Concept Of Learning Research Articles

Learning to support self-regulation: empowering preschool teachers through understanding by design and blended learning

ABSTRACT In this study, preschool teachers’ self-regulation knowledge and practices were improved through a professional development program. The program was crafted using the Understanding by Design framework to facilitate the transfer of learned concepts into the classroom. A blended learning approach with synchronous and asynchronous components was used to accommodate time and location constraints. The program covered executive functions, metacognition, emotion regulation, and motivation regulation in five modules. Program effectiveness was assessed using a nested mixed-method design. The study included 68 preschool teachers from 26 Turkish cities. Pre- and post-tests were used to collect quantitative data, while electronic survey forms and focus group interviews were used to collect qualitative data. The program significantly increased teachers’ use of metacognition and emotional-motivational regulation practices. Teachers’ belief in children’s control potential improved, resulting in significant shifts in their understanding and application of self-regulation.

Integrating Digital Assistive Technologies Into Care Processes: Mixed Methods Study.

Current challenges in patient care have increased research on technology use in nursing and health care. Digital assistive technologies (DATs) are one option that can be incorporated into care processes. However, how the application of DATs should be introduced to nurses and care professionals must be clarified. No structured and effective education concepts for the patient-oriented integration of DATs in the nursing sector are currently available. This study aims to examine how a structured and guided integration and education concept, herein termed the sensitization, evaluative introduction, qualification, and implementation (SEQI) education concept, can support the integration of DATs into nursing practices. This study used an explanatory, sequential study design with a mixed methods approach. The SEQI intervention was run in 26 long-term care facilities oriented toward older adults in Germany after a 5-day training course in each. The participating care professionals were asked to test 1 of 6 DATs in real-world practice over 3 days. Surveys (n=112) were then administered that recorded the intention to use DATs at 3 measurement points, and guided qualitative interviews with care professionals (n=12) were conducted to evaluate the learning concepts and effects of the intervention. As this was a pilot study, no sample size calculation was carried out, and P values were not reported. The participating care professionals were generally willing to integrate DATs-as an additional resource-into nursing processes even before the 4-stage SEQI intervention was presented. However, the intervention provided additional background knowledge and sensitized care professionals to the digital transformation, enabling them to evaluate how DATs fit in the health care sector, what qualifies these technologies for correct application, and what promotes their use. The care professionals expressed specific ideas and requirements for both technology-related education concepts and nursing DATs. Actively matching technical support, physical limitations, and patients' needs is crucial when selecting DATs and integrating them into nursing processes. To this end, using a structured process such as SEQI that strengthens care professionals' ability to integrate DATs can help improve the benefits of such technology in the health care setting. Practical, application-oriented learning can promote the long-term implementation of DATs.

Federated learning of medical concepts embedding using BEHRT.

Electronic health record data is often considered sensitive medical information. Therefore, the EHR data from different medical centers often cannot be shared, making it difficult to create prediction models using multicenter EHR data, which is essential for such models' robustness and generalizability. Federated learning (FL) is an algorithmic approach that allows learning a shared model using data in multiple locations without the need to store all data in a single central place. Our study aims to evaluate an FL approach using the BEHRT model for predictive tasks on EHR data, focusing on next visit prediction. We propose an FL approach for learning medical concepts embedding. This pretrained model can be used for fine-tuning for specific downstream tasks. Our approach is based on an embedding model like BEHRT, a deep neural sequence transduction model for EHR. We train using FL, both the masked language modeling (MLM) and the next visit downstream model. We demonstrate our approach on the MIMIC-IV dataset. We compare the performance of a model trained with FL to one trained on centralized data, observing a difference in average precision ranging from 0% to 3% (absolute), depending on the length of the patients' visit history. Moreover, our approach improves average precision by 4%-10% (absolute) compared to local models. In addition, we show the importance of the usage of pretrained MLM for the next visit diagnoses prediction task. We find that our FL approach reaches very close to the performance of a centralized model, and it outperforms local models in terms of average precision. We also show that pretrained MLM improves the model's average precision performance in the next visit diagnoses prediction task, compared to an MLM without pretraining.

E!Canasta: A card game to teach ATP synthesis and metabolic regulation in biochemistry classes.

Understanding ATP formation is essential for learning metabolism and is central to grasping metabolic processes as a whole. However, due to the high level of abstraction, the number of intermediate substrates, the connections, and integrated regulation, its comprehension often poses a challenge. This and the fact that traditional teaching methods struggle when dealing with highly abstract concepts, game-based strategies present a more concrete and dynamic alternative, which led to the creation of E!Canasta (card game). Developed based on Canasta and adapted in order to improve the learning of concepts, including some of pathway's regulation and integration, E!Canasta motivates students and promotes engagement in a fun activity. Students assemble a sequence of cards representing the glycolysis, acetyl-CoA, Krebs cycle, and electron transport chain, which correspond to the card suits. Strategically, some of the cards hold special feats that simulate some aspects of metabolic regulation and integration (to give or take away points). At the end of the game, points are added up for sequences and cards with positive or negative effects. The game was played with two classes of students enrolled in biochemistry as part of their graduations (86 players). Student perception on gameplay, motivation and understanding was measured through an anonymous Likert scale questionnaire, with very positive results in all questions. Statistically significant correlations were observed regarding the perceived comprehension of pathways and their regulation, and in linking motivation with a positive gaming experience, showcasing that E!Canasta demonstrates considerable educational potential, along with an enjoyable experience for learning ATP synthesis.

The effect of mind mapping on the level of student learning success in the Pancasila course: a study on students of the UNY English department

The quality of a nation's human resources can be measured from the learning achievements of students. Learning achievement is an indicator of student learning success and also an indicator of educator success in conducting learning. Learning success reflects the success of students in mastering learning concepts expressed by grades. One way to increase student understanding of concepts is by applying mind mapping. This study aims to determine the effect of mind mapping on the success rate of student learning in the Pancasila course. The method used in this research is Pre-Experimental with One Group Pretest-Posttest Design. The research sample consisted of 36 students majoring in English literature at UNY by choosing purposive sampling. Analysis of the learning outcomes obtained shows that the value of ha is accepted which means that the effect of applying mind mapping has on student learning success in the Pancasila course. While the Gain value (the average increase as an indicator of learning success) obtained results of 0.33 in the medium category. The implication of this research is as a source of information or a reference for educators that the application of mind mapping in learning can increase learning success.

The Ludo-Learning Matrix: A Framework for Understanding Learning in Games

Games have been widely recognized as methods of making learning more engaging. This has proven to be very attractive to those attempting to get complex fields such as cybersecurity training to a wider audience. Unfortunately, there is a failure to understand that each type of game serves a different educational purpose, leading to the situation where the games are being selected incorrectly and are failing to fulfill their promise. To address this issue, key concepts in learning and games were identified from the literature and then operationalized into an analytical framework. The concepts of player agency, message broadcasting, the transfer of learning and the type of challenges present in the game were selected. This generated a framework that is player-focused rather than content-focused and could be applied to any potential learning game. To validate the framework, three cybersecurity games were selected and analyzed using a ludo-textual approach. Cyberciege, a single player game aimed at university students, Spoofy, a kids’ web browser game and Tallinn Soldier, a seminar wargame, were selected to give as wide a sample as possible. The paper goes on to report on the results of the analysis and discuss how this may be used to examine and design learning games for cybersecurity in the future. Each of the selected games focus on different mechanisms for learning and engagement, highlighting the design tradeoffs that were made in the creation of the game. The proposed analytical framework can be applied not just to cybersecurity training games but theoretically to understanding any game that has learning as its objective.

The Role of Generative Learning Strategy in Bridging the Gap between High and Low Achievers in Genetics Studies in Sefwi-Wiawso Municipality, Ghana

The study sought to investigate the use of the Generative Learning Strategy (GLS) in bridging the performance gap between high- and low-achieving students in genetics in Ghana's Western North region's capital, Sefwi-Wiawso Municipality. The study adopted an embedded research design involving the quasi-experimental pretest/posttest groups with a sample size of 106 SHS 3 Biology students. Two intact classes were randomly selected, treated as one group and exposed to the same treatment conditions. The Genetic Concepts Test and a semi-structured interview guide collected the quantitative and qualitative data, respectively. The GCT's internal consistency was 0.784, indicating a preferable internal consistency. The quantitative data was analyzed using the SPSS version 26, by employing descriptive and inferential statistics. The study found no significant performance difference between high and low achievers after using the GLS. The approach improved the lower achievers’ performance. The interview results revealed that Senior High School Biology students perceived that the Generative Learning Strategy improved the understanding, motivation, retention and self-directed learning of genetic concepts, thereby enhancing their learning outcomes. The study recommended that SHS Biology teachers employ the Generative Learning Strategy in teaching genetic concepts to bridge the performance gap between high and low achievers.

The Game of Dominoes as Teaching-learning Method of Basic Concepts of Differential Calculus

This paper will present teaching-learning mathematics strategies, specifically differential calculus in a variable, through a fun and competitive space in which action is manifested by the student and not only the repetition of information by the teacher. This action refers to motivate, problematise, summarise, and coordinate a dominoes game whose thematic cards are designed around the basic and main contents of differential calculus. The strategies for teaching this area are diverse and precisely the game of dominoes is one of the most used strategies in the practice of mathematics because it stimulates logical reasoning and mental skills. The objective is to identify the way in which the game of dominoes affects the learning and understanding of fundamental concepts of differential calculus in a variable by means of experimentation carried out on students of the first semester of the School of Engineering and Sciences of Technological of Monterrey. Finally, the results of this study will be presented and the use of this strategy in other topics around mathematics will be recommended to facilitate logical and meaningful learning in students.

Ai] Explore!: An Educational Game for Developing Programming Skills and Algorithmic Thinking

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

Tiers of Engagement: Achieved Learning From Business Simulations Reflected in Economics Students’ Experiences

This article investigates the impact of a business simulator in an online economics course, focusing on the relationship between student engagement and reflections on learning. Utilizing a mixed-methods approach, our study analyzes responses from 328 students who used the "Hubro Business Simulator" in a flexible online bachelor's program in economics and administration. Students provided reflections on the simulator as a learning activity, application of theoretical concepts and practical relevance. Qualitative analysis of 1640 responses further explored the depth of students' reflections, categorized into different tiers of engagement based on their responses. This analysis builds on Self-Regulated Learning (SRL) and motivational theory. The study sought to determine whether students reporting higher levels of engagement exhibited deeper metacognitive reflections compared to their less engaged peers. Findings support a link between the level of engagement and the depth of reflection. Students with higher engagement levels were more likely to reflect on specific ways in which the simulator had supported their learning and understanding of theoretical concepts. This paper discusses the limitations and implications of these findings for enhancing game-based learning interventions, emphasizing the need to foster higher engagement levels to improve educational outcomes in online learning environments. The study contributes to the literature on game-based learning by providing a large empirical study on how business simulators can foster higher-order thinking and deeper learning reflections among economics students. It also highlights the role of engagement in using business simulators as learning tools, offering insights that could improve the learning design and implementation in similar online and flexible learning program contexts.

Pendampingan Dan Pembinaan Pembelajaran Dengan Strategi “LESSUGAR” (Lingkungan Sebagai Sumber Belajar) Di TK Restu Ibu Kota Depok

Mentoring and coaching of learning at Restu Ibu Kindergarten is carried out by implementing the “Lessugar” strategy, which utilizes the environment as a source of learning. This strategy aims to realize the concept of Independent play and Independent learning, where children are free to explore and learn according to their interests and potential. Through direct interaction with the surrounding environment, children can develop curiosity, critical reasoning skills, and social skills. This contextual learning provides a more meaningful learning experience, so that children are able to connect the knowledge gained with their daily lives. The results of the mentoring show that this strategy is able to create a pleasant learning atmosphere and support the holistic development of early childhood. The lessugar strategy is expected to continue to be developed as an innovative learning model that supports inclusive and participatory early childhood education.

Synthetic biology education and pedagogy: a review of evolving practices in a growing discipline

Synthetic biology is a growing field with an increasing number of successful applications. Yet, synthetic biology (SynBio) education initiatives are underreported and disconnected from each other. In this review we survey the literature on SynBio education and stratify this body of work into three categories: classroom activities, course designs, and program-level curricula-planning. For each category, we discuss the methods used to assess students’ experiences and achievement of learning objectives. Throughout, we identify trends and opportunities for further development in SynBio education. We determined that the design of low-cost education kits is a growing opportunity to support student learning at the level of classroom activities. In support of that work, we present a mapping of published education kits onto Bloom’s taxonomy, taking into account increasing accumulation of knowledge through continued experience. We further found that project-based learning is used widely and has proven effective in course designs. To facilitate such activities, we provide a high-level guide for the conversion of a didactic course into a project-based learning course. Further, we note that, currently, programs are delivered primarily at the graduate level, taking inspiration from traditional degree programs while incorporating interdisciplinary training. Finally, we find that design-based research may provide an effective framework for an iterative, mixed-method study design. To support such efforts, we provide a schematic overview of design-based research and its application to a learning progression for interdisciplinary skills. We conclude with a discussion of specific learning concepts that may be useful to SynBio educators and education researchers.

A comparative study of early stage Alzheimer’s disease classification using various transfer learning CNN frameworks

ABSTRACT The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer’s disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers. These enhancements improve the model’s effectiveness and precision in addressing the specified medical issue. The proposed model is rigorously validated and evaluated using publicly available Kaggle MRI Alzheimer’s data consisting of 1280 testing images and 5120 patient training images. For comprehensive performance evaluation, precision, recall, F1-score, and accuracy metrics are utilized. The findings indicate that the Xception method is the most promising of those considered. Without employing five K-fold techniques, this model obtains a 99% accuracy and 0.135 loss score. In addition, integrating five K-fold methods enhances the accuracy to 99.68% while decreasing the loss score to 0.120. The research further included the evaluation of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) for various classes and models. As a result, our model may detect and diagnose Alzheimer’s disease quickly and accurately.

Detection of Brain Tumor using Medical Images: A Comparative Study of Machine Learning Algorithms – A Systematic Literature Review

Abstract: Background: Brain tumors are a significant global health concern impacting both adults and children. Tumors are characterized by abnormal or excessive growth resulting from uncontrolled cell division. Diagnosing brain tumors poses various challenges, including limited funding, a shortage of qualified professionals, and insufficient access to medical facilities in remote regions. Different learning techniques for detecting brain tumors have been developed due to their ease of use, cost-effectiveness, and non-invasive nature, in contrast to other invasive methods. Methods: This research conducts a systematic literature review to explore modern trends and concepts of machine learning in healthcare, aiming to identify effective techniques for brain tumor detection. It also compares and analyzes the most efficient machine learning methods currently in use, focusing on aspects such as machine learning algorithms, image augmentation, evaluation metrics, and the sizes of datasets employed. Results: The findings indicate that non-invasive methods, such as machine learning algorithms for brain tumor detection, are cost-effective and provide quick results. Conclusions: This systematic literature review offers a technical overview, demonstrating the efficiency and effectiveness of machine learning techniques in making brain tumor detection feasible. The study utilizes deep learning and machine learning methods to comprehensively analyse diagnosis, imaging, and clinical evaluations in medical fields related to brain tumor detection.

Detection of phishing portals through machine learning algorithms

Objective Analysis and practical implementation of the phishing portal detection functionality through machine learning algorithms. Method. Systematization of disparate information, analysis of the field, description of available developments are the main methods that were used in the study. The work is divided into three large sub-blocks. The first one analyzes the concept of machine learning, describes the main ways to correctly interpret the information entered, indicates the most popular techniques and databases. In the second part of the work, an analysis of artificial neural networks is carried out. In particular, their subspecies are shown with a description of the implementation features, and a comparison with living neurons is carried out. In the third part, the practical implementation of the two techniques and their comparison are carried out, recommendations are given regarding their use in detecting phishing portals. Result. The paper investigates the methods of analyzing phishing portals. The analysis showed that it is most rational to use a random forest, because it provides quality according to the precession, recall, F1-score, 98% metrics with a significant number of parametric values entered. Conclusions. When implementing various search methodologies for phishing portals, it is necessary to take into account their decrease in efficiency from the entered parameters. To do this, it is important to conduct preliminary tests. However, the test result can be interpreted in different ways. In particular, the effectiveness of the methods can be improved if you limit the number of input parameters, but at the same time rigidly structured according to one search criterion.

Pharmacy Undergraduates’ Epistemic Cognition and Medication Beliefs as Predictors of Conceptual Learning and Academic Progress

The importance of studies showing the impact of students' epistemic beliefs on their conceptual learning and academic progress is increasing. This study investigated pharmacy students’ domain-specific epistemic cognition and topic-specific beliefs related to their level of conceptual prior knowledge, learning and study progress during the first academic year. Data were collected from 125 students using a pre-test/post-test design with a multiple-choice questionnaire, an open-ended case task and a measure of domain-specific epistemic cognition and topic-specific beliefs concerning medication. The results showed that students’ prior knowledge predicted their academic performance and more sophisticated epistemic cognition was related to better conceptual understanding, faster study progress, and fewer anti-medication beliefs. Anti-medication beliefs hindered participants’ success in the case task and were related to weaker study progress. Our study shows that epistemic cognition and topic-specific beliefs play a role in students’ performance, learning, and study progress

Evaluation of Athlete Physical Fitness Based on Deep Learning

In order to promote deep learning in physical education for students, guided by the concept of deep learning, the author integrates the concept of deep learning into flipped classrooms. The author proposes a flipped classroom design and classroom implementation process for promoting deep learning, and conducts a semester long experimental research based on basketball courses, continuously optimizing the design during this period.Through the analysis of various parameters appearing in the operation, the test results are satisfactory. The study found that students in three classes were physically trained by using the classroom teaching method of ”deep learning”. The classroom teaching mode based on deep learning achieved the best teaching results in the first stage. One habit that inspires real emotion in children is their pace: running in different directions. Classroom teaching based on deep learning can make students understand the importance of cross-movement. Thus, after experiment, there was no significant difference between the flipped classes and the traditional class teaching method in the cross run compared to before experiment. The effectiveness of flipped classroom design for promoting deep learning has been verified, achieving a promoting effect on students’ deep learning. The flipped classroom designed by this research institute can effectively promote learners to achieve deep learning, specifically manifested as: The physical fitness level of students has improved, the level of cognitive structure has improved, and students are more able to learn independently. The goal of deep learning for students has been achieved.

HANDS-ON FLUIDIZED BED CLASSROOM IMPLEMENTATION AND ASSESSMENT

In this study we examined the first-time use of a miniaturized fluidized bed module in a chemical engineering classroom. Learning activities were developed to foster learning at the higher levels of Bloom's taxonomy and within the ICAP framework to provide interactive, constructive, and active engagement to promote a deeper understanding of concepts. A hands-on activity facilitated by a desktop-scale fluidized bed and reinforcing printed worksheet materials was deployed within a 50-min class to encourage student engagement. Results from module performance tests compare well to predictions based on theoretical models suggesting this tool can effectively demonstrate fundamental concepts related to pressure loss in a packed bed, minimum fluidization velocity, constant pressure drop in a fluidized bed, bed expansion and repacking below a top screen. Pre- and Posttests 1 and 2 show student learning was significantly improved after pre-homework and the hands-on activity compared to the learning after the lecture alone. Student responses to two open-ended questions on Pre- and Posttests 1 and 2 allowed us to identify persisting student misconceptions about packed and fluidized beds. Suggestions for future work to repair these misconceptions are included in this study. Tweetable AbstractA hands-on fluidized bed learning tool enabling visualization of packed and fluidized beds in a chemical engineering classroom, and associated impacts on conceptual learning and student engagement.

Policy and organisational learning in judicial reform: evidence from Italy

The Italian judicial system is well-known for its substandard performance by comparison with its European peers. In this article, we analyse the implementation of an important reform of the Italian judicial system designed and implemented in the context of the National Recovery and Resilience Plan funded by Next Generation EU. The reform, launched in February 2022, involves the introduction of a new organisational structure (The Office for the Trial) staffed with 16,500 newly appointed judicial assistants, representing an increase of about one third of the total judicial workforce employed in Italy. By leveraging the concepts of policy and organisational learning (OL), we show that variation of the modes of policy learning observed in implementation design is linked with variation in the organisational models adopted by different judicial offices to implement the reform. Then, by connecting organisational models to OL and focusing on the performance of five selected sections within the Court of Appeal of Milan, we show that organisational models informed by reflexivity are associated with better performance than models informed by hierarchy. The study contributes to scholarship on policy and OL, as well as providing a first, albeit limited and exploratory, empirical evaluation of a strategic public sector reform.

Finance Modeling Approach Using Machine Learning

The integration of machine learning (ML) into financial modeling represents a transformative shift in the finance sector, enhancing analytical capabilities and decision-making processes. This article explores the fundamental concepts of financial modeling and machine learning, illustrating how ML methodologies—both supervised and unsupervised—improve traditional financial models by enabling the analysis of extensive datasets to uncover intricate patterns. By leveraging historical data, ML enhances predictive accuracy in various applications, including risk assessment, algorithmic trading, credit scoring, and fraud detection. The article highlights significant advancements in predictive analytics through techniques such as regression analysis and time series forecasting, which allow financial analysts to navigate market uncertainties more effectively. Moreover, the automation of data processing and feature engineering through ML leads to increased operational efficiency, reducing human error and enhancing real-time decision-making capabilities. However, challenges such as data quality issues, model interpretability, and ethical considerations regarding algorithmic bias necessitate careful management to ensure responsible implementation. The article emphasizes the importance of transparency in ML models to foster trust among stakeholders while meeting regulatory requirements. Future research directions are proposed, focusing on the integration of explainable AI with blockchain technology and the implications of big data analytics on ML models. By addressing these emerging challenges and opportunities, finance professionals can leverage advanced analytical tools to drive innovation and enhance strategic initiatives in an increasingly complex financial landscape. Ultimately, this article underscores the pivotal role of machine learning in reshaping financial modeling practices and its potential to redefine the future of finance.