EDUKASIA: Jurnal Pendidikan dan Pembelajaran Vol.
7, 1 (January-June, 2.
, pp.
ISSN: 2721-1150.
EISSN: 2721-1169.
DOI: 10.
62775/edukasia.
Innovation in Blended Learning: A Solution to the Shortage of Computer Laboratories in Information.
Communication, and Technology Courses Sagiman1.
Laras Shesa1.
Muhammad Istan1.
Ruly Morganna1 Institut Agama Islam Negeri Curup.
Indonesia
ARTICLE INFO
Keywords:
instructional innovation.
blended learning.
learning model Article history:
Received 2025-11-06
Revised 2025-01-13
Accepted 2026-02-15
ABSTRACT
Advances in digital technology have created substantial opportunities for instructional innovation, particularly in higher education institutions facing limitations in physical facilities such as computer laboratories.
This study aims to develop and evaluate the effectiveness of a blended learning model supported by Google Classroom as a solution to the shortage of computer laboratories in the Information and Communication Technology course.
The research method employed was research and development with an instructional design approach based on the ADDIE model (Analysis.
Design.
Development.
Implementation.
Evaluatio.
Data were collected through observations, interviews, questionnaires, and analysis of student performance within the blended learning The findings revealed that the blended learning model not only enhanced studentsAo learning independence but also improved teaching effectiveness by optimizing digital Moreover, the implementation of Google Classroom provided flexibility in accessing materials, facilitated more dynamic interaction, and reduced the operational burden on computer laboratories.
The novelty of this research lies in the integration of digital instructional strategies with the specific needs of faith-based higher education institutions, which remain underexplored in the higher education literature.
Accordingly, this study contributes to the development of adaptive instructional models that can be applied in other educational institutions facing similar infrastructural constraints.
This is an open access article under the CC BY-NC-SA license.
Corresponding Author:
Sagiman Institut Agama Islam Negeri Curup.
Indonesia.
sagiman@iaincurup.
INTRODUCTION
The rapid development of information and communication technology (ICT) has reshaped educational systems worldwide, compelling universities to integrate digital tools to enhance learning https://jurnaledukasia.
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188 of 202 quality and strengthen graduatesAo competitiveness in the digital era (Arikarani & Amirudin, 2.
Alongside these global demands, higher education institutions are expected to prepare students with adequate digital literacy and practical technological skills to remain relevant in an increasingly interconnected and innovation-driven world (Farias-Gaytan et al.
, 2.
Despite these expectations, many universities still face significant infrastructure limitations that hinder effective ICT-based learning (Rasimin et al.
, 2.
At IAIN Curup, for instance, the availability of computer laboratories is far below the actual learning needs.
The existing laboratories can accommodate only about 20% of the total student demand, resulting in long queues, restricted access to essential practice sessions, and unequal learning opportunities between students who rely solely on campus facilities and those who have personal devices.
Such constraints directly affect the achievement of learning outcomes, particularly those requiring hands-on digital skills (Ulfah & Saeful Anwar, 2.
To address these challenges, higher education institutions must adopt learning strategies that reduce dependence on limited physical facilities without compromising instructional quality (Abekiri et al.
, 2.
Blended learning has emerged as a promising alternative that combines face-to-face and online instruction to provide flexible, accessible, and interactive learning experiences (Ilham et al.
Digital platforms such as Google Classroom support the distribution of materials, discussions, assessments, and feedback (Stokes, 2.
, enabling students to continue learning beyond classroom hours while maintaining structured guidance from lecturers.
A considerable body of research demonstrates the effectiveness of blended learning in improving learning quality.
Myller and Wulf .
emphasize that blending online and face-to-face instruction enhances flexibility and interaction, whereas Anthony et al.
highlight pedagogical richness, access to knowledge, social interaction, cost effectiveness, ease of revision, and flexibility as key advantages of the approach.
Kang and Kim .
further show that students engaged in blended learning tend to achieve better academic outcomes than those in traditional settings due to increased autonomy and active participation.
In faith-based higher education, however, blended learning implementation requires contextual Fakhri et al.
argue that technology integration must be aligned with value-based instructional practices, while Mahfiroh et al.
report that students in such institutions often encounter challenges in digital readiness and infrastructure availability.
Additional studies, such as Kumar et al.
, show that Google Classroom can enhance engagement and feedback, though it requires lecturer training and continuous evaluation.
Moreover.
Shohel et al.
and Xu .
reveal that blended learning effectively addresses limitations in physical laboratories and expands access to learning resources.
Although prior studies highlight the potential of blended learning across various contexts (Kayi.
Mizza et al.
, 2025.
Radovan et al.
, 2024.
Sala et al.
, 2.
, research remains limited on how blended learning can be systematically designed to address infrastructure shortages, particularly within faithbased institutions where instructional characteristics and value-oriented learning hold distinct This gap is evident in ICT-related courses at IAIN Curup, where inadequate computer laboratories and diverse levels of student digital readiness pose significant instructional challenges.
Previous research .
McCarthy & Palmer, 2.
has not yet offered a contextualized blended learning model tailored to these dual constraints: infrastructure limitations and value-based academic Therefore, this study explicitly aims to develop a Google Classroom-assisted blended learning model as a contextual solution to overcome computer laboratory shortages in ICT courses at IAIN Curup.
Specifically, the study seeks to .
design a pedagogical framework that strategically combines essential face-to-face practical sessions with structured online learning.
ensure equitable access to digital materials, simulations, and continuous feedback.
support independent learning beyond Sagiman.
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Muhammad Istan.
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189 of 202 classroom hours.
enhance teaching efficiency through digital management of learning activities.
This model is expected to provide a measurable and contextually appropriate strategy to maintain the quality of practical ICT instruction despite infrastructural constraints.
METHODS
Design This study adopted a research and development (R&D) design with a quasi-experimental pretestposttest control group approach to develop and test a blended learning model assisted by Google Classroom.
The design was chosen to address the shortage of computer laboratories in the Computer Applications course .
lso referred to as the Information and Communication Technology cours.
at IAIN Curup.
The R&D framework followed the ADDIE model (Analysis.
Design.
Development.
Implementation.
Evaluatio.
to systematically create, implement, and evaluate the instructional model.
A quasi-experimental component was integrated to compare the model's effectiveness against conventional teaching methods, using pretest and posttest scores to measure improvements in student learning outcomes and independence.
Participants and Locus The study was conducted at Institut Agama Islam Negeri Curup (IAIN Curu.
, specifically within the Faculty of Tarbiyah.
Participants were undergraduate students enrolled in the Computer Applications course from the Islamic education department in the 4th semester.
Convenience sampling was employed to select participants, as the groups were based on existing class assignments to minimize disruption to the academic schedule.
For the limited preliminary trial, 32 students from one class participated.
For the large-scale trial, 100 students were divided into two groups: an experimental group .
that received the blended learning intervention and a control group .
that followed conventional face-to-face methods.
Lecturers teaching the course were also involved in interviews and Procedure of Development (ADDIE) The development procedure was implemented empirically through the ADDIE stages, focusing on practical application in the context of limited laboratory access.
In the Analysis stage, needs were identified through direct classroom observations of 10 sessions, semi-structured interviews with 5 lecturers and 20 students, and questionnaires distributed to 100 These revealed that computer laboratories accommodated only 20% of demand, with key barriers including student digital readiness .
verage self-reported proficiency score of 3.
and inconsistent internet access.
This data informed the model's focus on Google Classroom for off-campus The Design stage involved creating a blueprint for the blended model, integrating online (Google Classroom for material distribution, discussions, and assignment.
and face-to-face sessions .
aboratory-based collaboration and consultatio.
The structure balanced theory .
nline readings and modelin.
with practice .
n-lab project.
, drawing from constructivist, behaviorist, cognitivist, and connectivist theories.
Specific features included phased learning sequences .
, knowledge construction online, application in-la.
and assessment rubrics for quizzes and projects.
During Development, the design was translated into materials: 14 digital modules, video tutorials, and worksheets uploaded to Google Classroom, aligned with the undergraduate curriculum.
A limited Sagiman.
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190 of 202 preliminary trial was conducted over three meetings with 32 students to test feasibility, resulting in iterative revisions such as adding mobile-friendly formats based on feedback.
Implementation occurred over 14 meetings .
ne online and one face-to-face per topi.
in the Computer Applications course.
Students in the experimental group accessed materials via Google Classroom, completed individual and group tasks, and received feedback, while the control group used traditional lectures.
Monitoring included weekly logs of student engagement .
, 85% completion rate in experimental grou.
The Evaluation stage used formative .
ngoing feedback via questionnaires after each cycl.
and summative approaches .
re/posttests, final interview.
Adjustments were made based on data, such as enhancing video clarity after the first cycle.
Instruments There were three main instruments utilized in this study.
The first referred to learning outcome tests .
retest/posttes.
with 40 multiple-choice and practical items.
The tests assessed cognitive, affective, and psychomotor domains (Cronbach's alpha = 0.
content validity index.
CVI = 0.
The testsAo indicators encompassed cognitive knowledge recall .
, understanding key concepts in computer application.
, affective attitudes toward technology integration .
, motivation and interest in ICT task.
, and psychomotor skills application .
, proficiency in software usage and problemsolving in digital environment.
The tests were adapted from multidimensional assessment frameworks in blended learning contexts (Jeong & Gonzylez-Gymez, 2.
The second was the student independence questionnaire .
Likert-scale items, alpha = 0.
construct validity confirmed via confirmatory factor analysis.
CFA fit indices: CFI = 0.
RMSEA =
This instrument measured self-directed learning, with indicators including self-management .
, planning and organizing learning activitie.
, desire for learning .
, initiative in seeking resource.
, and self-control .
, monitoring progress and adjusting strategie.
This instrument was drawn from validated scales correlating self-directed learning with academic performance in higher education (Dehghani & Ghaffarifar, 2.
The third was the perception questionnaire .
items, alpha = 0.
content validity ratio.
CVR = .
on model usability, featuring indicators such as ease of use .
, navigation and accessibility of Google Classroo.
, content quality .
, relevance and clarity of material.
, and interaction effectiveness .
, feedback and collaboration feature.
This instrument was based on the Blended Learning Usability Evaluation Questionnaire (BLUE-Q) validation studies (Arora et al.
, 2.
The fourth was the semi-structured interview guidance for 10 students and 5 lecturers exploring experiences, with indicators probing benefits .
, flexibility in learnin.
, challenges .
, technical barrier.
, and overall satisfaction .
, impact on engagement and outcome.
This instrument was informed by qualitative guides for blended learning implementations (Batista-Toledo & Gavilan, 2.
The fifth was the classroom observation checklist tracking engagement and interactions during 8 sessions .
nter-rater reliability kappa = 0.
face validity established through expert revie.
, including indicators for behavioral engagement .
, participation in task.
, emotional engagement .
enthusiasm and persistenc.
, and cognitive engagement .
, critical thinking and collaboratio.
This instrument was derived from observation protocols for blended environments (National Education Association, 2.
Data Collection Techniques Data were collected through mixed methods.
Quantitative data came from pre/posttests administered before and after implementation, and questionnaires distributed online via Google Forms Sagiman.
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191 of 202 to all participants post-intervention.
Qualitative data were gathered via interviews .
ecorded and transcribed, lasting 20-30 minutes eac.
and observations .
oted during face-to-face session.
All data collection occurred from February to September 2025, with ethical approval from IAIN Curup's research committee, ensuring informed consent and anonymity.
Data Analysis Techniques Quantitative data were analyzed using SPSS v.
Paired-sample t-tests examined within-group differences .
re/posttes.
for the limited trial and experimental group, while independent-sample ttests compared posttest scores between experimental and control groups .
ssuming normality via Shapiro-Wilk tests, p > 0.
Effect sizes were calculated using Cohen's d.
Qualitative data from interviews and observations underwent thematic analysis.
Transcripts were coded inductively .
themes like "increased autonomy" and "infrastructure barriers"), with inter-coder reliability .
appa = .
verified by two researchers.
Triangulation integrated quantitative and qualitative findings for a comprehensive evaluation of the model's effectiveness in addressing laboratory shortages.
FINDINGS AND DISCUSSION
Findings Through the flow of ADDIE model, this studyAos blended-learning model was developed through a couple of steps, namely analysis, design, development, implementation, and evaluation.
The analysis phase laid the groundwork for the developed blended-learning model in the Computer Applications course by specifying intended outcomes, structuring instruction, and situating delivery within learner and environmental realities.
Drawing from course competencies, the study set outcomes spanning cognitive, affective, and psychomotor domains: students should articulate the roles of computer applications in personal and professional contexts.
proficiently use productivity suites.
employ creative software for graphics, photo editing, and animation.
apply design principles to produce purposeful digital artefacts.
and diagnose authentic problems to select and justify suitable technological solutions.
Instructional analysis indicated needs for stronger digital literacy, analytical and creative thinking, problem-solving, time management, collaboration, and communication.
The modelAos architecture combined constructivist, behaviorist, cognitivist, and connectivist perspectives to sequence online and face-to-face work, with each phase serving a distinct instructional function.
Learner and context profiling showed heterogeneous preparation and digital readiness, alongside adequate institutional infrastructure .
aboratories and a learning platfor.
but variable connectivity.
these constraints were addressed through scaffolds, flexible pacing, and multimodal resources.
The synthesis aligned objectives, strategies, and performance standards with learner characteristics and setting, positioning subsequent design and development for coherent implementation and evaluation.
The design phase translated the analysis into a coherent framework for the developed blendedlearning model in the Computer Applications course.
It specified the instructional flow, clarified instructor and student responsibilities, set measurable performance objectives, and defined assessment The course integrated online and face-to-face sessions informed by complementary learning theories.
the instructional flow is summarized in Table 1.
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192 of 202 Table 1.
Instructional phases of the developed model Phase Knowledge Construction Mode Online Theoretical Basis Constructivism Modeling Online Behaviorism Collaborative Learning and Practice Individual Learning and Application Face-toface Constructivism & Connectivism Face-toface Cognitivism Consultation with Instructor Face-toface Constructivism & Cognitivism Learning Evaluation Online & Face-toface Behaviorism Main Activities Students engage with readings and prior experiences, supported by online Learners study cases and examples, followed by practice and discussion.
Small groups apply concepts, share perspectives, and complete projects.
Students work independently on tasks that involve problem analysis and decision making.
Instructor provides feedback, clarification, and mentoring through Progress is measured through quizzes, projects, and summative assessments with feedback.
This sequencing linked theory to practice and balanced digital preparation with in-lab application.
In practice, instructors curated resources, guided interaction, moderated activities, and provided timely feedback, while students engaged actively in discussions, collaborated on projects, completed individual tasks, and reflected during consultation.
Performance objectives required students to communicate the role of computer applications in professional and everyday contexts, demonstrate proficiency with productivity tools, produce creative digital outputs using specialized software, apply design principles to their work, and analyze authentic problems to select appropriate technological Assessment captured both process and outcomes through quizzes, online interaction, ongoing feedback, project/product evaluation, and final examinations, supported by explicit rubrics to ensure fairness and reliability.
The development stage translated the design into deployable materials, assessment tools, and a working prototype of the developed blended-learning model.
Digital modules, readings, case tasks, and practice worksheets were produced and uploaded to Google Classroom to support both online preparation and in-lab application.
group projects and individual assignments were aligned to ensure continuity, supplemented by visual aids, project briefs, and interactive media.
The model was consolidated into a handbook that sets out its conceptual basis, phased syntax, roles, performance benchmarks, and assessment procedures.
The overview of the developed blended learning model in this study can be seen in Table 2.
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193 of 202 Table 2.
Overview of the developed blended-learning model Component Goals Context Target Learners Learning Materials Sub-Component Learning Independence Academic Achievement Course Students Meeting 1 & 2 Meeting 3 & 4 Meeting 5 & 6 Meeting 7 & 8 Meeting 9 & 10 Meeting 11 & 12 Meeting 13 & 14 Learning Structure Phase 1 Ae Knowledge Construction Phase 2 Ae Modeling Phase 3 Ae Collaborative Learning Phase 4 Ae Independent Learning Phase 5 Ae Consultation Phase 6 Ae Evaluation Description Enhance studentsAo capacity for self-directed learning.
Improve overall learning performance in the Computer Applications course.
Computer Applications .
ndergraduate leve.
Undergraduate (BachelorAos degre.
Introduction to computer applications and word-processing software .
elivered in online and offline session.
Data processing applications and presentation tools .
nline and offlin.
Project management applications and photo-editing tools .
nline and offlin.
Basic and advanced graphic design .
nline and offlin.
Basic and advanced animation .
nline and offlin.
Design principles and their application in creative digital products .
nline and offlin.
Case studies: selecting appropriate applications to solve problems in business contexts .
nline and offlin.
Online mode.
Constructivist basis.
delivered via Google Classroom and supported by ICT media.
Students construct knowledge through readings, reflection, and online Online mode.
Behaviorist basis.
implemented through Google Classroom and ICT media.
Students learn from case examples and practice tasks.
Face-to-face mode.
Constructivist and Connectivist basis.
conducted in the computer laboratory using ICT media.
Students work in groups to complete tasks and projects.
Face-to-face mode.
Cognitivist basis.
conducted in the computer laboratory with ICT media.
Students complete individual assignments involving analysis and decision Face-to-face mode.
Constructivist.
Connectivist, and Cognitivist basis.
learning consultation with the instructor in the computer laboratory.
Students clarify concepts, receive feedback, and refine understanding.
Online and face-to-face mode.
Behaviorist basis.
conducted through formative and summative methods.
Evaluation includes quizzes, projects, and final examinations with continuous feedback.
To measure learning across cognitive, affective, and psychomotor domains, the team constructed knowledge tests, product rubrics, collaboration checklists, and structured feedback forms, then sought expert review and conducted pilot trials to establish validity and reliability.
Findings from expert appraisal and pilots informed iterative revisions to materials, sequencing, and evaluation criteria, ensuring that the developed model was feasible, pedagogically robust, and responsive to learner needs.
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194 of 202 The implementation stage applied the developed blended-learning model in the Computer Applications course through a structured sequence of 14 meetings, each topic delivered in two sessions, namely one online via Google Classroom and one face-to-face in the computer laboratory, to balance conceptual preparation with hands-on practice.
Online sessions emphasized knowledge construction and modeling through readings, tutorials, case demonstrations, and guided discussion.
offline sessions focused on collaborative work, individual application, and consultation with the instructor.
The sequence progressed from introduction to computer applications and word processing (Meetings 1Ae.
, to data processing and presentation tools .
Ae.
, project management and photo editing .
Ae.
, basic and advanced graphic design .
Ae.
, basic and advanced animation .
Ae.
, design principles and their application .
Ae.
, and business-oriented case studies requiring tool selection and solution design .
Ae Throughout, instructors curated resources, moderated interaction, supervised lab activities, and provided timely feedback.
students engaged actively online, collaborated on projects, completed individual tasks, and refined outputs during consultation.
Formative feedback was integrated continuously, and summative assessments concluded the cycle.
The staged delivery effectively linked theory to practice, supported both individual mastery and group performance, and demonstrated the modelAos feasibility and potential to strengthen learner independence and achievement.
The subsequent evaluation phase was conducted to measure the effectiveness of the developed blended learning model.
Based on the evaluation results, the following quantitative data support the effectiveness of the blended learning model assisted by Google Classroom in enhancing student independence and academic achievement in the Information and Communication Technology course at IAIN Curup.
A limited trial was carried out over three meetings using pretest and posttest methods to measure the improvement in studentsAo learning independence.
The trial results are presented in the following table:
Table 3.
Results of the limited trial Meeting Mean Pretest Mean Posttest Difference t-value Sig.
-valu.
From the table above, the results of the t-test indicate that the Sig.
-valu.
is smaller than 0.
= 0.
, which means that studentsAo learning independence increased significantly after participating in the blended learning model.
The higher mean posttest scores demonstrate that this model supported students in better understanding the material.
A large-scale trial was then conducted using a pretestAeposttest control group design involving two groups of students, namely the experimental group .
hich used blended learnin.
and the control group .
hich used conventional learning method.
The mean posttest results of the two groups are presented in the following table:
Table 4.
Results of the large-scale trial Group Experimental Control N (Number of Student.
Mean Posttest Score These results indicate that students who learned through the blended learning model obtained higher mean scores compared with the control group that used conventional teaching methods.
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195 of 202 further examine the effectiveness of the model, an independent sample t-test was conducted.
The results are presented in the following table:
Table 5.
Independent sample t-test results t-value Sig.
-taile.
Mean Difference 95% Confidence Interval 558 Ae 17.
The t-test results show that the p-value = 0.
000 < 0.
05, which indicates a significant difference between the experimental and control groups.
This finding demonstrates that the blended learning model supported by Google Classroom is significantly more effective in improving studentsAo learning outcomes compared with conventional methods.
Based on the questionnaire administered to students after the implementation of the blended learning model, the results are as follows:
Table 6.
Student questionnaire results after the implementation of blended learning Response Category Very interesting and enjoyable Interesting and easy to follow Fairly interesting Less interesting Percentage of Students (%) The majority of students .
66%) reported that the model was very interesting and enjoyable, while 44% stated that learning through the blended learning model was interesting and easy to Only 4.
33% of students considered the model to be fairly interesting, and none of the students indicated that the method was less interesting.
The following figure illustrates a comparison of the mean posttest scores between the experimental group and the control group:
Comparison of Mean Posttest Scores Mean Posttest Scores N (Number of Student.
Mean Posttest Score Column1 Experimental Group Control Group Figure 1.
Comparison of mean posttest scores Sagiman.
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196 of 202 From the diagram above, it is evident that the mean score of the experimental group was higher than that of the control group, confirming that the blended learning method enhanced studentsAo learning outcomes.
Based on the quantitative data obtained, it can be concluded that the blended learning model supported by Google Classroom proved effective in improving both student independence and learning achievement.
Statistical tests demonstrated significant differences between students who learned through this model and those who continued with conventional teaching In addition, student responses to this model were overwhelmingly positive, with the majority reporting that the method was more engaging and facilitated their understanding of the material.
Therefore, the blended learning model can serve as an innovative solution to overcome the shortage of computer laboratories at the Faculty of Tarbiyah.
IAIN Curup.
Building on these quantitative findings, classroom observations corroborated a clear shift in learning behaviors.
Students demonstrated greater autonomy, participated more actively in asking questions and engaging in discussion, and managed their study time more efficiently.
Face to face sessions consequently pivoted toward problem solving and conceptual deepening, while foundational content delivery was accommodated in the online space.
Lecturers were thereby able to devote more time to academic mentoring and individualized guidance, since baseline explanations had already been addressed through the digital materials.
Interview data further reinforced these patterns.
Both students and lecturers reported that the model improved conceptual understanding and expedited feedback cycles, which in turn sustained momentum between sessions.
At the same time, participants highlighted several practical constraints that tempered implementation, notably the stability of internet access, uneven availability of personal devices, and the continuing need for training in platform use and in the design of high-quality digital Questionnaire evidence aligned with the observational and interview results and indicated strong acceptance of the model.
A majority of students evaluated the learning experience as very interesting and enjoyable at 51.
66 percent, while 44.
00 percent rated it interesting and easy to understand.
Only 33 percent considered it fairly interesting, and none rated it less interesting.
Taken together, these qualitative and survey-based findings provide convergent evidence that the blended learning approach supported by Google Classroom fostered higher engagement and more effective learning processes, while also identifying concrete infrastructural and capacity building priorities for sustained adoption.
Discussion The blended learning model was developed that integrates face-to-face sessions with online learning through Google Classroom, addressing the shortage of computer laboratories in ICT courses at IAIN Curup.
This model demonstrated higher achievement relative to conventional instruction, evidenced by a significant posttest mean difference of 13.
33 points .
= 7.
02, df = 98, p < 0.
, with a 95% confidence interval from 9.
56 to 17.
10, indicating not just flexibility but measurable learning gains through timely feedback and iterative practice.
In limited trials, pretestAeposttest gains of nine to eleven points per session were consistently significant .
= 0.
, supporting enhanced conceptual understanding and autonomy.
The instructional structure balanced theory and practice by shifting face-to-face time to higher-order activities like problem-solving, while offloading foundational content online, aligning with observed increases in participation and time management.
Google Classroom served as an enabling infrastructure for rapid feedback, structured interaction, and multi-device accessibility, shortening response times and driving efficiency in technology-supported instruction (Gupta & Pathania, 2021.
Hussein et al.
, 2022.
Perrotta et al.
, 2.
Digital resources, including e-books, articles, and interactive exercises, afforded spaced review and self-paced repetition, which interviewees linked to improved outcomes and triangulated with posttest advantages.
The digital evaluation system Sagiman.
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197 of 202 enhanced transparency, allowing immediate results and longitudinal monitoring, explaining faster task completion and self-regulation (Cao et al.
, 2025.
Cosa & Torelli, 2024.
Vancoillie et al.
, 2.
Synthesizing evidence, the model's theory of change, flexible access, rapid feedback, and structured interaction, explains improvements in outcomes, task efficiency, and motivation, positioning it as a viable solution for infrastructural constraints.
The results of this study align with and extend prior research on blended learning, particularly in the context of ICT courses in higher education.
For instance, several similar studies underscore the effectiveness of blended approaches in enhancing student outcomes through technology integration.
Chen et al.
reported increased participation and problem-solving skills in blended ICT courses at Chinese universities, mirroring our findings as both emphasize balancing online preparation with lab-based application to boost cognitive and psychomotor domains.
Godsk and Myller .
demonstrated that educational technologies in blended formats raised motivation and pass rates in European higher education, akin to our results through interconnected behavioral and affective engagement via platforms like Google Classroom.
Ndibalema .
highlighted digital literacy enhancements in sub-Saharan African HEIs via blended methods, paralleling our independence gains as both address infrastructural gaps with multimodal resources.
Mei et al.
showed ARCSmotivated blended designs improved self-efficacy in STEM courses, consistent with our feedback cycles fostering self-regulation.
Alam and Ogawa .
noted ICT readiness boosts in South African graduates through blended training, similar in promoting employability skills amid connectivity Attridge and OAoMahony .
reported LMS integration in apprenticeships enhanced collaboration, echoing our collaborative phases in lab sessions.
Egara and Mosimege .
found blended simulations improved analytical thinking in business ICT, comparable to our project-based tasks translating theory to practice.
Finally.
Chaves-Yuste and de-la-Peya .
evidenced costeffective flexibility in Asian HEIs' blended models, reinforcing our equity-oriented rationale for lab These similarities arise from a common focus on hybrid modalities in higher education to leverage digital tools for better access and interaction, teaching us that contextual scaffolding, like our ADDIE-based design, amplifies gains in diverse settings by mitigating reliance on physical In contrast, five differing studies reveal challenges or suboptimal outcomes in blended learning implementations, highlighting variances in context or execution.
Fisher et al.
showed delayed feedback in Australian accounting hybrids reduced motivation beyond 10 days, contrasting our rapid cycles as it lacked platform automation, emphasizing timely tech integration to sustain engagement.
Simon et al.
reported subjectivity and bias in participation assessment during Philippine onlineoffline shifts, unlike our rubric-supported evaluations, revealing that clear criteria prevent demotivation in large classes.
Fabian et al.
noted disconnection and rushed pacing in Belgian hybrid lectures, diverging from our balanced sequencing due to poor modality alignment, teaching the value of phased designs for social cohesion.
Lastly.
Godsk and Myller .
highlighted informal learning disruptions from tech in UK universities, differing as our model preserved face-to-face depth, illustrating that over-digitization risks affective isolation without hybrid complementarity.
These differences stem from inadequate infrastructural support or rushed adoptions in non-ICT-focused or under-resourced contexts, from which we learn that proactive fidelity checks and adaptive training can transform potential pitfalls into strengths, ensuring broader applicability.
As implications, the blended learning model significantly enhanced student learning independence and motivation through three core mechanisms: on-demand access to materials, rapid feedback cycles, and structured collaborative spaces that normalized question-asking and active Quantitative results from limited and large-scale trials, combined with qualitative evidence from interviews and observations, showed that students developed stronger self-regulation, better time Sagiman.
Laras Shesa.
Muhammad Istan.
Ruly Morganna / Innovation in Blended Learning: A Solution to the Shortage of Computer Laboratories in Information.
Communication, and Technology Courses EDUKASIA: Jurnal Pendidikan dan Pembelajaran.
Vol.
7, 1 (January-June, 2.
198 of 202 management, and higher autonomy, as they shifted from passive recipients to self-directed learners capable of revisiting content and refining work independently.
Survey data revealed overwhelmingly positive perceptions, with 51.
66% rating the experience as very interesting and enjoyable and 44.
as interesting and easy to understand, triangulating with increased participation and aligning with expectancy-value theory, where perceived control and task value sustain engagement.
For lecturers, the model reallocated effort from content delivery to high-value facilitation and individualized mentoring, supported by Google ClassroomAos automation features .
ssignment scheduling, instant grading, and activity log.
, allowing deeper focus on problem-solving during face-to-face sessions.
Overall, the model not only raised academic performance but also fostered lasting motivational and self-regulatory gains, offering a scalable pathway for resource-constrained institutions to cultivate 21stcentury learning competencies while preserving meaningful instructorAestudent interaction.
This study contributes a novel Google Classroom-supported blended learning model, developed through the ADDIE framework, specifically tailored for ICT courses in faith-based higher education institutions facing acute computer laboratory shortages.
It delivers robust quasi-experimental evidence .
osttest mean difference 13.
33 points, t.
= 7.
02, p < .
that the six-phase design .
nowledge construction, modeling, collaborative and independent practice, consultation, and evaluatio.
significantly outperforms conventional teaching while markedly enhancing student independence and By contextualizing digital pedagogy within the value-oriented environment of IAIN Curup, the research fills a critical gap in the literature and provides a replicable, equity-focused blueprint readily adaptable by other resource-constrained universities, especially Islamic higher education institutions across Indonesia and similar developing contexts.
As limitations, despite positive outcomes, the study faced constraints in device ownership and internet stability, which created unequal access to online components, interrupted feedback cycles, and potentially threatened internal validity and equity of impact.
Conducted at a single faith-based institution (N = .
with a specific ICT course focus, results may not fully generalize to secular or better-resourced settings.
Future implementations should therefore incorporate device-lending programs, expanded campus Wi-Fi, data subsidies, and systematic lecturer training while larger, multisite studies are needed to establish minimum connectivity thresholds and strengthen external validity.
CONCLUSION
The innovation of blended learning supported by Google Classroom represents a strategic solution to the shortage of computer laboratories, which has long been a barrier to technology-based instruction.
This model integrates face-to-face and online learning, enabling students to access materials, complete assignments, and interact with lecturers flexibly without relying on the availability of computer Through blended learning, students are able to use their personal devices to study essential concepts, while face-to-face sessions are more focused on discussion, problem-solving, and indepth academic guidance.
In addition, the use of Google Classroom facilitates the distribution of materials, assignment submission, and evaluation of learning outcomes in an efficient manner.
The implementation of this model has been shown to improve studentsAo learning independence, strengthen the effectiveness of lecturersAo teaching, and optimize the use of available resources in higher education.
Although challenges remain in its implementation, such as the readiness of students and lecturers to adopt technology, the findings indicate that with appropriate training and adaptation, blended learning can serve as an innovative, effective, and sustainable instructional method to enhance the quality of higher education.
Based on the findings of this study, several recommendations can be made for policymakers to enhance the effectiveness of technology-based learning.
First, training programs should be provided for both lecturers and students in the use of educational technologies.
Such training should include the Sagiman.
Laras Shesa.
Muhammad Istan.
Ruly Morganna / Innovation in Blended Learning: A Solution to the Shortage of Computer Laboratories in Information.
Communication, and Technology Courses EDUKASIA: Jurnal Pendidikan dan Pembelajaran.
Vol.
7, 1 (January-June, 2.
199 of 202 use of Google Classroom, the development of digital instructional materials, and interactive technology-based teaching strategies.
With this training, both lecturers and students will be better prepared to optimize the use of blended learning as an integral part of their academic activities.
Second, improving digital infrastructure in higher education institutions is essential to ensure the sustainability of this model.
Universities may provide additional computer laboratories that students can access outside of regular class hours and strengthen campus internet networks to guarantee more stable access to online learning.
In addition, universities can collaborate with internet service providers to offer special data packages for students participating in online courses.
Third, the curriculum must be further developed to support the implementation of blended learning.
Curricula should be designed to enable an effective combination of online and face-to-face instruction.
Technology-related courses in particular need to be developed with more innovative approaches so that students can gain learning experiences that are more relevant to current advancements.
Fourth, continuous evaluation of the blended learning model should be conducted regularly to identify strengths as well as challenges that still need to be Higher education institutions can administer surveys and interviews with both students and lecturers to assess the effectiveness of this model and adapt teaching methods based on the feedback received.
Through regular evaluations, the blended learning model can continue to evolve and be adjusted to meet the changing academic needs.
By implementing these recommendations, the blended learning model supported by Google Classroom is expected to serve as a long-term solution for improving instructional quality.
Although challenges remain, with proper institutional support and the readiness of all stakeholders, this model holds significant potential to become a sustainable teaching method that is highly relevant to the demands of education in the digital era.
REFERENCES