Professional development (PD) is widely recognised as a crucial factor in fostering the quality of teachers' professional practice. Today, online PD is often chosen over offline PD because it offers advantages related to cost, time, and location efficiency. However, compared with offline PD, less research has examined the quality of online PD. This study aimed to investigate the quality attributes of online PD and their impacts on teachers' satisfaction and changes in their professional practice. A cross-sectional design and an online questionnaire were used to collect data from 206 economics teachers in Indonesia. Structural equation modelling was conducted using SmartPLS 3.0 to examine the research hypotheses. The results show that three elements--collaboration, cognitive activation, and clarity and structure--contribute to explaining and establishing the quality of online PD. Furthermore, from the teachers' perspectives on satisfaction and changes in professional practice, online PD was effective in enhancing teachers' pedagogical knowledge and teaching practice. Finally, the study shows that the quality attributes of online PD positively affect participants' satisfaction and changes in teachers' professional practice. Theoretical and practical implications are discussed.
In the era of digital transformation, the success of educational management systems increasingly depends not only on technology but also on users' attitudes and creativity. Despite growing research on technology adoption in education, few studies have examined how attitudes toward digitalization (ATD) and digital creativity (CTD) interact to influence the efficiency, effectiveness, and responsible implementation of Educational Management Information Systems (EMIS) in educational institutions. To address this gap, the present study investigated how CTD mediates the relationship between ATD and EMIS. A total of 347 respondents participated in the study by completing an online questionnaire. Structural Equation Modeling (SEM) was employed to test four hypotheses. The results revealed that ATD significantly enhanced EMIS performance both directly and indirectly through CTD. Furthermore, CTD emerged as a critical mediator, indicating that while a positive digital attitude provides the motivational foundation for technology adoption, creativity transforms this attitude into innovative, efficient, and contextually responsible EMIS applications. The study highlights how fostering positive attitudes and digital creativity among users can improve operational efficiency and promote responsible management practices in educational settings.
Critical thinking skills (CTS) are essential competencies for 21st-century students; however, empirical evidence indicates that their development remains suboptimal. This study aimed to map the CTS profile of junior high school students in Bali Province, Indonesia, and to examine differences across CTS dimensions. A survey design was used, involving 403 students selected through multistage random sampling from public and private schools across eight regencies and one city in Bali Province, Indonesia. Data were collected using a content-free CTS test developed by the authors, which demonstrated content validity and high reliability (Cronbach’s α = 0.920). The data were analysed using descriptive statistics and repeated-measures analysis of variance after assumption testing. The results showed that students’ overall CTS remained low, with average mastery below 60%. Significant differences were found across dimensions: evaluation and interpretation had the highest scores, whereas analysis and explanation had the lowest. These findings provide a provincial-level empirical basis for developing more targeted strategies, assessments, and educational policies to strengthen CTS.
Practical experience is a key component of teaching and learning in project management. In 2023–2025, we organized two practical project management seminars as part of a bachelor’s introductory project management course. In these seminars, we used serious management games to practise waterfall and agile approaches and to improve students’ teamwork, communication, and soft skills. We applied the Profile of Mood States method to evaluate changes in total mood and individual mood factors during the seminars. We collected and analysed data from 2024–2025 (n24 = 139, n25 = 98) and compared these results with prior findings from 2023 (n23 = 49). We found that students’ total mood improved significantly during both waterfall and agile seminars in all eligible years. Fatigue, depression, and confusion decreased significantly for both seminar types in all years, and tension decreased in most seminars. We will use these results to further improve the practical seminars and to introduce the solution to other areas.
Mathematics education increasingly requires teaching approaches that strengthen students’ creativity, problem-solving skills, and positive attitudes toward learning. However, limited evidence exists on the effectiveness of AI-supported inquiry-based learning in developing multiple dimensions of mathematical competence among secondary school students. This study examined the impact of AI-supported inquiry-based learning on creative mathematical performance, critical problem-solving skills, and attitudes toward mathematics. Using a quasi-experimental design, students with a mean age of 12.79 years (SD = 0.68) were assigned either to an experimental group receiving AI-supported inquiry-based learning or to a control group receiving conventional instruction. Data were collected through validated tests and questionnaires. The results showed that AI-supported inquiry-based learning significantly improved students’ creative mathematical performance and attitudes toward mathematics compared with traditional instruction, but it did not produce a statistically significant improvement in critical problem-solving skills. Multivariate analysis confirmed a significant overall group effect, while correlation analysis showed positive relationships among all variables in both groups. Overall, the findings suggest that AI-supported inquiry-based learning mainly supports creativity and affective development, while its effect on problem-solving skills remains limited. It may also improve instructional efficiency through guided exploration, adaptive feedback, and reduced cognitive load.
Architectural Design Education (ADE) in India has changed little over the past two decades and still relies on an outdated curriculum. As a result, students and graduates may be underprepared for real-world professional challenges. Previous research from Europe, the USA, India, Australia, and South Asia shows considerable variation in ADE curricula, which generally fall into two areas: art and design, and technical practice. This study examines the curricular focus of Indian and international universities, compares frameworks developed by Indian regulatory bodies such as COA and AICTE with those of NAAB, RIBA, and AIA, and evaluates how closely Indian university curricula align with these standards. By comparing five leading architecture schools in India with four international institutions, the study identifies both shared features and major differences. The findings show that Indian ADE emphasises foundational theory and design skills but pays less attention to emerging technologies and practical experience than international programs do. The study also reveals gaps between COA standards and their implementation. In response, it proposes a competency assessment framework to support curriculum standardisation, improve assessment efficiency, and promote consistency and accountability.
High student dropout rates remain a significant impediment to achieving the United Nations SDG 4 (equitable education). While Artificial Intelligence (AI) offers robust early risk prediction, the intrinsic black-box nature of high-performing models constrains their transparency. This study designs and investigates a multi-layered Explainable AI (XAI)-based assessment framework to generate actionable insights for student retention. We utilized AutoGluon to construct high-performing multiclass classification models (Graduated, Dropout, or Enrolled) on a higher education dataset. To address the complexity of the AutoGluon-generated models, we employed a hybrid XAI framework that couples global interpretability via a decision tree surrogate model and local interpretability via LIME (Local Interpretable Model-agnostic Explanations). The analysis revealed that models from the Boosting family, particularly XGBoost with bagging level 2, achieved the highest predictive performance (exceeding 0.890 across all metrics). The global analysis demonstrated that academic factors were the primary drivers of prediction, but critical socio-economic factors, such as Tuition fees, also exerted significant influence. Local LIME analysis provided granular, case-specific insights, strongly linking dropout status to first-year academic challenges and to features such as age at enrollment. This integrated XAI approach transforms complex models into an interpretable system, supporting student retention and educational equity (SDG 4).
Higher education institutions need timely, explainable tools to identify students at risk of low performance on large-scale examinations and to guide targeted academic support strategies. In response to this challenge, this study proposes an explainable machine learning framework to predict undergraduate students' performance levels in Colombia's SABER PRO examination. Using student background variables (e.g., gender, region, school type, parental education, and occupation) and SABER 11 standardised test scores (Critical Reading, Mathematics, Citizenship Skills, Science, and English), we formulate a binary classification problem that distinguishes desirable outcomes (levels 3–4) from non-desirable outcomes (levels 1–2). We benchmark baseline models against non-linear learners, including XGBoost, GLMNET, SVM, DT, and LDA, using a 10-fold cross-validation protocol with systematic hyperparameter tuning. Model performance is assessed through confusion matrices and AUC scores. To support educational decision-making, we complement predictive results with explainability analyses, including global feature importance and individual-level explanations via SHAP, enabling transparent identification of the key drivers behind performance levels. The proposed approach provides actionable learning analytics to guide early academic support, promote responsible and transparent educational decision-making, and improve the likelihood of desirable SABER PRO achievement.
Persistent disparities in student learning outcomes across Czech municipalities highlight the challenge of ensuring equitable access to quality education. These disparities are not only associated with demographic and economic conditions but also with the responsibility of municipalities and institutions to address structural inequalities. This study applies machine learning and SHAP analysis to predict student learning outcomes across municipalities with extended jurisdiction (MEJs), using demographic, economic, social, and housing indicators. Results highlight the dominant role of educational structure, with the share of people without secondary education and the proportion of younger adults holding college degrees emerging as the most influential predictors. Social and housing stressors, including parental executions, poverty destabilization, and housing allowances, further moderate outcomes, revealing nonlinear threshold effects that refine the explanatory narrative. The combined model achieved an R² of 0.629, confirming that while demographic and educational indicators explain most of the variance, contextual vulnerabilities add interpretive richness by identifying vulnerable subgroups. These findings underscore the dual influence of structural educational attainment and social stressors on student performance, while emphasizing educational responsibility as a key dimension in promoting equity and sustainable development.
Student study status prediction, including drop-out and graduation, is a widely studied topic in higher education. Yet, evidence across studies remains difficult to compare due to differences in targets, imbalance treatment, metrics, and validation strategies. This systematic literature review synthesizes 70 peer‑reviewed articles published between 2017 and 2025 that apply machine learning or deep learning to predict study outcomes under class imbalance. Results reveal a strong dominance of binary targets, while multi‑class experiments are relatively rare, though they better reflect institutional categories and expose larger performance gaps across classes. Reported imbalance handling includes data‑level resampling, algorithm‑level class weighting, and ensemble or hybrid designs, but many studies lack sufficient procedural detail. Evaluation practices vary considerably; studies reporting per-class measures and imbalance-aware metrics, such as macro F1 and balanced accuracy, provide more decision-relevant evidence than those relying mainly on accuracy. Validation strategies range from hold‑out and stratified cross‑validation to nested validation, temporal splits, and external testing, shaping the credibility of reported performance for deployment. We propose an integrative taxonomy linking target formulation, imbalance degree, handling strategy, and evaluation design to enhance intervention efficiency through capacity‑aware prioritization, while strengthening responsibility through transparent reporting, defensible validation, and explicit attention to minority class performance.
Assessing academic productivity in higher education is challenging because performance depends on multiple correlated competencies and evolves across heterogeneous regional contexts. This is especially relevant in Colombian Social Science programs, where territorial disparities may mask differences in academic efficiency. This study analyses academic productivity dynamics from 2020 to 2023 using a combined PCA–Malmquist Index approach based on 11,099 observations. First, PCA was used as an unsupervised learning technique to reduce dimensionality and identify latent performance profiles. Second, the Malmquist Index was used to estimate productivity change through technological change (TC), pure technical efficiency change (PECH), and scale efficiency change (SECH). The findings show that the strongest profile was associated with Critical Reading, English, and Written Communication, increasing from 20% to 27% in the final period. Technological change explained 76% of productivity improvements, with Magdalena showing the best performance, while Huila lagged due to lower TC and PECH levels. The results highlight that academic productivity in Colombian Social Science programs is shaped by both educational performance and unequal regional capacity for academic modernization.
Evaluating the effectiveness of social support programs in higher education requires moving beyond homogeneous assessments of student performance. This study integrates intersectionality with dynamic efficiency analysis to examine how academic efficiency evolves across diverse student profiles within the Líderes del Mañana full-scholarship program in Mexico. Using a longitudinal dataset of 1,796 students (22,718 student–term observations), we apply a two-stage approach. First, Window Data Envelopment Analysis (DEA) estimates relative academic efficiency over time. Second, Gaussian Mixture Modeling identifies intersectional student profiles based on efficiency trajectories and contextual characteristics. Results reveal five distinct efficiency trajectories. While most students converge toward high-efficiency levels, one cluster exhibits a clear negative efficiency slope, greater variability, and limited institutional alignment, indicating it is a priority for intervention. Other clusters display stable high performance, continuous improvement, or moderate but non-accelerating trajectories. Findings demonstrate that efficiency differences are not explained by single demographic factors but by configurations of social background and institutional context. This study provides a scalable, data-driven framework for aligning equity and efficiency objectives in higher education policy and scholarship programs.
The rapid expansion of digital learning has generated large volumes of educational data, creating new opportunities to apply machine learning (ML) and data mining techniques to predict student academic performance. This study synthesizes 58 empirical studies that used Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, and Artificial Neural Networks to identify at-risk students and improve educational outcomes. The review focuses on predictor variables, validation methods, accuracy rates, and performance metrics. Findings suggest that the most effective predictive models combine four categories of variables: demographic factors, academic indicators, digital behavioral features, and psychosocial attributes. Among the algorithms examined, Random Forest and Artificial Neural Networks demonstrated the strongest predictive performance, achieving accuracy rates of 85%–93% across k-fold cross-validation and train-test split validation. Performance measures such as precision, recall, F1 score, and AUC further confirm the robustness and generalizability of these models. ML-based academic prediction systems can strengthen early warning systems, support data-driven policymaking, and enable personalized learning interventions. The study concludes that combining multidimensional predictors with explainable AI can improve equity, personalization, operational efficiency, and accountability in educational decision-making.
This study employed a convergent parallel mixed-method design to examine the teaching beliefs of secondary school teachers and determine the relationship between their scientific epistemological beliefs and pedagogical approaches. Semi-structured interviews were utilized to explore teachers’ teaching beliefs, while quantitative analysis involved a descriptive-correlational approach, employing two adapted questionnaires: Scientific Epistemological Belief Questionnaire (SEBQ) and Approaches to Teaching (ATI). Analysis of interview responses reveals that most teachers prioritize creating a student-involved classroom environment, typically teacher-initiated learning, rather than allowing student-led initiatives. They generally view themselves as facilitators of learning, base their teaching decisions on the curriculum, and believe that students demonstrate understanding by reiterating what has been taught. Quantitative analysis indicated that science teachers in the region largely demonstrate traditional beliefs regarding the origins and characteristics of scientific knowledge while predominantly employing transitional teaching approaches in their practice. Furthermore, the study found a correlation between teachers' SEBs and adopting learner-focused teaching approaches. Integration and meta inference of qualitative and quantitative findings bear significant implications for science education, suggesting avenues for enhancing, restructuring, and reforming teachers' teaching and epistemological beliefs. Hence, efforts should focus on fostering teachers' deeper understanding of the nature of science.
Improving student achievement is one of the most important components of learning, and it is influenced by several variables, including academic resilience, self-regulation, and students' perceptions. This study examined how high school academic resilience, self-regulation, and students' perceptions affect their academic achievement in chemistry classes. Even though chemistry is regarded as a crucial subject for learning, most students find it complicated, making it challenging to comprehend. This explains why students' academic achievement in chemistry is so low. Using cluster random sampling techniques, 791 students participating in chemistry classes formed the sample. The linear relationship model between academic resilience, self-regulation, student perceptions, and achievement in chemistry is examined in this research using the Structural Equation Modelling (SEM) method. The results indicate that chemical achievement correlates negatively with academic resilience, significantly positively with self-regulation, and negatively and insignificantly with student perception. To ensure that students in chemistry learn at their best, teachers should focus more on the qualities of their students and incorporate learning activities.
The study aimed to assess levels of "artificial intelligence literacy" (AIL) and "artificial intelligence anxiety" (AIA) among pre-service teachers and to examine their relationship. The study used an explanatory sequential design, a mixed-methods design. Quantitative data were collected from 136 pre-service teachers using the "Artificial Intelligence Literacy Scale" and "Artificial Intelligence Anxiety Scale" through convenience sampling. Qualitative data were collected through semi-structured interviews with nine pre-service teachers using criterion sampling. Quantitative data were analyzed using ANOVA and correlation analyses, while qualitative data were subjected to content analysis. The results indicated that pre-service teachers' AIL levels did not differ significantly by GPA, AI knowledge level, or emotional state towards AI. However, they varied significantly by skill level with technological tools. Additionally, AIA levels did not differ significantly by skill level in using technological tools or AI knowledge level. However, they varied considerably based on GPA and emotional state variables. Correlation results revealed no significant relationship between AI levels and AIA levels. The qualitative data from interviews supported the quantitative results, indicating no relationship between AIL and AIA levels. As a suggestion, training can be provided to increase pre-service teachers' awareness of AI.
Scrum is a framework that supports the development of various student skills, yet its application in chemistry learning within the Manado local cultural context remains rare. This study aimed to examine the effect of the scrum method, combined with ethnochemistry, on students' creative thinking skills and entrepreneurial attitudes in green chemistry. Using a quasi-experimental pretest-posttest control-group design, the study involved 110 senior high school students in Manado, divided into an experimental group (55 students) using Scrum with ethnochemistry and a control group (55 students) using conventional learning. Data were analyzed using MANOVA and paired sample t-tests. Results showed that the experimental group experienced significant improvements in creative thinking skills and entrepreneurial attitudes compared to the control group. However, the method's complexity posed challenges that affected its overall implementation. Despite these challenges, the Scrum method within the ethnochemistry context proved to have a positive influence on students' skills. Therefore, this method is recommended for broader application in schools to enhance students' creative and entrepreneurial competencies.
Informed Nature of Science (NOS) conception is among the professional competencies of science teachers. As a result, extensive research is being conducted on the development of NOS conceptions among pre-service science teachers (PSSTs). However, it remains a significant challenge, particularly because NOS is a meta-concept that necessitates higher-order cognitive skills. In this study, we explored the influence of explicit Science Process Skill (SPS) instruction on the PSST's NOS conception using a quasi-experimental pretest-posttest design with experimental and control groups. SPS is instructed using the four-component instructional design (4C/ID) model. Findings indicated that PSSTs had a less informed conception of NOS, its various themes, and laws vs. theories and methodologies in scientific investigation. Observation and inference, the tentativeness of scientific theories/knowledge, the existence of creativity and imagination in science, and scientific methodology were significant themes of NOS. On the other hand, laws vs. theories and society and cultural influence on science themes do not show significant improvements. This study demonstrated that explicit SPS instruction is a better framework for developing specific themes of NOS conception. However, it also highlighted the limitations of a single method in altering entire themes, emphasizing the need for an appropriate method for each theme.
Even though integrated science education (ISE) has been advocated globally for decades to enhance students’ scientific literacy, developing countries with their own contextual conditions still face ongoing struggles in transitioning from subject specialization to integration. To ensure educational efficiency, specifically by reducing disciplinary fragmentation and optimizing resource use, understanding teachers’ attitudes, along with diverse contextual factors, plays a pivotal role in the ISE movement. This quantitative study examines the attitudes of Vietnamese science teachers at various school levels, emphasizing differences in their attitudes across contextual variables, which informs strategies to enhance ISE promotion. The questionnaire-based methodology was employed to collect 203 responses, and the data were analyzed using one-way ANOVA. Our findings proved that (1) science teachers favored ISE despite the obstacles and anxiety of an educational reform; (2) traditional assumptions of contextual variables such as gender, years of teaching experience, and educational qualifications do not statistically differ in teachers’ attitudes; (3) the quality of professional development might relate to teachers’ perceived difficulty, anxiety, and self-efficacy towards ISE. Therefore, a sustainable, high-quality provision of professional development is essential to help teachers achieve ISE instructional objectives, alongside more practical solutions.
In today’s vocational education, preparing prospective vocational teachers requires developing innovative, creative thinking that strengthens pedagogical competence and classroom management. These abilities help address the complexity of modern vocational classes and support a positive, engaging learning environment. This study examines how prospective vocational teachers manage ICT-based learning resources to encourage creativity and innovation in their professional practice. A quantitative approach was applied with 178 participants, evenly assigned to a control group and an experimental group. The experimental group used ICT-based learning tools, including interactive multimedia and various digital technologies. Results showed that participants who effectively used ICT resources achieved higher levels of creative and innovative thinking. The experimental group’s N-Gain score reached 0.756, compared with only 0.049 in the control group. This significant difference strongly suggests that appropriate use and management of ICT learning resources can help future vocational teachers create more productive classroom conditions and strengthen their entrepreneurial abilities. The notable learning gains also demonstrate that ICT-based multimedia improves instructional effectiveness compared with traditional methods. Overall, the findings clearly highlight that future TVET educators who master digital learning resources will be better prepared for teaching demands and the challenges of contemporary TVET.
