous topics	Active listening, conversation with classmates, reading segmented text and sending voice in the group, group discussion and collaboration, report of cooperative activities in the group	Collaborative and question and answer
The third week: Three one-hour sessions	Grammar	Pre-training activity, grammar training, and practice, and questions from previous topics	Listening and conversation, using grammar points in the text, discussion and question and answer, report of cooperative activities in the group	Collaborative and question and answer
The fourth week: Three one-hour sessions	Practice	Pre-training activities, presenting exercises to groups, solving problems, and asking questions from previous topics	Listening and conversation, solving exercises presented in group, report of collaborative activities in the group	Collaborative and question and answer

In the last stage, after 4 weeks of training, the questionnaires and post-test questions were implemented equally among the samples, and the data obtained from the experiment were collected and analyzed.

Instruments

Expert-based content evaluation: To evaluate the initial pedagogical model, a 7-item Likert-scale questionnaire was developed based on the model’s core components. The instrument was reviewed by subject-matter experts to establish content validity, and internal consistency reliability was found to be acceptable (Cronbach’s α = 0.78).

Academic enthusiasm

In this study, academic enthusiasm is treated as an engagement-related construct reflecting students’ behavioral, emotional, and cognitive involvement in learning, consistent with the school engagement framework of²⁴. Academic enthusiasm was measured using a 15-item self-report questionnaire with three subscales: behavioral (4 items), emotional (6 items), and cognitive (5 items). Items are rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), yielding total scores from 15 to 75, with higher scores indicating higher academic enthusiasm; negatively worded items were reverse-scored. Prior Iranian studies have reported acceptable reliability for the total scale and supported the three-factor structure using EFA/CFA (e.g., Bagheri et al., 2020), and the measure has been used in multiple Iranian and international studies as an engagement-related academic variable (e.g., Safari et al., 2016; Izadpanah & Rezaei, 2022; Kim et al., 2021). In the present sample, internal consistency for the total scale was acceptable (Cronbach’s α = 0.78).

We conducted a confirmatory factor analysis (CFA) using the current sample to evaluate construct validity. Because the factor structure of the Academic Enthusiasm Questionnaire has been established in prior research, CFA (rather than EFA) was considered the appropriate method to test the hypothesized measurement model. The three-factor model demonstrated acceptable-to-good fit (CFI = 0.98; GFI = 0.93), and RMSEA also indicated acceptable fit (RMSEA = 0.070). In addition, the questionnaire showed acceptable internal consistency in the present study (Cronbach’s α = 0.78). The standardized CFA model was shown in Appendix B (Figure B1).

Learning outcomes: A researcher-developed test aligned with the 10th-grade English curriculum was used to measure learning outcomes before and after the intervention. The test consisted of 37 items, and content validity was established through expert review. Evidence of convergent validity was examined by correlating test scores with a related measure of English learning administered concurrently, yielding a correlation coefficient of r = 0.63. The instrument demonstrated high internal consistency reliability (Cronbach’s α = 0.89).

Collaborative learning: Students’ perceptions of collaboration were assessed using a modified version of²⁵ Collaborative Learning Questionnaire. The adapted instrument comprised 13 items across three dimensions (group dynamics, project work, and feedback), rated on a 5-point Likert scale. Content validity was supported through expert evaluation. Evidence of convergent validity was examined via correlations with a theoretically related measure administered concurrently (r = 0.58). Internal consistency reliability was strong (Cronbach’s α = 0.83).

Data analysis

The primary objective of this study was to design and examine a pedagogical model for mobile phone use in education. The model was developed based on a systematic review of domestic and international research. Prior to implementation, the model underwent an expert-based content evaluation to examine its perceived adequacy and relevance. The instructional effects of the model were subsequently examined using a quasi-experimental pretest–posttest design with student participants. Given the clustered structure of the data, intraclass correlation coefficients (ICC) were examined at the school level. The ICC for academic enthusiasm was 0.06, indicating modest clustering of students within schools. To test the study hypotheses, univariate analysis of covariance (ANCOVA) was conducted after verifying the required statistical assumptions. All data analyses were performed using SPSS version 25. To examine the instrument’s construct validity (academic enthusiasm) and test the questionnaire’s factor structure, confirmatory factor analysis (CFA) was conducted. Model fit was evaluated using the Comparative Fit Index (CFI), Goodness-of-Fit Index (GFI), and the Root Mean Square Error of Approximation (RMSEA). In this study, CFI and GFI values ≥ 0.90 were considered acceptable, and RMSEA ≤ 0.08 was considered indicative of an acceptable model fit.

Results

What are the main components of the educational design model for mobile phone use?

Based on the systematic review and analysis of relevant theoretical and empirical studies, four main components were identified: human factors, technological factors, educational factors, and supporting factors. Each component includes several subcomponents, illustrated in Fig. 1.

Fig. 1.

Conceptual network of an educational design model of mobile phone usage.

The proposed mobile phone–based educational model comprises four main, interconnected components: human factors, educational factors, technological factors, and supporting factors (Fig. 1).

Human factors include teachers and students. Students are the primary users, while teachers are responsible for designing the learning environment, setting objectives, determining content, and guiding effective mobile tool usage. Teacher effectiveness depends on pedagogical expertise, subject knowledge, moral qualities, and patience. Educational factors relate to instructional processes that deliver content and facilitate learning. Their effectiveness depends on the interplay with human, technological, and supporting factors.

Technological factors include software availability, accessibility, and usability. While students may have access and interest, varying skill levels can influence outcomes (Rehman, 2025). Supporting factors reinforce and facilitate education within the mobile technology environment, ensuring successful implementation. Unlike learning theories, which explain learning processes, educational design theories are prescriptive, providing guidelines for structuring instruction to achieve desired learning outcomes. This conceptual framework is grounded in such design principles and informs the development of the mobile phone–based instructional model Figs. 2, 3, and 4:

Fig. 2.

illustrates the general stages of the educational design model for using mobile phones. The model includes four stages—preparation, design, implementation, and evaluation—along with their respective subcomponents, as shown in Fig. 3.

Fig. 3.

Educational design model of mobile phone use.

Fig. 4.

Prescriptive model of training through mobile phone.

Figure 3 illustrates the key components of the mobile phone–based educational model. The model integrates elements from multiple educational design theories to maximize effectiveness, academic enthusiasm, and cooperative learning.

Key features include:

Four sequential, practical stages that are user-friendly.

An integrated approach selecting the most relevant elements from existing models.

Implementation emphasizes academic enthusiasm, collaboration, interaction, staged assessment, and continuous feedback.

Mobile phone technology is central to the implementation phase.

Comprehensive coverage of factors affecting mobile-based learning.

Ongoing evaluation informs iterative improvements.

Steps of the model

Step 1: Preparation

The preparation stage focuses on creating student academic enthusiasm, providing training on relevant software, and ensuring the installation of necessary applications. Training materials and sessions are organized to facilitate effective use of mobile tools.

Step 2: Designing learning through mobile phones

This stage incorporates educational design elements tailored to student characteristics.

Analysis of Learner Needs: The model begins with a comprehensive analysis of learner characteristics, including general attributes, learning styles, preferences, motivation, and goals. This informs the design of educational objectives and activities.

Technology Analysis: The suitability of technology is evaluated to ensure accessibility, usability, collaboration support, and content reuse. Students’ technological readiness and familiarity are assessed. Telegram, as a widely used platform in Iran, was selected for implementation due to its alignment with these requirements.

Setting goals

Clear, specific learning goals are essential for effective instruction. Establishing unambiguous objectives at the outset enhances learning outcomes, guides the sequence of instruction, and improves evaluation quality.

Selecting the presentation channel

The choice of communication channels—such as text, images, audio, video, or animation—affects how effectively content and activities are delivered. Selecting appropriate channels supports academic enthusiasm and learning success.

Educational message

Instructional messages should be clear, structured, and interactive to prevent aimless navigation of the mobile environment. Key considerations include:

Providing an overview of the lesson, keywords, and main topics.

Delivering concise presentations with pauses to encourage active participation.

Allowing learners to take conscious control, adapting content to individual preferences.

Preparation of teaching and learning strategies

Strategies encompass both teacher-directed instructional methods and student learning approaches, aligned with predefined objectives. Mobile learning strategies enhance collaboration, retention, and learner academic enthusiasm, and should be tailored to the mobile medium.

Selection of educational media

Appropriate media facilitate effective communication of educational messages. Media choice should consider the objectives, target audience, timing, and usage processes. For mobile learning, audio and written text are primary media.

Determining the evaluation system

Evaluation measures the reliability, effectiveness, efficiency, and overall value of educational programs²⁶. In mobile learning, it assesses instructional materials, activities, and learner outcomes, informing refinements to improve program effectiveness²⁷. The model incorporates formative evaluation to guide ongoing improvements, summative evaluation to measure overall success, and self-evaluation to actively engage learners. Learner satisfaction with course implementation is also evaluated.

The third step: implementation of training

After the design stage is ready, the teacher must implement the predictions of the previous stage. Executive activities at this stage are specified in the following figure:

The six process steps in Fig. 4 outline the training sequence, while four guiding principles—collaboration, interaction, cognitive load reduction, and academic enthusiasm—ensure effective learning, meaningful academic enthusiasm, and learner satisfaction. Mobile learning is central to all activities, supported by mobile phone technology.

Pedagogical goals

Pedagogical goals in mobile learning should be broken into manageable units to enhance comprehension and ensure attainability, preventing fatigue or dropout. Pre-training strategies communicate these goals to learners in advance, clarifying the skills and knowledge they are expected to acquire by the end of the session.

Implementation of pre-training strategy

Pre-training strategies enhance mobile learning by familiarizing students with key concepts and terminology, helping them form a strong mental model. Effective implementation includes posing questions in advance, discussing lessons beforehand, providing reminders of essential points, teaching organizational skills, explaining key terms, allowing peer-assisted pre-training, and reviewing vocabulary and concepts post-instruction. These approaches improve learners’ readiness, comprehension, and retention in mobile learning contexts.

Prerequisites

Understanding learners’ prior knowledge is crucial for effective teaching, particularly in mobile learning. Prior knowledge provides the foundation for new learning, while entry behaviors or prerequisites define the essential skills students must possess before starting a course. Identifying these prerequisites helps educators determine where to begin instruction and ensures learners can effectively engage with mobile-based materials. In this model, prerequisite knowledge and skills, including proficiency in using mobile devices, are assessed and prioritized to tailor teaching strategies and enhance the learning experience.

Providing a pedagogical strategy

There are different types of pedagogical strategies that a teacher can use depending on the type of pedagogical goals and available conditions and facilities to be most effective. Many strategies that can be suitable for teaching students through mobile phones include the strategy of presentation, practice and repetition, discussion, and collaboration. To maximize the capacity of existing strategies, it is advisable to select a combination of strategies. Simple presentation of content lowers the academic enthusiasm of learners, does not provide conditions for interaction and collaboration, and has lower pedagogical effectiveness than a combination of strategies. Therefore, it is better to select and apply a combination of the best strategies according to the goals, needs of students, type of content, and technological capacity.

Providing learning strategies

Learning guides support students in achieving pedagogical goals. Effective strategies include integration, organization, and explanation strategies, which initially serve as scaffolding before students can select and apply the most appropriate strategies for specific learning tasks²⁸ .

Call for action and feedback

Immediate and well-structured feedback enables learners to evaluate their understanding of course content. Technologies can facilitate discussions and various forms of feedback, including reminders and interactive quizzes via SMS or other platforms^29–31. Feedback should be clear, detailed, and timely to enhance learning outcomes.

Applying the principles of academic enthusiasm

A key responsibility of pedagogical designers and instructors, especially in e-learning environments, is fostering student academic enthusiasm. This is particularly important in mobile learning, where face-to-face interaction is limited. Mobile devices can enhance academic enthusiasm by enabling participation in group discussions and providing quick, effective feedback, which strengthens learning and retention. Strategies to enhance academic enthusiasm include:

Providing immediate feedback.

Encouraging learners to answer peers’ questions to increase collaboration.

Encouraging learners to express opinions to maintain academic enthusiasm.

Supporting independent activities to build self-confidence and motivation.

Assigning learners roles in managing the class to create a sense of ownership and academic enthusiasm.

Cognitive load

Cognitive load refers to the strain on working memory caused by learning tasks designed to achieve specific goals. Effective training requires that the combined internal and external cognitive load does not exceed the learner’s working memory capacity. Psychological comfort also affects cognitive load and task performance. Techniques to reduce cognitive load include:

Free target: Avoiding undefined goals to maintain focus and attention.

Divided attention: Integrating separated information sources in time and space.

Redundancy: Eliminating unnecessary details.

Additional techniques to support learning include:

Worked examples: Providing solved examples to guide learners and establish patterns.

Partial problem completion: Allowing learners to complete parts of a problem to experience success, boosting academic enthusiasm.

In mobile learning environments, identifying factors that increase cognitive load and implementing strategies to reduce them is essential for enhancing both learning effectiveness and learner motivation.

Collaboration and interaction

Learners should be encouraged to communicate through mobile tools, such as text messaging, email, instant messaging, and voice communication, to connect and support each other³². Interaction, communication, support, and collaboration are key advantages of mobile learning. Collaborative activities can occur between learners, between learners and teachers, or between learners and other resources. Teachers can communicate with students in multiple ways and adjust group cooperative learning strategies. This encourages learners to share knowledge, communicate, and solve problems together. To increase collaboration, it is recommended that: 1) one student asks a question while others respond, and 2) learners are encouraged to communicate with both the teacher and peers.

The fourth step: evaluation

Evaluation is essential for understanding student progress and guiding instructional strategies³³. Managing assignments, providing feedback, and assessing learning are key components in both face-to-face and remote courses. Mobile devices facilitate more comfortable evaluation, especially for learners who participate less in class. In mobile learning, teachers can employ multiple evaluation methods, including quantitative and qualitative assessments. Effective evaluation involves self-evaluation, staged evaluation, final assessment, and measuring learners’ satisfaction with the course.

Self-Evaluation

Self-evaluation allows learners to assess their own performance, identify problems, and seek solutions with peer or teacher support³⁴. In mobile environments, methods can include checklists, completing partial sentences, describing performance, and correcting tests.

Satisfaction

Student satisfaction is a critical principle in mobile learning. Satisfied learners are more likely to recommend courses, contributing to higher course participation and overall quality. Satisfaction can be assessed across three dimensions: satisfaction with the course itself, with the pedagogical environment (online or hybrid), and with the course components, including access, materials, technology, and interaction²⁶.

The results of the quantitative part

Data were analyzed using SPSS 25, with descriptive statistics presented as frequency tables, measures of central tendency, and dispersion indices. Inferential analyses were conducted using univariate analysis of covariance (ANCOVA). First, the results of the expert-based content evaluation of the proposed model are presented, followed by the analysis of data obtained from the experimental implementation.

Expert-based content evaluation of the proposed model

To address this question, the opinions of 30 experts in the field of educational technology were collected and analyzed.

The results of the data analysis are reported in Table 3:

Table 3.

Examining the results of the opinions of technology experts regarding model the validity of the pedagogical of using mobile phones.

Questions	N	M	Std. error	SD	t-value	df	p
Question 1	30	4.33	0.16	0.92	7.91	29	0.001
Question 2	30	4.30	0.10	0.59	11.94	29	0.001
Question 3	30	4.50	0.12	0.68	12.04	29	0.001
Question 4	30	4.60	0.09	0.49	17.58	29	0.001
Question 5	30	4.56	0.11	0.62	13.70	29	0.001
Question 6	30	4.26	0.11	0.64	10.84	29	0.001
Question 7	30	4.76	0.07	0.430	22.49	29	0.001

According to the means reported in Table 3, experts evaluated the designed model positively across all dimensions. To determine whether the average responses for each question were significantly higher than the midpoint score of 3, a one-sample t-test was conducted. These results confirm that the proposed pedagogical model demonstrates strong content validity and expert acceptance, indicating that its theoretical alignment and structural components are regarded as appropriate for educational implementation.

Descriptive findings

Data analysis was performed using SPSS 25 and included both descriptive and inferential statistics. First, demographic statistics were examined, and the frequency and percentage distributions of the research variables are presented in Table 4.

Table 4.

Distribution table of frequency and percentage of research variables.

		Experimental group		Control group
Variable		Frequency	Percent (%)	Frequency	Percent
Age	16 years	183	81.3	186	82.7
	17 years	42	18.7	39	17.3
Father’s education	High school	141	62.7	134	59.6
	Diploma	54	24.0	56	24.9
	Bachelor’s degree	29	12.9	32	14.2
	Master’s degree	1	0.4	3	1.3
Mother’s education	High school	178	79.1	171	76.0
	Diploma	31	13.8	33	14.7
	Bachelor’s degree	15	6.7	18	8.0
	Master’s degree	1	0.4	3	1.3

Table 4 summarizes participants’ demographic characteristics in the experimental and control groups. The distributions show comparable demographic profiles across the two groups, suggesting baseline equivalence and supporting the internal validity of subsequent group comparisons.

Table 5 reports pretest and posttest descriptive statistics for the experimental and control groups for academic enthusiasm, learning outcomes, and collaboration. As summarized in Table 5, ANCOVA results indicate that, after controlling for pre-test scores, the experimental group significantly outperformed the control group across all three dependent variables, academic enthusiasm, collaborative learning, and English learning outcomes (p < 0.01). These results provide direct empirical support for the effectiveness of the mobile phone–based pedagogical model, addressing all three research Table 6.

Table 5.

Pretest and posttest central and dispersion indices in control and experimental groups.

			Experimental group						Control group
		N	M	SD	Min	Max	Skewness	Kurtosis	M	SD	Min	Max	Skewness	Kurtosis
Pre-test	Academic enthusiasm	225	51.62	10.86	23	70	− 0.26	− 0.44	52.16	10.93	23	70	− 0.32	− 0.35
	Learning	225	102.11	21.71	51	143	− 0.22	− 0.44	102.70	21.495	51	143	− 0.27	− 0.25
	Collaboration	225	105.77	23.18	41	145	− 0.46	− 0.17	106.96	22.92	41	145	− 0.45	− 0.28
Post-test	Academic enthusiasm	225	55.34	9.50	26	72	− 0.50	− 0.17	52.54	10.89	23	72	− 0.33	− 0.33
	Learning	225	108.49	18.51	55	144	− 0.32	− 0.06	103.38	21.09	51	143	− 0.26	− 0.21
	Collaboration	225	112.46	21.13	46	147	− 0.70	0.27	107.79	22.51	41	145	− 0.465	− 0.18

Table 6.

The result of covariance analysis in research variables.

Tests of between-subjects effects
Variable	Period	M			Analysis covariance
		Experimental group	Control group	Type III sum of squares	df	M square	F	P	Partial η2
Academic enthusiasm	Pretest	51.62	52.16	1214.25	1	1214.25	123.63	0.001	0.21
	Posttest	55.34	52.54
Learning	Pretest	102.11	102.70	3566.32	1	3566.32	100.957	0.001	0.18
	Posttest	108.49	103.38
Collaboration	Pretest	105.77	106.96	3653.59	1	3653.59	46.89	0.001	0.09
	Posttest	112.46	107.79

Table 6 reports ANCOVA results for each outcome after adjusting for pretest scores. Group differences were statistically significant for academic enthusiasm (F = 123.63, p = 0.001, partial η² = 0.217), English learning outcomes (F = 100.957, p = 0.001, partial η² = 0.18), and collaborative learning (F = 46.89, p = 0.001, partial η² = 0.09). Descriptive pretest and posttest means are reported in Table 6.

Inferential analysis

Assumptions of univariate covariance analysis (ANCOVA)

Detailed ANCOVA assumption-check outputs (Shapiro–Wilk, Levene’s test, Q–Q plots, and regression-slope tests) are provided in Appendix B (Figure B1; Tables B1–B2).

Intervention effects on study outcomes

Covariance analysis was used to test the above hypothesis. As seen, the assumptions necessary for covariance analysis have been checked, and these assumptions are valid. The result of covariance analysis is recorded in the following table:

Discussion

The present study evaluated a mobile phone–based pedagogical model targeting academic enthusiasm, English learning outcomes, and collaborative learning among 10th-grade female EFL learners. Rather than treating mobile learning as inherently beneficial, the findings are interpreted through a theory-mapped explanation of how specific design features can influence motivation, interaction, and cognitive processing.

Outcome 1: academic enthusiasm

H1 proposed that the model would increase students’ academic enthusiasm. The observed increase can be explained through Self-Determination Theory, which emphasizes autonomy, competence, and relatedness as key supports for intrinsic motivation¹⁴. In the present intervention, the mobile-based pedagogical model incorporated structured interaction routines and mobile-supported participation formats that appeared to support learners’ sense of agency and peer connection. Specifically, students were given choices in task selection (supporting autonomy), received immediate feedback on their language use (building competence), and engaged in regular peer collaboration through Telegram groups (fostering relatedness). These design features likely contributed to sustained motivational involvement across the four-week intervention period.

This interpretation aligns with prior research showing that mobile learning environments can strengthen motivational outcomes when they provide engaging, learner-centered experiences^4,10,35. However, the present study extends this work by demonstrating how specific, theory-informed design features—rather than mobile technology itself—may drive enthusiasm gains in high school EFL contexts.

Outcome 2: english learning outcomes

H2 proposed that the mobile phone–based pedagogical model would improve students’ English learning outcomes. The experimental group showed measurable gains in English proficiency compared to the control group, which can be attributed to several design features embedded in the intervention. Specifically, the model organized learning content into discrete, manageable units—such as weekly vocabulary sets, grammar modules segmented by rule type, and reading passages broken into scaffolded sections. This chunking strategy appeared to reduce unnecessary cognitive demands during instruction, allowing students to process and retain information more effectively. The Cognitive Load Theory framework¹⁵ provides a useful lens for understanding this mechanism: by limiting extraneous load through clear task structures and worked examples, the model likely freed working memory capacity for deeper language processing.

Beyond cognitive efficiency, the mobile platform enabled students to access materials and review content outside classroom hours through the Telegram group. This extended practice opportunity may have supported consolidation through spaced repetition—students could revisit vocabulary, re-listen to pronunciation models, and complete self-paced exercises during their own time. The observed improvements align with broader evidence that mobile-assisted learning can support achievement when learning experiences are structured and curriculum-aligned^1,36,37 though the present study’s contribution lies in explicitly linking these outcomes to theoretically motivated design decisions rather than to mobile technology per se.

Outcome 3: collaborative learning

H3 proposed that the model would enhance students’ collaborative learning. The experimental group demonstrated measurable gains in collaborative learning, though the effect was smaller than those observed for academic enthusiasm and English learning outcomes (partial η² = 0.095 vs. 0.217 and 0.184, respectively). This difference in effect magnitude warrants careful interpretation of what the mobile-supported tasks achieved and what remained challenging in this implementation.

The collaboration gains that did occur appeared linked to specific design features that structured peer interdependence through the Telegram platform. Students participated in weekly group tasks—such as collaborative vocabulary building exercises, peer review of written assignments, and discussion threads requiring each group member to contribute a unique perspective—that established clear accountability and shared responsibility for outcomes. These activities positioned the mobile tool as a mediator of collective meaning-making rather than simply a communication channel, consistent with sociocultural views of learning as mediated activity in which tools and structured peer interaction support shared problem-solving¹³.

However, the smaller collaboration effect likely reflects inherent constraints of asynchronous text-based interaction. Unlike the immediate, multimodal exchanges possible in face-to-face settings, Telegram-based collaboration introduced temporal delays between contributions and limited students’ access to social cues such as tone, facial expression, and real-time negotiation of meaning. Additionally, the degree of task interdependence achieved in this initial four-week implementation may not have been sufficiently deep to drive stronger collaborative outcomes. While tasks required peer input, they may not have demanded the intensive co-construction of knowledge that characterizes more ambitious collaborative designs. This interpretation aligns with evidence that mobile tools can foster collaboration when interaction is intentionally designed with high interdependence²⁰ but suggests that our implementation represented an intermediate level of collaborative intensity.

Future iterations of the model could strengthen collaboration outcomes by incorporating synchronous communication features—such as voice or video calls within the Telegram platform—and by redesigning tasks to require more substantive knowledge co-construction, such as collaborative problem-solving projects where students must collectively produce a single deliverable that cannot be completed through division of labor alone.

Across all three outcomes, the pattern of effects suggests that the mobile phone–based pedagogical model functioned most effectively when design features directly addressed specific learner needs through structured routines. Enthusiasm gains were strongest, likely because SDT-aligned features (autonomy, competence feedback, peer relatedness) were embedded throughout all activities and required minimal technological sophistication to implement. Learning gains were similarly robust, supported by CLT-informed chunking and scaffolding that operated independently of platform constraints. Collaboration gains, while statistically significant, were more modest—suggesting that asynchronous, text-based mobile interaction may inherently limit the depth of peer interdependence achievable without additional design innovation. This graduated pattern of effects provides evidence that mobile pedagogy works not through technology access alone, but through the alignment of specific design features with targeted learning mechanisms.

Taken together, the findings support the view that mobile-assisted pedagogy is most effective when technology use is guided by explicit pedagogical design rather than added as a stand-alone tool¹⁶. The contribution of the present study is therefore the evaluation of a structured model that links design features to theoretical mechanisms (motivation support, interaction/scaffolding, and cognitive load management) and examines their outcomes simultaneously in a school-based EFL context.

Conclusion

This study designed, validated, and evaluated a mobile phone–based pedagogical model for high school-level EFL instruction. Grounded in Sociocultural Theory, Self-Determination Theory, and Cognitive Load Theory, the model was tested through a quasi-experimental intervention involving 450 10th-grade female students over four weeks. Results demonstrated statistically significant improvements in academic enthusiasm (partial η² = 0.217), English learning outcomes (partial η² = 0.184), and collaborative learning (partial η² = 0.095) compared with traditional instruction.

The pattern of effect sizes reveals an important design insight: motivation-focused features (autonomy support, competence feedback) produced the strongest gains, followed by cognitive load management (task segmentation, scaffolding), while collaboration outcomes—though significant—were more modest. This suggests that initial mobile learning implementations should prioritize SDT-aligned design features before investing heavily in complex collaborative structures, which may require synchronous communication tools not tested in this study.

Importantly, the study moves beyond generic “mobile learning works” claims by specifying which design features (structured peer tasks, autonomy-supportive choices, segmented content) were implemented, which theoretical mechanisms they targeted, and which outcomes they influenced most strongly. This theory-to-design mapping provides educators with actionable guidance that can be adapted to resource-constrained school contexts using free platforms like Telegram or WhatsApp.

While these findings are promising, they represent an initial validation within a specific context, single-gender classes in Iranian public schools over a four-week period. Sustained effects, applicability across diverse populations, and long-term implementation fidelity remain open questions requiring further research. Nevertheless, the study demonstrates that systematically structured mobile-assisted pedagogy can function as a scalable, evidence-based approach to language education when grounded in explicit instructional design principles.

Implications

For Classroom Teachers. Teachers can adopt micro-redesigns instead of full curricular overhauls by integrating short, theory-aligned mobile tasks into existing lessons. Brief audio-based vocabulary previews, structured peer dialogues, and feedback exchanges on platforms such as Telegram can meaningfully enhance learning. These strategies embed essential principles from Self-Determination Theory (choice and competence feedback), Cognitive Load Theory (segmented tasks and clear instructions), and Sociocultural Theory (peer co-construction) without additional cost or technical complexity.

For School Administrators. The greatest returns derive from pedagogical training rather than hardware investment. Administrators should prioritize design-focused professional development over device procurement. A phased rollout—beginning with one mobile-enhanced lesson per week—supports effective scaling, monitoring, and iterative improvement while keeping financial and logistical demands modest.

For Policymakers. Policy efforts should emphasize instructional design quality and equitable access rather than device mandates. Key actions include funding teacher training aligned with SDT, CLT, and SCT principles; developing open repositories of validated task templates; and ensuring mobile data support for disadvantaged students. National guidelines could define minimum quality criteria—such as clear success indicators and competence-oriented feedback—to standardize mobile pedagogy across contexts.

For Researchers. Future work should identify which mobile-design features (autonomy support, segmentation, or collaboration) drive particular outcomes, extend testing across genders and cultural contexts, and assess long-term sustainability beyond novelty effects. Streamlined monitoring protocols would further enable reliable school-based replication.

Future research

Future studies should investigate the scalability of the model in diverse cultural and gender contexts, examine long-term effects, and evaluate its impact on specific English skills. Exploring other mobile platforms, gamification strategies, and teacher professional development models would further advance the field.

Author contributions

Authorship Contribution Statement S.I. (Siros Izadpanah), M.C. (Mahdis Charmi), and D.W. ( Dai Wenji) collaboratively contributed to the study design, data analysis, and manuscript development. D.W. conducted re‑analysis and refinement of the Confirmatory Factor Analysis (CFA), integrated Sociocultural, Self‑Determination, and Cognitive Load Theories within the pedagogical framework, and substantively revised both the theoretical and methodological sections. S.I. conceptualized the pedagogical model, supervised data collection and validation, and prepared the final version of the manuscript for submission. M.C. performed the initial statistical analyses, organized figures and tables, and assisted in drafting and proofreading the manuscript. All authors reviewed and approved the final manuscript.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethical approval

Approval ID: Research Ethics Committees of Islamic Azad University-Zanjan Branch: IR.IAU.Z.REC.1402.087. Written informed consent was obtained from all participants’ parents or legal guardians, and written assent was obtained from the students prior to data collection. The consent process explicitly addressed the use of Telegram for instructional communication. To protect participants’ privacy, students were instructed to adjust Telegram settings to hide phone numbers, no personal identifiers were collected, and online interactions were monitored by teachers and the research team.

Competing interests

There is no ethical or conflict of interest in this research. All the participants filled out consent forms.