Learning Process Research Articles

Utilization of the use of technological devices in delivering communication information in the learning process

The development of increasingly sophisticated technology today brings education to participate in using the features available in today’s media such as the transition from face-to-face learning communication in schools to face-to-face assisted by technology such as laptops, tablets, cellphones and other multimedia. Technology currently provides innovation in the learning process both from home and from school. However, there are still many teachers and students who have not utilized technology such as laptops, tablets, cellphones and other multimedia in the learning process. The purpose of the study was to analyze the benefits and relationships of the four main variables of communication assessment elements with digital devices. The research method used was quantitative with a sample of 148 teachers randomly selected from schools that use technology in the learning process. Data collection techniques with instruments. The instruments used were four indicator instruments, namely technology from laptops, tablets, cellphones and other multimedia. Data analysis techniques with descriptive statistics using SPSS version 26.0 calculated the mean, standard deviation, and correlation test. The results of the study found that the four indicators had high reliability and the four indicators had significant utilization, were mutually positive and had a high relationship with each other. The conclusion is that the four technological devices are good for use in digital communication during the learning process and laptops and tablets are more recommended in this study.

Research trends about Visual Basic as a programming language in the learning process: a bibliometric analysis

Research trends about Visual Basic as a programming language in the learning process: a bibliometric analysis

Comparison on Applications and Impact of AI in English Grammar Learning

English grammar plays an important role in the learning of English language skills. However, it has always been challenging for students to understand grammar right from school. Complicated rules make the learning process even harder. Teachers have tried to come up with many ways to make the learning process interesting and simple yet there exists a gap. Artificial Intelligence bridges the gap and achieves the goal of attaining grammar learning in interesting ways. The integration of artificial intelligence (AI) in grammar learning applications represents a transformative approach to language education. Many applications focus on teaching grammar by gamifying the learning process. AI-powered applications provide personalized learning experiences, immediate corrective feedback, and adaptive difficulty adjustments, leading to improved grammatical competence and sustained user engagement. This research attempts to make a comparative study of English grammar learning platforms and applications by analyzing their impact on the autonomous learning of students. Applying AI has been considered learner-centric as it stimulates learning and fascinates the learners with expected learning outcomes.

Dreams and fears in the process of creative thinking in children from areas affected by the armed conflict in Colombia

The development of creativity and creative thinking in early school-age children is a strategy to foster future capabilities for innovation and change. It is also a strategy to instill resilience in these children, enabling them to face the challenges posed by the various environments that surround teaching and learning processes. This article analyzes the dreams and fears expressed by children from educational institutions located in municipalities affected by the armed conflict in Colombia, within the framework of a route designed to develop creativity and innovative thinking. A qualitative narrative analysis methodology was used to characterize and analyze the narratives of 68 co-creativity teams, involving 408 children. The results show that the narratives are influenced not only by the context in which the children live but also by the consequences of the armed conflict in the areas where the schools are located. The findings suggest that educational strategies like the one studied must incorporate elements that allow for adaptation to the context from the design stage, with a differential approach, to generate the expected positive effects in the territories where they are implemented.

Application of Extreme Machine Learning for Smart Agricultural Robots to Reduce Manoeuvering Adaptability Errors

The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adaptable Task Processing Model (MATPM). This model is designed to improve the adaptability and precision of agricultural robots coinciding with farming and climatic conditions. For this purpose, extreme machine learning is employed to learn, align, and respond to external conditions to improve precise manoeuvring. The external impacting features and their adaptability to the crop and climatic conditions are valued using the learning process. In this process, maximum adaptability is considered to improve precision agriculture. The tasks are then classified based on adaptability and executed sequentially. If an unpredictable impacting factor hinders a task, then the adaptability is paused from training and it improves the chances of training by using different completed and paused tasks from the previous manoeuvring processes. Therefore, precision in smart farming and robot system adaptability is ensured with fewer adaptability errors. From the comparative assessment, the proposed model improves adaptability by 8.71 % and precision by 11.44 %, with 8.96 % less adaptability error for various tasks/ day.

Automatic kidney disease prediction using deep learning techniques

The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection.

Inteligencia artificial como estrategia en la educación superior

Artificial intelligence (AI) is becoming a key tactic in higher education to enhance teaching and learning processes. This study aims to assess student's thinking about implementing AI in their academic experiences. A qualitative approach was conducted with students from the Autonomous University of Sinaloa, considering it as one of the higher education institutions, focusing on their level of knowledge, expectations, and various concerns about its use. The results highlight that most students are aware of the various potential applications of AI in education, with significant variations in acceptance and trust towards it. Students recognize advantages such as personalized learning and administrative efficiency but also express concerns about data privacy and potential insensitivity in education. The findings indicate that for AI to have a substantial positive impact on higher education, it is essential to consider both the expectations and concerns of students, and to promote an equitable and ethical integration of this technology into educational processes.

Development and evaluation of the active learning game "Who Am I?-Cellular Signal Transduction Edition": the positive impact on undergraduate education.

The teaching of physiology plays a crucial role in the education of health care professionals. However, traditional approaches to physiology classes in undergraduate health courses in Brazil often result in passive student participation. Research has shown that active methodologies are more effective in the learning process. In this study, we introduce the game "Who Am I?-Cellular Signal Transduction Edition" as an educational tool. This game follows a popular format with well-known rules and aims to enhance understanding of basic concepts related to hormones, cell signaling, and the functioning of the endocrine system. Our findings demonstrate that the game improves student knowledge and fosters enthusiasm and active engagement among participants. Additionally, student feedback has indicated a high level of appreciation for the game. By incorporating active learning strategies and a gamified approach, "Who Am I?-Cellular Signal Transduction Edition" provides a practical and enjoyable way of teaching physiology. This innovative educational tool has the potential to revolutionize physiology instruction. Demonstrating significant improvement in students' understanding, the game underscores its efficacy in enhancing knowledge acquisition and comprehension of cellular signaling and endocrine physiology topics.NEW & NOTEWORTHY We developed "Who Am I?-Cellular Signal Transduction Edition" to assist students in comprehending concepts of cellular signal transduction. This simple and cost-effective tool is perfect for educational settings with limited resources, and it encourages active learning for both small and large groups. Pre- and posttests have shown that it effectively enhances knowledge of hormonal actions and cellular signaling. Positive feedback from students emphasizes its value in reinforcing understanding and improving classroom engagement, making it a promising educational tool.

Impact of videos and traditional teaching methods on fifth grade students’ achievement in fractions

Mathematics teachers employ a diverse range of tools, resources, and materials to enhance the teaching and learning processes through various instructional strategies. In this quantitative study, the effectiveness of utilizing videos and traditional lecturing as distinct strategies for teaching fifth-grade students in fractions addition and subtraction was investigated. The researchers employed a quasi-experimental pre-test and post-test single group design, conducting the study in four schools with different curricula in Abu Dhabi over a two-week period. The same group of students underwent both pre- and post-tests after being exposed to lessons on fractions addition and subtraction through videos and lectures. The results indicated that using videos to teach fractions had a more positive impact on students’ performance across different schools compared to traditional lecturing. This bears significant implications for enhancing students’ proficiency in fractions through improved teaching strategies.

Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.

Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects' underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domain specific, leading to data being represented in dissimilar subspaces, and (ii) fused data are often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multimodal artificial intelligence model with a novel fusion module inspired by a bottleneck attention module. We use deep neural networks to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multimodal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (p < .0001), outperforming state-of-the-art unimodal and multimodal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural network's decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multimodal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to SZ. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies.

Inductive Meta-Path Learning for Schema-Complex Heterogeneous Information Networks.

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm.

A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-Based Graph Contrastive Learning.

Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools have some limitations, including dependency on empirical users, weak consistency in repeated experiments and time-consuming processes. In this work, a diffusion-based brain network pipeline, DGCL is designed for end-to-end construction of brain networks. Initially, the brain region-aware module (BRAM) precisely determines the spatial locations of brain regions by the diffusion process, avoiding subjective parameter selection. Subsequently, DGCL employs graph contrastive learning to optimize brain connections by eliminating individual differences in redundant connections unrelated to diseases, thereby enhancing the consistency of brain networks within the same group. Finally, the node-graph contrastive loss and classification loss jointly constrain the learning process of the model to obtain the reconstructed brain network, which is then used to analyze important brain connections. Validation on two datasets, ADNI and ABIDE, demonstrates that DGCL surpasses traditional methods and other deep learning models in predicting disease development stages. Significantly, the proposed model improves the efficiency and generalization of brain network construction. In summary, the proposed DGCL can be served as a universal brain network construction scheme, which can effectively identify important brain connections through generative paradigms and has the potential to provide disease interpretability support for neuroscience research.

A recommendation model for optimizing transfer learning hyper-parameter settings in building heat load prediction with limited data samples

The transfer learning method has gained increasing attention in the domain of building load prediction, particularly in scenarios with limited data samples. Its core principle involves leveraging knowledge obtained from abundant data in source buildings to aid the learning process of models for the target buildings. Existing research has predominantly concentrated on optimizing the selection of source building data to improve transfer learning effectiveness, while the optimization of transfer learning hyper-parameter settings is often neglected. This study proposes a recommendation model tailored for transfer learning hyper-parameter settings in the context of small sample prediction for building heat loads. The objective is to automatically suggest suitable transfer learning hyper-parameter combination based on the specific features of the building heat load data samples. In this study, 200 real building profiles were utilized to generate the input–output dataset required for the recommendation model. By employing data mining techniques such as clustering and classification, the correlation between the features of source building data and the most effective transfer learning hyper-parameter combination is investigated. The developed recommendation model for optimal transfer learning hyper-parameter settings achieves a classification accuracy of 90.5%，and the performance evaluation was conducted using an additional dataset of 30 source buildings. The results show that by employing this recommendation model, the prediction error of the target buildings can be reduced by 0.12% to 6.64% compared to the conventional method of empirically determining transfer learning hyper-parameter settings.

Assembling a physical model helps students grasp human somatosensory pathways.

Mastering the complexity of the nervous system is essential for education programs in physiology, anatomy, and neuroscience. Students often struggle when learning somatosensory pathways, which convey information from sensory neurons to the somatosensory cortex in the brain. Active learning activities incorporating physical models have been shown to increase content comprehension as well as enjoyment of the learning process. Here, we present a three-dimensional physical model of somatosensory pathways constructed of durable, affordable, and widely available materials. In an upper level human physiology lab, students assembled the model and then used it to complete case study questions connecting damage to the spinal cord to resulting sensory deficits. This model-based activity was highly effective as indicated by significant increases in content knowledge and positive responses to survey questions on the effectiveness of the activity. This activity incorporates many evidence-based teaching practices that have been shown to increase engagement, inclusion, and mastery of content and provides an effective and fun way for students to learn a challenging topic.NEW & NOTEWORTHY This model-building activity for learning somatosensory neural pathways increases students' content knowledge and is an enjoyable way to learn a complex system. This activity adds to an ever-growing collection of evidence-based learning activities for human physiology courses.

ChatGPT as a tool for self-learning English among EFL learners: A multi-methods study

This study investigates the factors influencing ChatGPT acceptance in self-directed English language learning and explores its potential to enhance the learning process. A multi-methods approach was employed, comprising a systematic review of 40 empirical articles, a quantitative survey of 344 English as a Foreign Language (EFL) learners, and 19 semi-structured interviews. Results indicated that interactivity, enjoyment, trust, and subjective norms significantly affect continued use through perceived usefulness, while human-likeness, self-efficacy, and technology anxiety influence it through perceived ease of use. EFL learners utilize ChatGPT for both primary and secondary English learning purposes, supporting various language dimensions including reading, writing, vocabulary, and grammar. The study also identified both passive and active language learning engagement facilitated by ChatGPT. This research contributes to the field by proposing an extended Technology Acceptance Model and a conceptual framework for chatbot-assisted language learning. For chatbot developers, the findings underscore the need to enhance interactivity, human-likeness, enjoyment, and trust while minimizing technology anxiety to increase sustained use. Educators can utilize these insights to better integrate ChatGPT into their curricula, supporting diverse language skills and engaging learners more effectively. Together, these findings provide concrete guidance for enhancing the design and implementation of AI chatbots in language learning contexts.

Influence of feedback mechanism on trainee academic performance in national examinations in TVET Institutions in Western Kenya

Feedback mechanism is an integral part of the teaching and learning process since it enables learners and teachers to know where they are in the process, identify whether they have or have not achieved the learning objectives and ultimately enhance the learning process. The purpose of this study was to analyze the nexus of feedback mechanism and trainee academic performance in national examinations in Technical and Vocational Education Training (TVET) institutions in Western Kenya. The study examined how feedback mechanisms from formative assessment influenced trainee academic performance in technical national examinations in TVET institutions in Western Kenya. The study was guided by the social cognitive theory, constructivism theory, and the diffusion of innovation theory. The target population consisted of 406 respondents who included 29 principals, 29 deputy principals in charge of academics, 29 registrars, 29 examination officers, and 290 heads of academic departments in the 29 public TVET institutions in Western Kenya. To have a representative sample, this study employed stratified sampling to select 21 respondents in National Polytechnics and 179 respondents in Technical and Vocational Colleges(TVCs) constituting a sample size of 200 respondents. Questionnaires and interviews were used as data collection instruments. Validity was established through expert opinion with the help of the supervisors. Using Cronbach’s alpha, a reliability coefficient of 0.886 was established based on the results of the piloting of the research instruments. Data was analyzed using descriptive and inferential statistics. The study established that formative assessment mechanisms played a critical role in enhancing trainees' academic performance in TVET institutions. It found out that there are strong correlations between feedback clarity, management techniques, and formative assessment scores, emphasizing their predictive value for academic success. The study concludes that the combination of continuous feedback highly influences trainee performance in final examinations in TVET institutions. It advocates for structured feedback systems to support trainees, improve learning experiences, and achieve better academic outcomes.

The virtual modality and its impact on the training of medical sciences students

Introduction: The social restrictions that countries imposed to contain COVID-19 generated challenges in all areas of human development, including higher education. University institutions had to adapt abruptly to the new pedagogical demands, resorting obligatorily to the use of the virtual modality in all their academic offerings. Objective: To evaluate the impact of the virtual modality in the academic and professional training of students of Medical Sciences, in order to identify its advantages, disadvantages and potentialities in the current educational context. Methodology: A systematic review was carried out to analyze virtual health education, using databases such as REDALYC, SCOPUS, DIALNET and SCIELO. Relevant articles were identified and grouped, excluding those that did not focus on virtual education related to health careers. Several search terms were analyzed and multiple articles were obtained in each database. Results: Although there is uncertainty in the formative quality of students of health careers due to the eminently practical condition of the knowledge imparted, it is evident that telematic education has overcome acceptably the obstacles posed from the dogmatic criterion, so it should be recognized for its support to the continuity of teaching and learning processes. Conclusions: The university community is obliged to encourage the development of the virtual modality and its application for the benefit of strengthening the professions related to health.

Integrative and Collaborative Assessment: A Contemporary Shift of Improving Students' Academic Performance

Assessment is crucial in the academe. The Department of Education (DepEd) has adopted different formative and summative assessment tools but continuously encouraging teachers to innovate school-based projects that could improve the teaching-learning process. The use of integrative and collaborative assessments is beneficial to both learners and students. Evaluations can be drawn to improve the student performance at same time as a basis to curriculum change. Interdisciplinary academic learning is one of the main advantages of integrative and collaborative assessment. This is to provide realization to students on how learning process can be meaningful in the interplay of different subject areas within or across the curriculum.

Generative AI and Education: A Symbiotic Relationship

Generative AI, with its capacity to create diverse content formats, holds immense potential to revolutionize the educational landscape. This research paper delves into the multifaceted ways in which generative AI can enhance teaching and learning, fostering a symbiotic relationship between technology and human ingenuity. Personalized Learning: Generative AI can analyse vast datasets of student performance and engagement to create adaptive learning paths, tailoring instruction to individual needs and preferences. This personalized approach ensures that each learner receives the optimal level of support and challenge, maximizing their potential for growth and development. Engaging Learning Experiences AI-powered tools can generate a wide range of interactive and immersive learning materials, such as simulations, virtual labs, and gamified experiences. These innovative resources can make education more engaging and enjoyable, capturing students' attention and motivating them to actively participate in the learning process. Streamlining Administrative Tasks Generative AI can automate time-consuming administrative tasks, such as grading assignments, providing personalized feedback, and generating comprehensive reports on student progress. By freeing up valuable time and resources, educators can focus on more meaningful interactions with students, fostering a deeper connection and a more supportive learning environment. Ethical Considerations While generative AI offers numerous benefits, it is essential to address potential challenges and ethical concerns. Data privacy, algorithmic bias, and the responsible use of AI are critical issues that must be carefully considered. By establishing clear guidelines, developing robust safeguards, and providing educators with the necessary training and support, we can mitigate risks and ensure that AI is used ethically and effectively in education.

Children’s human rights education: conceptual foundations of school students’ learning processes

ABSTRACT The study of human rights education has emerged in recent years, but few studies have addressed students’ learning processes regarding children’s human rights education (CHRE). This paper conceptualises the interrelated features of these processes in school, subsumed under three conceptual levels of analysis. The first highlights the individual child, whose learning is influenced by developmental and socio-cultural factors and should consider child-centred aims, content and approaches. The second level accentuates the prominent role of interactions and relationships in students’ learning of CHRE, which requires educators to share power. The third emphasises the role of the school environment as a multidimensional space where students’ CHRE learning processes transpire, stresses the importance of whole-school approaches, and analyses the institutional challenges that may constrain students’ ability to make sense of CHRE. This conceptualisation highlights how CHRE may be adapted to children as learners in school.