Search Results (354)

Mining activities have long-term impacts on the environment even after the active stage. Historical mines developed in the 19th and 20th centuries for tin, copper, and mainly iron ore are located in the Pitkäranta area (Karelia, Russia). These objects are considered in our research as natural–anthropogenic sites of long-term water–rock interaction. Waters from flooded mines are the subject of this research. Redox conditions, pH, dissolved oxygen content, conductivity, and water temperature were determined during field work. The chemical composition of natural waters was determined by ICP-MS, ICP-AES, ion chromatography, potentiometric titration, and spectrophotometry. Our investigation showed that the mine waters are fresh and predominantly calcium–magnesium hydrocarbonate; most samples showed elevated sulfate ion contents. Circumneutral pH values and the absence of extremely high concentrations of heavy metals indicate neutral mine drainage. However the calculation of the accumulation coefficient showed the highest levels for siderophile elements relative to the corresponding data of the geochemical regional background. Moreover, zinc has the highest content in the series of heavy metal(loid)s considered. The maximum concentration of zinc was determined in the water of one of the shafts of the Lupikko mine, i.e., 5205 µg/L. The accumulation of heavy metals occurs in the process of long-term interaction of water–rock–organic matter under conductive redox conditions. Overall, the research highlighted the relevance of investigating the geochemistry of historical mines in the Pitkäranta area both from the perspective of environmental safety and the preservation of mining sites for scientific and educational purposes. Full article

Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article

Predicting undergraduate student success is critical for informing timely interventions and improving outcomes in higher education. This study leverages over a decade of historical data from Louisiana State University (LSU) to forecast graduation outcomes using advanced machine learning techniques, with a focus on convolutional autoencoders (CAEs). We detail the data processing and transformation steps, including feature selection and imputation, to construct a robust dataset. The CAE effectively extracts meaningful latent features, validated through low-dimensional t-SNE visualizations that reveal clear clusters based on class labels, differentiating students likely to graduate from those at risk. A two-year gap strategy is introduced to ensure rigorous evaluation and simulate real-world conditions by predicting outcomes on unseen future data. Our results demonstrate the promise of CAE-derived embeddings for dimensionality reduction and computational efficiency, with competitive performance in downstream classification tasks. While models trained on embeddings showed slightly reduced performance compared to raw input data, with accuracies of 83% and 85%, respectively, their compactness and computational efficiency highlight their potential for large-scale analyses. The study emphasizes the importance of rigorous preprocessing, feature engineering, and evaluation protocols. By combining these approaches, we provide actionable insights and adaptive modeling strategies to support robust and generalizable predictive systems, enabling educators and administrators to enhance student success initiatives in dynamic educational environments. Full article

This study presents the validation of the Player Personality and Dynamics Scale (PPDS), designed to identify player profiles in educational gamification contexts with narrative elements. Through a sample of 635 participants, a questionnaire was developed and applied, covering sociodemographic data, lifestyle habits, gaming practices, and a classification system of 40 items on a six-point Likert scale. The results of the factorial analysis confirm a structure of five factors: Toxic Profile, Joker Profile, Tryhard Profile, Aesthetic Profile, and Coacher Profile, with high fit and reliability indices (RMSEA = 0.06; CFI = 0.95; TLI = 0.91). The resulting classification enables the design of personalized gamified experiences that enhance learning and interaction in the classroom, highlighting the importance of understanding players’ motivations to better adapt educational dynamics. Applying this scale fosters meaningful learning through the creation of narratives tailored to students’ individual preferences. Full article

Knowledge tracing (KT), a core educational data mining task, models students’ evolving knowledge states to predict future learning. In online education systems, the exercises are numerous, but they are typically associated with only a few concepts. However, existing models rarely integrate exercise information with high-order exercise–concept correlations, focusing solely on optimizing models’ final predictive performance. To address these limitations, we propose the Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism (HGKT), a novel framework that (1) captures correlations between exercises and concepts through a two-layer hypergraph convolution; (2) integrates hypergraph-driven exercise embedding and temporal features (answer time and interval time) to characterize learning behavioral dynamics; and (3) designs a learning layer and a forgetting layer, with the dual-gating mechanism dynamically balancing their impacts on the knowledge state. Experiments on three public datasets demonstrate that the proposed HGKT model achieves superior predictive performance compared to all baselines. On the longest interaction sequence dataset, ASSISChall, HGKT improves prediction AUC by least 1.8%. On the biggest interaction records dataset, EdNet-KT1, it maintains a state-of-the-art AUC of 0.78372. Visualization analyses confirm its interpretability in tracing knowledge state evolution. These results validate HGKT’s effectiveness in modeling high-order exercise–concept correlations while ensuring practical adaptability in real-world online education platforms. Full article

Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed. Full article

While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R² of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article

This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. We comparatively evaluate logistic regression, Random Forest, and XGBoost, integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and 5-fold cross-validation for robust model training. A comprehensive fairness analysis is conducted, considering sensitive attributes such as gender, school type, and socioeconomic factors, including parental education (Medu and Fedu), cohabitation status (Pstatus), and family size (famsize). Using the AIF360 library, we compute the demographic parity difference (DP) and Equalized Odds Difference (EO) to assess model biases across diverse subgroups. Our results demonstrate that XGBoost achieves high predictive performance (accuracy: 0.789; F1 score: 0.803) while maintaining low bias for socioeconomic attributes, offering a balanced approach to fairness and performance. A sensitivity analysis of bias mitigation strategies further enhances the study, advancing equitable artificial intelligence in education by incorporating socially relevant factors. Full article

This paper presents a conceptual and methodological framework for using Geographical Information Systems (GIS) to “deep map” cultural heritage sites along Ireland’s Wild Atlantic Way, with a focus on the 1588 Spanish Armada wrecks in County Kerry and archaeological landscapes in County Sligo’s “Yeats Country.” Drawing on interdisciplinary dialogues from the humanities, social sciences, and geospatial sciences, it illustrates how digital spatial technologies can excavate, preserve, and sustain intangible cultural knowledge embedded within such palimpsestic landscapes. Using MAXQDA 24 software to mine and code historical, literary, folkloric, and environmental texts, the study constructed bespoke GIS attribute tables and visualizations integrated with elevation models and open-source archaeological data. The result is a richly layered cartographic method that reveals the spectral and affective dimensions of heritage landscapes through climate, memory, literature, and spatial storytelling. By engaging with “deep mapping” and theories such as “Spectral Geography,” the research offers new avenues for sustainable heritage conservation, cultural tourism, and public education that are sensitive to both ecological and cultural resilience in the West of Ireland. Full article

Background: The integration of mining and urban spaces in coal-resource-based cities holds significant implications for urban transformation and sustainable development. However, existing research lacks an in-depth analysis of its characteristics and driving factors. Methods: This study takes the central urban area of Huaibei City as a case, utilizing historical documents, POI data, and spatial analysis methods to explore the evolution patterns and influencing factors of mining–urban spatial integration. Standard deviation ellipse analysis was employed to examine historical spatial changes, while a binary logistic regression model and principal component analysis were constructed based on 300 m × 300 m grid units to assess the roles of 11 factors, including location, transportation, commerce, and natural environment. Results: The results indicate that mining–urban spatial integration exhibits characteristics of lag, clustering, transportation dominance, and continuity. Commercial activity density, particularly leisure, dining, and shopping facilities, serves as a core driving factor. Road network density, along with the areas of educational and residential zones, positively promotes integration, whereas water surface areas (such as subsidence zones) significantly inhibit it. Among high-integration areas, Xiangshan District stands as the most economically prosperous city center; Lieshan–Yangzhuang mining area blends traditional and modern elements; and Zhuzhuang–Zhangzhuang mining area reflects the industrial landscape post-transformation. Conclusions: The study reveals diverse integration patterns under the synergistic effects of multiple factors, providing a scientific basis for optimizing spatial layouts and coordinating mining–urban development in coal-resource-based cities. Future research should continue to pay attention to the dynamic changes of spatial integration of mining cities, explore more effective integrated development models, and promote the rational and efficient use of urban space and the sustainable development of cities. Full article

Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved k-NN data mining algorithm. Firstly, we establish the naive Bayes machine learning algorithm to achieve accurate classification of the students in the class and then implement student grouping based on this accurate classification. Then, relying on the student grouping, we use the matching features between the students’ interest vector and the practical topic vector to construct an intelligent teaching recommendation model based on an improved k-NN data mining algorithm, in which the optimal complete binary encoding tree for the discussion topic is modeled. Based on the encoding tree model, an improved k-NN algorithm recommendation model is established to match the student group interests and recommend discussion topics. The experimental results prove that our proposed recommendation algorithm (PRA) can accurately recommend discussion topics for different student groups, match the interests of each group to the greatest extent, and improve the students’ enthusiasm for participating in practical discussions. As for the control groups of the user-based collaborative filtering recommendation algorithm (UCFA) and the item-based collaborative filtering recommendation algorithm (ICFA), under the experimental conditions of the single dataset and multiple datasets, the PRA has higher accuracy, recall rate, precision, and F1 value than the UCFA and ICFA and has better recommendation performance and robustness. Full article

The increasing development of coal mining subsidence wetland parks has led to a growing focus on assessing their ecological, economic, and social benefits following ecological restoration. This study establishes an assessment framework for the social landscape performance of coal mining subsidence wetland parks based on the landscape performance series (LPS), cultural ecosystem services (CES), and the unique characteristics of coal mining subsidence wetland parks. The framework integrates expert opinions and field research to select indicators, resulting in a comprehensive evaluation system comprising 28 indicators across five dimensions. Taking the Pan’an Lake National Wetland Park (PLNWP) in Xuzhou, China, as an example, we conducted empirical research by collecting data through questionnaires and on-site interviews. Using the fuzzy comprehensive evaluation method, the social landscape performance score of PLNWP was 3.511, which is rated as “good.” The importance–performance analysis (IPA) was applied to identify differences in the perceptions of visitors and local residents regarding the social landscape performance of the PLNWP. Local residents highlighted the need to enhance the amenity of waterside spaces, while visitors focused on the accessibility. Finally, based on the performance score and the perceptions from different stakeholders, optimization strategies were proposed in four aspects: enhancing waterside space amenity, optimizing accessibility, improving educational facilities, and addressing diverse user needs. This study could provide a feasible assessment framework and optimization guidance for other coal mining subsidence wetland parks. Full article

Artificial intelligence (AI) has found applications across diverse sectors in recent years, significantly enhancing operational efficiencies and user experiences. Educational data mining (EDM) has emerged as a pivotal AI application to transform educational environments by optimizing learning processes and identifying at-risk students. This study leverages EDM within a Moroccan university (Hassan First, University Settat, Morocco) context to augment educational quality and improve learning. We introduce a novel “Hybrid approach” that synthesizes students’ historical academic records and their in-class behavioral data, provided by instructors, to predict student performance in initial coding courses. Utilizing a range of machine learning (ML) algorithms, our research applies multi-classification, data augmentation, and binary classification techniques to evaluate student outcomes effectively. The key performance metrics, accuracy, precision, recall, and F1-score, are calculated to assess the efficacy of classification. Our results highlight the long short-term memory (LSTM) algorithm’s robustness achieving the highest accuracy of 94% and an F1-score of 0.87 along with a support vector machine (SVM), indicating high efficacy in predicting student success at the onset of learning coding. Furthermore, the study proposes a comprehensive framework that can be integrated into learning management systems (LMSs) to accommodate generational shifts in student populations, evolving university pedagogies, and varied teaching methodologies. This framework aims to support educational institutions in adapting to changing educational dynamics while ensuring high-quality, tailored learning experiences for students. Full article

Understanding how students’ online homework behaviors relate to their academic success is increasingly important, especially in elementary education where such research is still emerging. In this study, we examined three years of online homework data from fourth-grade students enrolled in an after-school math program. Our goal was to see whether certain behaviors—like how soon students started their homework, how many times they tried to solve problems, or whether they uploaded their written work—could help explain differences in homework completion and test performance. We used multiple regression analyses and found that some habits, such as beginning homework soon after class and regularly attending lessons, were consistently linked to better homework scores across all curriculum levels. Test performance, however, was harder to predict and showed fewer consistent patterns. These findings suggest that teaching and encouraging specific online study behaviors may help support younger students’ academic growth in digital learning environments. Full article

The linguistic summarization of data is one of the study trends in data mining because it has many useful practical applications. A linguistic summarization of data aims to extract an optimal set of linguistic summaries from numeric data. The blended learning format is now popular in higher education at both undergraduate and graduate levels. A lot of techniques in machine learning, such as classification, regression, clustering, and forecasting, have been applied to evaluate learning activities or predict the learning outcomes of students. However, few studies have been examined to transform the data of blended learning courses into the knowledge represented as linguistic summaries. This paper proposes a method of linguistic summarization of blended learning data collected from a learning management system to extract compact sets of interpretable linguistic summaries for understanding the common rules of blended learning courses by utilizing enlarged hedge algebras. Those extracted linguistic summaries in the form of sentences in natural language are easy to understand for humans. Furthermore, a method of detecting the exceptional cases or outliers of the learning courses based on linguistic summaries expressing common rules in different scenarios is also proposed. The experimental results on two real-world datasets of two learning courses of Discrete Mathematics and Introduction to Computer Science show that the proposed methods have promising practical applications. They can help students and lecturers find the best way to enhance their learning methods and teaching style. Full article