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Al-Hijr: Journal of Adulearn World
Published by Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar
ISSN : 28293894     EISSN : 28294351     DOI : https://doi.org/10.55849/alhijr
Core Subject : Science, Education,
Computer Science & IT Education Languange, Linguistic, Communication & Media Other
Al-Hijr: Journal of Adulearn World is a multi-disciplinary, peer-refereed open-access international journal which has been established for the dissemination of state-of-the-art knowledge in the field of education, teaching, development, instruction, educational projects and innovations, learning methodologies, and new technologies in education and learning. The journal publishes state-of-art papers in fundamental theory, experiments, and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion, and concise conclusion. As our commitment to the advancement of science and technology, the Al-Hijr follows the open access policy that allows the published articles freely available online without any subscription. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Umum - Umum
Articles 155 Documents
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Evaluation of the Impact of Educational Games on The Literacy Capabilities of Elementary School Students Zara Ali
Al-Hijr: Journal of Adulearn World Vol. 5 No. 1 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i1.849

Abstract

The integration of educational games into the learning process has garnered significant attention due to their potential to enhance student engagement and learning outcomes. Particularly in elementary education, where foundational skills such as literacy are developed, educational games are seen as a promising tool for improving literacy capabilities. However, the impact of educational games on literacy skills in elementary school students remains underexplored. This study aims to evaluate the impact of educational games on the literacy capabilities of elementary school students. The primary objective is to assess how different types of educational games influence the development of reading, writing, and comprehension skills among young learners. A quasi-experimental research design was employed, involving 120 elementary school students across four classes. The students were divided into an experimental group, which engaged in educational game-based learning, and a control group, which followed traditional literacy instruction. Data was collected through pre- and post-tests measuring literacy skills, as well as surveys to gather student feedback. The results showed that the experimental group demonstrated significant improvements in reading comprehension, vocabulary, and writing skills compared to the control group. Students in the experimental group reported increased engagement and motivation towards literacy activities. This study concludes that educational games can significantly enhance literacy capabilities in elementary school students, providing a more engaging and effective approach to teaching foundational literacy skills.
APPLICATION OF ADAPTIVE LEARNING IN IMPROVING THE LEARNING OUTCOMES OF JUNIOR HIGH SCHOOL STUDENTS Choi Sun Hui
Al-Hijr: Journal of Adulearn World Vol. 5 No. 1 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i1.857

Abstract

Adaptive learning technologies have emerged as a promising solution to address the diverse learning needs of students, offering personalized educational experiences that can improve learning outcomes. In the context of junior high school education, where students possess varied academic abilities and learning styles, adaptive learning can be particularly effective in promoting engagement and academic success. This study investigates the application of adaptive learning in improving the learning outcomes of junior high school students, focusing on its impact on both academic performance and student engagement. A mixed-methods research design was employed, combining quantitative assessments of student performance with qualitative surveys on student engagement. The study involved 200 junior high school students who participated in adaptive learning programs in subjects such as mathematics and science. The results indicate that students who engaged with adaptive learning tools demonstrated a significant improvement in academic performance, with an average increase of 18.5% in test scores. Additionally, students reported higher levels of engagement and motivation in learning activities. This study concludes that the implementation of adaptive learning systems can significantly improve learning outcomes by providing personalized, data-driven support tailored to individual student needs. Educational institutions are encouraged to integrate adaptive learning technologies to enhance student success.
ESTABLISHING THE FACTORIAL VALIDITY, RELIABILITY AND USABILITY OF META-COGNITIVE AWARENESS INVENTORY IN PHYSICS (MAIP) FOR MALE AND FEMALE STUDENTS IN NIGERIA Barnabas Obiaje Ellah
Al-Hijr: Journal of Adulearn World Vol. 5 No. 1 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i1.882

Abstract

The study was on establishment of factorial validity, reliability and usability of Meta-cognitive Awareness Inventory in Physics (MAIP) for male and female students in Nigeria. Three research questions which guided the study were answered. One null hypothesis was formulated and tested at 0.05 level of significance. The study adopted instrumentation research design. The population for the study comprised all the 15030 Senior Secondary Two (SSII) science students in the 2319 Secondary Schools. The sample comprised 1382 SSII male and female students that offered Physics as a school subject for 2020/2021 Academic Session. The instrument for data collection was Meta-cognitive Awareness Inventory in Physics (MAIP). The Meta-cognitive Awareness Inventory in Physics (MAIP) was face validated by five experts and the observations of these experts were used for the review of the items of the instrument. Construct validation was carried out on the Meta-cognitive Awareness Inventory in Physics (MAIP) for secondary schools to establish its construct validity using factor analysis. This is based on the extraction method of principal component analysis and rotation method of Varimax with Kaiser Normalization. The item selection was done using the rotated component matrix. The items with factor loading of 0.40 and above on any of the factors were identified and selected to form part of the instrument. Coefficient of internal consistency was obtained through Cronbach’s Alpha and found to be 0.78. Research question 1 was answered using factor analysis. Research question 2 was answered using coefficient of internal consistency obtained through Cronbach’s Alpha. Research question 3 was answered using mean and standard deviations. The null hypothesis was tested using independent t-test at 0.05 level of significance. The findings revealed that Meta-cognitive Awareness Inventory in Physics (MAIP) for Senior Secondary students in Benue State has 52 factorially simple or pure items and 23 factorially complex items in terms of their factor loadings. The coefficient of internal consistency of Meta-cognitive Awareness Inventory in Physics (MAIP) for Senior Secondary students in Benue State was found to be 0.98. It was also found that there was statistical significant difference in the mean meta-cognitive awareness ratings of male and female students in favour of female students using Meta-cognitive Awareness Inventory in Physics (MAIP). It was recommended among others that Physics teachers should use the Meta-cognitive Awareness Inventory in Physics (MAIP) to determine their students’ level of meta-cognitive awareness in the schools. This may enable them to gain awareness and control over themselves as learners for effective learning of Physics in secondary schools irrespective of their gender.
A DOUBLE-EDGED SWORD: WEIGHING THE POTENTIAL AND RISKS OF GENERATIVE AI IN MAINTAINING ACADEMIC INTEGRITY IN HIGHER EDUCATION Multajimah; Mohamed Shifaz; Aminath Nafeeza
Al-Hijr: Journal of Adulearn World Vol. 4 No. 4 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v4i4.1147

Abstract

Generative artificial intelligence has rapidly penetrated higher education, reshaping academic writing, assessment practices, and knowledge production, while simultaneously raising serious concerns about academic integrity. This study aims to examine generative AI as a double-edged phenomenon by analyzing its potential benefits and associated risks for maintaining academic integrity in higher education institutions. The research employed a qualitative-dominant mixed analytical design, combining systematic literature review, secondary statistical analysis, policy document analysis, and a focused institutional case study to capture conceptual, empirical, and governance dimensions of AI use. The findings reveal that generative AI does not inherently erode academic integrity; instead, integrity risks emerge primarily from unclear institutional policies, assessment models reliant on final textual outputs, and limited faculty preparedness. Institutions that implemented explicit AI guidelines, faculty training, and process-oriented assessment redesign demonstrated lower perceived misconduct and higher confidence in integrity enforcement. The study concludes that generative AI should not be addressed through prohibition-driven approaches but through adaptive governance, pedagogical innovation, and ethical literacy development. Academic integrity in the AI era depends less on technological restriction and more on institutional capacity to align policy, pedagogy, and assessment with evolving human–AI academic practices. These findings offer guidance for universities navigating responsible AI integration globally.
THE ASSESSMENT REVOLUTION: THEORIES AND METHODOLOGIES OF AUTOMATED ASSESSMENT USING MACHINE LEARNING FOR EVALUATING LEARNING PROGRESS Misbahul Khairani; Cedric Butler; Markus Rolle
Al-Hijr: Journal of Adulearn World Vol. 4 No. 4 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v4i4.1153

Abstract

Rapid advances in artificial intelligence and machine learning have fundamentally transformed educational assessment practices, shifting evaluation from episodic, human-centered measurement toward continuous, data-driven monitoring of learning progress. This study aims to examine the theoretical foundations and methodological approaches underlying automated assessment systems that employ machine learning to evaluate learning progress in diverse educational contexts. A qualitative systematic review with an integrative analytical framework was employed, drawing on peer-reviewed studies from international journals across education, learning analytics, and computer science. The selected literature was analyzed to identify dominant assessment purposes, theoretical alignments, data sources, modeling techniques, and validation strategies. The results indicate that most automated assessment systems prioritize predictive accuracy and efficiency, frequently conceptualizing learning progress through performance-oriented metrics while offering limited alignment with established assessment theories such as formative assessment and construct validity. Theory-informed and interpretable models remain underrepresented despite their pedagogical relevance. The findings reveal a persistent gap between technological innovation and educational meaning-making in automated assessment research. This study concludes that the assessment revolution driven by machine learning will remain incomplete without stronger integration of educational assessment theory, methodological transparency, and interpretability. Aligning machine learning methodologies with robust assessment principles is essential to ensure that automated systems support meaningful evaluation of learning progress, instructional decision-making, and educational equity.
INTERPRETIVE PERSPECTIVES ON HOW TO DEAL WITH STRESS AND ANXIETY IN MODERN LIFE Puja Miranda; Hasan Zaini; Irman Irman
Al-Hijr: Journal of Adulearn World Vol. 5 No. 3 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i3.1273

Abstract

Stress and anxiety are becoming significant problems in modern life, especially among individuals exposed to social and work pressures. This research aims to explore the perspective of interpretation regarding the ways taught in the Qur'an and Hadith to overcome stress and anxiety. The purpose of this research is to explore and identify spiritual solutions that can be applied in the context of daily life, especially in facing the challenges of modern times. The method used in this study is a literature review with a tafsir approach, which examines verses of the Qur'an and hadith related to peace of mind and emotion management. This study found that the Qur'an and Hadith provide very relevant guidelines, such as the importance of tawakkul (surrender) to Allah, maintaining a balance in life, and the practice of dhikr and prayer as a way to relieve stress and anxiety. In conclusion, the interpretive perspective offers a profound spiritual approach to addressing stress and anxiety in modern life, which can be implemented as part of mental health prevention and management efforts in society.
PREDICTIVE ANALYTICS IN EDUCATION: HOW MACHINE LEARNING IS SHAPING FUTURE CLASSROOMS Kasran; Kasim Yahijii; Mujahid Damopolii
Al-Hijr: Journal of Adulearn World Vol. 5 No. 2 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i2.1285

Abstract

The integration of predictive analytics in education is transforming the learning environment by enabling personalized learning pathways. Machine learning algorithms, through real-time data analysis, have the potential to forecast student performance, identify at-risk learners, and suggest timely interventions. Traditional educational methods often fail to address the diverse needs of students, and predictive analytics offers a more adaptive approach to teaching and learning. This study explores how machine learning is shaping future classrooms by assessing its impact on student outcomes, engagement, and overall learning experiences. The research aims to determine how AI-driven predictive tools can optimize learning by providing tailored content and real-time feedback. A mixed-methods approach was employed, combining quantitative assessments and qualitative interviews with students and teachers. The results indicate that students who interacted with AI-powered learning tools showed a significant improvement in academic performance and engagement. However, concerns were raised regarding the depersonalization of learning due to the AI’s lack of emotional intelligence. The study concludes that while predictive analytics offers significant benefits in personalizing education, it should complement rather than replace human interaction in the classroom. To fully harness the potential of machine learning, future research should explore the long-term impacts of AI on student development and address ethical concerns related to data privacy.
EFFECT OF PQRST METHOD ON FIFTH-GRADE STUDENTS’ SPEAKING SKILLS AT MIN 12 BANDAR LAMPUNG Adinda Thalia; Uswatun Khasanah; Yuli Yanti
Al-Hijr: Journal of Adulearn World Vol. 5 No. 2 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i2.1286

Abstract

This study aims to analyze the effect of the Preview, Question, Read, Summarize, and Test (PQRST) method on students’ speaking skills. The participants of this study were fifth-grade students who experienced difficulties in expressing ideas, organizing speech, and demonstrating confidence during speaking activities. A quantitative approach with a quasi-experimental design using a posttest-only control group was employed. The sample was selected through purposive sampling techniques. Data were collected through speaking tests and documentation. The findings revealed that students taught using the PQRST method obtained a higher average score (59.0) than students taught using the SQ3R method (31.8). Statistical analysis further indicated that the PQRST method had a significant effect at the 5% significance level. The novelty of this study lies in the implementation of the PQRST method as a structured learning strategy that improves not only reading comprehension but also speaking skills. The results suggest that the PQRST method can serve as an innovative alternative in language learning. This study contributes to the development of effective active learning strategies that encourage students’ participation, confidence, and communication skills in educational settings.
THE ROLE OF AI IN TRANSFORMING THE TRADITIONAL CURRICULUM INTO A PERSONALIZED LEARNING EXPERIENCE Nur Fauzia Dauango; Nong Chai; Jhems Richard Hasan
Al-Hijr: Journal of Adulearn World Vol. 5 No. 3 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i3.1301

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in contemporary education, creating new opportunities to address the limitations of traditional curriculum models that often fail to accommodate diverse learner needs, abilities, and learning preferences. Increasing demand for learner-centered education has encouraged educational institutions to explore AI-driven solutions capable of supporting adaptive instruction, individualized learning pathways, and data-informed decision-making. This study aims to examine the role of AI in transforming the traditional curriculum into a personalized learning experience and to explore its implications for learner engagement, self-directed learning, and educational satisfaction. A mixed-methods sequential explanatory design was employed. Quantitative data were collected from 450 participants, including students, teachers, curriculum developers, educational technology specialists, and academic administrators. Qualitative interviews were subsequently conducted to gain deeper insights into curriculum transformation processes and stakeholder experiences. Structural Equation Modeling and thematic analysis were used to analyze the data. Findings revealed that AI utilization significantly enhanced curriculum flexibility, personalized learning effectiveness, learner engagement, self-directed learning, and learning satisfaction. Personalized learning effectiveness emerged as a key mediating factor linking AI implementation to positive educational outcomes. Qualitative evidence further demonstrated that adaptive learning systems and learning analytics facilitated more responsive and individualized educational experiences. The study concludes that AI serves as a strategic enabler of curriculum transformation, supporting the development of flexible, learner-centered, and personalized learning ecosystems.
AI, BIAS, AND EQUITY IN EDUCATION: ADDRESSING CHALLENGES IN ALGORITHMIC DECISION MAKING Yayu Laode; A. Asdar; Najamuddin Petta Solong
Al-Hijr: Journal of Adulearn World Vol. 5 No. 3 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i3.1302

Abstract

Artificial Intelligence (AI) has become increasingly integrated into educational systems through predictive analytics, automated assessments, learning management platforms, admissions processes, and student support services. Growing reliance on algorithmic decision-making technologies has created opportunities to improve efficiency, consistency, and scalability in educational administration and instruction. Concerns regarding algorithmic bias, transparency, accountability, and educational equity have simultaneously emerged as critical challenges, particularly when AI systems are trained on historical data that may reflect existing social and institutional inequalities. This study aims to examine the relationships among AI implementation, algorithmic bias, transparency, accountability, trust, and educational equity in educational decision-making contexts. A mixed-methods convergent design was employed. Quantitative data were collected from 480 participants, including students, teachers, administrators, policymakers, and educational technology specialists. Qualitative interviews were conducted to explore stakeholder experiences and perceptions regarding algorithmic fairness and governance. Structural Equation Modeling and thematic analysis were used to analyze the data. Findings revealed that transparency significantly enhanced trust in AI systems, while accountability positively influenced perceptions of educational equity. Perceived algorithmic bias demonstrated a strong negative effect on fairness and trust. Qualitative evidence further indicated that inadequate oversight and biased datasets may contribute to unequal educational outcomes. The study concludes that equitable AI implementation requires transparent governance, accountability mechanisms, continuous bias auditing, and meaningful human oversight to ensure that algorithmic decision-making supports educational justice rather than reinforcing existing inequalities.

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