English Learning Behavior Research Articles

Developing a self-determination motivation scale for English learners with Chinese background

“Interest” is one of the most important factors that trigger English learning behavior, and self-determination theory believes that “interest” and “value” are important factors associated with target behavior. Therefore, the purpose of this study is to construct and establish a “Self-Determination Motivation Scale of English Learners” based on the core concept of self-determination theory, which encompasses the three innate needs. There were 169 participants, all university students from various majors with Chinese backgrounds, who were taking English classes at a university. The research tool is extracted from the Learning Climate Questionnaire (LCQ) (six questions on autonomy), perceived competence for learning (PCS) (four questions on competence), and basic psychological needs (BPN) in general (eight questions on relatedness) and developed an 18-item questionnaire. The data were obtained from the existing three different self-determination questionnaires and analyzed using confirmatory factor analysis (CFA). The results show that after CFA, 15 items of the scale, which were extracted from the existing three different self-made questionnaires, were maintained, and the removed three items are all reverse items. The reliability of the developed 15-item scale is 0.95. The conclusion is that the developed 15-item scale is more suitable for university English learners with a Chinese background, although the existing three different self-determination questionnaires are still valid and reliable. Three suggestions for future research include adding expert validity for item review, adjusting the number of reverse items, and having more participants to master the scale. The implications, limitations, and future research plans are presented in this paper.

An analysis of English learning behavior based on big data and its impact on teaching

This study explores the application of big data in the field of English learning, focusing on its influence on the analysis of English learning behavior and the effect of teaching intervention. Through experimental design and data analysis, the research results show that big data analysis can reveal the learning behavior pattern of learners, and provide personalized teaching intervention according to individual characteristics, so as to improve the learning effect. The study also found that the experimental group received personalized teaching intervention, English learners’ academic performance and learning motivation significantly improved. However, this study faces the limitations of sample representativeness and consistency of teaching interventions. Future studies can further expand the sample size and strengthen teacher training to improve the generalization and reliability of research results.

English learning behaviors in computer-mediated communication during COVID-19 in Thailand’s Deep South

ABSTRACT COVID-19 has transformed how teaching is organized, with computer-mediated communication (CMC) implemented to overcome physical constraints from social distancing. This study explored behaviors and approaches employed by 10 English teachers from three private schools to optimize ELT with CMC in southern Thai provinces. Data collection involved focus group discussions and semi-structured interviews with the participants. Findings revealed that the students had transformed their behaviors of learning English through CMC. Consequently, CMC was utilized in two ways, including as auxiliary and primary instruments. Firstly, CMC was employed to supplement synchronous learning in the early stages of COVID-19 with social media to assign and return completed tasks. Nevertheless, online classes saw low student participation, notable learning fatigue, limited English practice opportunities, and inadequate self-practicing for language development. Secondly, after CMC was utilized as a primary instrument; the students had a broader opportunity to study English; demonstrated a higher commitment to learning; and adopted autonomous learning, self-assessment, and digital skills. This study further found that CMC-ELT performance could be maximized by allowing students to actively design their online learning, using diverse applications to promote participation, introducing creative content to reduce limitations, increasing spaces for autonomous learning, and providing channels for publishing student outcomes.

An evaluation of college students' involvement in English learning behavior based on fuzzy comprehensive evaluation

An evaluation of college students' involvement in English learning behavior based on fuzzy comprehensive evaluation

Analysis of Higher Vocational English Learners’ Behavioral Characteristics and Teaching Content Optimization Strategies Based on Big Data Mining

Abstract In the context of the big data era, the study of the massive data accumulated in the information construction of higher vocational colleges and universities can provide convenience for the teaching and management of colleges and universities. By improving the K-means clustering algorithm and Apriori algorithm in big data mining technology, the article mines the English learning behaviors and laws of higher vocational English learners and explores the correlation between learners’ behavioral characteristics and teaching performance. Finally, through empirical testing, this paper proposes an optimization strategy for teaching content in higher vocational English education. In the comparative analysis of reading and writing pre-test and post-test scores between the experimental class and the control class, the pre-test score of reading comprehension of the students in the experimental class is 31.25, and the post-test score is 32.84, and the average score of reading comprehension has increased by 1.59, which can be obtained that the English reading comprehension of the students has been improved after teaching with the teaching content optimization strategy proposed in this paper.

Analysis on the Design and Timeliness of English Teaching Models in Colleges and Universities under the Background of Informatization

Abstract The informationization era has accelerated people’s learning changed their thinking and cognitive styles, and the English teaching mode is also evolving and innovating. The purpose of this paper is to develop an English learning behavior analysis model, select an improved K-means algorithm for clustering learners, and examine the association rules between learning behaviors and learning effects. In addition, based on the sequence of learners’ behaviors, the article also combines the EduHawkes prediction model with multi-type features to quantify the learning effect in terms of learning engagement and predict the learning effect of students. The blended English teaching model is designed by combining learning behavior analysis and learning effect prediction models. In the experimental study of the timeliness of the English teaching model, the dimensions of students’ English literacy in terms of thinking quality, learning ability, and language ability showed significant differences before and after the experiment (P<0.05). In the evaluation of teaching methods, the dimensions of teaching materials, teaching tools, evaluation of learning outcomes assessment methods, and evaluation of teaching level showed significant differences (P<0.05). The English teaching model proposed in this paper is timely.

Instilling Growth Mindset to Promote Students' English Learning Behaviors and Oral Communication Learning Achievement

Instilling Growth Mindset to Promote Students' English Learning Behaviors and Oral Communication Learning Achievement

Clustering Algorithm in English Language Learning Pattern Matching under Big Data Framework.

The Internet era has brought new challenges and opportunities for English learning and English teaching. At the same time, basic education is fully implementing quality education and respecting students' individual differences. The same teacher teaches the same content to the same class of students, but some students perform well, and some students perform poorly due to the influence of intellectual and nonintellectual factors. The uneven performance of students in the same class makes it very difficult for teachers to teach. In view of the current situation of university English teaching and the trend of respecting students' individual development in the new era, this study investigates the basic concept of English language learning pattern matching, its main features, and practical application in the process of university English teaching. The clustering algorithm based on the big data framework is proposed for English language learning pattern matching, which is fault-tolerant and can quickly acquire and process the big data information in English teaching. By analyzing the characteristics of the data mining method of students' English learning behavior, the method of clustering processing for students' English learning data mining and the processing method of students' English learning clustering data are explored. The method is highly adaptable and can be used for actual English language learning pattern matching, and actively explores the main path of English teaching change and innovation.

English Education Tutoring Teaching System Based on MOOC.

In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming characteristics. Among these emerging teaching modes, massive open online course (MOOC) is a relatively advanced teaching mode with a better application effect. Quickly and accurately detecting the cheating behavior of MOOC learners is of great significance for maintaining the development of the MOOC platform and English education counseling. This paper studies a deep-learning-based hybrid model for MOOC cheating detection. The model greatly improves the detection performance of a single model by integrating CNN, a bidirectional gated recurrent unit, and an attention mechanism. The proposed model selects the English learning behavior data of a MOOC platform to verify the performance of the algorithm. Simulation results show that the proposed scheme can greatly help MOOC-based English education tutoring.

Formative Evaluation of College Students’ Online English Learning Based on Learning Behavior Analysis

To measure the achievements and progress of college students in online learning, online learning platforms and teachers must pay attention to the formative evaluation of the learning process. The relevant data should be fully utilized to analyze the online English learning behavior of college students, such that online learning platforms and teachers can make formative evaluation of the students’ online learning. However, the existing studies on formative evaluation are mostly theoretical. To solve the problem, this paper explores the formative evaluation of college students’ online English learning based on learning behavior analysis. Firstly, the density-based spatial clustering of applications with noise (DBSCAN) was adopted to analyze the data samples of college students’ online English learning behavior, the evaluation indices were selected for the formative evaluation of the said behavior, and the index weighting method was explained in details. Next, the school precaution function of online English learning was realized through the graph structure data prediction of students’ online learning behavior. Based on the proposed graph neural network, the clustering Euclidean distance weight was introduced to measure the similarity between two nodes. In addition, the weight update process was illustrated for the distance weight-based attention mechanism. The proposed formative evaluation approach was proved effective through experiments.

Toward a Better Understanding of Language Learning Motivation in a Study Abroad Context: An Investigation Among Chinese English as a Foreign Language Learners.

Motivation has been recognized as a vital component in successfully learning a second or foreign language. However, research on language learners’ motivation in a study abroad context requires more attention in an era in which international mobility is becoming a new normal. This study investigated 217 Chinese overseas university students’ L2 motivation during their one-year postgraduate study in the United Kingdom. by examining a range of motivational variables in relation to their motivated English language learning behaviors. Integrating results from both questionnaires and interviews from nine participating students, the study revealed that international posture showed the strongest positive power, followed by the ideal L2 self, in explaining the learners’ willingness to communicate, frequency of communication, and intended learning effort. Additionally, instrumentality and parental encouragement exerted prominent promotional influence in shaping their intended learning effort. However, the ought-to L2 self-displayed a significant negative impact on their L2 learning in this study abroad context, and the role of attitudes toward L2 speakers/community and culture was not evident in this case. The findings shed light on a more comprehensive understanding of L2 motivation in a study abroad context, and offer insightful implications for English as a Foreign Language education in cultivating language learners’ motivation to prepare for study abroad.

Research on the Influencing Factors of College English Learning Willingness Based on the Theory of Planned Behavior

Since learning willingness is one key factor affecting foreign language learning, this article constructs a theoretical model of different influencing factors affecting the learning willingness, and uses structural equation model to test some hypotheses, based on the theory of planned behavior, through exploring the relationship among Willingness of Behavior (WB), Attitude toward the Behavior (AB), Subjective Norms (SN), Perceived Behavioral Control (PBC), Behavior of students. The study found that important others and groups have the greatest influence on individual behavior; and high demand pressure can positively affect students’ learning willingness; individual differences lead to differences in the learning willingness; the influence of learning attitude on behavior willingness is non-dominant. Findings highlight the importance of learning willingness in college English education, and provide theoretical guidance for the formulation of intervention measures for English learning behavior.

College English Online Teaching Model Based on Deep Learning

In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.

Exploration of the Compilation of English Learning Materials for Chinese College Students Based on Big Data under the Guidance of Complex Dynamic Theory

With the help of big data and Citespace software, this research makes a statistical analysis of the journals and dissertations on College English teaching and learning materials collected by CNKI from 2011 to 2020. This paper, based on the knowledge map drawn by the visualized analysis of literatures volume, authors, research institutions and keywords clustering, analyzes the current research status and hotspots in the compilation of China’s College English textbooks. It is found that there are six problems in the compilation. Because of the complexity of teaching and learning materials and the dynamic progress of language learning, the nature of English teaching and learning materials is bound to turn from a learning tool to learning resources. Thus, this research, from the perspective of Complex Dynamic System Theory, attempts to develop big data-based College English learning materials with digital, individual and multi-dimensional characteristics by three paths: the establishment of big data-based English learning behaviors index system, the development of big data-based College English learning materials and the application of big data-based College English learning materials. This paper will explore a new way of developing China’s College English learning materials, improve and optimize the compilation and development of College English learning materials in China.

A Study on the New Compilation of College English Learning Materials Based on Big Data in China

This paper finds that there are six problems in the compilation. Because of the complexity of teaching and learning materials and the dynamic progress of language learning, the nature of English teaching and learning materials is bound to turn from a learning tool to learning resources. Thus, this research, from the perspective of Complex Dynamic System Theory, attempts to develop big data-based College English learning materials with digital, individual and multi-dimensional characteristics by three paths: the establishment of big data-based English learning behaviors index system, the development of big data-based College English learning materials and the application of big data-based College English learning materials. This paper will explore a new way of developing China’s College English learning materials and improving and optimising the compilation and development of College English learning materials in China.

Factors that Influence College-Level English Language Learning: A Structural Equation Perspective

The development of Internet technology has made it difficult for the traditional English teaching model of college students to meet the latest requirements. It is therefore necessary to explore the factors that influence college student English learning and to provide scientific support that will promote efficient English teaching. This paper collects data related to five aspects of the English learning situation of college students, specifically learning attitude, environment, input, motivation and status. It then constructs a structural equation model of college student English learning behavior. It distributes questionnaires to freshmen and sophomores at Hohai University’s Changzhou Campus and draws on data obtained from 326 questionnaires (386 were distributed). It combines the model's path system and constructs eight hypotheses (H1-H8) that are found to significantly influence the five aspects. The results show that learning attitude and input exert significant positive effects on learning. The paper concludes that educators who teach English to college students should focus on active learning, as this will drive improvements in the effect of English teaching.

An investigation of factors involved in Japanese students’ English learning behavior during test preparation

Japan has recently been promoting university entrance examination reform with the goal of positively influencing students’ English learning, but the extent to which entrance examinations themselves affect English learning is not known. The promotion of better learning requires changing the factors that affect learning behavior, rather than merely modifying existing examinations or introducing new ones. This study investigated the factors determining Japanese students’ English learning while they prepared for high-stakes university entrance examinations, aiming to construct a model that explicates how test-related and test-independent factors are intertwined. Semi-structured interviews were conducted with 14 first-year university students asking how they had prepared for their examinations and why they had chosen particular preparation methods. After thematic analysis, four main factors in student learning behavior were identified (examination, student views, school, and examination-independent factors) and their relationships explored. The study findings provide useful insights for policymakers in English as a foreign language (EFL) educational contexts, where English tests are used as part of language education policies. Furthermore, the proposed model is theoretically important as it explains the complex washback mechanism and deepens our understanding of why intended washback effects on learning are not necessarily achieved.

The Relationships among the English Learning Motivation, English Learning Barriers and English Learning Behavior of University (Athlete) Students: Social Support as Moderator

The Relationships among the English Learning Motivation, English Learning Barriers and English Learning Behavior of University (Athlete) Students: Social Support as Moderator

中学生の英語学習に及ぼす自尊感情,原因帰属および期待・感情の影響 : 認知・情意面での学習支援の観点から

中学生の英語学習に及ぼす自尊感情,原因帰属および期待・感情の影響 : 認知・情意面での学習支援の観点から

Effects of Facebook usage on English learning behavior of Thai English teachers

This research investigated the use of Facebook to enhance the effectiveness of English writing and learning among English language teachers. English language learning through Facebook design was evaluated using the results from data collected from 403 completed copies of a questionnaire. The questionnaire sought responses on the problems, needs, readiness, and suggestions of the respondents—Thai teachers of English in 437 Bangkok Metropolitan Administration (BMA) schools. Seventeen BMA teachers who exhibited a keen interest in using Facebook over other social media were selected as test subjects. One facilitator was included. All participants used the English language to interact on Facebook every day for six weeks. A mixed method consisting of qualitative and quantitative approaches was employed to analyze the collected data. The frequency, percentage, and content analysis were used to analyze the qualitative data consisting of the survey questionnaires, observations on interaction through Facebook sites, in-depth interviews, participants' reflections, and self-notes. A t-test was used to analyze quantitative pre- and post-test data. The findings of this study indicated that Facebook could significantly help improve writing skills, namely accuracy, meaningfulness, clarity, and relevance. It also effectively enhanced teachers' English learning attributes, namely fluency, confidence, satisfaction, value, and self-efficacy belief.