Autonomous Learning Model Research Articles

Utilization of Computer Techonolgy-Based Video (School Tube) as Learning Media

The utilization of technology in digital era has been beneficial for all aspects of human live including education. Technology is also used as the problem solvers and solutions for educational problems faced by teachers and students in schools. Based on that situation, this research proposed to analyze and discover the impact of School Tube utilization as the learning media at Madrasah Aliyyah Negeri (MAN) 1 in Lhoksemwe. This qualitative research used a descriptive approach and employed two data collection techinques that are interview and observation. The respondents of this research were teachers and school administrators at MAN 1 in Lhokseumawe. This research found that the utilization of SchoolTube at MAN 1 Lhokseumawe has positive impact to help students in learning their subjects. It directs students to learn independently that promotes autonomous and asynchronous learning model. Besides that, Shool Tube also helps teacher in providing materials for students to learn when the teachers need to attend their administrative duties outside the classroom. On the contrary, some teachers were still found not proficient in using this media. Moreover, as a free learning media, School Tube still has limitation access, so the students could not use it outside the school compound. Thus, it is suggested to the school authorities to train and enhance teachers’ ability through workshop in using School Tube and other audiovisual application as learning media.

Deep reinforcement learning for pairs trading: Evidence from China black series futures

Pair trading is one of the main methods of statistical arbitrage, mainly by taking advantage of the temporary price anomalies between related financial products with long-term equilibrium relationships to obtain arbitrage opportunities. In this paper, based on the co-integration method for the selection of allotment, the deep reinforcement learning method is integrated. The deep reinforcement learning method establishes an autonomous learning model and develops paired trading rules under different cycles. At the same time, the deep neural network model is used to implement the learning and training algorithm. This paper uses the data of Shanghai Commodity Exchange and Dalian Commodity Exchange on black futures, and the period is from January 2, 2014 to December 31, 2021. Three different periods were set for five models, namely simple threshold method (ST), simple threshold method based on pairwise cointegration (CA-ST), simple threshold method based on tripartite Cointegration (CA-ST-ALL), deep reinforcement learning method (DRL), and Deep reinforcement learning method based on pairwise cointegration (CA-DRL), and economic indicators were compared. The final results show that, combining the results of the three time periods, the deep reinforcement learning method based on pairwise cointegration performs better regarding return and risk control, validating the feasibility of deep reinforcement learning in China's future market.

The role of autonomous learning in the study of grammar by economics and foreign language students (pedagogical profile with two areas of training)

Currently, an integral part of the learning process is the development and honing of the skills of competent use of a foreign language – the most important criterion in any field. The sphere of economics and pedagogy is no exception, because specialists in the presented direction should formulate thoughts succinctly, both in written and oral forms. In pedagogical science, there is a search for effective means to improve grammatical skills and abilities. In this paper, the authors present autonomous learning as an effective tool in the study of grammar, which implies that students should have the opportunity to learn the rules themselves and consolidate them in practice. Autonomous learning implies the improvement of self-regulation and critical thinking, which, in turn, is extremely important in possible non-standard situations that may arise in the course of professional activity. The limitless possibilities of autonomous learning are various online resources, interactive tasks, audio and video materials, electronic literature, grammar exercises. It is thanks to such aspects that students enrich their vocabulary, as well as significantly improve their pronunciation and text construction skills. The aim of the work is to reveal the specifics of the format of autonomous learning by students of the direction "Economics and foreign language" in the study of grammar. The object of the study is the field of formation of autonomous learning skills in the study of grammar. The subject of the study is the introduction of M. Schmitt’s autonomous model "Integrated Learning" in the process of studying grammar of students in the specialties "Economics and foreign language". The scientific novelty of this research is expressed in the creation of its own autonomous learning model and its components. The practical significance of the work is in the possible application of the proposed exercises in autonomous learning by students of the specialization under study. The results of this study will contribute to the field of linguodidactics, as well as attract the attention of teachers, students and all interested parties to innovative approaches in teaching grammar. In addition, the findings allow us to identify effective strategies for autonomous learning that can be used in teaching practice in any field.

Development of Autonomous Learning Model to Enhance Inflight Safety-Based Competence for Cabin Crew

Currently, the aviation business is a popular service that helps people travel conveniently, quickly, and safely. People in the airline business have become more stressed with the need to learn about inflight safety-based competence to effectively serve the broad range of passengers’ needs. One reason for this is the nature of their jobs, which also affects their learning. Therefore, this research study developed an autonomous learning model to enhance inflight safety-based competence for cabin crew and then studied the effects of using an autonomous learning model to enhance inflight safety-based competence for cabin crew. This study was conducted in two parts using research and development methodology (R&D). The first phase identified problems and needs in learning about inflight safety-based competence, while the second stage examined the results of using the developed learning model. The results revealed that an autonomous learning model consisted of the model’s purpose, identification and management of learning goals, knowledge development of learning strategies, the trainer’s role, practical ideas, and reflection on and evaluation of the learner’s learning. Furthermore, the cabin crew members had improved scores in inflight safety-based competence learning in four areas: safety policy, risk management, safety promotion, and safety assurance, compared before participating in the autonomous learning model testing. The vital learning promotion course for cabin crew dealing with the inflight safety of the passengers. The developed autonomous learning model should enhance the inflight safety-based competence of cabin crew.

Research on the Construction and Application Effect of Autonomous Learning Model in the Teaching of Tourism English Majors in Colleges and Universities

Abstract Autonomous learning is a hot issue in current research in learning. The article firstly constructs an autonomous learning model based on dynamic fuzzy logic (DFL) theory and describes the independent behavior rules in detail. Further, a learning algorithm for the independent learning subspace is proposed. To verify the effectiveness of the DFL-based autonomic learning model, the authors selected college students majoring in English for Tourism as experimental subjects, divided the students into experimental and control groups, and compared their test results before and after the autonomic learning teaching experiment. The study results show that in the six-month experiment, the experimental group significantly improved in three aspects: study time, independent learning ability, and learning achievement. Specifically, upper-middle and middle-level students excelled in independent learning ability. In contrast, students at the lower-middle level showed significant improvement in terms of study time and academic performance, and these differences reached the level of extreme significance (p<0.01). In addition, in terms of the level of learning autonomy, the experimental group has higher indicators of learning autonomy level than the control group in all six dimensions, especially in the dimension of learning environment, the difference between the experimental group and the control group is as high as 52.53%. The study proves the effectiveness of the autonomy learning model based on dynamic fuzzy logic in enhancing students’ autonomy learning ability, especially in promoting the learning development of students at different levels showing significant advantages.

Technology‑supported Blended Problem-based Learning Model for Problem-Solving and Autonomous Learning

Problem-solving skills and autonomous learning are crucial abilities for 21st-century students. Yet, due to work and family obligations, mature students face time constraints for education. This study developed an online course platform and mind mapping technology-supported blended problem-based learning (Tech Blended PBL) model to investigate how it affects mature students' achievement, problem-solving skills and autonomous learning. A pre-post test design tested the model effect, using 70 students, randomly assigned to similarly sized experimental and control groups. Data analysis, using MANOVA and t-tests, confirmed that there were significant differences in all three aspects: the average scores of the experimental vs control groups for course achievement were 85% vs 74%, for problem-solving abilities were 83% vs 72% and for autonomous learning 86% vs 75% (Sig. < 0.05). Overall, the Tech-Blended PBL model had a positive impact on all three aspects. A questionnaire answered by students in the experimental class showed that they were satisfied with the new model.

메타버스를 활용한 한국·싱가포르 국제교류 수업의 실천

This study aims to practice international exchange classes between Korea and Singapore using the metaverse and to seek the meaning of learning and encounters in the metaverse space. As a result of the elements of the metaverse to the autonomous cooperative learning model, discussion between learners on topic research and presentation methods was more emphasized than in conventional classes. Since the introduction of data in the metaverse is mainly done in the form of web postings, effective information delivery strategies that take this into account have been discussed a lot. In addition, since the entire learning- sub-topic- mini-subject learning spaces are separated and connected, communication between group members was greatly required to create a harmonious space composition with overall learning while maintaining the individuality of the subject selected by the learner. As a result of class execution, in terms of learning, the strengthening of learning accountability due to the occupation of learning space, the creation of a two-sided space of connection and exclusion, and the autonomy of learning between exploration and wandering were meaningfully observed. As for the dimension of meeting, it was possible to confirm the aspects of hospitality experienced as hosts and guests and the new ideas of hospitality discovered by the learners.

Autonomous learning model in online English classes: The benefits and challenges

The post-pandemic phase has provided choices in the teaching and learning process, whether offline, online, or hybrid. Autonomous learning has become one of the preferred learning models dealing with online learning. This model of learning is believed to enhance students’ proficiency in EFL learning and enable learners to direct their learning. This qualitative study aims to investigate the benefits and challenges of implementing an autonomous learning model post-COVID-19 outbreak to EFL learners. There were three teacher-participants from Sukma Bangsa and Modal Bangsa high schools in Aceh. The teachers participated in a semi-structured interview, and the data from the interview were transcribed and coded through thematic analysis using the Coding system. The data analysis results indicate three benefits of autonomous learning through online platforms: meeting human needs, creating motivated and reflected learners, and developing communicative competence in second/foreign language learners. Furthermore, the challenges of autonomous learning are related to teachers’ roles, students’ beliefs, and social perspectives on autonomous learning. Based on the result, Autonomous Learning Model could be a choice as a learning model for online learning.

Research on English Autonomous Learning Model of College Students in Baoding

The rise of "Internet +" in today's era has provided a great port for the English learning mode of contemporary college students. Bilibili bullet-screen website (hereinafter referred to as "B Website"), as a new media platform that has been booming in recent years, has attracted a large number of contemporary college students to study on this platform, featuring rich learning resources, high participation sense of bullet-screen and strong course selection. High-quality videos and precise push algorithm make Station B more professional and create a better education and teaching area. Literature research shows that there are few studies on new English learning models for college students on new media platforms. In order to better explore college students' independent English learning mode under the reform of education mode in the new era, this study is based on the APP Bilibili, focusing on college students themselves, to explore the beneficial new mode of knowledge sharing platform such as Station B for college students' English learning.

后疫情时代下依托自媒体平台自主学习模式构建研究

后疫情时代下依托自媒体平台自主学习模式构建研究

Five Highs, Four Lows, and Three Optimizations: A Study on the Integrated Online and Offline Autonomous Learning Model

Five Highs, Four Lows, and Three Optimizations: A Study on the Integrated Online and Offline Autonomous Learning Model

The Effect of Using Autonomous Learning Model by Duolingo-Assisted to Improve Students’ Vocabulary Mastery at VIII Class of SMPN 3 Buton Tengah

The aim of this study was to find out whether the use of the Autonomous Learning Model by Duolingo-assisted has an effect on increasing the vocabulary mastery of the eighth grade students of SMPN 3 Buteng. Purposive sampling was used to select 29 students in VIII grade class as sample of this research. This study applied a pre-experimental method using one class pretest and posttest. To collect data and information, researchers used tests, distributed questionnaires, and conducted interviews. From the analysis result, the researcher found that the students' average score increased from 60.94 to 86.83. Hypothesis analysis at a significant level of 0.05 showed that the value of Tcount was higher than the value of Ttable (14,662>2,048) so that H0 was rejected and H1 was accepted. This means that the treatment given to students, namely the Autonomous Learning Model assisted by the Duolingo application has an effect to increase vocabulary. Autonomous Learning assisted by the Duolingo application is very appropriate to use in teaching vocabulary because with this learning model students were encouraged to be more independent and responsible in learning English without others help. Duolingo as a learning media made students more interested in learning English because this application is equipped with complete features that make it easier for students to learn vocabulary.

Big Data-Based Behavior Analysis of Autonomous English Learning in Distance Education

Distance English education platforms like ABC360 and Rocky English provides a new learning model, which facilitates the autonomous English learning. This study aims to clarify the relationship between learning process and learning efficiency of distance English education, and provide personalized services for distance English teaching. To this end, we carried out a big data analysis on the behavior of autonomous English learning in distance education, in a bid to reveal the learning law and behavior pattern of the learners. Section 2 provides the research strategy for the motivation and behavior of autonomous English learning in distance education, and establishes a research model for such motivation and behavior. Section 3 constructs a behavior analysis model for autonomous English learning in distance education, computes the correlation between the target autonomous learning resources (ALRs) and the series of clicked ALRs, and calculates the probability for autonomous English learners in distance education to click on new resources. The proposed network model can handle a massive amount of learning behavior data, i.e., boast a certain ability to process the big data. The three parts of the model were introduced in details. The effectiveness of the model was demonstrated through experiments.

IMPLEMENTATION OF OBE TEACHING CONCEPT BASED ON EMOTIONAL BEHAVIOR CHANGE IN COLLEGE ENGLISH CURRICULUM

IMPLEMENTATION OF OBE TEACHING CONCEPT BASED ON EMOTIONAL BEHAVIOR CHANGE IN COLLEGE ENGLISH CURRICULUM

PEMBELAJARAN AUTONOMOUS LEARNING DENGAN DUOLINGO MEMUPUK MINAT DAN KEMANDIRIAN BELAJAR SISWA KELAS X SMA NEGERI 6 MALANG

This study aims to observe the application of the Autonomous Learning model with Duolingo media for listening skills in German lessons. In addition, this research will also focus on investigating the impact of such learning on students' interest and independence in learning. The method used in this study is a qualitative descriptive method. The data in this study were obtained from observations of the learning process and the results of students’ interviews. This study will collect data from online and asynchronous learning in class X IPS 2 SMAN 6 Malang. This study took the data during German language lessons in two meetings with listening skills learning. The results showed that the Autonomous Learning model with Duolingo media made students enthusiastic in learning German. Students’ responses to the Autonomous Learning model with Duolingo media were positive. Students can determine their own way of learning so that it is easier to understand the material.

Research on the Construction of English Autonomous Learning Model Based on Computer Network-Assisted Instruction.

As a supplement to traditional teaching methods, computer-assisted teaching methods can reflect modern educational concepts, such as creating student-led, teacher-led environments. The goal of college English education is to enable them to communicate effectively in English in their future academic, work, and social interactions, while also developing students' self-learning skills. Chinese society improves overall cultural competence and adapts to the needs of international communication. Self-directed learning is not static and will increase or decrease with time, discipline, and conditions and is an evolving process. Understanding learning, taking responsibility for one's own learning, and learning how to learn are all beneficial. Students abound in school life and even throughout their lives. In this paper, we try to propose a computer-based method for constructing an independent English learning model based on a practical study of computer network technology for the development of self-learning ability of non-English majors in a university. This paper uses comparative analysis techniques to compare traditional paper-and-pencil examinations and computer-based online evaluations and analyzes the effects of each. The survey showed that 81% of the students preferred the computer-based assessment. Therefore, the focus of this research is to strengthen the oral English training in college and create an authentic English learning environment for students to really feel the standard English pronunciation, intonation, and knowledge of grammar, listening, reading, writing, and translation.

Construction of OBE Concept Autonomous Learning Mode in University Teaching Based on the Internet

In order to meet the needs of life-long learning of the Internet of Things and the economic and social development of digital learning countries, we must actively explore the teaching mode of digital self-learning courses for college students. This article aims to explore the construction of an autonomous learning model based on the OBE concept of university teaching based on the Internet of Things technology. Through self-study of college students in the environment of Internet of Things and information technology, as well as the micro-course self-directed learning that carries the OBE concept, the results show that most students hold a positive attitude towards the autonomous learning model in the information environment. A survey that carries the OBE concept shows that the scoring dimensions of the content of the students' autonomous learning attitude survey before class are both 4.11. It can be considered that the micro-curriculum resources that carry the OBE concept can effectively help students to conduct autonomous learning.

A Deep Learning-Based Classification Method for Different Frequency EEG Data.

In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the EEG data. Thus, the adaptability of EEG classification methods has become significant. This study proposed a deep network model for autonomous learning and classification of EEG signals, which could self-adaptively classify EEG signals with different sampling frequencies and lengths. The artificial design feature extraction methods could not obtain stable classification results when analyzing EEG data with different sampling frequencies. However, the proposed depth network model showed considerably better universality and classification accuracy, particularly for EEG signals with short length, which was validated by two datasets.

Classification of Lathe’s Cutting Tool Wear Based on an Autonomous Machine Learning Model

Classification of Lathe’s Cutting Tool Wear Based on an Autonomous Machine Learning Model

Hybridization of harmonic search algorithm in training radial basis function with dynamic decay adjustment for condition monitoring

In recent decades, hybridization of superior attributes of few algorithms was proposed to aid in covering more areas of complex application as well as improves performance. Condition monitoring is a major component in predictive maintenance which monitors the condition and identifies significant changes in the machinery parameter to perform early detection and prevent equipment defects that could cause unplanned downtime or incur unnecessary expenditures. An effective condition monitoring model is helpful to reduce the frequency of unexpected breakdown incidents and thus, facilitates in maintenance. ANN has shown effective in various condition monitoring and fault detection applications. ANN is popular due to its capability of identifying the complex nonlinear relationships among features in a large dataset and hence, it can perform with an accurate prediction. However, a drawback is that the performance of ANN is sensitive to the parameters (i.e., number of hidden neurons and the initial values of connection weights) in its architecture where the settings of these parameters are subject to tuning on a trial-and-error basis. Hence, a wide range of studies have been focused on determining the optimal weight values of ANN models and the number of hidden neurons. In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. This paper presents an intelligent system integrating a Radial Basis Function Network with Dynamic Decay Adjustment (RBFN-DDA) with a Harmony Search (HS) to perform condition monitoring in industrial processes. RBFN-DDA performs incremental learning wherein its structure expands by adding new hidden units to include new information. As such, its training can reach stability in a shorter time compared to the gradient-descent based methods. To achieve optimal RBFN-DDA performance, HS is proposed to optimize the center and the width of each hidden unit in a trained RBFN. By integrating with the HS algorithm, the proposed metaheuristic neural network (RBFN-DDA-HS) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA by 2.2% up to 22.5% in two benchmarks datasets, which are numerical records from a bearing and steel plate system and a condition-monitoring system in a power plant (i.e., the circulating water (CW) system). The results also show that the proposed RBFN-DDA-HS is compatible, if not better than, the classification performances of other state-of-the-art machine learning methods.