Easy Learning Research Articles

Hybrid network with difficult–easy learning for concealed object detection in imbalanced terahertz image dataset

Hybrid network with difficult–easy learning for concealed object detection in imbalanced terahertz image dataset

UNDERSTANDING TEACHERS' MOTIVATION: PROFILES OF EARLY CHILDHOOD TEACHERS CONTINUING THEIR STUDIES

This study aims to examine and analyze the motivation of early childhood education (ECE) teachers in pursuing higher education. The research method employed is descriptive qualitative, involving students from semesters 1 to 7 who meet the criteria of being ECE teachers and continuing their studies in the PIAUD Department at Ibrahimy University Situbondo. Data collection was carried out using three methods: interviews, observations, and document studies. Subsequently, the researcher analyzed the collected data through three stages: data reduction, data presentation, and conclusion drawing. After conducting the research steps mentioned above, the researcher concluded that the dominant motivation for teachers to continue their studies is primarily driven by external factors, such as educational alignment, institutional demands, and improving their quality of life. Internal motivations include enhancing personal quality as educators and support from their families. Lastly, the university's easy learning system and affordable tuition fees also contribute as motivational factors. Keywords: Motivation, Learning, ECE Teachers

Developing an Adaptive Learning Recommendation Algorithm and System for MOOCs

Massive Open Online Courses (MOOC) based learning platform had totally changed the educational environment by providing easy and accessible learning opportunities for global learners. But even such environment display high dropout and low learner engagement which remain a significant challenge to be addressed. To handle the challenge of this study, propose an Adaptive Learning Recommendation System (ALRS) that is designed to personalize learning paths based on individual preferences and performance metrics. The study employed Open University Learning Analytics Dataset (OULAD) and build recommendation model that combine k-means Clustering, Content-based Filtering, Collaborative Filtering, and Random Forest (RF) classifiers to make course recommendations. The proposed model have shown better recommendation when compared to other models with Precision of 0.92, Recall of 0.89, F1 Score of 0.90, and AUC of 0.95. Also the proposed model had shown the lowest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) at 0.042 and 0.205, respectively.

Enhancing Autonomous Agents Through Narrative Memory: A Comprehensive Framework for Improved Performance and Interaction

The process of organizing information and experiences into a narrative style is known as narrative memory, and it presents a cutting-edge method for improving the performance of autonomous agents. This essay describes the idea of narrative memory, as well as its elements, construction of narrative structures, and uses. The four main elements of narrative memory are timelines, characters, events, and context. Events are important things that happen, such interactions or acts, and characters are people, other agents, and the autonomous agent. The context gives the underlying knowledge required to comprehend these events and individuals, while the chronology sets the order of occurrences, creating a cause-and-effect link. The process of creating narrative frameworks includes gathering data, identifying events, crafting stories, and refreshing memories. Three methods are used to collect data: external sources, user input, and sensors. This data is used to identify significant events, which are subsequently arranged into logical tales. New situations and experiences are constantly added to the narrative memory. Improved predictive analysis, easier learning and adaptability, and improved human-robot interaction are just a few of the uses of narrative memory in autonomous agents. Robots can interact with people more successfully if they can comprehend human narratives and reply with responses that are contextually relevant. Agents may learn and adapt by identifying patterns in their prior experiences, and they can make better decisions by forecasting future occurrences with the help of historical narratives. Managing the complexity of real-world events, addressing ambiguity and uncertainty, and guaranteeing the ethical use of narrative data are challenges in the implementation of narrative memory. In order to improve narrative memory frameworks, future research options include creating sophisticated story generating tools, integrating narrative memory with other memory systems, and working across disciplines. The goal of this roadmap is to enhance autonomous systems' comprehension, communication, and decision-making skills by utilizing the power of tales.

Blended learning challenges of EFL undergraduate students: Student learning experience in an AI-integrated ESP course

Blended learning can facilitate ESP learning, yet challenges in AI-integrated ESP learning are largely unexplored. This study aims to investigate the blended learning challenges of EFL undergraduate students from the perspective of student learning experience in an AI-integrated ESP course. The participants were 57 Thai mixed-level undergraduate students studying English for economics at a public university in Thailand. Using a mixed-method approach, student data were collected and analysed using Microsoft Teams learning analytics and triangulated with a questionnaire and focus group interview data. Study results showed similarities and differences in students’ blended learning challenges in the diverse target participants. Participants generally viewed technological and online learning aspects as the least challenging, in contrast with social aspects as the most challenging, followed by motivation and willingness to participate in the blended learning ESP course. Despite students’ academic achievement, data revealed students’ negative emotions and negative blended learning experiences amongst higher-proficiency students, but lower level of blended learning challenges due to easy learning tasks and lack of teacher support for more advanced language skills. In contrast, lower-proficiency students recorded more positive emotions and blended learning experiences but a higher level of blended learning challenges. Despite lower-level English, lower-proficiency students experienced teacher support both in using AI and ESP. The study highlights the importance of teacher support, which fulfils the incomplete roles of AI in ESP blended learning, and calls for a reconceptualisation of blended learning challenges. Implications for pedagogy and research on AI-integrated blended learning in ESP contexts are provided.

Improving linguistic competence through learning based on language error analysis and problems

The success of an educational institution, especially those related to teaching, depends on various components, including materials and learning models or approaches. Materials and learning models or learning approaches must not only be able to attract students' attention but must also be following their needs and development. One learning model that pays attention to the needs and development of students is problem and error analysis-based learning. This learning model presents a real problem for students at the beginning of learning, then resolved through investigation and applied using a problem-solving approach. Therefore, the research "Improving Linguistic Competence through Learning Based on Language Error Analysis and Problems" is very interesting to be done immediately. This study aims to identify linguistic errors and problems found in everyday life and describe solutions to understand these problems in improving students' linguistic competence. The study of learning based on error and problem analysis can contribute to the development of linguistics, especially on linguistic problems that are still unsolved. In addition, the results of this study can contribute to the improvement and development of learning models based on error and problem analysis. This will help create a fun, challenging, and easy learning environment.

Students Challenges in Learning Public Speaking

This research aims to determine students' challenges in learning public speaking. This research method is descriptive quantitative. The subjects of this research are 5 male and 15 female students from University of Muhammadiyah Semarang in Kedung Mundu. The data collection technique uses questionnaires and interviews by giving a series of written and verbal questions or statements to respondents to then answer. The results show that there is easy learning when using Manuscript and Comunication in learning English. By applying the Manuscript method in learning public speaking, it shows an increase in self-confidence. Meanwhile, conversation can improve English pronunciation skills, habits, and increase English vocabulary which previously was one of the biggest challenges for students. Their tendency was to more easily improve Public Speaking by using the Manuscript model.

Application of the Self-Assembling Peptide Hydrogel RADA16 for Hemostasis during Tonsillectomy: A Feasibility Study.

Tonsillectomy is a common surgical procedure but carries a high risk of readmission for secondary bleeding and pain. This study evaluated the feasibility and effectiveness of using the hemostatic self-assembling peptide hydrogel RADA16 (PuraBond, 3-D Matrix SAS; Caluire et Cuire, France) to control bleeding from the tonsillectomy wound bed. Readmission/re-operation rates were compared between a prospective case series of 21 primarily adult tonsillectomy patients treated with topical RADA16 and an untreated historical Control group of 164 patients who underwent tonsillectomy by 10 surgeons at a single tertiary hospital in the UK between March 2019 and June 2022. Cumulative readmission rates for any reason were 2-fold elevated in Control subjects (18.9%; n = 31/164 subjects) compared to patients treated intra-operatively with RADA16 hemostatic hydrogel (9.5%; n = 2/21) (p = 0.378). Readmission rates for postoperative bleeding were 3-fold higher in Controls (14.6%; n = 24/164 subjects) than in the RADA16-treated group (4.8%; n = 1/21) (p = 0.317). A similar rate of retreatment for pain was recorded in the Control (4.3%; n = 7/164) and RADA16 (4.8%; n = 1/21) groups (p = 0.999). Two Control subjects (1.2%) required re-operation for recalcitrant bleeding; no RADA16 subject (0.0%) required re-operation for any reason. No device-related adverse events occurred in the RADA16 group. Surgeons were pleased with the easy learning curve and technical feasibility associated with intra-operatively administering RADA16 hemostatic hydrogel. Intra-operative hemostasis using RADA16 peptide hydrogel was straightforward and was associated with a trend of 3-fold lower rates of readmission for postoperative bleeding events than untreated Control subjects.

E-PINN: A fast physics-informed neural network based on explicit time-domain method for dynamic response prediction of nonlinear structures

The physics-informed neural networks (PINNs), serving as the surrogate models, have emerged in the dynamic response prediction of nonlinear systems. However, in the conventional PINN model, the global differential equation of motion of the nonlinear system is directly integrated into the loss function, which requires the network to capture the intricate global dynamic evolution mechanism of the system and thus poses significant challenges for network learning. In this study, inspired by the explicit time-domain method (ETDM), a novel PINN based on ETDM, called E-PINN, is proposed to address the challenges arising from the network learning of the existing PINNs. A lightweight long short-term memory (LSTM) module is trained to learn the nonlinear evolution mechanism of restoring forces, while a single convolutional layer (SCL) module is utilized to reflect the linear evolution mechanism of the primary structure under the combined action of the external excitations and the nonlinear restoring forces. The weights of the SCL module can be directly retrieved from the discrete convolutional formulation of ETDM, and only the parameters in the LSTM module need to be optimized through a loss function solely based on the nonlinear restoring forces, thereby enabling easy network learning of the proposed E-PINN by decoupling the linear and nonlinear evolution mechanisms embedded in the nonlinear system. Three numerical examples are investigated by the present approach, and the results demonstrate the superior training efficiency and prediction accuracy of E-PINN in dynamic response prediction of nonlinear systems.

Cultivating Creativity and Improving Coding Skills in Primary School Students via Domain-General and Domain-Specific Learning Scaffoldings

The transformative wave of generative AI is reshaping the creative thinking processes of learners, posing a significant challenge to education and industry in cultivating technological literacy and creativity. This study delves into the exploration of how learners can effectively tackle new challenges by deconstructing fragments from a macro perspective and generating innovative methods or concepts. In the Scratch visual programming environment, learners in the self-regulated learning mode observed entire functioning projects, facilitating easy disassembly and learning, namely by using Code Decomposed by Learner (CDBL). A total of 104 fifth-grade students were divided into two learning scaffoldings: (1) domain-general, learning from the top down (CDBL-TD), and (2) domain-specific, learning from the bottom up (CDBL-BU). Students in the CDBL-TD group exhibited a high degree of completion, strong exploration abilities, and the willingness to experiment with unlearned functions. Although there was no significant difference in originality between the two groups, students in the CDBL-TD group showcased greater uniqueness in designing characters or items within the game. This study introduces a novel programming learning scaffolding, offering instructors a tool to guide students’ creativity and enhance their programming capabilities.

Students attitudes on STACK content: A pilot study at the university of Trieste, Italy

Background: Online examinations are increasingly prevalent in educational settings, offering innovative assessment methods. Understanding student perceptions and experiences with such systems is essential for enhancing their effectiveness and acceptance in educational practice.Aims: This pilot study aimed to examine the impact of STACK-based online exams on various facets such as student comfort, perceived value for learning and exam preparation, preferences between pen-and-paper versus STACK formats, confidence in online math tests, readiness to adopt online assessment platforms, and overall perspectives on online evaluations.Method: An experimental within-subjects design and convenience sampling were used to involve 117 first-year biology students enrolled in a Probability and Statistics course, who were already familiar with the STACK system. Data were collected using pre- and post-exam online surveys featuring five-point Likert scale questions and an open-ended query.Results: The findings indicate that students felt more comfortable using the STACK system after the study and preferred it over traditional exams. Nevertheless, some students expressed uncertainty about using STACK content for final exams due to concerns about its effectiveness in evaluating critical thinking and potential technical difficulties. However, concerns regarding technical challenges decreased significantly post-exam, with no technical issues reported during the exam. Positive feedback highlighted STACK's benefits for formative assessment, easier learning, immediate feedback, and its practicality and innovation. Some students even suggested incorporating STACK into final exams for convenience and advocated for further investment in the STACK system, possibly with improved content. Conclusion: In summary, students preferred using STACK for exams, though concerns about technical glitches and the need to refine content for assessing critical thinking persist. Future efforts should focus on enhancing content quality, starting this summer.

Learning Arabic Language Improves Student's Memorization of the Al-Qur’an

Learning Arabic for non-Arab people still has many obstacles and problems to face because Arabic is not an easy language to master completely. But studying it is something that cannot be ignored. The need for Arabic for the world community is currently quite high, both for Muslims and non-Muslims.The specificity of the language of the Al-Qur'an has given people enthusiasm and willingness to learn Arabic. The efforts made are to make it easier to memorize the Al-Qur'an. Apart from that, understanding Arabic allows a person to easily understand and understand the contents of the Al-Qur'an. An interest in memorizing the Al-Qur'an and the ability to speak Arabic can be an influence on someone's achievement in memorizing the Al-Qur'an. This research was conducted at Ma'hadRubbaht Al-Futuhat Jepara, Central Java. This research aims to determine the effect of Arabic language mastery and the ability to memorize the Al-Qur'an of Santriwati Ma'had Rubbaht Al-Futuhat Jepara, Central Java.So this research is expected to provide good benefits for Santriwati, namely increasing motivation in memorizing and mastering the Al-Qu'ran, for teachers it is hoped that it can provide benefits in helping provide easy learning motivation for Santriwati in mastering and memorizing the Al-Qu'ran. The research method used is a quantitative approach, namely interview guides, test papers, observation guides and archives.Keywords: Learning, Arabic, Memoriation, Al-Qur'an.

Implementation of an Academic Software for Children from 6 to 8 Years Old for Easy Learning in Basic Mathematics and Reading Comprehension

Nowadays teaching has not improved talking about the methods to get to interest the studentor want to learn, it is not common for schools to have a didactic and innovative curriculum, we continue to maintain in do the same learning format of years ago and it is one of the areas in which we do not evolve compared to others. As we well know, among the most important foundations in education are the reading comprehension and mathematics courses, because these are the pillars to have facilities in learning with other courses in the future and that is why it is important to ensure the knowledge of these courses in childhood so that these students do not present difficulties to learn in the future. That is why in the part of basic education we see important the implementation of an educational software for children, to improve their motor capacity in terms of learning the area of mathematics and reading comprehension, since relying on statistical data we realized that the levels of knowledge in these courses, They are not the desired ones, therefore, we agreed to project a new way of learning so that children can be interested in wanting to learn, for this we develop a plan in which it is proposed to implement educational games, memory games, didactic reading, among others, all this to ensure that children improve their learning through new learning methodologies.

Maize leaf disease recognition based on improved MSRCR and OSCRNet

Accurate identification of maize pests and diseases plays a crucial role in the growth and yield of maize. The recognition method of maize leaf diseases based on deep learning is currently a research hotspot. In practical applications, it is difficult to ensure a high recognition accuracy due to the impact of environmental factors (such as light intensity, background noise) and insufficient training data. To address this challenge, this paper proposes a maize image recognition method based on image enhancement and OSCRNet. Firstly, a maize leaf image enhancement framework and algorithm based on improved MSRCR were designed to enhance the characteristics of the maize leaf in a complex environment and to solve the problems of high noise and blur of maize images. Subsequently, the OSCRNet maize leaf recognition network model was established, which uses octave convolution with characteristics to accelerate network training and reduce unnecessary redundant spatial information in maize leaf images. Additionally, a self-calibrated convolution with multi-scale features was employed to realize the interactions of different feature information in the maize leaf images, enhance feature extraction, and solve the problems of similarity of maize disease features and easy learning disorders. Concurrently, batch normalization was employed to prevent network overfitting and enhance the robustness of the model. The experimental results showed that the highest identification accuracy of the proposed method for rust, gray leaf disease, northern wilt disease, and healthy maize are 96.67%, 94.86%, 92.81%, and 95.63%, respectively. Our methods were beneficial in solving the problems of slow efficiency, low accuracy and insufficient training data, and also outperformed other comparison models. The proposed method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.

Intermixed levels of visual search difficulty produce asymmetric probability learning.

When performing novel tasks, we often apply the rules we have learned from previous, similar tasks. Knowing when to generalize previous knowledge, however, is a complex challenge. In this study, we investigated the properties of learning generalization in a visual search task, focusing on the role of search difficulty. We used a spatial probability learning paradigm in which individuals learn to prioritize their search toward the locations where a target appears more often (i.e., high-probable location) than others (i.e., low-probable location) in a search display. In the first experiment, during a training phase, we intermixed the easy and difficult search trials within blocks, and each was respectively paired with a distinct high-probable location. Then, during a testing phase, we removed the probability manipulation and assessed any generalization of spatial biases to a novel, intermediate difficulty task. Results showed that, as training progressed, the easy search evoked a stronger spatial bias to its high-probable location than the difficult search. Moreover, there was greater generalization of the easy search learning than difficult search learning at test, revealed by a stronger bias toward the former's high-probable location. Two additional experiments ruled out alternatives that learning during difficult search itself is weak and learning during easy search specifically weakens learning of the difficult search. Overall, the results demonstrate that easy search interferes with difficult search learning and generalizability when the two levels of search difficulty are intermixed.

Development and Validation of an LC-MS/MS Method for the Determination of Plasma and Red Blood Cell Omega Fatty Acids: A Useful Diagnostic Tool

Development and Validation of an LC-MS/MS Method for the Determination of Plasma and Red Blood Cell Omega Fatty Acids: A Useful Diagnostic Tool

Research on the design and application of 3D scene animation game entertainment system based on user motion sensing participation

Developed a 3D indoor virtual scene assembly system based on dual touch screen. The system adopts a combination of 2D views and 3D views. The 2D and 3D views are displayed on the two touch screens. We have comprehensively designed various interactive operations of 2D view and 3D view from the perspectives of simplicity, easy learning and easy to use, and reduced the complex 3D interactive operations to 2D interactive operations. Users can conduct two-dimensional interactive operation and 3D interactive operation based on two display screens, which can realize the efficient assembly of 3D virtual scene. The results indicate significant differences in the number of modifications of mode A and B (t = 2.982, p = 0.006 < 0.01), effective design time (t = 4.990, p < 0.001), first design satisfaction (t = 5.074, p = <0.001) and final design satisfaction (t = 2.787, p = 0.010 < 0.05). Participants in mode A had fewer modifications and shorter effective design times compared to mode B. Study and design a new workbench supporting the collaborative design of 3D virtual scenes. The workbench can support a three-dimensional indoor virtual scene assembly system based on dual touch screen, and designers and users can assemble the virtual scene together. The workbench mainly consists of two touch displays and a holographic display device. The holographic display device displays the three-dimensional scene designed by the user in real time, and supports the designer and the user to operate the three-dimensional scene through the touch film attached to it. Touch display enables designers and users to assemble scenes on 2D views through simple touch operations such as drag and drop. The face-to-face collaborative design method provided by the workbench is more conducive to the communication between designers and users, so as to improve the design efficiency of 3D scenes.

Exploring the Link Between Emotional Intelligence and Training Efficacy from Supervisor’s Rated Performance in the Retail Industry

The retail sector is a dynamic and demanding environment, requiring employees to possess a diverse skillset beyond product knowledge and sales techniques. Emotional Intelligence (EI), encompassing selfawareness, self-regulation, social awareness, and relationship management, emerges as a crucial factor impacting both training efficacy and performance, as judged by supervisors. EI holds significance in forecasting human behaviour in the workplace. To investigate this, 204 full-time employees were surveyed on their self-reported EI, training experiences, and supervisors’ evaluations of their job performance in both task-related and contextual aspects. Researchers designed a questionnaire based on existing studies and adjusted it to ensure it accurately measured the intended concepts. They tested the questionnaire on a small group of experts, who provided feedback that led to further improvements. Through Sequential Equation Models (SEM), hypotheses were examined. The study showed that EI significantly boosts learning outcomes, how easy learning feels, and how well people perform at work. Additionally, it found that perceiving learning as useful and easy plays a key role in overall learning and ultimately job performance. This suggests that training effectiveness can be improved by people being able to manage their own and others’ emotions. Previous research by Khokhar and Kush (2009) already established a strong link between learning and performance. However, no prior studies have explored how training influences personality traits or looked at the impact of EI on how easy learning feels.

Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning

Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains. To tackle the challenges of dataset bias and domain domination, numerous MDL approaches have been proposed from the perspectives of seeking commonalities by aligning distributions to reduce domain gap or reserving differences by implementing domain-specific towers, gates, and even experts. MDL models are becoming more and more complex with sophisticated network architectures or loss functions, introducing extra parameters and enlarging computation costs. In this paper, we propose a frustratingly easy and hyperparameter-free multi-domain learning method named Decoupled Training (D-Train). D-Train is a tri-phase general-to-specific training strategy that first pre-trains on all domains to warm up a root model, then post-trains on each domain by splitting into multi-heads, and finally fine-tunes the heads by fixing the backbone, enabling decouple training to achieve domain independence. Despite its extraordinary simplicity and efficiency, D-Train performs remarkably well in extensive evaluations of various datasets from standard benchmarks to applications of satellite imagery and recommender systems.

Utilization of Interactive Learning Multimedia for Arabic Language Teachers in Madrasah Tsanawiyah (MTs) Cirebon City

Technological advances, as contained in learning multimedia, must be accompanied by teacher competence in innovating and utilizing learning media in the classroom. The role of learning multimedia is very important to support the learning process. Interactive learning multimedia is a combination of media that aims to make it easier for teachers to convey learning material. However, teaching and learning activities in schools have not carried out conventional learning and used effective, efficient and easy learning media. The present study discusses the Utilization of Interactive Learning Multimedia for Arabic Language Teachers at Madrasah Tsanawiyah (MTs) in Cirebon City. The purpose of this community service is to increase the knowledge of Arabic language teachers in MTs-Kota Cirebon about the concept of Canva, Quiziz, and PPTx learning media, and to improve the skills of teachers in MTs-Kota Cirebon to create these learning media. The service method used is Service Learning (SL), which is an activity that involves practical experience, academic learning and community involvement. The results of the Community Service activities showed that service activities had an impact on participants by increasing the knowledge of teachers at MTs about the concepts of Canva, Quiziz, and PPTx learning media. As well as being able to develop these learning media so as to produce audio-visual teaching material products using Powerpoint Show, Canva and Quiziz which were compiled based on the service and training modules.