A BIM-Linked Mathematical Decision Model for Energy Retrofit Prioritisation in Existing Building Portfolios

Ashraf Elhendawi; Moustafa Metwally

doi:https://doi.org/10.54216/IJBES.120204

International Journal of Artificial Intelligence and Education Technology | 2026

From Signals to Action: Explainable AI for Engagement-Responsive Instructional Support in Digital Higher Education

Artificial intelligence is increasingly used to monitor learning processes in higher education; however, many analytics pipelines still terminate at prediction and provide limited support for instructional action. The research establishes an explainable artificial intelligence framework which utilizes digital learning environment behavioral data and contextual information to create customized instructional support solutions. The analysis uses xAPIEdu-Data dataset which contains 480 records to build engagement index and create support profiles and predict multiclass performance through rule based action allocation. The study tests three classification models using stratified cross validation. The study selects Random Forest as the most effective system because it delivers superior results across all tests. The selected model demonstrates 0.8021 accuracy and 0.8204 macro precision and 0.8010 macro recall and 0.8084 macro F1 score and 0.9140 macro area under the curve on the hold-out sample. The analysis shows that student absence and composite engagement index andgender and student guardian relationship and support profile and digital resource access arethe most important factors that determine student performance. The final decision layer manages student assignment to instructional support plans which contain attendance-first intervention and adaptive engagement support and family-engagement reinforcement and structured progression coaching and challenge-and-extend pathways. The study develops an analytical framework which connects explainable artificial intelligence to digital higher education instructional decision support systems.

groups

Andino Maseleno mail -

Meinhaj Hussain mail -

Aygul Z. Ibatova mail

link https://doi.org/10.54216/IJAIET.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Early Detection of Student Dropout Risk in Higher Education through Optimized Machine Learning

Student retention in higher education institutions is a critical problem that causes academic and financial challenges to individual students and to schools and entire countries. The field of study should be in the area of student retention as it enables educational facilities to provide appropriate intervention. The present study implements a comparative analysis of five machine learning classifiers; Linear Discriminant Analysis, K-Nearest Neighbours, Support Vector Machine, Random Forest and Gradient Boosting classifiers on dataof 4424 students who were selected from the Realinho et al. (2022) data set which contains demographic and socioeconomic, and macroeconomic and academic performance data from a Portuguese higher education institution over a decade. The mutual information feature selection step reduces the 22-dimensional feature space prior to model trainingby selecting 12 features that have, statistically, the highest discriminative power. Five-fold stratified cross-validation shows that the best overall performance is achieved by a SVM with a radial basis function kernel with accuracy of 97.1% and F1 score of 0.954 and all five models achieve AUC greater than 0.981. The importance analysis reveals that the combination of four measures of academic success from the first two semesters constructs 87.6% of the signal that Random Forest model uses for prediction which is driven by the most important predictor - number of curricular units that the student passes during the secondsemester (importance= 0.335). The impact of all socioeconomic and demographic and macroeconomic factors is less than 13%. The findings of the study have three implications about risk factors in student retention via empirical measurement.

groups

Aa Hubur mail -

Aygul Z. Ibatova mail

link https://doi.org/10.54216/IJAIET.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Analytic Trait Scoring of English Language Learner Essays Using Fine-Tuned DeBERTa-v3 on the ELLIPSE Corpus

Automated Essay Scoring (AES) technologies have been extensively researched for holistic, topic-specific scoring, but their use to predict multiple analytic writing quality traits of English Language Learner (ELL) student essays has received less attention. This research contributes to this knowledge gap by systematically investigating multi-trait AES on the ELLIPSE corpus (Learning Agency Lab, 2022), a publicly accessible dataset of 6,482 argumentative essays written by grades 8-12 ELLs and rated by human raters on six analytic traits: cohesion, syntax, vocabulary, phraseology, grammar, and conventions. We experiment with five models: Ridge regression, Support Vector Regression (SVR) with a radial basis function (RBF) kernel, Random For-est, fine-tuned BERT-base-uncased and fine-tuned DeBERTa-v3-base. The mean Quadratic Weighted Kappa (QWK) across six traits is highest for DeBERTa-v3 (0.726) - a 26.5-point improvementover the Ridge base-line (0.461) and a 6-point improvement over BERT (0.666). Phraseology is the most difficult trait to score automatically (DeBERTa QWK = 0.701) and cohesion the easiest (DeBERTa QWK = 0.742). Analysis of inter-trait correlations reveals high co-variation between vocabulary and phraseology (r = 0.79), which may reflect common linguistic skills that can be leveraged by multi-task learning. Thisresearch sets a replicable baseline for multi-trait AES on the ELLIPSE corpus, and suggests that phraseology scoring is the most urgent area for future architectural innovation.

groups

Andino Maseleno mail -

Meinhaj Hussain mail

link https://doi.org/10.54216/IJAIET.040103

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Educational Value Formation Around Intelligent Learning Tools: Student Readiness, Usage Archetypes, and Support Pathways in Higher Education

Modern higher education campuses now use intelligent learning tools as standard educational resources yet students learning results depend on their understanding of these tools and their implementation in academic work. The study analyzes how students pre-pare to use educational tools while investigating the connection between their preparedness and their judgment of educational benefits. The study uses an open student-perception dataset to conduct empirical research which includes developing constructs and profiling readiness and creating predictive models and establishing pathways. The study introduces two measurement methods which include source breadth to measure how students acquire knowledge about intelligent tools through different information channels and an advantage score to present perceived benefits for educational activities. The three-profile segmentation method shows that different groups in the sample display distinct levels of preparedness and value assessment. The Random Forest model demonstrates superior performance because it achieves the highest accuracy among all tested models in the predictive stage. The selected model exhibits an accuracy rate of 0.789 and a precision rate of 0.714 and a recall rate of 1.000 and an F1 score of 0.833 and an area under the receiver operating characteristic curve of 0.806 in hold-out evaluation. The analysis of variable importance indicates that AI knowledge and grade-point average and information breadth and profile membership serve as the main factors that explain the results. The final stage of the process transforms analytical results into distinct educational pathways which focus on developing essential literacy skills and implementing structured curriculum materials and providing support for governance matters and enabling advanced collaborative learning. The results demonstrate that the educational benefits of intelligent tools depend more on students’ preparedness to use them than on their initial exposure to the tools.

groups

Marina Sagatovna Abdurashidova mail -

Muhammad Eid Balbaa mail

link https: // doi. org/ 10. 54216/ IJAIET. 040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Green Finance, Renewable Energy Adoption, and Carbon Emissions: Evidence from a Ten-Country Panel Dataset (2010–2022)

The capital mobilisation needed to achieve the energy transition has put green finance on the agenda of climate policy discussions, but it is empirically unclear whether financial flows branded green translate into emissions abatement. Drawing on publicly available World Bank World Development Indicators and Climate Policy Initiative data for ten major economies over 2010–2022, this paper quantifies the relationships among green finance flows, renewable energy share, and per-capita CO2 emissions using pooled ordinary least squares regression, panel correlation analysis, and a cross-sectional multiple regression model. Renewable energy share is a strong negative predictor of per-capita emissions (r = −0.661; p < 0.001; βˆ = −0.1104), with each percentage-point increase in renewable penetration associated with a reduction of approximately 0.11 metric tons of CO2 per capita. A three-variable cross-sectional model combining renewable energy share, green finance, and GDP per capita accounts for 61% of cross-country variance in emissions in 2022 (R2 = 0.613). There is a positive and heterogeneous relationship between green finance flows and renewable adoption, which is most notable when the financial commitment is combined with strong regulatory frameworks. The country level trajectory analysis shows that all the ten economies have been on the rise in their renewable share between 2010 and 2022, but at a rate varying between 0.37 and 2.88 percentage points annually, and that the institutional environment is a moderating factor. The results have immediate implications on the design of green bond markets, the lending policy of multilateral development banks, and the structures of additionality that support net-zero transition schemes.

groups

Ihtisham ul Haq mail -

Andino Maseleno mail

link https://doi.org/10.54216/JSDGT.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Machine Learning for At-Risk Student Identification in Virtual Learning Environments: A Multi-Classifier Analysis Using the Open University Learning Analytics Dataset

The detection of students who will face academic difficulties or leave their studies during their initial course period provides universities with a brief time frame to develop effective solutions. This research paper conducts a systematic analysis which tests multiple machine learning classifiers on the Open University Learning Analytics Dataset (OULAD) which serves as one of the most widely used public educational datasets that presents data from 32593 students who studied 22 different courses through distance learning. The four classification methods include logistic regression decision tree random forest and gradient boosting which use a feature set that combines student demographic information and virtual learning environment (VLE) clickstream-based engagement data. The primary discovery shows that VLE behavioral characteristics constitute the most important elements for Random Forest which identifies total click volume and active VLE days and typical daily click volume as its top four elements which make up 92.8% of total importance while demographic information has less impact. Random Forest achieves the strongest held-out test performance (AUC = 0.998, F1 = 0.978, accuracy = 98.2%) while Decision Tree shows lower results with AUC = 0.959 which demonstrates how performance losses occur when systems need to be understandable. At-risk students in the two groups present a 75.8% decrease in total VLEclicks which results in an average of 49.0 clicks compared to 203.0 clicks with a t value of 104.0 and a p value less than 0.001. The research describes its complete end-to-end prediction pipeline which includes details about its model evaluation framework and its dataset to enable future researchers to reproduce the study. The results have direct implications for the design of early-alert systems and the ethical deployment of predictive analytics in higher education.

groups

Emad Bashkail mail -

Nesrin Merhi mail

link https://doi.org/10.54216/IJAIET.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Risk-Aware Cyberattack Analytics for Unmanned Aerial Vehicle Communications: A Publication-Ready Gradient- Boosting Framework

Cyberattack detection in unmanned aerial vehicle environments has become an essential requirement for dependable digital operations. Security analytics for these environments should not only separate benign and malicious traffic, but should also provide interpretable evidence that can support timely triage and intervention. This paper presents a risk-aware classification framework for UAV communication security based on a leakage-screened feature design and a gradient-boosting ensemble model. The framework combines multiclass discrimination, probability-based decision logic, and feature-level interpretation within one coherent workflow. The study demonstrates that a carefully designed ensemble approach can provide balanced and operationally meaningful cyberattack recognition while remaining transparent enough for practical cybersecurity management. The results also show that communication-structure variables provide strong discriminatory power and that replay-type activity remains more difficult to separate than benign or denial-of-service behavior. The proposed framework therefore contributes a reproducible analytical design and a managerial reading of cyberattack classification for UAV operations.

groups

Andino Maseleno mail -

Aa Hubur mail

link https://doi.org/10.54216/JCIM.180105

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Multi-Sector Circular Economy Integration and Transition: Evidence from Textile, Construction, Energy, Chemical Manufacturing, and Agri-Food Systems in Uzbekistan

The transition from a linear economic model to a circular economy (CE) marks a profound change, driving toward better resource management and ecological robustness. Although much previous investigation has concentrated on specific industries, this research takes a broader, multi - sector approach. It delves into how circular economy principles are being incorporated across crucial industries such as textiles, construction, energy, chemical production and agriculture in Uzbekistan. Employing a qualitative research methodology, which involved synthesizing existing literature and scrutinizing policies, the study pinpoints the primary forces driving this change, the obstacles encountered and the connections between different sectors that influence the move toward a circular economy. The results highlight that shared circular practices - like industrial symbiosis, optimizing resource use, adopting renewable energy and developing circular business models - are essential for boosting both environmental sustainability and economic viability. Nevertheless, this transition faces hurdles due to inadequate infrastructure, disjointed governance structures and insufficient skilled personnel, especially within developing nations. Examining Uzbekistan specifically, we observe both growing policy dedication and ongoing structural difficulties, underscoring the need for synchronized governance, investment in green initiatives and robust innovation systems. This work adds to the existing body of knowledge by introducing a conceptual framework that spans multiple sectors. This framework illustrates how industrial systems are interconnected and how these connections contribute to achieving sustainable development goals. Moreover, it offers practical policy suggestions aimed at speeding up circular economy adoptions in Uzbekistan and comparable developing countries.

groups

Muhammad Eid Balbaa mail -

Olim Astanakulov mail -

Habibe Elif Kutlugun mail

link https://doi.org/10.54216/JSDGT.060201

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2026

Geometry-Driven BIM Element Category Recognition from Open IFC Property Records: A Reproducible Engineering Science Study

Accurate object semantics are essential for building information modeling (BIM) workflows to enable interoperability, model checking, quantity take-off, performance analysis, and other downstream engineering applications. However, in practice, Industry Foundation Classes (IFC)-based model exchanges often feature limited or poorly identified semantic tags, particularly during interoperability with authoring and reviewing tools. This research proposes a re-producible, geometry-based learning algorithm for the automatic recognition of BIM element categories based on publicly available IFC-based property data. The empirical analysis is based on 780 object instances from ten BIM categories from a publicly available sample of IFC object records. A rule based parser translates semi-structured BIM text exports into engineering features as bounding box dimensions, coordinates, elevations and object-status. The study compares three supervised machine-learning baselines via stratified five-fold cross-validation: logistic regression, random forest and extra trees. Random forest performed best overall with an accuracy of 0.992, balanced accuracy of 0.971, a weighted F1-score of 0.992, and a macro F1-score of 0.970. The analysis of feature importance shows that bounding-box height, width, length, spatial coordinates and externality related descriptors are the most important features. The results demon-strate significant semantics can be extracted from minimal engineering descriptors without the need for deep learning of meshes. This work provides an interpretable and efficient baseline for BIM enrichment, assessment, and interoperability-focused preprocessing for engineering science use-cases.

groups

Sonia Ahmed mail -

Marek Salamak mail

link https://doi.org/10.54216/IJBES.120203

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2026

A BIM-Linked Mathematical Decision Model for Energy Retrofit Prioritisation in Existing Building Portfolios

Building information modelling is increasingly applied to structure engineering information across the life cycle of built assets, but existing buildings are often underconnected to operational data for retrofit prioritisation. This research proposes a BIM-connected retrofit prioritisation model that converts building-performance information into an engineering information layer for initial screening. The method integrates BIM-aligned feature organisation, transparent machine learning, diagnostic validation, and scenario-driven screening to flag buildings for further assessment by engineers. The paper proposes a workflow for institutions and cities seeking to transition from disparate disclosure records to evidence-based retrofit prioritisation without relying on the immediate availability of digital twins. The results suggest that operational, geometric, and typological features can be used to generate interpretable screening markers that help guide engineering judgement, benchmarking, and incremental retrofit strategies. This research offers a replicable model that supplements, rather than substitutes for, in-depth audit and modelling.

groups

Ashraf Elhendawi mail -

Moustafa Metwally mail

link https://doi.org/10.54216/IJBES.120204

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new