Active Learning Model Research Articles

Need Analysis for Development of a Role Playing Learning Model to Increase Student's Learning Motivation in Social Pathology Course

This study aims to (1) determine the needs of students regarding learning media that can help students improve learning outcomes and motivation; (2) the implementation of Social Pathology learning. It is research and development that is being done here. The Borg Gall development model—which includes preliminary and development studies, expert validation, model updates, hypothetical models derived from validation results, and model testing—is the one that is employed. According to Borg and Gall, the two primary goals of research and development procedures are product development and feasibility testing. Nevertheless, the scope of this research is restricted to the analytical phase, specifically examining the necessity of developing a role-playing learning model in social pathology courses to enhance the desire of students to learn. The participants in this research were third-semester social pathology students. 37 individuals made up the samples using the non-probability sampling method (saturated sample). A modified Likert scale was utilized to create the needs questionnaire, which had four response options: strongly agree, agree, disagree, and disagree strongly. Students and lecturers need a role-playing model. Students and lecturers in the Community Education Study Program need a role-playing model. This is demonstrated by the percentage that was found while examining the financial requirements of the student responses—90% of which were classified as strongly agreeing. Applying the role-playing learning paradigm can boost students' motivation and enhance their academic performance. This needs analysis can be used as the basis of learning innovations that will be applied to have an impact positive learning, which can improve learning motivation students significantly.

DIFFERENCES IN THE EXPERIENCE OF SCIENCE CONTENTS AND ITS IMPLEMENTATION IN CLASSROOM TEACHING

The educational process for every individual has an important role in life. An even more important role is played by the teacher, on whom the quality of teaching depends a lot. Teachers fight their own battles along the way - how to interest the student and develop positive attitudes about teaching and teaching content, how to implement teaching as high-quality as possible, how to contribute to better student achievements, etc. One of the key things for quality acquisition of knowledge is students’ awareness of the importance and applicability of the teaching content they learn. The concept of the content of natural science, on which we will base this work, refers to the content of natural sciences that are covered within the subject My environment (in the first three grades) or Nature and society (fourth and fifth grade) in the Republic of Srpska according to the Curriculum for elementary education (2021). The aim of the paper is to examine the differences in experiencing the content of science and their realization in the teaching of nature and society among students of younger grades of elementary school. A survey questionnaire was conducted with 343 students of grades II-V. The Likert-type questionnaire consisted of 13 statements to which the students responded by circling one of the provided answers on a five-point scale - “strongly agree”, “partially agree”, “not sure”, “mostly disagree” and “disagree”. The following research tasks were set: examine the homogeneity of the variance in student answers; perform a one-factor analysis of variance according to the independent variable “class”; examine the existence of statistically significant differences in responses between groups; interpret and analyze the obtained results. The results of the research indicate that the differences appear in favor of second-grade students, who experience the teaching of nature and society more positively, and adopt the material with more enthusiasm, than students of other grades. Classes organized according to some of the active learning models leave a better impression and effect, as found. Enthusiasm for learning declines already in the third grade, but returns with a slightly lower intensity in the fourth and fifth grade. What they all have in common is that they can be used by active learning models, of which ambiental teaching of nature and society, followed by multimedia teaching, stands out. Since through the research we have gained knowledge about the experience of science content and its implementation among students of younger school age, in the next research we should deal with each grade individually, trying to find out what actually happens at the transition from the second to the third grade, and what from the third to in the fourth, or in the fifth, regarding the adoption of the contents of nature and society, with special reference to specific learning models.

Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning.

There is growing interest in predictive coding as a model of how the brain learns through predictions and prediction errors. Predictive coding models have traditionally focused on sensory coding and perception. Here we introduce active predictive coding (APC) as a unifying model for perception, action, and cognition. The APC model addresses important open problems in cognitive science and AI, including (1) how we learn compositional representations (e.g., part-whole hierarchies for equivariant vision) and (2) how we solve large-scale planning problems, which are hard for traditional reinforcement learning, by composing complex state dynamics and abstract actions from simpler dynamics and primitive actions. By using hypernetworks, self-supervised learning, and reinforcement learning, APC learns hierarchical world models by combining task-invariant state transition networks and task-dependent policy networks at multiple abstraction levels. We illustrate the applicability of the APC model to active visual perception and hierarchical planning. Our results represent, to our knowledge, the first proof-of-concept demonstration of a unified approach to addressing the part-whole learning problem in vision, the nested reference frames learning problem in cognition, and the integrated state-action hierarchy learning problem in reinforcement learning.

Navigating materials chemical space to discover new battery electrodes using machine learning

Investigating the role of electrodes' physiochemical properties on their output voltage can be beneficial in developing high-performance batteries. To this end, this study uses a two-step machine learning (ML) approach to predict new electrodes and analyze the effects of their physiochemical properties on the voltage. The first step utilizes an ML model to curate an informative feature space that elucidates the relationship between physiochemical properties and voltage output. The second step trains an active learning model on the informative feature space using Bayesian optimization to screen potential battery electrodes from a dataset of 3656 materials. This strategy successfully identified 41 electrode materials that exhibit good electronic conductivity and host highly electronegative anions. This work provides an efficient strategy to discover novel electrode materials while integrating domain knowledge of chemistry and material science with ML in materials research.

Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification

This study presents a novel approach to medical text classification using a deep active incremental learning model, aiming to improve the automation of the preauthorization process in medical health insurance. By automating decision-making for request approval or denial through text classification techniques, the primary focus is on real-time prediction, utilization of limited labeled data, and continuous model improvement. The proposed approach combines a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with active learning, using uncertainty sampling to facilitate expert-based sample selection and online learning for continuous updates. The proposed model demonstrates improved predictive accuracy over a baseline Long Short-Term Memory (LSTM) model. Through active learning iterations, the proposed model achieved a 4% improvement in balanced accuracy over 100 iterations, underscoring its efficiency in continuous refinement using limited labeled data.

Active and Innovative Indonesian Language Learning Model for 8-Year-Old Children

Mastery of Indonesian language skills for children is still a challenge for teachers and parents. Teachers need an effective learning model to improve Indonesian language skills and knowledge. This research aims to develop an active and innovative Indonesian language learning model for children aged 8 years by collaborating with the Numbered Heads Together, Sample Non-Examples, and Guess Words (NET) learning models. This research is a test of the effectiveness of the learning model with a small group trial study carried out at MI Kompa, and a large group trial carried out at MI Adda'wah and MIN I Sukabumi Parungkuda. Data collection techniques were carried out using a mixture of two types of data, qualitative and quantitative. The research results show that the development of the NET learning model in Indonesian language subjects has succeeded in improving children's learning outcomes. Therefore, the results of this study can be used as a starting point for further research. This study has limitations in using descriptive statistical data that do not yet explain the factors that influence the success of the proposed learning model. The results can also provide a basic approach to support further analysis of the impact of using learning strategies in the classroom, both in increasing student and teacher activity. Keywords: numbered heads together, non-examples, guess the words, Indonesian, eight-year-old children

Multi-principal element alloy discovery using directed energy deposition and machine learning

Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy design. Additive manufacturing via directed energy deposition is demonstrated as a high-throughput technique for synthesizing alloys in the Cr-Fe-Mn-Ni quaternary system. More than 100 compositions are synthesized in a week, exploring a broad range of compositional space. Uniform compositional control to within ±5 at% is achievable. The rapid synthesis is combined with conjoint sample heat treatment (25 samples vs 1 sample), and automated characterization including X-ray diffraction, energy-dispersive X-ray spectroscopy, and nano-hardness measurements. The datasets of measured properties are then used for a predictive strengthening model using an active machine learning algorithm that balances exploitation and exploration. A learned parameter that represents lattice distortion is trained using the alloy compositions. This combination of rapid synthesis, characterization, and active learning model results in new alloys that are significantly stronger than previous investigated alloys.

A Chinese power text classification algorithm based on deep active learning

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep active learning (CCTP-DAL). Our core idea is to apply a hierarchical confidence strategy to a deep active learning model, to balance the trade-offs between the amount of training data and the accuracy of text classification. Our CCTP-DAL (1) trains the Bert model using a small amount of labeled data to calculate the confidence level of each short text, (2) selects high-confidence text data with optimal model generalization capability based on the hierarchical confidence level, and (3) fuses deep learning models and active learning strategies to ensure high text classification accuracy with less labeled training data. We benchmark our model on a real crawler data on the web with extensive experiments. The experimental results demonstrate that our proposed model can achieve higher text classification accuracy with less labeled training data compared with other deep learning models.

Pendampingan Model Pembelajaran Kooperatif Bagi Guru Matematika SMP/MTs se-Kabupaten Maros

Teacher quality can be improved by increasing knowledge about various methods, approaches and strategies that can be applied in learning to overcome learning problems in the classroom so that innovative learning can be realized which will maximize student achievement. In connection with this problem, the cooperative learning model was socialized to improve students' mathematical generalization abilities. This community service aims to socialize the application of the Active, Collaborative and Inductive (AKI) Cooperative learning model to teachers in Maros Regency. This activity was attended by 23 teachers and lecturers from various schools and universities. During the activity, the teachers were interested and motivated to develop creative mathematics teaching skills using the AKI type cooperative learning model as a solution to the low mathematical generalization abilities experienced by students.

Pengaruh Model Pembelajaran Active Team Quiz Terhadap Hasil Belajar Siswa Mata Pelajaran Pendidikan Agama Islam Kelas Xi Sma Muhammadiyah 9 Sedayulawas Brondong

This study discusses the Effect of the Active Team Quiz Learning Model on Student Learning Outcomes of Class XI Islamic Religious Education Subjects of SMA Muhmaadiyah 9 Sedayulawas Brondong Student Year 2022/2023. In the background, students tend to be passive and not in accordance with what is expected, as well as teachers who still use conventional learning or teachers who are very dominating in the learning process, especially in PAI subjects. To address these problems, researchers are interested in using the Active Team Quiz Learning Model as the main model in the PAI learning process. The formulation of the problem behind the research is "Is there an influence of the Active Team Quiz learning model on the Learning Outcomes of Students in Islamic Religious Education class XI of SMA Muhammadiyah 9 Sedayulawas Brondong for the 2022/2023 academic year?". This study used a type of quantitative research. Using the Pre-Experiment design method with one group pretest-posttest design technique. Data collection techniques in the form of observations and tests with a sample of 31 students from class XI of SMA Muhammadiyah 9 Sedayulawas Brondong. The test used the Pearson Product Moment correlation which was calculated at 0.763 the number of respondents 31 with dk – 2 = 29 who obtained r table 0.367 at a significant level of 5%. So rcalculate r>table (0.763 0.355). This shows that H 0 is > rejected and H1 is accepted with an interpretation of the correlation coefficient assessment found at 0.60 – 0.799 which is included in the strong category. As for the significance test using the t test obtained the results of t count 6.3579 and t table 1.699 so that it can be concluded that t count t>table which means that H 0 is rejected and H1 is accepted. Thus, it can be concluded that there is a strong and significant influence of the Active Team Quiz learning model on student learning outcomes in class XI Islamic Religious Education (PAI) subjects of SMA Muhammadiyah 9 Sedayulawas Brondong for the 2022/2023 academic year.

Efficient estimation of structural probability of failure with adaptive kriging and multi-layered hyperball-based importance sampling

ABSTRACT The need for more resilient infrastructures entails an accurate and efficient structural reliability analysis. In recent years, Kriging-based meta-modeling has constantly been adopted to reduce the computational cost in estimating the probability of failure. Active learning models focus on most informative samples to increase computational efficiency. Nevertheless, the efficiency gained from the models may diminish when dealing with a rare event with a relatively small probability of failure. To resolve the difficulty, this study proposes a new method, Adaptive Kriging with Multi-layered Hyperball-based Importance Sampling (AK-MHIS), to estimate the structural probability of failure. AK-MHIS uses an adaptive multi-layered hyperball as its candidate sample pool with a new sample filtering mechanism and a more robust stopping condition for the active-learning phase. The performance of AK-MHIS method is validated in benchmark cases. The verification results confirm the superior performance of AK-MHIS to previous methods.

Active learning-based random forest algorithm used for soil texture classification mapping in Central Vietnam

In this study, an active learning model is introduced to map soil textures. We integrate density-based clustering (DBC) with a random forest (RF) algorithm to develop an active learning model. A dataset of 88 soil samples and eight environmental covariates, geology, altitude, slope, land use, distance to the sea, distance to waterways, distance to roads, and sediment transport index (STI), was used for training and testing at a scale of 70:30. Our results indicated that the traditional model achieved poor performance (accuracy = 53.3 %) with weak agreement (kappa = 0.40), whereas, the active learning model achieved excellent performance (accuracy = 96.7 %) with almost perfect agreement (kappa = 0.96). The five environmental covariates of distance to the sea, distance to waterways, distance to roads, altitude, and slope were the most important in explaining the soil texture classification. Approximately 45 % of the total area was categorized as soil textures with high sand content, including sand, loamy sand, and sandy loam, distributed mainly along the coast. This work contributes a novel approach for soil texture classification mapping under limited budget, time, and human resource conditions.

Human-centred design on crowdsourcing annotation towards improving active learning model performance

Active learning in machine learning is an effective approach to reducing the cost of human efforts for generating labels. The iterative process of active learning involves a human annotation step, during which crowdsourcing could be leveraged. It is essential for organisations adopting the active learning method to obtain a high model performance. This study aims to identify effective crowdsourcing interaction designs to promote the quality of human annotations and therefore the natural language processing (NLP)-based machine learning model performance. Specifically, the study experimented with four human-centred design techniques: highlight, guidelines, validation and text amount. Based on different combinations of the four design elements, the study developed 15 different annotation interfaces and recruited crowd workers to annotate texts with these interfaces. Annotated data under different designs were used separately to iteratively train a machine learning model. The results show that the design techniques of highlight and guideline play an essential role in improving the quality of human labels and therefore the performance of active learning models, while the impact of validation and text amount on model performance can be either positive in some cases or negative in other cases. The ‘simple’ designs (i.e. D1, D2, D7 and D14) with a few design techniques contribute to the top performance of models. The results provide practical implications to inspire the design of a crowdsourcing labelling system used for active learning.

Efforts to Improve Community Economy Through Utilization of Young Coconut Waste Into Cocofiber and Cocopeat on the Beach Sunur Pariaman Indonesia

This paper aims to describe the utilization of young coconut waste into coco fiber and coco peat on Sunur Beach Pariaman, Indonesia. This training activity is based on the lack of optimal waste management at Sunur Pariaman Beach. Even though it has good enough facilities, the awareness of keeping a clean environment has not been implemented well by the community. This causes visitors to feel uncomfortable with the trash everywhere and smashes the beautiful view of Sunur Beach. Young coconut waste is the most common waste found. The lack of trash cans and information boards that can educate people about clean living is one of the problems that must be resolved. Likewise, there is a lack of awareness of society's charms. The problems experienced by the community will surely hinder the process of developing Sunur Beach as a marine tourism location that is of interest to tourists. Based on this problem, training activities were carried out on the use of young coconut waste to make it economically valuable by turning it into coco fiber and coco peat. This activity seeks to contribute to waste management, especially young coconut waste, and educate the public about clean living by creating information boards and providing rubbish bins by utilizing the potential of local communities by providing training on processing young coconut waste into coco fiber and coco peat, education on clean living, and making information boards on cleaning living education, and throwing rubbish in its place. An active and participatory learning and KAP (Knowledge, Attitude, Practice) model approach is used to implement this program. The impact of this training is increasing public awareness of clean living, being able to utilize young coconut waste into cocopeat and coco fiber, improving families' economy, and supporting the realization of Sapta Pesona.

Designing the Classic Teaching Session for Architectural Student via New BOPPPS Model with the Spirit “S” or Seventh Seal

Purpose is to analyze BOPPPS as a model for participatory learning and development (PLD) activities for architectural students. 
 Methodology. The methods of analysis, classification, and descriptions are used. 
 Results. BOPPPS model consists of the following components: Bridge-in: An opening session to introduce the topic, participants, and objectives of the PLD activity. Objectives: Defining the specific goals and outcomes that the PLD activity aims to achieve. Post-test: An evaluation tool used to assess participants' knowledge and understanding of the topic prior to the PLD activity. Participatory Learning: A process where participants actively engage in learning activities, such as group discussions, role-plays, and hands-on exercises, to acquire new knowledge and skills. Post-assessment: An evaluation tool used to assess participants' knowledge and understanding of the topic after the PLD activity. Summary: A closing session where the facilitator summarizes the key takeaways from the PLD activity and highlights areas for further improvement or follow-up. The BOPPPS model is designed to ensure that PLD activities are well-structured, participatory, and effective in promoting learning and development. By incorporating evaluation tools such as pre- and post-tests, the model allows organizers to measure the impact of the PLD activity and assess its effectiveness. The addition of the "S for Spirit" in the BOPPPS model emphasizes the importance of designing PLD activities with the spirit, or the essence and guiding principle, of the project. This means that the PLD activity is not only structured and evaluated effectively, but also aligns with the purpose, values, and vision of the project. Designing with the spirit can add a more meaningful and personal touch to the PLD activity, creating a more impactful learning experience for participants. 
 Conclusions. The updated BOPPPS model could include one more component Spirit (S). They are as followings: Bridge-in, Objectives, Post-test, Participatory Learning, Post-assessment, Summary, and definitely Spirit (S) – the guiding principle and essence of the project that shapes the PLD activity.

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20–82.66%) and semi-supervised learning (81.39–85.26%), active learning models were able to achieve higher accuracy (82.85–85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.

Pengaruh Model Pembelajaran Role Playing Terhadap Minat Belajar Pak Siswa Di Kelas VIII SMP Negeri 2 Siborongborong Tahun Pembelajaran 2022/2023

The aim of this research is to find out whether there is an influence of the role playing learning model on students' interest in learning PAK in class VIII of SMP Negeri 2 Siborongborong for the 2022/2023 academic year. The research method used is a quantitative method of inferential statistics. The population was all 221 students of class VIII of SMP Negeri 2 Siborongborong who were Christians and a sample of 42 people was determined using random sampling techniques. Data was collected using a positive closed questionnaire with 36 items, namely 20 items for variable : 1) Test the analysis requirements: a) positive relationship test obtained rxy = 0.407 > rtable(=0.05,n=42) = 0.304. b) Testing a significant relationship obtained tcount= 4.815 > ttable(=0.05,dk=n-2=40)= 2.021. 2) Test the effect: a) Test the regression equation, obtained the regression equation Y ̂="18.38"+0.51X. b) Regression coefficient of determination test (r2) = 16.5%. 3) Test the hypothesis using the F test to obtain Fcount > Ftable=(=0.05, dk numerator k=19, dk denominator=n-2=42-2=40) namely 7.92 > 1.51. Thus Ha is accepted and H0 is rejected.

Pengaruh Model Active Learning Tipe Team Quiz Terhadap Motivasi Belajar Siswa Pada Mata Pelajaran Pendidikan Agama Kristen dan Budi Pekerti Kelas XI SMA Negeri 1 Uluan Kabupaten Toba Tahun Pembelajaran 2023/2024

The aim of this research is to determine the influence of the team quiz type active learning model on students' learning motivation in the Christian Religious Education and Character Education subjects in class The research method used is a quantitative method, pre-experimental design type one shot case study. The population is all students of class Data was collected using a positive closed questionnaire with 52 items, namely 24 items for variable SMA Negeri 1 Uluan, Toba Regency, Academic Year 2023/2024: 1) Test the analysis requirements: a) positive relationship test obtained a value of rxy = 0.596 > rtable(a=0.05,n=35) = 0.334. b) Testing a significant relationship obtained tcount= 4.267 > ttable(a=0.05, dk=n-2=33)= 2.042. 2) Test the effect: a) Test the regression equation, obtained the regression equation Y ̂="41.27"+0.66X. b) Regression coefficient of determination test (r2) = 35.6%. 3) Test the hypothesis using the F test to obtain Fcount > Ftable=(a=0.05, dk numerator k=20, dk denominator=n-2=35-2=33) namely 18.21 > 1.62. Thus Ha is accepted and H0 is rejected.

The Effect of Active, Creative, Effective, and Enjoyable Learning Models on the Fourth-Grade Students’ Learning Outcomes

This paper discusses the effect of using the PAKEM learning model on students’ learning outcomes on the theme of the area where I live in class IV of SD Negeri 097795 Perasmian in the academic year 2022/2023. The study uses quantitative methods. The research population is 132 students from the entire class I-VI of SD Negeri 097795 Perasmian. Sampling uses a purposive sample with a number of 30 students from class IV. The research results indicate that students’ learning outcomes using the PAKEM model are included in the very good category with an average score of 85.13 and a correlation test result of 0.841, which means rcount (0.841) ≥ rtable (0.361), hence Ha is accepted. Thus, there is a significant effect between the use of the PAKEM learning model and students’ learning outcomes on the theme of the area where I live in class IV of SD Negeri 097795 Perasmian. It can be seen from the research results of the T-test, where tcount 8,215 ≥ ttable 2,048, Ha is accepted. It reveals that there is a significant positive effect from the use of the PAKEM learning model on students’ learning outcomes in the theme of the area where I live in class IV of SD Country 097795 Perasmian in the academic year 2022/2023.

The Flipped Classroom: An Effective Model of Active Learning

A a result of the development of humanistic psychology in the 1960s, there has been a growing recognition that affective factors can significantly impact the outcomes of education (Lu, 2001). Affective factors in language learning, particularly in second language acquisition, have been reckoned as non-cognitive factors and along with cognitive factors, have sparked substantial attention among academics. Affective variables that may influence foreign language acquisition include a variety of emotions and feelings arising in foreign language classroom such as anxiety, fear, embarrassment, or sense of inferiority. Among them, second language anxiety has long been a popular topic in foreign language instruction research.