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Journal articles on the topic 'Action Model Learning'

Author: Grafiati

Published: 22 June 2024

Last updated: 31 July 2025

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1

Rao, Dongning, and Zhihua Jiang. "Cost-Sensitive Action Model Learning." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 02 (2016): 167–93. http://dx.doi.org/10.1142/s0218488516500094.

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Action model learning can relieve people from writing planning domain descriptions from scratch. Real-world learners need to be sensitive to all kinds of expenses which it will spend in the learning. However, most of previous studies in this research line only considered the running time as the learning cost. In real-world applications, we will spend extra expense when we carry out actions or get observations, particularly for online learning. The learning algorithm should apply more techniques for saving the total cost when keeping a high rate of accuracy. The cost of carrying out actions and

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Wang, Zhenyi, Ping Yu, Yang Zhao, et al. "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12281–88. http://dx.doi.org/10.1609/aaai.v34i07.6911.

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Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are

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Wang, Ziyi, Xinran Li, Luoyang Sun, Haifeng Zhang, Hualin Liu, and Jun Wang. "Learning State-Specific Action Masks for Reinforcement Learning." Algorithms 17, no. 2 (2024): 60. http://dx.doi.org/10.3390/a17020060.

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Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL), especially for Markov Decision Processes (MDPs) with vast action spaces. Previous approaches have commonly involved projecting the original action space into a latent space or employing environmental action masks to reduce the action possibilities. Nevertheless, these methods often lack interpretability or rely on expert knowledge. In this study, we introduce a novel method for automatically reducing the action space in environments with discrete action spaces while preserving interpretability. Th

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Chang, Kyungwon. ""A Model of Action Learning Program Design in Higher Education"." Journal of Educational Technology 27, no. 3 (2011): 475–505. http://dx.doi.org/10.17232/kset.27.3.475.

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Funai, Naoki. "An Adaptive Learning Model with Foregone Payoff Information." B.E. Journal of Theoretical Economics 14, no. 1 (2014): 149–76. http://dx.doi.org/10.1515/bejte-2013-0043.

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AbstractIn this paper, we provide theoretical predictions on the long-run behavior of an adaptive decision maker with foregone payoff information. In the model, the decision maker assigns a subjective payoff assessment to each action based on his past experience and chooses the action that has the highest assessment. After receiving a payoff, the decision maker updates his assessments of actions in an adaptive manner, using not only the objective payoff information but also the foregone payoff information, which may be distorted. The distortion may arise from “the grass is always greener on th

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Mordoch, Argaman, Brendan Juba, and Roni Stern. "Learning Safe Numeric Action Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 12079–86. http://dx.doi.org/10.1609/aaai.v37i10.26424.

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Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Yet obtaining such an action model is a notoriously hard task. This task is even more challenging in mission-critical domains, where a trial-and-error approach to learning how to act is not an option. In such domains, the action model used to generate plans must be safe, in the sense that plans generated with it must be applicable and achieve their goals. Learning safe action

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Pandey, Ritik, Yadnesh Chikhale, Ritik Verma, and Deepali Patil. "Deep Learning based Human Action Recognition." ITM Web of Conferences 40 (2021): 03014. http://dx.doi.org/10.1051/itmconf/20214003014.

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Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications. Action recognition is the identification of different actions from video clips (an arrangement of 2D frames) where the action may be performed in the video. This is a general construction of image classification tasks to multiple frames and then collecting the predictions from each frame. Different approaches are proposed in literature to improve the accuracy in recognition. In this pa

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Bong, Hyeon-Cheol, Yonjoo Cho, and Hyung-Sook Kim. "Developing an action learning design model." Action Learning: Research and Practice 11, no. 3 (2014): 278–95. http://dx.doi.org/10.1080/14767333.2014.944087.

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Chalard. "Developing Learner Centered Action Learning Model." Journal of Social Sciences 7, no. 4 (2011): 635–42. http://dx.doi.org/10.3844/jssp.2011.635.642.

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Amir, E., and A. Chang. "Learning Partially Observable Deterministic Action Models." Journal of Artificial Intelligence Research 33 (November 20, 2008): 349–402. http://dx.doi.org/10.1613/jair.2575.

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We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model(the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and

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Lewis, Alan, and Tim Miller. "Deceptive Reinforcement Learning in Model-Free Domains." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (2023): 587–95. http://dx.doi.org/10.1609/icaps.v33i1.27240.

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This paper investigates deceptive reinforcement learning for privacy preservation in model-free and continuous action space domains. In reinforcement learning, the reward function defines the agent's objective. In adversarial scenarios, an agent may need to both maximise rewards and keep its reward function private from observers. Recent research presented the ambiguity model (AM), which selects actions that are ambiguous over a set of possible reward functions, via pre-trained Q-functions. Despite promising results in model-based domains, our investigation shows that AM is ineffective in mode

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Rahmawati, Sitti, Detris Poba, Magfirah Magfirah, and Kusrini Burase. "Application of Cooperative Learning Jigsaw Model to Improve Student's Learning Achievement in Chemistry Learning." Jurnal Akademika Kimia 11, no. 1 (2022): 39–45. http://dx.doi.org/10.22487/j24775185.2022.v11.i1.pp39-45.

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This study aims to improve student achievement in learning chemistry in class X MIA4 at SMA Negeri 1 Palu by applying the Jigsaw Cooperative Learning Model. The Classroom Action Research (CAR) problem can be formulated as follows: Is the Jigsaw Cooperative Learning Model application able to improve student achievement in learning chemistry in class X MIA4 SMAN 1 Palu? CAR is carried out with the following stages to answer the problem: 1. Planning, 2. Implementation. 3. Observation, and 4. Evaluation and Reflection. The study results can be explained as follows several fundamental aspects of le

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Andriani, Anisa, Tiki Aqsha, Endang Juliana, Sahrul Pahmi, and Karlina. "Increasing Students' Learning Concentration Through The PBL Learning Model." ICEETE Conference Series 2, no. 1 (2024): 315–21. https://doi.org/10.36728/iceete.v2i1.206.

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Student learning concentration is an important factor in the learning process that can influence learning outcomes. However, it is often found that students have difficulty maintaining concentration during the learning process. This research aims to describe how the application of the Problem Based Learning (PBL) learning model can increase the learning concentration of fifth grade elementary school students. The background to this research is based on the importance of concentration in the learning process which can influence student learning outcomes. However, many students experience diffic

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Kd, Manik Suriti. "PENERAPAN MODEL PEMBELAJARAN BERBASIS STEM DALAM UPAYA MENINGKATKAN HASIL BELAJAR KIMIA SISWA KELAS XI MIPA 4 SMA NEGERI 7 DENPASAR TAHUN PELAJARAN 2019/2020." WIDYADARI 22, no. 1 (2021): 382–94. https://doi.org/10.5281/zenodo.4661896.

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<em>The purpose of this study was to determine whether the application of STEM-based learning models can improve learning outcomes and how students respond to dynamic equilibrium material. This research is a classroom action research designed through action research in classroom learning activities. This research was conducted at SMA Negeri 7 Denpasar in October 2019 in the odd semester. The subjects of this study were students of class XI MIPA 4 SMA Negeri 7 Denpasar, and the object of this study was the application of STEM-based learning models and student responses to chemistry learning out

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Zuber‐Skerritt, Ortrun. "A model for designing action learning and action research programs." Learning Organization 9, no. 4 (2002): 143–49. http://dx.doi.org/10.1108/09696470210428868.

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Gaikwad, Suhani, Rutuja Ghodekar, Nikhil Gatkal, and Atharv Prayag. "Human Action Recognition using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1888–92. http://dx.doi.org/10.22214/ijraset.2023.51960.

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Abstract: The aim of this project is to recognize human actions for monitoring and security purposes. This project is mainly focused on building a system that is helpful for doctors to monitor patients .Human Action Recognition is required to recognize a set of human activities by training a supervised learning model and displaying the activity/action result as per the input action received. It has wide range of applications such as patient monitoring system, ATM/ Bank security system, etc. Human Action Recognition model can be mainly used for security and monitoring purposes. We can use vario

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Zhu, Zheng-Mao, Shengyi Jiang, Yu-Ren Liu, Yang Yu, and Kun Zhang. "Invariant Action Effect Model for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 9260–68. http://dx.doi.org/10.1609/aaai.v36i8.20913.

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Good representations can help RL agents perform concise modeling of their surroundings, and thus support effective decision-making in complex environments. Previous methods learn good representations by imposing extra constraints on dynamics. However, in the causal perspective, the causation between the action and its effect is not fully considered in those methods, which leads to the ignorance of the underlying relations among the action effects on the transitions. Based on the intuition that the same action always causes similar effects among different states, we induce such causation by tak

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Hollinger, David, Ryan S. Pollard, Mark C. Schall, Howard Chen, and Michael Zabala. "A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition." Sensors 24, no. 23 (2024): 7857. https://doi.org/10.3390/s24237857.

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Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.g., ankle plantarflexion angle) motion. Although action-level or joint-level information is frequently the focus of movement intent prediction, contextual information is necessary for a more thorough approach to intent recognition. Therefore, a combination of action-level and joint-level information may offer a more comprehensive approach to predicting movement intent. In this study, we devised a novel h

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Muhrin, Lisna. "Meningkatkan Hasil Belajar Peserta Didik Kelas VIII SMP Negeri 3 Satap Maba Menggunakan Model Pembelajaran Problem Based Learning (PBL) Pada Konsep Bahasan Cahaya." KUANTUM; Jurnal Pembelajaran dan Sains Fisika 4, no. 1 (2023): 103–29. https://doi.org/10.5281/zenodo.8401228.

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This research is a classroom action research that aims to find out whether the application of the problem based learning (PBL) learning model can improve the learning outcomes of physics students in class VIII students of SMP Negeri 3 Satap Maba. The subject of this study was class VIII SMP Negeri 3 Satap Maba, which consisted of 25 students. This research was conducted in 2 cycles consisting of 4 components, namely: planning, implementation, observation and reflection. Collecting data by means of observation, evaluation and tests at the end of each teaching cycle; analyze the data through the

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Xi, Kai, Stephen Gould, and Sylvie Thiébaux. "Neuro-Symbolic Learning of Lifted Action Models from Visual Traces." Proceedings of the International Conference on Automated Planning and Scheduling 34 (May 30, 2024): 653–62. http://dx.doi.org/10.1609/icaps.v34i1.31528.

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Model-based planners rely on action models to describe available actions in terms of their preconditions and effects. Nonetheless, manually encoding such models is challenging, especially in complex domains. Numerous methods have been proposed to learn action models from examples of plan execution traces. However, high-level information, such as state labels within traces, is often unavailable and needs to be inferred indirectly from raw observations. In this paper, we aim to learn lifted action models from visual traces --- sequences of image-action pairs depicting discrete successive trace s

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Krishnan, Abhijeet, Aaron Williams, and Chris Martens. "Towards Action Model Learning for Player Modeling." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (2021): 238–44. http://dx.doi.org/10.1609/aiide.v16i1.7436.

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Player modeling attempts to create a computational model which accurately approximates a player’s behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player’s cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstr

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22

Comfort, Louise K. "Action Research: A Model for Organizational Learning." Journal of Policy Analysis and Management 5, no. 1 (1985): 100. http://dx.doi.org/10.2307/3323415.

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23

Jie Yang, Yangsheng Xu, and C. S. Chen. "Human action learning via hidden Markov model." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 27, no. 1 (1997): 34–44. http://dx.doi.org/10.1109/3468.553220.

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Comfort, Louise K. "Action research: A model for organizational learning." Journal of Policy Analysis and Management 5, no. 1 (2007): 100–118. http://dx.doi.org/10.1002/pam.4050050106.

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Joshila Grace, L. K., K. Rahul, and P. S. Sidharth. "An Efficient Action Detection Model Using Deep Belief Networks." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3232–36. http://dx.doi.org/10.1166/jctn.2019.8168.

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Computer Vision and image processing have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Action detection and Pattern Recognition. Action recognition is a new advancement of pattern recognition approaches where the actions performed by any action or living being is tracked and monitored. Action recognition still encounters some challenges that needs to be looked upon and perform recognize the actions is a very minimal time. Networks like SVM and Neural Networks are used to train the network in such a way they

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Mordoch, Argaman, Enrico Scala, Roni Stern, and Brendan Juba. "Safe Learning of PDDL Domains with Conditional Effects." Proceedings of the International Conference on Automated Planning and Scheduling 34 (May 30, 2024): 387–95. http://dx.doi.org/10.1609/icaps.v34i1.31498.

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Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designing such an action model is a notoriously challenging task. An alternative is to automatically learn action models from observation. Such an action model is called safe if every plan created with it is consistent with the real, unknown action model. Algorithms for learning such safe action models exist, yet they cannot handle domains with conditional or univers

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Edmonstone, John. "Learning and development in action learning: the energy investment model." Industrial and Commercial Training 35, no. 1 (2003): 26–28. http://dx.doi.org/10.1108/00197850310458216.

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In action learning sets participants bring their personal energy and attitudes. These produce identifiable behaviour styles (not types of people). In the energy investment model four behaviour styles of set members are identified, showing participants’ typical feelings and reactions, the support needed and what helpful questions may be.

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Gregory, Michael. "Accrediting Work‐based Learning: Action Learning – A Model for Empowerment." Journal of Management Development 13, no. 4 (1994): 41–52. http://dx.doi.org/10.1108/02621719410057069.

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Fardell, Jill. "Bringing Learning to Life: A User‐led, Action‐Learning Model." Journal of Integrated Care 11, no. 2 (2003): 36–42. http://dx.doi.org/10.1108/14769018200300026.

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Liu, Zhenxian, Peixi Peng, and Yonghong Tian. "Visual Reinforcement Learning with Residual Action." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19050–58. https://doi.org/10.1609/aaai.v39i18.34097.

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Learning control policy from continuous action space by visual observations is a fundamental and challenging task in reinforcement learning (RL). An essential problem is how to accurately map the high-dimensional images to the optimal actions by the policy network. Traditional decision-making modules output actions solely based on the current observation, while the distributions of optimal actions are dependent on specific tasks and cannot be known priorly, which increases the learning difficulty. To make the learning easier, we analyze the action characteristics in several control tasks, and

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Long, Jiahuai, and Shuguang Rong. "Application of Machine Learning to Badminton Action Decomposition Teaching." Wireless Communications and Mobile Computing 2022 (April 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/3707407.

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The study was aimed at realizing the identification of athletes’ actions in badminton teaching. The teaching process is segmented into many independent actions to help learners standardize their movements in badminton play, improving the national physical quality. First, the principle and advantages of machine vision sensing are introduced. Second, the images and videos about the action decomposition of badminton teaching are collected, and the image data are extracted by Haar-like. Subsequently, badminton players’ actions are recognized and preprocessed, and a dataset is constructed. Furtherm

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Larombo, Sapiudin fiun, and Basuki Wibawa. "INCREASED ACTIVITY AND LEARNING OUTCOMES THROUGH BIOLOGY WITH GUIDED DISCOVERY LEARNING MODEL." Asia Proceedings of Social Sciences 4, no. 3 (2019): 8–10. http://dx.doi.org/10.31580/apss.v4i3.808.

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The purpose of this study is to improve the activities and student learning outcomes on biology subjects through the application of guided discovery learning models. This research is a type of Classroom Action Research. Classroom Action Research is carried out in 2 cycles. The results showed that the application of guided discovery learning models can improve the activity and learning outcomes of students of class XI IPA 1 SMAN 1 Asera. Learning outcomes in the cognitive realm increased by 22.77% after the action of the first cycle and amounted to 11.50% after the second cycle of action and ps

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Mtk, Agnes Lugfi Wulandari, Paraniah Paraniah, Subhanudin Subhanudin, and A. Rasul. "Application Model Learning Team Games Tournament on Students' Mathematics Learning Results." Indo-MathEdu Intellectuals Journal 4, no. 1 (2023): 29–37. http://dx.doi.org/10.54373/imeij.v4i1.47.

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This study aims to determine the success of increasing the application of the team games tournament (TGT) learning model to mathematics learning outcomes in grade VIII students of YKK Ebenhaezer Mimika Papua Junior High School. This research included the type of Classroom Action Research with the research subjects of grade VIII A students at SMP YPK Ebenhaezer Mimika Papua totaling 29 students. The research design used the Kemmis and Mc. Taggart model. The results showed that mathematics learning outcomes in grade VIII A students at SMP YPK Ebenhaezer could be improved using the team games tou

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Desak, Putu Kartiwi. "UPAYA MENINGKATKAN AKTIVITAS DAN PRESTASI BELAJAR MELALUI PENERAPAN MODEL PROBLEM BASED LEARNING PADA SISWA KELAS XI MIPA 8 SMA NEGERI 7 DENPASAR TAHUN PELAJARAN 2020/2021." WIDYADARI 22, no. 1 (2021): 371–81. https://doi.org/10.5281/zenodo.4661880.

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<em>The purpose of this study was to determine the increase in activity, learning achievement, and student responses of class XI MIPA 8 to the application of problem-based learning models in mathematics learning. This type of research is a classroom action research conducted online via Zoom and Google Classroom. The research subjects were all students of class XI MIPA 8 at SMA Negeri 7 Denpasar in the academic year 2020/2021 even semester, totaling 40 people. This study uses two cycles to improve students' mathematics learning outcomes through four stages of the activity process which incl

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Huang, Xinting, Jianzhong Qi, Yu Sun, and Rui Zhang. "MALA: Cross-Domain Dialogue Generation with Action Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7977–84. http://dx.doi.org/10.1609/aaai.v34i05.6306.

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Response generation for task-oriented dialogues involves two basic components: dialogue planning and surface realization. These two components, however, have a discrepancy in their objectives, i.e., task completion and language quality. To deal with such discrepancy, conditioned response generation has been introduced where the generation process is factorized into action decision and language generation via explicit action representations. To obtain action representations, recent studies learn latent actions in an unsupervised manner based on the utterance lexical similarity. Such an action l

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Rozhana, Kardiana Metha, Adi Tri Atmaja, Nathasa Pramudita Irianti, Nila Kartika Sari, and Kardiana Zendha Avalentina. "Implementation of the STEAM model in mathematics subjects to improve learning outcomes." Jurnal Bidang Pendidikan Dasar 7, no. 2 (2023): 142–48. http://dx.doi.org/10.21067/jbpd.v7i2.8540.

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The purpose of this research is to implement the STEAM learning model to improve the mathematics learning outcomes of fourth grade elementary school students. The method used is classroom action research with research procedures including implementation, among others, preparing action plans, implementing actions, observing, and reflecting. The results obtained in cycle 1 of the student's average score were only 58.5 with an incomplete scale and the need for improvement/reflection. After the action in cycle 1, cycle 2 found a significant increase in learning completeness, namely reaching 85.75.

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I, Komang Sukendra, and Made Surat I. "PENERAPAN MODEL PEMBELAJARAN OPEN ENDED UNTUK MENINGKATKAN HASIL BELAJAR ALJABAR LINIER MAHASISWA PENDIDIKAN MATEMATIKA DENGAN PEMBELAJARAN DARING." Widyadari 22, no. 2 (2021): 439–48. https://doi.org/10.5281/zenodo.5550348.

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<em>Online learning is a system of learning activities that are carried out without going through face-to-face directly but through the internet network. The purpose of this study was to determine the effect of applying the open-ended learning model with online learning in improving student learning outcomes and responses. This type of research is classroom action research. Action research is designed using two cycles, where each cycle consists of four stages, namely planning, implementing actions, observing and reflecting. The subjects of this study were students of the sixth semester of Math

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Blagojević, Marija, and Marjan Milosević. "Collaboration and Learning Styles in Pure Online Courses: an Action Research." JUCS - Journal of Universal Computer Science 19, no. (7) (2013): 984–1002. https://doi.org/10.3217/jucs-019-07-0984.

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Collaboration provides numerous possibilities for realisation of active learning/teaching concepts in e-learning. For this reason it is recomendable to determine the optimal way in which to develop collaborative activities, with the possibility of adapting the appropriate modules' use in accordance with learners' characteristics. The paper presents a description of a behaviour pattern analysis, which deals with learners with different learning styles using collaborative modules. An action research was conducted using data from Master degree program that is conducted purely online. The research

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Muslikhin, Nasuha Aris, Arifin Fatchul, Suprapto, and Winursito Anggun. "Troop camouflage detection based on deep action learning." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 859–71. https://doi.org/10.11591/ijai.v11.i3.pp859-871.

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Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with

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Vince, Russ, and Linda Martin. "Inside Action Learning: an Exploration of the Psychology and Politics of the Action Learning Model." Management Education and Development 24, no. 3 (1993): 185. http://dx.doi.org/10.1177/135050769302400302.

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Vince, Russ, and Linda Martin. "Inside Action Learning: an Exploration of the Psychology and Politics of the Action Learning Model." Management Education and Development 24, no. 3 (1993): 205–15. http://dx.doi.org/10.1177/135050769302400308.

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Kholis, Nur Hidayat, and Rofiq Zainur. "The Effect of Implementation Project-Based Learning Model on Vocational Students' Machining Learning Outcomes." International Journal of Social Science and Human Research 08, no. 01 (2025): 395–406. https://doi.org/10.5281/zenodo.14709573.

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This study aims to analyze the improvement of the learning process, competencies achieved by students, and student learning outcomes in the implementation of the Conventional Lathe Machining Technique learning process with a sample of class XI TM 4 Mechanical Engineering Department of SMK Negeri 3 Yogyakarta through the treatment of implementing the Project-Based Learning learning model. This research uses the Classroom Action Research (CAR) method. This research will be conducted in two cycles; the first cycle will include one meeting, and the second cycle will consist of two sessions to eval

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HISAN, NAILUL. "UPAYA MENINGKATKAN KOMPETENSI NEGOSIASI MELALUI MODEL PEMBELAJARAN ACTION LEARNING." TEACHING : Jurnal Inovasi Keguruan dan Ilmu Pendidikan 2, no. 3 (2022): 348–58. http://dx.doi.org/10.51878/teaching.v2i3.1662.

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Action learning is one form of implementation of the Ministry of Finance's Corporate University. Action learning activities are implemented after the completion of structured learning. The purpose of this study is know the opinion of participants about action learning activities. The types of action learning activities in PJJ Effective Negotiation Skills are summarizing negotiation books, sharing negotiation knowledge with colleagues, or negotiation practices in the workplace. The research method used qualitative research whose data is obtained from action learning activity reports. The data i

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Zhao, Quanbin, and Hanqi Wang. "Application of Unsupervised Transfer Technique Based on Deep Learning Model in Physical Training." Computational Intelligence and Neuroscience 2022 (April 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/8679221.

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The research purpose is to study the standardization and scientizing of physical training actions. Stacking denoising auto encoder (SDAE), a BiLSTM deep network model (SDAL-DNM) (a kind of training action model), and an unsupervised transfer model are used to deeply study the action problem of physical training. Initially, the physical training action discrimination model adopted here is a combination of stacked noise reduction self-encoder and bidirectional depth network model. Then, this model can collect data for five actions in physical training and further analyze the importance of action

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Yana, Lisa, and Wati Oviana. "Problem based Learning: A Learning Model to Improve Student Learning Outcomes." Indonesian Journal of Education and Social Humanities 1, no. 1 (2024): 19–27. http://dx.doi.org/10.62945/ijesh.v1i1.24.

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Learning is an individual's effort to change his knowledge, attitudes and skills. Each learning process targets the results that must be achieved. This research aims to improve student learning outcomes. The type of research used is classroom action research with four steps, namely planning, action, observing and reflection. The subjects of this research were elementary school students. Data was obtained using observation and test techniques. The data obtained was then analyzed using descriptive statistical techniques. The research results show that the problem based learning model can help im

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Makhfudhoh, Nurul, and Khoirotul Idawati. "Model Pembelajaran Pasti Aksi Hanifida dan Profesionalisme Guru Pendidikan Agama Islam." An-Nidzam : Jurnal Manajemen Pendidikan dan Studi Islam 11, no. 2 (2024): 240–55. https://doi.org/10.33507/an-nidzam.v11i2.2160.

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This study aims to describe the professionalism of Islamic Religious Education (PAI) teachers through innovative learning models (Hanifida Action Definite Learning Model). The type of research used is a literature study, where the researcher utilizes various relevant literature to obtain research data in the form of descriptions. Based on the results of the literature review, the researcher concluded that the Hanifida Action Definite Learning Model is one of the approaches in Islamic Religious Education that focuses on the implementation of real actions in learning. This approach emphasizes th

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Desi Yunita Putri and Arie Ramadhani. "Implementation of the 7e Learning Cycle setting Peer Learning Learning Model to Improve Student Learning Outcomes." Santhet (Jurnal Sejarah Pendidikan Dan Humaniora) 8, no. 2 (2024): 2747–55. https://doi.org/10.36526/santhet.v8i2.4798.

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This research aims to improve the learning outcomes of VII class sttudent the subjects of Pancasila and Citizenship Education through the application of the Learning Cycle 7e learning model in a Peer Learning setting. The type of research used is Classroom Action Research (PTK). In this research, it was carried out in two cycles and consisted of several stages, namely introduction or planning, action implementation, observation and reflection. The material used in this research is about cooperation in various fields of life. The subjects in this research were class VII students consisting of 2

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Dandoti, Sarosh. "Learning to Survive using Reinforcement Learning with MLAgents." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3009–14. http://dx.doi.org/10.22214/ijraset.2022.45526.

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Abstract: Simulations have been there for a long time, in different versions and level of complexity. Training a Reinforcement Learning model in a 3D environment lets us understand a lot of new insights from the inference. There have been some examples where the AI learns to Feed Itself, Learns to Start walking, jumping etc. The reason one trains an entire model from the agent knowing nothing to being a perfect task achiever is that during the process, new behavioral patterns can be recorded. Reinforcement Learning is a feedback-based Machine Learning technique in which an agent learns how to

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B. Luspe, Almae Joy, and John Manuel C. Buniel. "BLENDED LEARNING IN LEARNING ACTION CELL." International Journal of Applied Science and Engineering Review 06, no. 03 (2025): 83–97. https://doi.org/10.52267/ijaser.2025.6306.

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This study explored how a blended learning approach in Learning Action Cell (LAC) sessions influences teachers’ professional development. Recognizing the crucial role of the Individual Development Plan (IDP) in guiding professional growth, the research aimed to pinpoint teachers’ specific developmental needs and evaluate how tailored blended learning materials could support them. Using descriptive research design, the study first analyzed IDPs and gathered teachers' responses to identify common areas for development. Teachers, who update their IDPs annually, confirmed that these documents were

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Suleman, Muh Asharif, and Zulfi Idayanti. "Improving Student Learning Outcomes Through the Picture and Picture Cooperative Learning Model." Ideguru: Jurnal Karya Ilmiah Guru 9, no. 3 (2024): 1939–47. http://dx.doi.org/10.51169/ideguru.v9i3.1155.

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This research aims to describe the application of the Picture and Picture Cooperative Learning Model and improve student learning outcomes after implementing this model. This type of research is Classroom Action Research (PTK), carried out over two cycles on class II elementary school students, totaling 24 students. Research procedures include planning, implementation, observation, evaluation, and reflection stages. Data collection uses observation, testing, and documentation techniques, which are then analyzed descriptively and qualitatively. The research results show that the implementation

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