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Journal articles on the topic 'Autonomous learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sun, Qiuyun. "Cultivation Strategy of Higher Vocational Students' Autonomous Learning Ability under the Background of "Internet +"." Advances in Education, Humanities and Social Science Research 9, no. 1 (2024): 238. http://dx.doi.org/10.56028/aehssr.9.1.238.2024.

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Autonomous learning ability is an essential ability for outstanding talents. The future society under the background of the "Internet +" is a learning society that requires everyone to have the ability to learn autonomously. The learning status, career development, and social environment of higher vocational students require them to have a certain degree of autonomous learning ability. In the process of education, emphasis should be placed on cultivating students' autonomous learning ability, enabling them to develop good learning habits, master correct learning methods, and improve effective

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Liaq, Mudassar, and Yungcheol Byun. "Autonomous UAV Navigation Using Reinforcement Learning." International Journal of Machine Learning and Computing 9, no. 6 (2019): 756–61. http://dx.doi.org/10.18178/ijmlc.2019.9.6.869.

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Maslakhatin, Maslakhatin, and Ayong Lianawati. "The Implementation of Autonomous Learning in English Pronunciation of Guidance and Counseling Departmentâ€™s Students." JET ADI BUANA 2, no. 2 (2017): 115–24. http://dx.doi.org/10.36456/jet.v2.n2.2017.1052.

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Most Indonesian students have difficulties in learning English pronunciation. Autonomous learning can be as an alternative strategy to help the students in learning pronunciation independently by utilizing Breakingnewsenglish.com. This study aimed to analyze the implementation of autonomous learning in learning pronunciation, the studentsâ€™ response toward autonomous learning, and the impact of autonomous learning on the studentsâ€™ pronunciation achievement. This study used descriptive qualitative method and the subject was Guidance and Counseling department students. The data was collected

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Ababo, A. B., and A. K. Animaw. "Students’ Practices and Perceptions of Autonomous Language Learning: the Case of Addis Ababa Science and Technology University." Язык и текст 10, no. 2 (2023): 115–28. http://dx.doi.org/10.17759/langt.2023100210.

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<p>This study aimed to investigate freshmen students' practices and perceptions of autonomous English language learning. The study investigates the actual language learning activities students carry out inside and outside the classroom with a view to determining their perceptions towards autonomous language learning, their readiness to take responsibility for their learning, and their motivation level of learning English autonomously. 313 students participated in the survey questionnaire where classroom observations and FGDs were used for triangulation. The obtained quantitative data fro

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Yang, Liangjun. "Autonomous Driving Control Strategy Based on Deep Reinforcement Learning." Applied and Computational Engineering 128, no. 1 (2025): 79–85. https://doi.org/10.54254/2755-2721/2025.20209.

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This paper discusses an autonomous driving control strategy based on Deep Reinforcement Learning (DRL), which aims to improve the decision-making ability of autonomous driving system in complex traffic environments. Deep reinforcement learning has a wide range of applications in many fields, such as robotics and medicine. Autonomous driving has emerged as a significant research focus in recent years. By combining deep learning and reinforcement learning, the model is able to autonomously learn and optimize driving behavior under dynamically changing road conditions. The DRL-based control strat

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Heide, Nina Felicitas, and Janko Petereit. "Machine learning for the perception of autonomous construction machinery." at - Automatisierungstechnik 71, no. 3 (2023): 219–32. http://dx.doi.org/10.1515/auto-2022-0054.

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Abstract Robotic systems require holistic capabilities to sense, perceive, and act autonomously within their application environment. A safe and trustworthy autonomous operation is essential, especially in hazardous environments and critical applications like autonomous construction machinery for the decontamination of landfill sites. This article presents an enhanced combination of machine learning (ML) methods with classic artificial intelligence (AI) methods and customized validation methods to ensure highly reliable and accurate sensing and perception of the environment for autonomous cons

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Masouleh, Nima Shakouri, and Razieh Bahraminezhad Jooneghani. "Autonomous learning: A teacher-less learning!" Procedia - Social and Behavioral Sciences 55 (October 2012): 835–42. http://dx.doi.org/10.1016/j.sbspro.2012.09.570.

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Ma, XiaoRui. "Influence Study of Learners’ Independent Learning Ability on Learning Performance in Online Learning." International Journal of Emerging Technologies in Learning (iJET) 17, no. 09 (2022): 201–13. http://dx.doi.org/10.3991/ijet.v17i09.30925.

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In this paper, questionnaires regarding the influence of learners’ autonomous learning ability on learning performance in online learning were designed, and the mediating role played by deep learning orientation in the effect of autonomous learning ability on learning performance was analyzed. The results revealed that the overall Cronbach’s α coefficient of the questionnaire was 0.884, the KMO value was 0.817, and the corresponding P value was 0.000, manifesting the good reliability and validity of this questionnaire. The learning performance could be obviously influenced by four aspects of a

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Marsevani, Maya. "LEARNERS’ PERCEPTION AND PRACTICES ON AUTONOMOUS LANGUAGE LEARNING IN EFL SETTINGS." International Journal of Language and Literature 5, no. 1 (2021): 54. http://dx.doi.org/10.23887/ijll.v5i1.32598.

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This research was aimed to investigate learners’ perception and practices of understanding teachers’ teaching objectives and requirements related to autonomous language learning, setting up their objectives and study plans to autonomous language learning, and find out learners perceive in learning English autonomously. To gain a deep understanding and utilizing the objectives, the researcher employed descriptive qualitative research. The research findings were extracted through both an online questionnaire and interview. Some constraints were reported in this research, but more positive respon

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R, Ragıp BAŞBAĞI, and YILMAZ Nagehan. "Autonomous foreign language learning." Journal of Languages and Culture 6, no. 8 (2015): 71–79. http://dx.doi.org/10.5897/jlc2015.0338.

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Bagnell, James, David Bradley, David Silver, Boris Sofman, and Anthony Stentz. "Learning for Autonomous Navigation." IEEE Robotics & Automation Magazine 17, no. 2 (2010): 74–84. http://dx.doi.org/10.1109/mra.2010.936946.

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Hexmoor, Henry, and Lisa Meeden. "Learning in autonomous robots." Knowledge Engineering Review 11, no. 4 (1996): 361–64. http://dx.doi.org/10.1017/s0269888900008043.

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The definitions above, separated by ten years, represent two very different conceptions of learning. For Simon learning depends on an internal change in representation, and for Kaelbling it is instead measured in terms of an external change in behaviour. Furthermore, Kaelbling's focus on the situatedness of the learning system being embedded in its environment reflects the recent experience gained by much direct experimentation with physical robots.

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Walter, Oliver, Reinhold Haeb-Umbach, Bassam Mokbel, Benjamin Paassen, and Barbara Hammer. "Autonomous Learning of Representations." KI - Künstliche Intelligenz 29, no. 4 (2015): 339–51. http://dx.doi.org/10.1007/s13218-015-0372-1.

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Crome, Keith, Ruth Farrar, and Patrick O’Connor. "What is Autonomous Learning?" Discourse: Learning and Teaching in Philosophical and Religious Studies 9, no. 1 (2009): 111–25. http://dx.doi.org/10.5840/discourse20099112.

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Leinen, Philipp, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, and F. Stefan Tautz. "Autonomous robotic nanofabrication with reinforcement learning." Science Advances 6, no. 36 (2020): eabb6987. http://dx.doi.org/10.1126/sciadv.abb6987.

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The ability to handle single molecules as effectively as macroscopic building blocks would enable the construction of complex supramolecular structures inaccessible to self-assembly. The fundamental challenges obstructing this goal are the uncontrolled variability and poor observability of atomic-scale conformations. Here, we present a strategy to work around both obstacles and demonstrate autonomous robotic nanofabrication by manipulating single molecules. Our approach uses reinforcement learning (RL), which finds solution strategies even in the face of large uncertainty and sparse feedback.

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Nurhaeni and Pupung Purnawarman. "THE USE OF SMARTPHONE AND LEARNING STRATEGIES IN AUTONOMOUS LEARNING." Indonesian EFL Journal 4, no. 1 (2018): 43. http://dx.doi.org/10.25134/ieflj.v4i1.797.

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Rapid penetration of smartphone in students� life currently leads to the extreme influence and addiction of smartphone use in their daily activities (Alexander, 2011; Hough, 2011). Later on, it raises concern about the negative impact of smartphone use towards students� learning performance and achievement (Kibona & Mgaya, 2015). On the other hand, several studies found that smartphone is a possible learning tool that can be utilized by students to learn on their own, or autonomously (Kim & Kwon, 2012; White & Mills, 2014; Woodcock, Middleton, & Nortcliffe, 2012). Hence, the pu

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Zhang, Tingyu. "The application of deep learning in autonomous driving." Applied and Computational Engineering 50, no. 1 (2024): 144–48. http://dx.doi.org/10.54254/2755-2721/50/20241379.

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Autonomous driving technology is currently a globally prominent subject, with its relevance and impact readily apparent. Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion. Furthermore, this technology is poised to accelerate innovation within the automotive sector, drive industrial advancement, and enhance people's convenience and safety in their travel experiences. The importance of autonomous driving technology is not only reflected i

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Jafari, Sakineh, Saeed Ketabi, and Mansoor Tavakoli. "Advanced and intermediate EFL learners’ perceptions and practices of autonomous learning." ITL - International Journal of Applied Linguistics 168, no. 1 (2017): 70–90. http://dx.doi.org/10.1075/itl.168.1.03jaf.

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Abstract This study examined Persian intermediate and advanced EFL learners’ perceptions regarding (a) their own and their teachers’ responsibility in learning language autonomously; (b) their decision making ability in learning language autonomously; and (c) their autonomous learning activities inside and outside the classroom. To this end, a questionnaire designed by Chan, Spratt, and Humphrey (2002) was distributed among 67 intermediate and 65 advanced EFL learners. Statistical analysis of students’ answers showed that overall, advanced learners tended to assume more responsibility for thei

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Phi, Minh Tuan. "Becoming Autonomous Learners to Become Autonomous Teachers." International Journal of Computer-Assisted Language Learning and Teaching 7, no. 4 (2017): 15–32. http://dx.doi.org/10.4018/ijcallt.2017100102.

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Autonomy appears to be a challenging concept for both language learners and teachers. This article attempts to ascertain the beliefs on learner autonomy (LA) and teacher autonomy (TA) of students on the MA in English Language Teaching and Applied Linguistics at Coventry University (UK) engaging with this concept while reflecting on a distributed MOOC blend flip. This article explores the extent to which a MOOC blended into an existing curriculum can support students in their transition between LA and TA for their professional practice. The paper discusses the interpretations of autonomy in lan

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Talok, Damianus, Kletus Erom, Maria Goreti Djehatu, and Isabel Coryunitha Panis. "Metacognitive Learning Strategies for EFL Autonomous Learning." Jurnal Komunikasi Pendidikan 7, no. 1 (2023): 1–13. https://doi.org/10.32585/jurnalkomdik.v7i1.2641.

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This study aims to determine whether the research subjects have practiced metacognitive strategies. The method used is qualitative. Eighteen (18) questions are provided, answers to individual questions are written on the guided question sheet in 2 hours - thirty minutes to get the required response. The data proves that planning, monitoring and evaluation is practiced i.e. totaling 100% checks the next day's schedule; (95%) checking what courses to attend; hence they review the previous and upcoming meeting materials. For the next meeting they write notes for questions or to confirm what they

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Cai, Jianxian, Xiaogang Ruan, and Pengxuan Li. "Autonomous Path Planning Scheme Research for Mobile Robot." Cybernetics and Information Technologies 16, no. 4 (2016): 113–25. http://dx.doi.org/10.1515/cait-2016-0072.

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Abstract An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the f

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Zhao, Lei, Ruijie Zhu, Xu Cai, and Junchao Zhang. "Improving Sustainability of Learning Outcomes: An Empirical Study of Medical Students’ Autonomous Learning." Sustainability 15, no. 7 (2023): 5668. http://dx.doi.org/10.3390/su15075668.

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Achieving sustainable learning outcomes for medical students requires the assessment of their engagement in autonomous learning, and the development of sustainable autonomous learning skills. This study examined the relationship among autonomous learning, academic support, school satisfaction, and learning outcomes. It used structural equation modeling to analyze data from 725 medical students studying at Huazhong University of Science and Technology, Wuhan. The findings showed that autonomous learning was positively related to academic support, school satisfaction, and learning outcomes. Furt

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Florin, Stoica. "Autonomous Driving using Stochastic Learning Automata." European Journal of Advances in Engineering and Technology 6, no. 1 (2019): 32–39. https://doi.org/10.5281/zenodo.10671948.

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<strong>ABSTRACT</strong> An automaton is a machine or control mechanism designed to execute a default sequence of operations or to respond to instructions. The term stochastic captures the adaptive nature of the automata described in this paper. Such an automaton does not follow predefined rules, but adapts to its environment. This adaptation is the result of a learning process. Through learning, is understood any permanent change in behavior as a result of past experience. For this reason, a learning system must have the ability to improve its behavior over time to achieve a final goal. A st

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Thao, Tran Quoc. "Non-English majors’ employment of autonomous English lexical learning strategies." SOCIAL SCIENCES 10, no. 1 (2020): 43–54. http://dx.doi.org/10.46223/hcmcoujs.soci.en.10.1.540.2020.

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The lexicon is one of the most core elements in a language system, so acquiring/learning it is a must-do job for ESL/EFL learners. Nevertheless, whether ESL/EFL learners can employ English lexical learning strategies (LLS) autonomously or not has concerned researchers and educators in different contexts. This study, therefore, endeavors to explore the use of autonomous English LLS by non-English majors at a technical Ho Chi Minh City-based university in Vietnam. It involved 200 students in answering a closed-ended questionnaire and ten students in semi-structured interviews. The results reveal

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Chen, Haiyan. "Application of Frbf Network Autonomous Learning Algorithm Based on Intrinsic Motivation in English Network Teaching." Journal of Electrical Systems 20, no. 3s (2024): 540–50. http://dx.doi.org/10.52783/jes.1327.

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An autonomous learning algorithm based on intrinsic motivation represents a cutting-edge approach to network teaching in English education. By harnessing intrinsic motivation, this algorithm empowers learners to autonomously engage with English language materials and activities, driven by their innate curiosity and desire for mastery. Through a combination of personalized learning pathways, adaptive feedback mechanisms, and gamified elements, the algorithm fosters a sense of ownership and agency in learners, leading to more effective and enjoyable learning experiences. Learners are encouraged

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Lee, Min-Fan Ricky, and Sharfiden Hassen Yusuf. "Mobile Robot Navigation Using Deep Reinforcement Learning." Processes 10, no. 12 (2022): 2748. http://dx.doi.org/10.3390/pr10122748.

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Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Unlike conventional approaches, this paper proposes an end-to-end approach that uses deep reinforcement learning for autonomous mobile robot navigation in an unknown environment. Two types of deep Q-learning agents, such as deep Q-network and double deep Q-network agents are proposed to enable the mobile robot to autonomously learn about collisio

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Chyzhykova, Inna. "The research of autonomous learning aptitude of students of economic specialities." International Science Journal of Education & Linguistics 1, no. 4 (2022): 32–37. http://dx.doi.org/10.46299/j.isjel.20220104.02.

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The article deals with the concept of autonomous learning in the context of teaching students majoring in economics and a study of the readiness of higher education students for autonomous learning was conducted. It is determined that autonomy means the ability to be responsible for a productive learning and provides an opportunity to choose materials, resources, learning strategies, taking into account students’ individual needs and opportunities, while predicting personal control over the content, process and results of studying. A survey on the readiness of students for the autonomous learn

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Azmi, Nur Afiqah Md, Nik Hanan Mustapha, Mohd Sobhi Ishak, and Ammar Khalil. "Enhancing Autonomous Learning in Vocabulary Learning Strategies: A Structural Equation Modeling Analysis." International Journal of Research and Innovation in Social Science IX, no. VI (2025): 4275–89. https://doi.org/10.47772/ijriss.2025.906000323.

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This study explores the relationship between autonomous learning and vocabulary learning strategies (VLS), emphasising its importance in enhancing language acquisition. There is a scarcity of empirical data confirming the influence of autonomous learning on vocabulary learning strategies, particularly within the realm of learning Arabic as a foreign language (AFL). This study seeks to examine how autonomous learning fosters the development and application of effective vocabulary learning strategies among Arabic language learners. The quantitative data was gathered using a questionnaire employi

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Chen, Jinyan, and Wenyuan Yang. "The Relationships Between Learning Styles and L2 English Learning." Lecture Notes in Education Psychology and Public Media 46, no. 1 (2024): 153–61. http://dx.doi.org/10.54254/2753-7048/46/20230678.

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The paper discusses the relationship between learning styles and L2 English learners. The authors mainly focus on how personality and autonomous learning abilities influence learning styles and learning performance. The authors read through pervious study and find that there are gaps in studies of personality and autonomous learning abilities. Then, two pieces of questionnaires are conducted through online platform. According to the questionnaires, for personality, students of judgement and perceiving personalities prefer different learning styles, achieving different English performance. Thei

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Wang, Tiening. "Research on the Cultivation of Students' Autonomous Learning Ability in Online Learning Environment." International Journal of Education and Humanities 18, no. 2 (2025): 242–45. https://doi.org/10.54097/b09n6940.

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This paper explores the cultivation of students' autonomous learning ability in an online learning environment. With the rapid development of information technology and the widespread adoption of online education, fostering students' ability to learn independently has become increasingly important. The study examines the concept and characteristics of autonomous learning, analyzes the current state of online learning environments, and investigates factors influencing the development of autonomous learning abilities. Based on theoretical foundations and practical considerations, the paper propo

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Roman, Daniel. "Motivația pentru învățarea autonomă a studenților." Revistă de Ştiinţe Socio-Umane = Journal of Social and Human Sciences 49, no. 3 (2021): 86–90. http://dx.doi.org/10.46727/jshs.2021.v49.i3.p86-90.

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The article develops ideas on the need to form motivation for autonomous students’ learning, a process aimed at the system of self-regulatory mechanisms, determined by motivating factors, focused on the study of needs and behaviors. Motivation being a strong force to stimulate the individual creativity of students, expresses, in fact, a set of motives: needs, interests, trends, intentions and ideals, levers triggering the motivation for learning, which allow the achievement of actions in line with vocational training objectives, designed in a social context. Explicitly and implicitly, the moti

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Orgován, László, Tamás Bécsi, and Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning." Periodica Polytechnica Transportation Engineering 49, no. 3 (2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.

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Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. Th

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Aripova, Shahnoza. "Students’ Motivation In Autonomous Learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (2021): 1095–98. http://dx.doi.org/10.17762/turcomat.v12i4.620.

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In this very article, teaching and learning a foreign language is not only about mastering all the theoretical rules and skills, but also the role of motivation in enhancing student performance is of importance. Particularly, it is devoted to the role of motivation in increasing students’ competence to learn autonomously. It also suggests some of the ways to motivate learners in order to be wholly involved in the learning process.

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Aripova, Shahnoza. "“STUDENTS’ MOTIVATION IN AUTONOMOUS LEARNING”." Psychology and Education Journal 58, no. 1 (2021): 4966–71. http://dx.doi.org/10.17762/pae.v58i1.1717.

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In this very article, teaching and learning a foreign language is not only about mastering all the theoretical rules and skills, but also the role of motivation in enhancing student performance is of importance. Particularly, it is devoted to the role of motivation in increasing students ’competence to learn autonomously. It also suggests some of the ways to motivate learners in order to be wholly involved in the learning process

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Duroc, Yvan. "Teaching Approaches Encouraging Autonomous Learning." Education 2, no. 4 (2012): 96–100. http://dx.doi.org/10.5923/j.edu.20120204.06.

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Span, Pieter. "Autonomous Learning as Educational Goal." Gifted International 6, no. 2 (1990): 108–14. http://dx.doi.org/10.1080/15332276.1990.11672741.

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Forsythe, Edo. "Autonomous language learning with technology." JALT CALL Journal 9, no. 3 (2013): 329–37. http://dx.doi.org/10.29140/jaltcall.v9n3.164.

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Pawaskar, Mr Mihir. "Reinforcement Learning in Autonomous Racing." International Journal of Scientific Research and Engineering Trends 10, no. 5 (2024): 2304–6. http://dx.doi.org/10.61137/ijsret.vol.10.issue5.293.

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Forsythe, Edward. "Autonomous language learning with technology." JALT CALL Journal 9, no. 3 (2013): 329–37. http://dx.doi.org/10.29140/jaltcall.v9n3.j164.

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Japan’s Ministry of Education, Culture, Sports, Science and Technology (MEXT) wants English language education to be more communicative. Japanese teachers of English (JTEs) need to adapt their instructional practices to meet this goal; however, they may not feel confident enough to teach speaking themselves. Using technology, JTEs have the ability to interact with native speakers around the world when it’s convenient for them. Three of the available tools are discussed in this article with specifics on how to use them for autonomous language learning and finding partners to practice speaking E

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Cotterall, Sara. "Roles in autonomous language learning." Australian Review of Applied Linguistics 21, no. 2 (1998): 61–78. http://dx.doi.org/10.1075/aral.21.2.04cot.

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Abstract The literature on autonomous learning indicates that role is a critical dimension in implementing learner autonomy. This paper examines the roles adopted by learners and teachers in language learning settings where the objective of promoting learner autonomy has been adopted. It does this first by exploring the ways in which different writers committed to autonomy have characterised learner and teacher roles. It then focuses specifically on language learning and considers how three variables – culture, learning mode and individual differences – might influence the roles which individu

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Stewart, Mel. "Autonomous Learning in Higher Education." Physiotherapy 79, no. 4 (1993): 238. http://dx.doi.org/10.1016/s0031-9406(10)60703-1.

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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space.

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Russell, Stuart J. "Prior knowledge and autonomous learning." Robotics and Autonomous Systems 8, no. 1-2 (1991): 145–59. http://dx.doi.org/10.1016/0921-8890(91)90019-h.

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Hammer, Barbara, and Marc Toussaint. "Special Issue on Autonomous Learning." KI - Künstliche Intelligenz 29, no. 4 (2015): 323–27. http://dx.doi.org/10.1007/s13218-015-0392-x.

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Lee, M. H., Q. Meng, and F. Chao. "Developmental learning for autonomous robots." Robotics and Autonomous Systems 55, no. 9 (2007): 750–59. http://dx.doi.org/10.1016/j.robot.2007.05.002.

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Joseph, Richard. "Autonomous Learning in the Workplace." Prometheus 35, no. 3 (2017): 244–46. http://dx.doi.org/10.1080/08109028.2018.1522827.

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Tammelo, E. "AUTONOMOUS LEARNING – A NEW APPROACH." Trames. Journal of the Humanities and Social Sciences 2, no. 2 (1998): 186. http://dx.doi.org/10.3176/tr.1998.2.03.

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Ali, Kamal S. "Self learning for autonomous systems." Computers & Industrial Engineering 25, no. 1-4 (1993): 401–4. http://dx.doi.org/10.1016/0360-8352(93)90306-i.

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Liljenström, Hans. "Autonomous learning with complex dynamics." International Journal of Intelligent Systems 10, no. 1 (1995): 119–53. http://dx.doi.org/10.1002/int.4550100109.

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Anca, Monica Iuliana. "AUTONOMOUS LEARNING – A THEORETICAL APPROACH." Journal Plus Education 33, Special Issue (2023): 301–9. http://dx.doi.org/10.24250/jpe/si/2023/mia/.

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Autonomy is a pedagogical concept with multiple educational dimensions, the most important correlative notions being involvement, freedom of choice, responsibility, critical thinking, and metacognition. Educational autonomy is a pedagogical concept with multiple educational dimensions, important correlative notions being activation, freedom of choice, responsibility, critical thinking, and metacognition. The common teacher-student actions aim to increase the student's autonomy and include the acquisition of meta-knowledge and the use of meta-cognitive strategies, reflection on knowledge, perfo

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