Learning Demands Research Articles

Education 4.0: Teachers’ Strategies, Needs, And Challenges

This study examines the evolving educational environment shaped by the Fourth Industrial Revolution, which necessitates innovative approaches to teaching and learning. The research focuses on the integration of technology in education and the corresponding competencies required by teachers to meet the demands of 21st-century learners. The study specifically investigates the strategies, needs, and challenges faced by secondary teachers in the Third Congressional District of Iloilo in transitioning towards Education 4.0. Grounded in the theoretical framework of Connectivism, the study employs a descriptive-correlational research design, utilizing a structured questionnaire to gather data from 296 respondents. Findings reveal that while teachers are eager to adopt Education 4.0 strategies, they face significant challenges, including limited access to resources, insufficient training, and varying levels of digital literacy among students. The study concludes with recommendations for enhanced professional development, improved access to technological resources, and the promotion of collaborative learning environments to better support teachers in this transition. These findings hold significant implications for stakeholders, including educational authorities, school administrators, and policymakers, in fostering a future-ready educational system. Keywords: Education 4.0, Fourth Industrial Revolution, Connectivism, digital literacy, teacher competencies, innovative teaching strategies, professional development, technological integration

Unveiling Students’ Mental Models and Learning Demands: an Empirical Validation of Secondary Students’ Model Progression on Plant Nutrition

Abstract Identifying the mental models held by students has been widely emphasized as being a pivotal aspect of effective science education. In fact, it allows us to understand students’ conceptions, detect teaching-learning difficulties and tailor instruction accordingly. Hence, in this study, the plant nutrition mental models held by upper secondary students were investigated and empirically validated with the aim of detecting the most pressing learning demands and providing instructional guidelines to improve the teaching-learning of the topic. In order to unveil students’ mental models a 5-question open-ended questionnaire was administered to 122 Spanish upper secondary students. Their responses were analyzed through an innovative approach that merged phenomenography and Item Response Theory. Three distinct models emerge from the analysis investigating the sequential development of students’ reasoning. The first and most basic comprises students with heterotrophic explanatory ideas based on intuitive and naïve conceptions. The intermediate model consists of participants who incorporate photosynthesis and plants’ gas exchanges to their models without fully grasping some key underlying concepts probably due to previous misconception inducing instruction. The upper model encompasses the few students who have autotrophic mental models and ideas which resemble the consensus Scientific Model of Plant Nutrition. The findings also reveal that the overall conceptualization level of the participants is relatively low and that several teaching-learning difficulties are strictly linked to each of the different mental models. Finally, the learning demands of the topic are described, and the implications for the teaching-learning designs aimed at overcoming those difficulties are discussed.

Personalized Learning Style Detection And Pathway Optimization Using Hybrid Machine Learning Approaches

The demand for personalized learning in education has led to the need for adaptive systems that cater to diverse student needs and learning styles. This paper proposes a hybrid framework that combines learning style detection with dynamic pathway optimization to enhance student engagement and outcomes. By leveraging data-driven techniques, the framework identifies individual preferences and adapts educational content accordingly, ensuring an effective and tailored learning experience. Through illustrative scenarios, we demonstrate how this approach can accommodate various learning styles, such as visual, auditory, and kinesthetic, while optimizing content delivery to improve satisfaction and performance. This work highlights the potential for scalable and adaptable solutions in e-learning platforms, offering significant benefits across diverse educational and training contexts. The proposed framework lays a foundation for future research in personalized education systems. Keywords: Artificial Intelligence (AI), Machine Learning in Education, Personalized Learning, Learning Style Detection, Student Segmentation, Clustering Techniques, Reinforcement Learning.

Research on the Analysis of students’ English Learning Behavior and Personalized Recommendation Algorithm based on Machine learning

The goal of this study is to create a personalized recommendation system for English learning resources by analysing students’ English learning behaviors using a Generalized Regression Neural Network (GRNN). For efficient language acquisition, tailored educational support is essential due to the diversity of students’ linguistic backgrounds and learning demands. Performance ratings, personal preferences, and the amount of time students spent on various content categories were among the data gathered for this study on how students interacted with English language learning materials. Our initial analysis of the patterns and discrepancies in the learning behaviors of the pupils involved the use of the GRNN model. Strong insights into the correlations between various aspects and learning results were obtained by the neural network, which was especially well-suited for this investigation due to its affinity for handling non-linear interactions and its low need for preprocessing data. These observations led us to create an individual recommendation engine that recommends educational resources and activities based on each user’s learning preferences and skill level. Using a varied set of students, a controlled study was conducted to assess the efficacy of the individual suggestions. Comparing the preliminary findings to conventional, non-personalized methods of learning, efficiency in learning and student engagement have significantly improved. In addition to showcasing GRNN’s potential for educational applications, this work offers an adaptable framework for adaptive learning systems across a range of academic fields.

Analysis of Student Needs for the Use of Anonymous Chatting in Liberal Arts Education

This study explores the impact of using anonymous open chatting systems in university liberal arts courses on learning quality, efficiency, and student satisfaction, while identifying the demands of learners. Data were collected through an online survey of 588 undergraduate students at Sangmyung University, comprising 39 questions about usage experience, pros and cons of anonymity, efficiency, satisfaction, learning quality improvement, technical aspects, and demands. The findings indicate that anonymous open chatting enhances the efficiency of large lectures, fosters interactions between instructors and students, and significantly improves student satisfaction. Anonymity was found to reduce the burden of class participation and facilitate active engagement in discussions. Meanwhile, the risk of inappropriate behavior due to anonymity was minimal. The study also revealed that anonymous open chatting is beneficial in diverse subjects and large-scale classes. These findings suggest important implications for adopting and expanding anonymous open chatting systems in liberal arts education.

Enhancing Pedagogy and Learning Outcomes in Financial Accounting: A Case Study of Higher Education Institutions in South Africa

This study explores innovative strategies to enhance instruction and learning in financial accounting within higher education institutions in South Africa. Acknowledging the dynamic nature of the business environment and the evolving learner demands, the research investigates various approaches for optimizing educational outcomes. Through a comprehensive literature review, the study identifies contemporary challenges in financial accounting education and underscores the importance of adapting teaching methods to foster a deeper understanding of complex accounting principles. This research aims to bridge the gap between secondary school outputs and the expectations of tertiary financial accounting programs. The study examines systemic factors affecting financial accounting education and employs a self-study narrative approach, utilizing reflexive praxis to delve into the lived experiences of accounting lecturers. The findings, presented through thematic analysis, illustrate the reflective practices of higher education lecturers, providing valuable insights for professional development and growth in the accounting field. The paper advocates for diverse pedagogical approaches to address the challenges faced by accounting educators, emphasizing strategies to overcome teaching impediments. This article aims to enhance the success of post-secondary teaching and learning in the accounting discipline in South Africa, considering the quality of students transitioning from secondary education and their expectations upon entering tertiary education.

Exploring the Value of Philosophy in Curriculum Implementation in Physical Education: A Scoping Review

Educational philosophy provides a strong pillar of support and guidance to an institution. The scoping review aims to examine the significance of educational philosophies to curriculum implementation in Physical Education. The purpose was to provide a comprehensive overview of other relevant literature to identify gaps and observe existing cases related to this review. Additionally, it explores how these theoretical-based approaches influence the selection of content, teaching pedagogy, and preferred learning styles to deliver instructions effectively. Arksey and O’Malley’s (2005) five-stage framework was employed for a comprehensive and transparent process. Moreover, the study covered a criterion for inclusion and exclusion studies. The researchers selected articles from 1998 to 2024 that focused on the accuracy and consistency of the scope and material and underwent a rigorous evaluation to identify peer-reviewed papers. Further, the references list was manually searched and extracted from Google Scholar. The results indicate that educational philosophies are the common basis of reflection on how certain curriculum content should be properly implemented. Though learning contents might vary, teachers could still find an effective way to sustain the demands of learners providing substantial experiences in academic pursuit.

Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy

The integration of generative artificial intelligence (AI) tools like ChatGPT in education has raised concerns that students may become dependent on AI-generated solutions, potentially stifling the development of critical thinking skills. Compounding this issue is the fact that Bloom’s Taxonomy—the widely used framework for designing educational goals—fails to address the cognitive demands of AI-assisted learning. This exploratory study presents a revised framework that incorporates AI-specific competencies, offering a more relevant model for nurturing critical thinking in an AI-driven environment. Using a conceptual approach supported by empirical evidence from MSc Marketing students’ interactions with AI tools over 4 weeks, the study found that AI can both enhance and challenge critical thinking across cognitive, affective, and metacognitive domains. Key elements such as melioration, ethical reasoning, collaboration, and reflective thinking were identified as critical for developing deeper engagement with AI-generated content. The framework proposes 12 propositions that inform future research and pedagogical strategies. This study outlines a research agenda for examining AI’s impact on cognitive development, serving as a resource for educators, policymakers, and researchers seeking to adapt teaching methods for AI-assisted education.

DEVELOPING ENGLISH MATERIALS WITH ISLAMIC CONTENT THROUGH TECHNOLOGICAL, PEDAGOGICAL AND CONTENT KNOWLEDGE APPROACH

The integration of technology in English teaching materials is essential to meet the growing demands of modern learners including those who learn in Islamic educational contexts. This study aimed to (1) identify the English learning needs related to Technological, Pedagogical, and Content Knowledge (TPACK); (2) develop an English language textbook based on the TPACK approach; and (3) evaluate its suitability for use in Islamic higher education. Employing the Research and Development (R&D) framework by Gall et al. (2003), the study was conducted in seventh phases: research and data collection, planning, preliminary product development, preliminary field testing, product revision, main field testing, and final product revision. The participants were 77 students from three Islamic higher education institutions in Lampung, Consist of 20 students from IAI Agus Salim, 30 students from IAIN Metro, and 27 students from IAI Ma’arif Metro.The subject in this research are expert jugment and user. Data were collected through questionnaires and interviews. The data were analyzed using both qualitative and quantitative techniques. Expert and user evaluations of the developed textbook, Basic English, demonstrated its good quality and effectiveness for use in Islamic higher education, with minor revisions recommended for optimization. The findings imply that the integration of the TPACK framework into textbook development can enhance the quality of English language learning in Islamic higher education. It is concluded that the textbook is suitable for use as an English Material in the learning process.

Application and Impact of Artificial Intelligence in Education: A Case Study of Programming Education

With the rising demand for personalized learning and increased digital natives, the traditional education model is under pressure to reform. the rapid development of artificial intelligence (AI) provides new education solutions, and the application of AI is becoming widespread, from intelligent tutoring to automated grading to learning analytics. However, the application of AI in education is not without challenges. This literature review aims to explore the impact of AI on education, analyze its potential advantages, and propose strategies to address the challenges and provide guidance for the future development of education. Understanding the impact of AI on education is instructive for the future direction of AI in education and reveals the importance of AI for education to the extent that it is important for formulating effective educational policies and cultivating future talents in AI technology. The final results of the study show that although AI in the education industry there are various difficulties and limitations, but AI offers great potential for smarter and more efficient education levels and education systems. In today's era, computer programming education is emerging as a pivotal force in shaping the future, as it not only hones students' logical thinking and innovative capabilities but also lays a solid foundation for their career paths in the digital world. By integrating knowledge across various disciplines, programming education stimulates students' enthusiasm for applying their learning to real-world problems and enhances their sense of social engagement, enabling them to tackle challenges through technological means.

Reducing Numerical Precision Requirements in Quantum Chemistry Calculations.

The abundant demand for deep learning compute resources has created a renaissance in low-precision hardware. Going forward, it will be essential for simulation software to run on this new generation of machines without sacrificing scientific fidelity. In this paper, we examine the precision requirements of a representative kernel from quantum chemistry calculations: the calculation of the single-particle density matrix from a given mean-field Hamiltonian (i.e., Hartree-Fock or density functional theory) represented in an LCAO basis. We find that double precision affords an unnecessarily high level of precision, leading to optimization opportunities. We show how an approximation built from an error-free matrix multiplication transformation can be used to potentially accelerate this kernel on future hardware. Our results provide a roadmap for adapting quantum chemistry software for the next generation of high-performance computing platforms.

Significance of Artificial Intelligence and Machine Learning in Business Enterprises

The rising demand for Artificial Intelligence (AI) and Machine Learning (ML) technologies in the corporate sector offers significant opportunity for firms to foster innovation and change. Incorporating generative AI capabilities into AI/ML corporate adoption plans can significantly improve the performance and efficacy of these technologies. By adopting AI/ML with generative AI functionalities, organizations may optimize content production workflows, provide synthetic data to enhance ML model training, and provide more captivating consumer experiences. This connection enhances creativity and innovation, accelerating model development and refinement, leading to expedited advancement in the creation of sophisticated AI systems. This article discusses the significance of AI and ML in business. Future trends and difficulties are also examined.

Errorless and errorful learning in people with aphasia across novel-object pairing and word retrieval tasks

ABSTRACT Background While linguistic deficits are key to diagnosing and treating aphasia, there is growing interest in the cognitive processes important for rehabilitation outcomes, particularly the role of learning. Of relevance to the current study, research has manipulated instructional methods (errorless vs. errorful) to assess their effects on outcomes. However, it is still unclear whether individualized profiles of errorless and errorful learning exist in aphasia and whether they might be meaningful for clinical practice. Aims The current study aimed to examine learning in people with aphasia, manipulating instruction method (errorless, errorful) and linguistic demands of learning. Methods & procedures Nine people with stroke-induced aphasia participated in this preliminary study. Participants engaged in errorless and errorful novel object pairing and word retrieval tasks. Learning outcomes were assessed on the same day, next day, and after one week. Participants also completed cognitive-linguistic assessments to investigate the contribution of memory, language, and executive functioning abilities on learning outcomes. Outcomes & results At the group level, participants performed significantly better following errorful training for novel object pairing (p = 0.001) relative to errorless training. An errorful advantage was observed at the individual level in 7 participants during same day testing, with the highest overall performers on the task showing the most persistent errorful learning benefits. In the word retrieval practice task, group and individual-level differences in scores following errorless and errorful practice were minimal. Scores in errorful novel object pair learning correlated with verbal short-term memory and nonverbal long-term memory assessments, while no other correlations were found between learning scores and cognitive-linguistic variables. Conclusions Findings are consistent with prior research that suggests that successful effortful retrieval may pose an advantage over errorless learning when acquiring novel information and the potential contributions of verbal short-term memory and nonverbal long term memory on learning. Results from the word retrieval practice task draw attention to differences between practicing lexical access and novel learning. The lack of an errorful advantage in word retrieval may alternatively be due to task design and merits further research. Results support the hypothesis that people with aphasia display variable learning profiles that may stem from differences in underlying cognitive-linguistic abilities. Continuing research is needed to characterize learning in aphasia to consider its potential influence on rehabilitation outcomes and support speech-language pathologists in considering and accounting for different learning and cognitive-linguistic abilities when individualizing treatment decisions.

Transition from primary to lower secondary school in Austria. Findings from a longitudinal study during COVID-19

AbstractTransition from the familiar environment of primary school to the larger, more complex setting of secondary school is, for most students, marked by social and academic challenges as well as a need to fit in. The aim of this contribution is to investigate the possible impact of achievement-related personal resources and social support in coping with the developmental task of transitioning from primary to lower secondary school during COVID-19. We make the assumption that a sudden shift to distance learning due to the pandemic intensified the challenges of transitioning to secondary school and led to educational disadvantage, in particular for students who were struggling with the demands of self-regulated distance learning.Data were generated from a longitudinal study carried out in a rural-alpine region of Austria in 2021 and 2022. During the time where COVID-19 was affecting education, 188 pupils were surveyed in the year prior to and after their transition from primary to lower secondary school. In order to analyse the impact of achievement-related personal resources and social support, a multivariate hierarchical regression analysis was conducted.Results showed that managing the transition to lower secondary school relied heavily upon pupils’ ability to cope with self-regulatory demands of distance learning, their achievement-related personal resources and social integration in class in primary school. However, from the findings, we can conclude that social support in secondary school also plays an important role. The ability to cope with social and academic demands in secondary school is at a higher level when pupils receive school-related support from their parents, recognise their school climate as supportive and perceive their teachers’ expertise in judging academic achievements as high.

Digital Teaching Material of Integrated Science with Blended-PBL Model for Independent Curriculum

The Independent curriculum demands student-centered learning by utilizing technology, so that students' 21st century skills can develop. However, what is found at school is very contrary to the demands of learning in the independent curriculum. As a solution, Digital Integrated Science Teaching Materials Integrated Blended-PBL Model (DTMIS-Blended-PBL) was developed for the development of the Independent Curriculum. This research aims to determine the needs analysis, validity, and practicality of integrated digital science model teaching materials with PBL models. The research method used is the development method with the Ploomp. Research data in the form of knowledge scores, creative thinking skills, and communicative skills of students, validity and practicality of DTMIS-Blended-PBL. Based on the research results, three conclusions can be presented. First, there are problems in learning, especially low student achievement and creative and communicative thinking skills. Second, DTMIS-Blended PBL is valid with an average score of 0.87. Third, DTMIS-Blended PBL is practical with an average score of 93.53. Therefore, the developed DTMIS-Blended PBL is valid and practical so it has the potential to improve the quality of science learning and support the implementation of the Independent Curriculum.

ANALYSIS OF ACADEMIC STRESS LEVELS OF HIGH SCHOOL STUDENTS

Academic stress is an inequality that occurs between the demands of learning with the ability of learners. The condition of learners in a state of stress can affect learning achievement. This research aims to understand the level of academic stress experienced by high school students. This study adopts a quantitative descriptive approach in research. The population that was the subject of this research consisted of 432 eleventh grade high school. Samples were taken using the methodsimple random sampling and using the significance of Slovin provisions of 5% so that the number of samples needed was found to be 208. Standard questionnaire that has been modified fromEducational Stress Scale for Adolescent (ESSA) consists of 16 statements. From the results of research, it can be drawn a meaning that high school students face academic stress with a high level that is influenced by various indicators of data collection instruments.

Effective warm‐ups in the German‐language classroom: Form and function

AbstractThis article explores three questions developed to be central to the creation and implementation of an effective classroom warm‐up: Does it help jumpstart students’ use of German? Can it capture students’ attention? And does it encourage building rapport through providing opportunities for personal expression or relevance? Based on my experience as an instructor for the first and second semesters of the German language sequence at the University of Illinois at Urbana‐Champaign, I argue that a well‐constructed warm‐up in a German language course is more than a low stakes invitation to cultivate a mindset conducive to the demands of language learning; it is an opportunity to begin learning from a place of genuine interest, trust, and confidence. By implementing warm‐ups with the guidelines outlined in this article in mind, mnemonically referred to as JARR (Jumpstarting language use, capturing Attention, and building Rapport through Relevance) warm‐ups, instructors can set students up for success not only for the next hour of learning, but also for their entire language journey—all in the first 5 minutes of class.

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation of target volumes is critical. To alleviate the data demand of deep learning and promote the establishment and promotion of auto-segmentation models in small and medium-sized oncology departments and single centres, we proposed an auto-segmentation algorithm to determine the cervical cancer target volume in small samples based on multi-decoder and semi-supervised learning (MDSSL), and we evaluated the accuracy via an independent test cohort. In this study, we retrospectively collected computed tomography (CT) datasets from 71 pelvic cervical cancer patients, and a 3:4 ratio was used for the training and testing sets. The clinical target volumes (CTVs) of the primary tumour area (CTV1) and pelvic lymph drainage area (CTV2) were delineated. For definitive radiotherapy (dRT), the primary gross target volume (GTVp) was simultaneously delineated. According to the data characteristics for small samples, the MDSSL network structure based on 3D U-Net was established to train the model by combining clinical anatomical information, which was compared with other segmentation methods, including supervised learning (SL) and transfer learning (TL). The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) were used to evaluate the segmentation performance. The ability of the segmentation algorithm to improve the efficiency of online adaptive radiation therapy (ART) was assessed via geometric indicators and a subjective evaluation of radiation oncologists (ROs) in prospective clinical applications. Compared with the SL model and TL model, the proposed MDSSL model displayed the best DSC, HD95 and ASD overall, especially for the GTVp of dRT. We calculated the above geometric indicators in the range of the ground truth (head-foot direction). In the test set, the DSC, HD95 and ASD of the MDSSL model were 0.80/5.85 mm/0.95 mm for CTV1 of post-operative radiotherapy (pRT), 0.84/ 4.88 mm/0.73 mm for CTV2 of pRT, 0.84/6.58 mm/0.89 mm for GTVp of dRT, 0.85/5.36 mm/1.35 mm for CTV1 of dRT, and 0.84/4.09 mm/0.73 mm for CTV2 of dRT, respectively. In a prospective clinical study of online ART, the target volume modification time (MTime) was 3–5 min for dRT and 2–4 min for pRT, and the main duration of CTV1 modification was approximately 2 min. The introduction of the MDSSL method successfully improved the accuracy of auto-segmentation for the cervical cancer target volume in small samples, showed good consistency with RO delineation and satisfied clinical requirements. In this prospective online ART study, the application of the segmentation model was demonstrated to be useful for reducing the target volume delineation time and improving the efficiency of the online ART workflow, which can contribute to the development and promotion of cervical cancer online ART.

The necessity and demand of machine learning in treatment and diagnostics in dentistry

Aim: to justify the necessity of machine learning technology in treatment and diagnostics in dentistry.Material and methods: the research was taken using the method of anonymous questionnaire to estimate the demand and necessity of machine learning technology in diagnostics and treatment in dentistry on the basis of the E.V Borovsky Institute of Dentistry I.M Sechenov First Moscow State Medical University (Sechenov University). 100 participants from different dental specialities aged 20 to 54 took part in the questionnaire. Wilson score interval and Student’s T Critical Values were used for the statistical analysis of the Results.Results: during the study, it was found that the majority of dentists who participated in the questionnaire (54%) have challenges in diagnosing oral mucosal diseases. Herewith dentists with work experience more than 5 years diagnose this kind of disease more frequently than specialists with less work experience (p<0.05). Surgical dentists (46,6%) and prosthetic specialists (50%) diagnose this pathology most often. Clinicians attribute diagnostic challenges to the lack of experience (85%) and low occurrence of patients with this group of diseases. During treatment and diagnostics 84% of residents mentioned that they compare their patients’ clinical cases with clinical cases from the Internet and other resources, 78% of residents believe that machine learning will help to increase the efficiency of diagnosing oral mucosal diseases in clinical work. During the held research, it was found that 85% of participating dentists would definitely use digital programs with machine learning in their clinical work for treatment and diagnostics.Conclusions: the presence of problems in treatment and diagnostics of oral mucosal diseases was confirmed, and to solve it, the demand and the need to develop and implement digital systems based on artificial intelligence using machine learning technology were substantiated.

Teacher Professional Development in the Digital Age: Strategies for Integrating Technology and Pedagogy

The rapid advancement of digital technologies has transformed the educational landscape, creating a critical need for teacher professional development (TPD) that integrates technology with pedagogy. This article explores strategies for enhancing teacher competencies in the digital age, focusing on the integration of educational technologies into pedagogical practices. It synthesizes key approaches from recent research, including blended learning models, professional learning communities (PLCs), and personalized learning pathways. The findings suggest that effective TPD programs are those that not only provide hands-on technology training but also emphasize pedagogical alignment, collaboration among educators, and ongoing support. The article argues that successful integration of technology in the classroom requires a shift in both teaching practices and teacher mindsets, supported by institutional policies that promote innovation and adaptability. Ultimately, this research highlights the importance of a holistic approach to TPD that fosters both technological fluency and pedagogical mastery, preparing educators to meet the demands of 21st-century learners.