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International Journal of Environment, Engineering, and Education
Published by Three E Science Institute
ISSN : -     EISSN : 26568039     DOI : https://doi.org/10.55151/ijeedu
Core Subject : Science, Education, Social, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science
The International Journal of Environment, Engineering, and Education [e-ISSN: 2656-8039] is a peer-reviewed, open-access journal that is published three times a year [in April, August, and December]; this journal provides the right platform for authors to update their knowledge, information, and share their research results with the more significant scientific community publishing research articles explaining the ecological, technical, and educational impact of research from various disciplines publishing research articles explaining the environmental, technical, and educational implications of research from multiple disciplines publishing research As an interdisciplinary scientific publication, this journal encourages collaboration between researchers, academics, practitioners, and policymakers in various sectors to develop sustainable solutions to address environmental, engineering, and educational problems and promote sustainable development.
Arjuna Subject : Umum - Umum
Articles 10 Documents
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Search results for , issue "vol. 8 no. 2 (2026)" : 10 Documents clear
Spatial Analysis and Visualization of Edu-Tourism Clusters in MAMMINASATA, South Sulawesi: A GIS Approach to Attraction Identification and Classification Haedar Akib; Didin Didin; Ahmad Wahidiyat Haedar; Khairil Asnan Haedar; Rudi Salam; Muh. Darwis; Muh. Ibrahim Halim
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.352

Abstract

Educational tourism is increasingly understood as a spatially embedded learning resource system, yet its metropolitan-scale planning remains underexamined. This study analyzes the spatial distribution and land suitability of edu-tourism attractions in the MAMMINASATA Metropolitan Area, South Sulawesi, Indonesia, comprising Makassar, Gowa, Maros, and Takalar. Using a GIS-based weighted overlay approach, 36 georeferenced attractions were inventoried and classified into cultural, natural, man-made, and special-interest tourism. Spatial suitability was assessed through four criteria relevant to metropolitan planning: road accessibility, slope, vegetation density (NDVI), and proximity to activity centers. All datasets were standardized to a common suitability scale and integrated into a composite land-suitability model, after which suitability values were extracted at each attraction location. The results show that the edu-tourism resource base is dominated by natural attractions (44.40%) and cultural attractions (27.8%), with the strongest concentration in Makassar, Gowa, and Maros. The weighted overlay indicates that 66.63% of the study area is very suitable and 29.84% is suitable for edu-tourism development, meaning that 96.47% of the metropolitan area possesses supportive territorial conditions. At the site level, 58.3% of attractions are located in very suitable areas, 36.10% in suitable areas, and only 5.60% in moderately suitable areas, while no site falls into the not suitable class. These findings demonstrate that MAMMINASATA has strong potential for integrated edu-tourism planning, particularly in the Makassar-Gowa corridor, while more selective accessibility and service improvements are needed in parts of Maros and Takalar.
Belief in a Just World Predicting Career Exploration through Future Time Perspective in Chinese Higher Education Students Wei Li; Ching-Lin Wu
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.449

Abstract

This study examined the relationship between belief in a just world, future time perspective, and career exploration among vocational college students in Guangxi, China. Drawing on Career Construction Theory, belief in a just world was conceptualized as an adaptive readiness factor, future time perspective as a cognitive-regulatory resource, and career exploration as an adapting response. A quantitative cross-sectional survey design was employed. Data were collected from 1,055 students enrolled in nine higher vocational institutions and analyzed using confirmatory factor analysis and structural equation modeling. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. The structural model showed an acceptable fit to the data, with χ²/df = 2.43, RMSEA = 0.037, SRMR = 0.042, CFI = 0.96, and TLI = 0.95. The results indicated that belief in a just world was positively associated with career exploration and future time perspective. Future time perspective was also positively associated with career exploration and partially mediated the relationship between belief in a just world and career exploration. The model explained 30% of the variance in future time perspective and 46% of the variance in career exploration. These findings suggest that students’ fairness-related beliefs and future-oriented cognition are important psychological correlates of proactive career development behavior. The study extends Career Construction Theory by integrating just-world beliefs and future time perspective into a mediation framework for vocational students’ career exploration. Because the study used a cross-sectional design, the findings should be interpreted as theoretically grounded associations rather than causal evidence.
Design, Environmental Validation, and Candidate Crop Screening in a Modular Micro-Lunar Greenhouse System under CELSS-Like Conditions Tao Wei; Olha Bakumenko; Yunfan Zhang
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.452

Abstract

Controlled Ecological Life Support Systems (CELSS) require reliable in situ crop production for long-duration lunar and planetary habitation. However, existing research infrastructure remains divided between large, expensive facilities and conventional growth chambers that lack the environmental realism needed for standardized crop screening. This study aimed to design, engineer, and experimentally validate a Micro-Lunar Greenhouse System (MLGS) as a mid-scale modular platform for evaluating functionally diverse candidate crops under CELSS-like conditions. The 1.52 m3 MLGS integrated automated control of atmospheric composition, temperature, relative humidity, airflow, hydroponics, and programmable LED lighting across three independent cultivation zones. Mesembryanthemum crystallinum, Anredera cordifolia, and Anoectochilus roxburghii were cultivated for 180 days under species-optimized MLGS conditions and compared with standard laboratory chamber conditions using three biological replicates per treatment. Platform performance and crop responses were assessed through environmental stability, growth, photosynthetic traits, nutritional and bioactive composition, resource-use efficiency, waste-processing capacity, and microbial safety. The MLGS maintained environmental setpoints within ±0.8°C, ±1.5% relative humidity, and 25-35 ppm CO2 of target values for more than 97% of operating time, with low spatial heterogeneity among cultivation zones. Relative to the control, the optimized MLGS treatment increased biomass by 38-62%, enhanced key nutritional or bioactive compounds by 24-46%, and improved water-use efficiency by 25-55%. Nitrogen and phosphorus removal from simulated waste streams reached 58-79% and 47-70%, respectively, and no foodborne pathogens were detected. These results validate the MLGS as a reproducible bridge platform between large CELSS facilities and conventional growth chambers, supporting standardized crop screening for future space agriculture applications.
AI Education Exposure and Its Effects on Innovation Capability and Career Adaptability Among Chinese Undergraduate Students Chengyi Zheng; Hui-Wen Tang; Zheyun Zheng
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.462

Abstract

Artificial intelligence (AI) is increasingly embedded in higher education, yet empirical evidence remains limited regarding whether students’ AI-Supported Learning Experience (ASLE) is associated with broader developmental outcomes such as innovation ability and career adaptability within a single explanatory model. This study employed a quantitative cross-sectional survey design involving 411 undergraduate students from four universities in China who had prior exposure to AI-supported learning environments. Data were analyzed using IBM SPSS and AMOS through descriptive statistics, reliability testing, confirmatory factor analysis (CFA), structural equation modelling (SEM), and bootstrapped mediation analysis with 5,000 resamples. The findings indicate that ASLE positively predicts innovation ability (β = 0.329, p < 0.001) and career adaptability (β = 0.231, p < 0.001). Innovation ability also positively predicts career adaptability (β = 0.284, p < 0.001). In addition, innovation ability significantly partially mediates the relationship between ASLE and career adaptability (indirect effect = 0.093, p < 0.001; 95% CI [0.05, 0.15]). The model explained 10.8% of the variance in innovation ability and 17.7% of the variance in career adaptability. The study suggests that AI-supported learning in higher education may contribute to students’ future-oriented development not only by familiarizing them with emerging technologies, but also by strengthening innovation-related capability that supports adaptive career readiness. The findings clarify the construct boundary of ASLE and highlight the importance of pedagogically meaningful AI integration in designing learning environments that better prepare students for technology-driven labor markets.
Service Quality and Perceived Platform Error Exposure in Digital Learning Platforms: An Extended SEM Study of Perceived Value and Continuance Intentions in Indonesia and Malaysia Zhuofan Chen; Sin Yin Teh; Theam Foo Ng
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.490

Abstract

Digital learning platforms have become essential academic service systems in higher education, yet e-learning continuance research has largely emphasized positive quality attributes while under examining recurring technical disruptions. This study investigates how service quality and perceived platform error exposure influence perceived value and behavioral intentions among university students in Indonesia and Malaysia. Drawing on the Information Systems Success Model, SERVQUAL, and post-adoption continuance theory, an extended structural equation modelling framework was tested using cross-sectional survey data from 123 undergraduate students, comprising 62 respondents from Indonesia and 61 from Malaysia. The sample included LMS users (69.9%) and MOOC users (30.1%). Data were analyzed using confirmatory factor analysis and SEM in IBM AMOS. The measurement model showed good fit: χ²/df = 1.74, CFI = 0.961, TLI = 0.954, RMSEA = 0.048, and SRMR = 0.042. In the extended model, service quality positively predicted perceived value (β = 0.593, p < 0.001) and behavioral intentions (β = 0.481, p < 0.001), while perceived value positively predicted behavioral intentions (β = 0.391, p < 0.001). Perceived platform error exposure negatively predicted perceived value (β = −0.314, p < 0.001), and perceived value significantly mediated the service quality–behavioral intention relationship (β = 0.232, p < 0.001). The extended model explained 59.8% of the variance in perceived value and improved model fit relative to the baseline model. These findings position platform error exposure as a distinct negative user-experience construct and highlight the need to improve service quality while reducing recurring technical failures.
AI Integration in Vocational EFL in China: A Qualitative Pedagogical Capability Framework from Teachers’ Perspectives Yi Xia; Saedah Binti Siraj; Siti Hajar Binti Halili
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.499

Abstract

Artificial intelligence (AI) is increasingly reshaping English as a Foreign Language (EFL) instruction, yet its integration in vocational education remains insufficiently explained by adoption-oriented or tool-centered models. In vocational EFL, teachers must ensure that AI-supported learning is pedagogically meaningful, workplace-relevant, and ethically governed. This study develops an empirically informed pedagogical capability framework for integrating AI into vocational EFL teaching in China, from teachers’ perspectives. A qualitative exploratory design was employed with 41 vocational EFL teachers from six vocational and technical institutions in Yunnan Province, China. Data were collected through 18 semi-structured interviews and 23 extended open-ended survey responses. Reflexive thematic analysis was used to generate themes and synthesize framework dimensions, supported by coding summaries, source comparison, and reflexive memoing. Three themes emerged: teacher pedagogical capability and readiness, institutional and ethical barriers, and instructional applicability of AI tools. Teachers distinguished general digital competence from AI-specific pedagogical judgment, particularly in validating AI feedback, designing workplace-oriented tasks, protecting learner agency, and addressing privacy, authorship, bias, and overreliance. AI tools were perceived as useful for writing feedback, chatbot-based workplace simulation, pronunciation practice, adaptive learning, learner analytics, and occupation-specific vocabulary support, but only under teacher mediation and institutional support. Meaningful AI integration in vocational EFL depends on four interdependent dimensions: teacher pedagogical capability, institutional readiness, instructional applicability, and ethical governance. The framework extends TPACK, TAM, UTAUT, digital competence, and AI literacy by repositioning AI integration as a relational, human-centered, and context-sensitive pedagogical process.
Multi-Criteria Performance Assessment of Rigid Pavement Concrete with High-Absorption Local Fine Aggregate Using Superplasticizer and Water-Reducing Admixture Abdul Karim Hadi; Amalia Nur Chasanah
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.511

Abstract

Rigid pavement concrete incorporating high-absorption local fine aggregate requires careful control of effective water availability, as aggregate moisture conditions may influence workability, setting behavior, and flexural performance. This study assessed the effects of superplasticizer and water-reducing admixture dosages on pavement concrete designed for a target compressive strength of 30 MPa and a target modulus of rupture of 45 kgf/cm² (4.41 MPa). A laboratory-based performance screening was conducted using a control mixture, superplasticizer mixtures at 0.60–1.50% by cement mass, water-reducing admixture mixtures at 0.15–0.35%, and one combined admixture mixture. Fresh properties were evaluated using slump, visual stability, bleeding and segregation observations, and initial setting time, whereas hardened performance was assessed through 7- and 28-day compressive and flexural strength tests. The control mixture achieved 31.71 MPa compressive strength at 28 days but failed the flexural strength requirement, reaching only 39.70 kgf/cm² (3.89 MPa). The 0.80% superplasticizer mixture achieved balanced performance, with 33.93 MPa compressive strength, 46.43 kgf/cm² (4.55 MPa) modulus of rupture, and an initial setting time of 4 hours. The 0.25% water-reducing admixture produced the highest compressive strength, 37.53 MPa, but did not meet the flexural criterion. The combined admixture mixture showed the best overall laboratory performance, achieving 33.75 MPa compressive strength, 56.90 kgf/cm² (5.58 MPa) modulus of rupture, and an initial setting time of 5 h 15 min. These findings indicate that pavement concrete mixture selection should integrate flexural strength, setting behavior, workability, and fresh-state stability rather than rely solely on compressive strength.
Map-Derived Agreement Assessment of Landsat-9 OLI-2 for Island-Scale Lithological Discrimination in a Humid Tropical Setting from Langkawi Island, Malaysia Yaokai Du; Ying Jia Teoh; Nur Azwin Ismail; Ismail Ahmad Abir; Haylay Tsegab Gebretsadik
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.512

Abstract

Lithological discrimination in humid tropical islands remains constrained by dense vegetation, deep weathering, regolith cover, mixed pixels, and discontinuous bedrock exposure, which collectively weaken diagnostic spectral responses. This study evaluates the capability and limitations of Landsat-9 OLI-2 for island-scale lithological discrimination in Langkawi Island, Malaysia, using an interpretable optical-only workflow. Atmospherically corrected Landsat-9 imagery was processed through false-color composites, Optimum Index Factor (OIF)-based band selection, band-ratio enhancement, Principal Component Analysis (PCA), Normalized Difference Vegetation Index (NDVI), Jeffries–Matusita separability analysis, and Maximum Likelihood Classification (MLC). The resulting MLC lithological map was assessed pixel-by-pixel against a published geological map; consequently, the reported statistics represent map-derived agreement rather than independent field-validated lithological accuracy. Results show that the OIF-selected RGB 6-5-2 composite, selected band-ratio combinations, and PCA enhanced broad contrasts among Quaternary Alluvium, granitic terrain, and carbonate-bearing formations. The MLC classification achieved an overall map-derived agreement of 51.72% and a kappa coefficient of 0.4177. Qal, Cm-SS/Sh/Md, and OS-Ls/SS showed relatively stronger agreement, whereas PT-Ls/Mb, DP-St/Md, and Tr-Gr were more affected by spectral overlap and class confusion. NDVI-stratified assessment further confirmed that vegetation cover influences classification performance, with low-vegetation areas producing higher agreement than moderate-vegetation areas. This study establishes a reproducible full-island baseline for evaluating optical multispectral lithological mapping under humid tropical conditions. These findings demonstrate that Landsat-9 OLI-2 can support reconnaissance-level lithological discrimination in humid tropical islands but remains insufficient for precise formation-level mapping without field validation and integration with SAR, DEM-derived, or higher-resolution spectral datasets.
The Interplay of Soft Skills, Digital Literacy, and Self-Efficacy in Shaping Work Readiness: A Structural Equation Modeling Study of Vocational High School Students Anas Arfandi; Musdalifah Musdalifah; Sudjani Sudjani; Abdul Haris Setiawan
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.418

Abstract

Work readiness in vocational education is increasingly shaped by transferable competencies and students’ confidence in applying them in technology-rich workplaces. Although soft skills and digital literacy are widely promoted, evidence remains limited in Indonesian VET contexts regarding how these competencies translate into work readiness through self-efficacy. This study tested a structural mediation model in which soft skills and digital literacy predict work readiness both directly and indirectly via self-efficacy among vocational students who participated in MOOC-based upskilling. A cross-sectional survey was administered to 225 students from the Construction and Housing Engineering program across 22 vocational high schools in 16 districts/cities in South Sulawesi, Indonesia. All constructs were measured using five-point Likert scales, and covariance-based SEM was estimated in AMOS using Maximum Likelihood. The model demonstrated excellent fit (CMIN/DF=1.248, RMSEA=0.001, SRMR=0.005, GFI=0.921, CFI=0.996, TLI=0.989, PNFI=0.762). Soft skills positively predicted self-efficacy (β=0.574; p<0.001) and work readiness (β=0.652, p<0.05), while digital literacy also predicted self-efficacy (β=0.274, p<0.05) and work readiness (β = 0.663, p<0.001). Self-efficacy exhibited the strongest direct effect on work readiness (β=0.778, p<0.05) and partially mediated the effects of soft skills (indirect β=0.447, p<0.05) and digital literacy (indirect β=0.213, p<0.001). Overall, these findings propose an integrated competency–belief model for Indonesian vocational students in a MOOC participation context, highlighting self-efficacy as a key mechanism through which competencies contribute to work readiness. This contribution extends SEM-based evidence on vocational competency development by underscoring the importance of confidence-building alongside skills and digital capability training.
Smart Composter for Experiential Learning in Sustainable Agriculture: System Design and Application for a Small-Scale Farm Setting Catherine Molloseau; Ira Woodring; Amy McFarland; Isak Davis; Yunju Lee
International Journal of Environment, Engineering and Education Vol. 8 No. 2 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i2.486

Abstract

Composting offers a practical pathway for recycling organic waste, yet maintaining favorable process conditions and translating composting into structured experiential learning remain challenging. This study designed and conducted a preliminary field evaluation of an automated, sensor-enabled composting system intended to process vegetative waste from a small-scale farm while supporting sustainability education. The prototype combined an aerated static-pile configuration with forced aeration, humidity-triggered irrigation, three vertically distributed temperature and relative-humidity sensors, oxygen and fill-level monitoring, a touchscreen interface, transparent viewing panels, multiple access points, and remote data logging. A 29-day trial was conducted in an unheated tunnel greenhouse using approximately 1 m³ of vegetative waste and wood chips mixed at a reported 1:1 mass ratio. Sensor data were recorded at 10-minute intervals and analyzed descriptively to examine temporal patterns, vertical variation, system performance, and data completeness. Temperatures showed recurrent diurnal fluctuations and clear vertical stratification, reaching maximum values of 54.39°C, 45.83°C, and 35.67°C in the upper, middle, and lower zones, respectively. Lower-zone measurements were incomplete because of a connection failure, while middle-zone relative humidity remained near sensor saturation, limiting interpretation. The observed temperature increases, absence of malodor, and visible material changes were consistent with ongoing aerobic decomposition, although compost maturity and educational outcomes were not directly assessed. These findings demonstrate the feasibility of integrating monitoring, user interaction, visual access, and real-time data communication in a smart composter. Future work should validate sensor placement, quantify actuator performance, assess compost quality, and evaluate learning outcomes aligned with the United Nations Sustainable Development Goals.

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