This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.
Geography education plays a crucial role in enhancing students’ environmental awareness. One of the applicable approaches is the integration of local wisdom into the learning process. Local wisdom reflects the traditional ways communities have preserved environmental balance across generations. This study aims to (1) analyse the types of local wisdom in Kerjo District, (2) examine the implementation of local wisdom in geography learning, and (3) assess the impact of local wisdom on students’ understanding of environmental issues. This research employs a qualitative approach using a case study method conducted at SMA Negeri 1 Kerjo, which has implemented local wisdom-based learning. The research subjects include geography teachers and students. Data collection techniques involve observation, interviews, and analysis of teaching materials and school policy documents. The collected data were analysed through data reduction, data display, and conclusion drawing. The findings of the study are as follows: (1) Kerjo District has eight types of local wisdom, namely Nyadran, Bersih Desa, Tirakatan, Suran, Gotong Royong, Wetonan, Sedekah Bumi, and Rasulan; (2) The implementation of local wisdom-based education at SMA N 1 Kerjo is not yet optimal; (3) Students’ understanding of environmental topics in geography learning at SMA N 1 Kerjo needs improvement. This study highlights the importance of integrating local wisdom into geography education to strengthen students’ comprehension of environmental issues. It serves as a reference for educators and policymakers in developing more contextual and applicable culturally-based curricula in environmental education.
Sport-related concussion (SRC) and its potential neurological sequela represent an emerging global health concern, requiring improved recovery management and strategies for return-to-play (RTP) to enhance brain health in athletes. Given the dynamic and multifaceted nature of SRC recovery, the purpose of this review is to synthesize existing literature on post-SRC outcomes in adult athletes, and to outline the temporal trajectories of key recovery indicators (symptoms, cognitive function, blood biomarkers) across distinct recovery phases until resolution. In the acute phase of SRC (first 48 h), symptom scores and brain damage markers peaked immediately, while cognitive impairments and neuroinflammation emerged with a slight delay. Following the initial rise, brain damage marker concentrations rapidly dropped below baseline levels at approximately 48 h following SRC injury. During the early recovery phase, neuroinflammation and most cognitive alterations resolved after 3–5 days, though symptom burden and attention deficits persisted for up to 7 days. Despite prolonged alterations reported in some individuals, recovery markers typically returned to pre-injury levels in the transition phase (≤ 2 weeks), though mild attention deficits were detected up to 3 weeks, and TNF-α concentrations remained elevated throughout late recovery (> 2 weeks). These results reveal distinct temporal discrepancies across recovery markers and emphasize that physiological disturbances can outlast symptom resolution, underscoring the need for both multimodal assessments and appropriately timed evaluations to accurately track recovery progression. Incorporating structured follow-ups at key time points, particularly beyond symptom resolution, may improve RTP decision-making and reduce the risk of premature return and long-term neurological consequences.
Experimental activities are an essential part of physics education. In addition to conveying scientific knowledge, they play a significant role in developing scientific literacy, inquiry skills, and critical thinking. In today's world, where students are exposed to vast amounts of information of varying quality, the ability to analyse, evaluate, and interpret information correctly has become increasingly important. This paper presents a series of physics experiments in the field of optics, specifically designed to foster critical thinking at various stages of the inquiry process. The topic of optics was chosen deliberately, as many optical phenomena occur naturally in everyday life, are familiar to students, and stimulate their curiosity. At the same time, they provide space for formulating hypotheses, designing experiments, interpreting data, and evaluating alternative explanations. Each experiment begins with a real-life problem situation that students are expected to explore and resolve through their own investigative work. The tasks are structured to encourage discussion, require argumentation, and promote reflection on both the process and the outcomes. The proposed experiments are suitable for students at both primary and secondary school levels and can be implemented in formal as well as non-formal educational settings. The aim of this contribution is to demonstrate how well-designed and pedagogically grounded experiments can not only enhance the understanding of physical concepts but also systematically develop critical thinking skills - one of the key competencies of 21st-century education.
As artificial intelligence-generated content (AIGC) reshapes knowledge acquisition, higher education faces growing inequities that demand systematic mapping and intervention. We map the AI divide in undergraduate education by combining network science with survey evidence from 301 students at Nanjing University, one of China's leading institutions in AI education. Drawing on course enrolment patterns to construct a disciplinary network, we identify four distinct student communities: science dominant, science peripheral, social sciences & science, and humanities and social sciences. Survey results reveal significant disparities in AIGC literacy and motivational efficacy, with science dominant students outperforming humanities and social sciences peers. Ordinary least squares (OLS) regression shows that motivational efficacy--particularly skill efficacy--partially mediates this gap, whereas usage efficacy does not mediate at the evaluation level, indicating a dissociation between perceived utility and critical engagement. Our findings demonstrate that curriculum structure and cross-disciplinary integration are key determinants of technological fluency. This work provides a scalable framework for diagnosing and addressing the AI divide through institutional design.
Mohamed Tolba, Olivia Kendall, Daniel Tudball Smith
et al.
Educational videos are widely used across various instructional models in higher education to support flexible and self-paced learning. However, student engagement with these videos varies significantly depending on how they are designed. While several studies have identified potential influencing factors, there remains a lack of scalable tools and open datasets to support large-scale, data-driven improvements in video design. This study aims to advance data-driven approaches to educational video design. Its core contributions include: (1) a workflow model for analysing educational videos; (2) an open-source implementation for extracting video metadata and features; (3) an accessible, community-driven database of video attributes; (4) a case study applying the approach to two engineering courses; and (5) an initial machine learning-based analysis to explore the relative influence of various video characteristics on student engagement. This work lays the groundwork for a shared, evidence-based approach to educational video design.
Mentoring software is a pivotal innovation in addressing critical challenges in teacher development within educational institutions. This study explores the transformative potential of digital mentoring platforms, evaluating their impact on enhancing traditional mentoring practices through scalable, data-driven, and accessible frameworks. The research synthesizes findings from existing literature to assess the effectiveness of key features, including structured goal setting, progress monitoring, and advanced analytics, in improving teacher satisfaction, retention, and professional growth. Using an integrative literature review approach, this study identifies both the advantages and barriers to implementing mentoring software in education. Financial constraints, limited institutional support, and data privacy concerns remain significant challenges, necessitating strategic interventions. Drawing insights from successful applications in healthcare and corporate sectors, the review highlights adaptive strategies such as leveraging open-source tools, cross-sector collaborations, and integrating mentoring software with existing professional development frameworks. The research emphasizes the necessity of integrating digital mentoring tools with institutional objectives to create enduring support systems for teacher development. Mentoring software not only enhances traditional mentorship but also facilitates broader professional networks that contribute to collective knowledge sharing.
James Alves de Souza, Michel Corci Batista, Marcello Ferreira
The rapid advancement of digital technologies in the first quarter of the 21st century has introduced significant transformations in various fields, such as communication, healthcare, and education. However, it has also led to an increase in the use and disposal of electronic devices, resulting in environmental challenges related to Waste Electrical and Electronic Equipment (WEEE), also known as e-waste. This phenomenon is observed in schools, where the integration and renewal of equipment have become essential for the development and implementation of new teaching strategies. Based on a technological research project, we present how students from a public school in São Paulo's countryside conducted e-waste reuse processes, applying the principles of metarecycling and physics knowledge to build a portable battery (power bank) and a smartphone charger powered by a dynamo attached to a bicycle. The appropriation of the relationships between science, technology, and social aspects was facilitated by validating the chargers through characterization tests of the charging time provided by the power bank and the bicycle-installed device under riding conditions. Educational actions within the community, involving concepts of sustainability, clean energy, and health benefits through physical exercise, were guided by the United Nations Sustainable Development Goals (SDGs).
Charith Jayasekara, Carlo Kopp, Vincent Lee
et al.
This paper presents the design and refinement of automated Moodle-based Problem-Solving Assessments (PSAs) deployed across large-scale computing units. Developed to replace traditional exams, PSAs assess applied problem-solving skills through parameterised, real-world tasks delivered via Moodle's quiz engine. Integrated with interactive workshops, this approach supports authentic learning, mitigates academic integrity risks, and reduces inconsistencies in marking. Iterative improvements have enhanced scalability, fairness, and alignment with learning outcomes. The model offers a practical and sustainable alternative for modern computing and engineering education.
Exsaris Januar, Nurhizrah Gistituati , Yanti Fitria
et al.
Background/purpose. Study addresses the problem of low scientific and digital literacy among Grade 6 students (Phase C) in the IPAS (Science and Social Studies) subject under the Merdeka Curriculum, particularly in the theme “Our Endangered Earth.” The main purpose of the study is to develop and evaluate the EXARIS learning model (Exploration, Analysis, Reflection, Integration, Sharing) as an innovative solution that enhances both scientific and digital literacy, aligned with 21st-century learning demands and curriculum goals.
Materials/methods. The EXARIS model was developed using a systematic instructional design approach based on the ADDIE framework (Analysis, Design, Development, Implementation, and Evaluation). Validation was conducted through expert review, while practicality was assessed by classroom teachers. The model's effectiveness was measured using a quasi-experimental design with a Paired Samples T-Test dan Anacova One Way.
Results. The expert validation showed a high level of validity (average score = 3.85 on a 4-point scale). Practicality testing indicated that teachers found the model easy to implement (average score = 3.70). Effectiveness testing revealed significant improvements in students’ digital literacy (Mean Difference = 1.79, p < 0.001) and scientific literacy (Mean Difference = 1.65, p < 0.05).
Conclusion. The EXARIS learning model is valid, practical, and effective for improving scientific and digital literacy among Grade 6 students in the IPAS subject within the Merdeka Curriculum. This model offers an innovative pedagogical strategy to strengthen students' foundational competencies and meet the needs of 21st-century education.
Thays Cristina dos Santos, Hellen Paulo Silva, Karen Rodrigues Lima
et al.
<b>Background:</b> Estrogen depletion alters bone mineralization and oxidative stress. Antioxidants like humic acids (HA) may help mitigate bone demineralization and redox imbalances. Thus, this study evaluated the effects of HA on bone mineral composition and oxidative stress markers in an experimental menopause model. <b>Methods:</b> Twenty-four female C57BL/6 mice were divided into four groups (n = 6/group): Sham; Sham + HA; Ovariectomized (OVX); and OVX + HA. The menopause model was induced by bilateral ovariectomy at the beginning of the experiment. HA derived from biomass vermicompost was administered daily by gavage for 28 days. After euthanasia, femurs and fragments of the gastrocnemius muscle, liver, and kidney were collected. Bone elemental composition was analyzed using scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS). Superoxide dismutase (SOD), catalase (CAT), and hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) activities were assessed in muscle, renal, and hepatic tissues. Data were analyzed using two-way ANOVA and Bonferroni’s post hoc test. <b>Results:</b> Untreated OVX mice exhibited a significant reduction in femoral calcium content (<i>p</i> < 0.05). However, HA treatment increased calcium levels and improved the Ca/P ratio (<i>p</i> < 0.05). H<sub>2</sub>O<sub>2</sub> activity was reduced in the liver and kidney of OVX + HA mice compared to untreated animals (<i>p</i> < 0.05). CAT activity in muscle increased in the OVX + HA group compared to the OVX (<i>p</i> < 0.05). <b>Conclusions:</b> HA treatment improved femoral elemental composition and modulated oxidative stress markers in an experimental menopause model.
The integration of Large Language Models (LLMs) in education offers both opportunities and challenges, particularly in fields like physics that demand precise conceptual understanding. This study examines the capabilities of six state-of-the-art LLMs in explaining the law of conservation of momentum, a fundamental principle in physics. By analyzing responses to a consistent, simple prompt in Japanese, we assess the models' explanatory approaches, depth of understanding, and adaptability to different educational levels.Our comprehensive analysis, encompassing text characteristics, response similarity, and keyword usage, unveils significant diversity in explanatory styles across models. ChatGPT4.0 and Coral provided more comprehensive and technically detailed explanations, while Gemini models tended toward more intuitive approaches. Key findings include variations in the treatment of critical concepts such as net force, and differing emphases on mathematical rigor and real-world applications.The results indicate that different AI models may be more suitable for various educational contexts, ranging from introductory to advanced levels. ChatGPT4.0 and Coral demonstrated potential for advanced discussions, while Gemini models appeared more appropriate for introductory explanations. Importantly, the study underscores the necessity of educator guidance in effectively leveraging these AI tools, as models varied in their ability to convey nuanced aspects of physical principles.This research establishes a foundation for understanding the educational potential of LLMs in physics, providing insights for educators on integrating these tools into their teaching practices. It also highlights the need for further investigation into AI-assisted learning in STEM fields, paving the way for more sophisticated applications of AI in physics education.
Transitioning from Education 1.0 to Education 5.0, the integration of generative artificial intelligence (GenAI) revolutionizes the learning environment by fostering enhanced human-machine collaboration, enabling personalized, adaptive and experiential learning, and preparing students with the skills and adaptability needed for the future workforce. Our understanding of academic integrity and the scholarship of teaching, learning, and research has been revolutionised by GenAI. Schools and universities around the world are experimenting and exploring the integration of GenAI in their education systems (like, curriculum design, teaching process and assessments, administrative tasks, results generation and so on). The findings of the literature study demonstrate how well GenAI has been incorporated into the global educational system. This study explains the roles of GenAI in the schooling and university education systems with respect to the different stakeholders (students, teachers, researchers etc,). It highlights the current challenges of integrating Generative AI into the education system and outlines future directions for leveraging GenAI to enhance educational practices.
Chun-Wei Huang, Si Ying Yau, Chiao-Ling Kuo
et al.
Study region: The Choushui River Fan, Taiwan. Study focus: Groundwater overdraft has led to not only groundwater depletion but also environmental disasters, such as subsidence and seawater intrusion in the Choushui River Alluvial Fan, Taiwan. The influence of land subsidence is gradually shifting from the coast to the center of the fan and threatening Taiwan high-speed rail. However, it remains a great challenge to manage and model the groundwater aquifer due to numerous unregulated wells. This study maps and locates private wells using deep learning technologies. We trained and validated convolutional-based deep learning neural networks (DNNs), using street view images. We applied the DNNs to a land subsidence area along the Taiwan high-speed rail, termed the Golden Corridor in Taiwan. The results showed that DNNs can recognize pumping wells with at least 90% accuracy. The testing cases showed their capability to recall all the pumping wells in three road segments along the Golden Corridor. Finally, we spatially estimated potential pumping of a subsidence area using the fine-trained DNNs. New hydrological insights for the region: Given the prevalence of unknown private pumping in the Choushui River Fan, our image data-driven computer vision approach not only eases labor-intensive private well investigations but also advances hydrologic understanding for groundwater modeling. We enhance comprehension of unknown sinks and provide their spatial distribution to improve groundwater modeling.