Learning Progress Research Articles

Multimedia Learning Strategy (Animated Video) for Children With Slow Learner

This study explores and applies multimedia learning strategies in the form of animated videos to overcome counting difficulties for students in class III-A at SDN Kemirimuka 2, Depok, by taking a research subject with the initials TZ. Numeracy difficulties can be a significant obstacle in learning mathematics, especially for students with slow learning characteristics such as TZ. This research aims to present an innovative and engaging solution through the application of multimedia learning media. The research began by specifically identifying the numeracy difficulties faced by TZ and designing a multimedia learning strategy that could adapt to the needs and characteristics of these students. The selection of animated video as learning media was based on its ability to present mathematical concepts in a visual and interesting way, suitable for the cognitive development level of grade III students. The research was conducted at SDN Kemirimuka 2, involving active participation from TZ, as well as monitoring her learning progress. The results showed that the multimedia learning strategy, specific to the use of animated videos, had a positive impact on TZ's understanding of math concepts and learning motivation. These findings can be used as a basis for further development of learning strategies that can be adopted for students with learning disabilities. Practical implications and recommendations for further development are discussed in this journal, making an important contribution to improving the mathematics learning of students with learning difficulties at the primary level.

Engaging for the future: challenge-based learning and stakeholder partnerships in sustainability education

This paper aims to illustrate a mechanism and the impact of collaboration between academia and external stakeholders using a challenge-based learning (CBL) approach, with ill-defined open challenges as the starting point of the learning journey. The context of the study is a sequence of three connected courses that enabled students to address pressing sustainability challenges both in the Global South and the Global North in a variety of fields, such as health, urban living, agriculture and waste management. The courses aimed to equip students with knowledge and skills on how to tackle the challenges and design responsible innovations, as well as inspire them to make an impact in the world, while collaborating with external stakeholders. The insights gained from this study are that the courses effectively fostered critical thinking, organizational skills, and teamwork capabilities among students. The collaboration with external partners was particularly valued by the students for its relevance to their learning and potential career progression, as it allowed them to take ownership of the challenges to tackle, thereby enhancing motivation and deepening their learning experience. Furthermore, the courses were successful in enabling students to apply knowledge to real-world cases and engage in meaningful debates on responsible innovation and SDGs.

Integration of Ukrainian refugees in Belgium thanks to learning French

This paper considers the integration of Ukrainian refugees in Belgium – French Language assistance in the province of Walloon Brabant in Belgium. The learning of the local language is essential to enable them to orientate themselves, communicate and integrate into French-speaking society. Learning of French plays an essential role in the integration of these refugees in Belgium. It facilitates social interactions, access to employment and understanding of local culture. Initiatives such as the Reception and Schooling Scheme for Newly Arriving and Assimilated Students, the French Language of Education support system, the Local Integration Initiative as well as the applications and sites: Wallangues, “Bonjour Belgique”, First Aid Communication Tool, Alpha French Foreign Language Portal were developed to help Ukrainian refugees learn French, thus promoting their integration into French-speaking Belgian society. Also, several systems and structures studied to teach French quickly and effectively to Ukrainian refugees who arrive in Belgium. As a result of the study, it was found that the level and speed of progression in learning depend on the initial notions of French previously received, the personal motivation and the commitment of Ukrainian learners.

Social entertainment robot based on neural network algorithm in personalized music course simulation

This study focuses on the simulation of personalized music courses using social entertainment robots based on neural network algorithms, and introduces the current research status of music processing algorithms based on neural networks. A detailed explanation was provided on the training methods for neural network algorithms, including BP neural network training and music generation algorithms. Research was conducted on activation functions, music output detection, and loss function detection. A virtual music course system framework has been designed to provide students with personalized learning experiences. In a social entertainment robot based on neural network algorithms, a complete control node system was reconstructed, aiming to achieve interactive and entertainment effects of music courses through sensing technology. The control node system includes multiple key components, and the robot is able to perceive and recognize the actions, facial expressions, and sounds of students. Social entertainment robots have intelligent classification and recommendation functions, and this algorithm can intelligently classify and recommend music resources based on students’ music preferences, levels, and needs. By analyzing the personal preferences and learning progress of students, robots can provide customized music course content and learning materials for each student, improving learning effectiveness and personal interests.

DINAMIKA LINGKUNGAN PESANTREN DAN KECERDASAN EMOSIONAL TERHADAP PRESTASI BELAJAR AKIDAH AKHLAK DI PESANTREN DAARUL FALAAH BANJARNEGARA

Education is a collaboration between society and educational institutions towards wise and noble values for human progress. The pesantren environment is a significant consideration in realizing the learning progress of mentally and emotionally practical students. Seeing how important the ability to appreciate someone in everyday life is, it must be guided in every learner, one of which is through learning moral creed. The relationship between the ability to understand students and learning moral creed is significant because the ability to understand students essentially lies in character and moral creed. This research is a type of field research. The subjects in this study were boarding school administrators, moral creed teachers, and several students of MTs Muhammadiyah Merden. The data were collected using observation, interview, and documentation methods. The data were analyzed descriptively and qualitatively through data reduction, presentation, and conclusion drawing. The results of this study indicate that 1) the pesantren environment has a strategic role in improving Aqidah akhlak learning achievement because students can be controlled regularly with various daily activities in the pesantren. 2) Emotional intelligence is formed from the students' interaction patterns in several activities and learning in the classroom so that the students can understand and tolerate various differences that arise. 3) The importance of the role of Akidah Akhlak teachers in strengthening emotional intelligence and achievement in Akidah Akhlak learning for students.

Transformasi Digital dalam Pendidikan: Meningkatkan Kualitas Pembelajaran Melalui Teknologi

Digital transformation is changing the paradigm of the world of education and has a major impact on learning and teaching methods. This article discusses key aspects of transformation such as increasing accessibility to information, developing 21st century skills, digital literacy, and various forms of lifelong learning online, distance, and personal. Digital assessments are also an important element in measuring student learning progress. These changes not only bring opportunities but also challenges that require collaboration, innovation and interesting learning experiences. This is very important in understanding the impact of digital transformation on the world of education in order to face rapid technological changes. The research method used in this research is library research obtained from books and journals to ensure the truth. Many changes have occurred along with advances in technology, information comes faster, is easier to learn, and easier to interact with.

Machine Learning and Applications a Review Study

The proliferation of information and the construction of many databases have made the extraction of data from usable information a pressing issue that has to be addressed. Machine learning is the central focus of research in artificial intelligence. This paper provides an introduction to the definition of machine learning and its fundamental structure. Machine learning techniques including rote learning, inductive learning, explanatory learning, learning using neural networks, and knowledge discovery are also covered. In addition to outlining the present course of machine learning's progress, this article provides an introduction to its aims.

Deep learning-based 3D reconstruction from multiple images: A survey

Reconstructing the three-dimensional structure of a scene is a classic and fundamental problem in computer vision, but it has been revolutionized by recent advancements in deep machine learning. In this paper, we survey this rich and growing area. We divide the work into four main threads: 3D reconstruction from two calibrated images from a binocular camera; 3D reconstruction from more than two images taken by the same camera or more than two calibrated cameras; object-focused 3D reconstruction with relaxed camera calibration; and SLAM-based techniques. We summarize each approach along four salient dimensions: algorithmic and deep network characteristics, output representation, datasets, and quantitative comparisons among different methods. We also discuss key challenges and future directions.

Variegated racism: exploring experiences of anti-Black racism and their progression in medical education.

Addressing anti-Black racism in medical education in Canada has become increasingly urgent as more Black learners enter medical institutions and bring attention to the racist harms they face. We sought to gather evidence of experiences of racism among Black medical learners and to explore the contexts within which racism is experienced by learners. Drawing on critical race and structural violence theories, we conducted interviews with Black medical faculty, students, residents, and staff at the University of Saskatchewan College of Medicine between May and July 2022. We thematically analyzed interviews using instrumental case study methodology. Thematic analyses from 13 interviews revealed 5 central themes describing experiences of racism and the compounding nature of racist exposures as learners progressed in medicine. Medical learners experienced racism through uncomfortable encounters and microaggressions. Blatant acts of racism were instances where patients and superiors harmed students in various ways, including through use of the N-word by a superior in 1 instance. Learners also experienced curricular racism through the absence of the Black body in the curriculum and the undue pathologizing of Blackness. Medical hierarchies reinforced anti-Black racism by undermining accountability and protecting powerful perpetrators. Finally, Black women medical learners identified intersecting oppressions and misogynoir that compounded their experience of racism. We propose that experiences of racism may worsen as learners progress in medicine in part because of increases in the sources of and exposure to racism. Anti-Black racism in medical education in Canada is experienced subtly through microaggressions or blatantly from different sources including medical faculty. As Black learners progress in medicine, anti-Black racism may become worse because of the compounding effects of exposures to a wider range of sources of racist behaviour.

Meningkatkan Motivasi Belajar Siswa Melalui Media Pembelajaran

Learning motivation is the general main drive in students that produces learning practice, ensures learning progress, and guides learning practice to achieve learning goals. The aim of the exam to be achieved is to find out how to increase students' learning inspiration through learning media. This research method uses a subjective system to describe and understand what the use of realization media means for student learning inspiration. Student inspiration can be broadened by utilizing various techniques and media that suit learning targets. There are several examples of systems and media that can be used to increase student learning inspiration, namely true evaluation and accompanying evaluation, character teaching, teachers as solace, intuitive learning methodology, and the use of creative learning media.

Exploring the impact of nonverbal social behavior on learning outcomes in instructional video design

Online education has become increasingly popular in recent years, and video lectures have emerged as a common instructional format. While the importance of instructors’ nonverbal social cues such as gaze, facial expression, and gestures for learning progress in face-to-face teaching is well-established, their impact on instructional videos is not fully understood. Most studies on nonverbal social cues in instructional videos focus on isolated cues rather than considering multimodal nonverbal behavior patterns and their effects on the learning progress. This study examines the role of instructors’ nonverbal immediacy (a construct capturing multimodal nonverbal behaviors that reduce psychological distance) in video lectures with respect to learners’ cognitive, affective, and motivational outcomes. We carried out an eye-tracking experiment with 87 participants (Mage = 24.11, SD = 4.80). Results of multilevel path analyses indicate that high nonverbal immediacy substantially increases learners’ state motivation and enjoyment, but does not affect cognitive learning. Analyses of learners’ eye movements show that learners allocate more attention to the instructor than to the learning material with increasing levels of nonverbal immediacy displayed by the instructor. The study highlights the importance of considering the role of multimodal nonverbal behavior patterns in online education and provides insights for effective video lecture design.

Optimising Arabic Language Learning Management: Digitalisation Efforts

The background of this research includes the complexity of challenges in managing Arabic language learning, especially with limited resources and the massive development of digital technology. This study aims to identify the strategies and steps the Head of the study program took in utilizing technology to digitalize Arabic language learning. This research methodology uses a qualitative approach with data collection techniques through in-depth interviews with the Head of the study program and participatory observation of the digitalization efforts made. The results obtained from the research show that there are three stages of digitalization efforts. First, the collaboration between Arabic Language Education lecturers is planned to revise the curriculum and upgrade semester implementation plans. Second, implementation should be done using interactive Arabic learning models and technology-based learning media. Third, monitoring the learning process between lecturers and students, student learning progress, reviewing the results of midterm and final semester exams and facilitating communication between lecturers and students regarding learning problems. Hopefully, this research can provide recommendations for other educational institutions in optimizing Arabic language learning management.

Evaluation of feedback as an assessment for learning strategy in accounting education

Feedback is essential for the development and progress of learners in subjects like accounting. Teachers provide the necessary information about completed work and an opportunity for learners to improve. However, the literature indicates that teachers often struggle to provide effective feedback due to a lack of knowledge in feedback practices that facilitate learning. This study utilised critical pedagogy as a theoretical framework to create a meaningful, just, and conducive learning environment that can change and improve the educational conditions of accounting teachers. Furthermore, the study employed a qualitative research approach with Participatory Action Research (PAR) as the research design, allowing participants to take action to create a transformed teaching and learning environment that provides effective feedback. The study included five participants, selected using purposeful sampling, and data was generated from focus group meetings and workshops. Thematic analysis was employed to analyse the qualitative data. The findings indicated that accounting teachers used procedural and evaluative feedback methods to provide feedback, which was found to be ineffective, discouraging, and unclear to learners. However, as PAR was used as a research design, the subsequent findings after the workshop revealed that accounting teachers found descriptive feedback to be an effective method to improve learning. Learners found it to be specific and provide guidance and support.

Stochastic Latent Transformer: Efficient Modeling of Stochastically Forced Zonal Jets

AbstractWe present a novel probabilistic deep learning approach, the “stochastic latent transformer” (SLT), designed for the efficient reduced‐order modeling of stochastic partial differential equations. Stochastically driven flow models are pertinent to a diverse range of natural phenomena, including jets on giant planets, ocean circulation, and the variability of midlatitude weather. However, much of the recent progress in deep learning has predominantly focused on deterministic systems. The SLT comprises a stochastically‐forced transformer paired with a translation‐equivariant autoencoder, trained toward the Continuous Ranked Probability Score. We showcase its effectiveness by applying it to a well‐researched zonal jet system, where the interaction between stochastically forced eddies and the zonal mean flow results in a rich low‐frequency variability. The SLT accurately reproduces system dynamics across various integration periods, validated through quantitative diagnostics that include spectral properties and the rate of transitions between distinct states. The SLT achieves a five‐order‐of‐magnitude speedup in emulating the zonally‐averaged flow compared to direct numerical simulations. This acceleration facilitates the cost‐effective generation of large ensembles, enabling the exploration of statistical questions concerning the probabilities of spontaneous transition events.

1DCAE-TSSAMC: Two-Stage Multi-Dimensional Spatial Features Based Multi-View Deep Clustering for Time Series Data

At present, as a research hotspot for time series data (TSD), the deep clustering analysis of TSD has huge research value and practical significance. However, there still exist the following three problems: (1) For deep clustering based on joint optimization, the inevitably mutual interference existing between deep feature representation learning progress and clustering progress leads to difficult model training especially in the initial stage, the possible feature space distortion, inaccurate and weak feature representation; (2) Existing deep clustering methods are difficult to intuitively define the similarity of time series and rely heavily on complex feature extraction networks and clustering algorithms. (3) Multidimensional time series have the characteristics of high dimensions, complex relationships between dimensions, and variable data forms, thus generating a huge feature space. It is difficult for existing methods to select discriminative features, resulting in generally low accuracy of methods. Accordingly, to address the above three problems, we proposed a novel general two-stage multi-dimensional spatial features based multi-view deep clustering method 1DCAE-TSSAMC (One-dimensional deep convolutional auto-encoder based two-stage stepwise amplification multi-clustering). We conducted verification and analysis based on real-world important multi-scenario, and compared with many other benchmarks ranging from the most classic approaches such as K-means and Hierarchical to the state-of-the-art approaches based on deep learning such as Deep Temporal Clustering (DTC) and Temporal Clustering Network (TCN). Experimental results show that the new method outperforms the other benchmarks, and provides more accurate, richer, and more reliable analysis results, more importantly, with significant improvement in accuracy and spatial linear separability.

Mastery Learning in Preclinical Endodontics Using Customized 3D-Printed Tooth Models for Deliberate Practice: An Application of Educational Design Research

IntroductionThis study, conducted within an educational design research (EDR) framework, assessed the use of customized 3D-printed tooth models at various difficulty levels to enhance mastery learning and deliberate practice in preclinical endodontic training. MethodsThe EDR was conducted in a preclinical endodontic training involving 42 third-year students. The study focused on developing and evaluating 3D-printed tooth models customized into 3 difficulty levels to facilitate mastery learning for the access opening procedure on upper anterior teeth. To promote deliberate practice, we ensured ample availability of these models. The evaluation combined quantitative analysis, using Friedman and Wilcoxon Signed-Rank tests to assess practice volume and performance, with qualitative feedback from interviews and focus groups, analyzed via content analysis. ResultsThere was a significant reduction in practice time across the different model levels, with students using an average of 1.5–1.8 models per level. No significant differences in performance scores were observed across levels and natural tooth (P = .333). Feedback highlighted the models' educational value in enhancing dental training, indicating their effectiveness in improving learning experiences and skill development in endodontic education. ConclusionsThe innovative design of a 3D-printed tooth model system, which features 3 levels of difficulty and was developed within an EDR framework, allowed for tailored learning progressions and ample practice opportunities. This significantly enhanced the endodontic training experience and skill development by providing varied and realistic challenges.

Understanding self-directed learning in AI-Assisted writing: A mixed methods study of postsecondary learners

This study investigates how postsecondary learners employ generative AI, specifically ChatGPT, to support their self-directed learning (SDL) for writing purposes. Following a sequential mixed methods design, we analyzed 384 survey responses and 10 semi-structured interviews with postsecondary writers. Findings suggest that the major learning task that the learners used ChatGPT for writing is brainstorming and seeking inspiration for ideas. While the entering motivation for using ChatGPT varies from curiosity about innovative technologies to fulfilling academic requirements, such entering motivation transformed into task motivation when the learners perceived the potential benefits of ChatGPT for assisting their writing. In terms of self-management, participants mostly demonstrated a high responsibility towards their own learning with ChatGPT and employed various strategies for SDL. Although survey respondents demonstrated a comparatively low level of self-monitoring, most interviewees claimed that they critically reflected on their learning process and validated information provided by ChatGPT. There are mixed opinions regarding whether the writing skills have improved as a result of using ChatGPT. Some participants suggested that the benefits brought by ChatGPT, such as alleviating social pressure and receiving instant feedback at any time, encouraged them to spend more time practicing writing and making revisions. However, some argue that assessing their AI-assisted SDL learning progress in the short term is challenging. This study addresses gaps in the existing literature where there is scarce, large-scale empirical research on self-directed AI usage in writing, shedding light on the emerging phenomenon of utilizing generative AI as a means of SDL in writing.

Tahap Development dalam Metode ADDIE Pengembangan Bahan Ajar berbasis Mobile

Teaching materials are sources of knowledge that are useful as sources of information used by teachers and students in teaching and learning activities. Printed teaching materials are currently expensive and difficult to obtain. Current technological developments support the development of modern teaching materials and can be accessed anytime and anywhere. Mobile-based teaching materials are a solution to this problem. Through this product, teachers can provide complete material accompanied by a guide to conducting practicums. Teachers can monitor students' learning progress from the application and provide guidance if there are questions from students. Students can use the product with direct guidance from the teacher. So that the learning process will be well directed. This article discusses the development stage of the ADDIE method (Analyze, Design, Development, Implementation and Evaluation). The analyze and design stage has been published in another article. This development stage discusses the initial formation of the product to become a product. The product being developed can be seen starting from the initial login display, the main menu containing the features available in the application, the material menu containing various materials and function buttons inside along with the quiz menu with buttons that are useful as supporting quizzes.

Utilizing Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity

The decline in water conditions contributes to the crisis in clean water biodiversity. The interactions between water conditions indicators and the correlations among these variables and taxonomic groupings are intricate in their impact on biodiversity. However, since there are just a few kinds of Internet of Things (IoT) that are accessible to purchase, many chemical and biological measurements still need laboratory studies. The newest progress in Deep Learning and the IoT allows for the use of this method in the real-time surveillance of water quality, therefore contributing to preserving biodiversity. This paper presents a thorough examination of the scientific literature about the water quality factors that have a significant influence on the variety of freshwater ecosystems. It selected the ten most crucial water quality criteria. The connections between the quantifiable and valuable aspects of the IoT are assessed using a Generalized Regression-based Neural Networks (G-RNN) framework and a multi-variational polynomial regression framework. These models depend on historical data from the monitoring of water quality. The projected findings in an urbanized river were validated using a combination of traditional field water testing, in-lab studies, and the created IoT-depend water condition management system. The G-RNN effectively differentiates abnormal increases in variables from typical scenarios. The assessment coefficients for the system for degree 8 are as follows: 0.87, 0.73, 0.89, and 0.79 for N-O3-N, BO-D5, P-O4, and N-H3-N. The suggested methods and prototypes were verified against laboratory findings to assess their efficacy and effectiveness. The general efficacy was deemed suitable, with most forecasting mistakes smaller than 0.3 mg/L. This validation offers valuable insights into IoT methods' usage in pollutants released observation and additional water quality regulating usage, specifically for freshwater biodiversity preservation.

Research on E-learning interactive English vocabulary recommendation education system based on naive Bayes algorithm

E-learning is an interactive online learning mode that can enhance students' interest in learning in an entertainment environment. This article aims to develop an English vocabulary learning recommendation system based on decision tree algorithm and naive Bayesian algorithm to provide personalized learning suggestions and help learners learn and remember words more effectively. Firstly, a large amount of English vocabulary data was collected, preprocessed, and feature extracted. Decision tree algorithms were selected as the foundation, and through analysis of existing data, a decision tree was generated to classify new data. Using learners' characteristics (such as age, learning objectives, etc.) and learning history (such as learned words, learning time, etc.) as inputs, a personalized recommendation model was constructed, This model can recommend suitable learning content for learners based on their personalized needs and learning situation. In order to better understand learners' learning progress, a naive Bayesian model was trained using learners' learning history and progress information to analyze their current learning progress and predict their future learning situation. Through testing and evaluation of actual learners, the recommendation system performs well in providing personalized learning suggestions, and has a significant improvement effect on learners' vocabulary learning.