This study investigates whether and how exemplars can facilitate student engagement in self-assessment tasks.An intact class of 32 undergraduates majoring in Chinese-English translation participated in the study.After...This study investigates whether and how exemplars can facilitate student engagement in self-assessment tasks.An intact class of 32 undergraduates majoring in Chinese-English translation participated in the study.After some preliminary training,the students performed three translation self-assessment tasks,each involving comparison with authentic exemplars of varying quality and filling out a structured self-assessment report.Our analysis of multiple sources of data reveals the multi-dimensional nature of student engagement in self-assessment activities and the potential for using authentic exemplars of different qualities to enhance students’cognitive and behavioral engagement and mitigate negative emotions in the process.Pedagogical implications for implementing exemplars and self-assessment to promote student engagement and support student learning are discussed.展开更多
This study examined the impact of teacher-student relationship quality on students’risk of bullying victimiza-tion and the mediating roles of student-student relationships and student engagement in this relationship....This study examined the impact of teacher-student relationship quality on students’risk of bullying victimiza-tion and the mediating roles of student-student relationships and student engagement in this relationship.A total of 656 Chinese junior high school students(females=361,mean age=13.75,SD=0.98)completed validated measures of teacher-student relationship quality,student-student relationship quality,student engagement,and bullying victimization.Regression analysis results indicated that higher teacher-student relationship quality predicted a lower risk of student bullying victimization.Serial mediating effect testing of the student-student relationship quality and student engagement revealed that these factors fully mediated the relationship between teacher-student relationship quality and bullying victimization,resulting in a lower risk of bullying victimization.The results showed that student-student relationship quality had a more substantial mediating effect than student engagement.Thefindings support the Socio-Ecological Framework,suggesting that within the Microsystem,interactions between individuals and their immediate environments significantly impact their behavior.Specifically,thesefindings suggest that good teacher-student relationships can enhance the quality of student-student relationships and student engagement,thereby preventing and reducing the occurrence of bullying victimization.展开更多
Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing ...Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos.The evaluation of these models utilizes the DAiSEE dataset,a public repository capturing student affective states in e-learning scenarios.The initial model integrates EfficientNetV2-L with Gated Recurrent Unit(GRU)and attains an accuracy of 61.45%.Subsequently,the second model combines EfficientNetV2-L with bidirectional GRU(Bi-GRU),yielding an accuracy of 61.56%.The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory(LSTM)and bidirectional LSTM(Bi-LSTM),achieving accuracies of 62.11%and 61.67%,respectively.Our findings demonstrate the viability of these models in effectively discerning student engagement levels,with the EfficientNetV2-L+LSTM model emerging as the most proficient,reaching an accuracy of 62.11%.This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement,thereby contributing to advancements in online education quality.展开更多
In the past few years, the phenomenon of smartphone addiction has become more serious especially among university students. In order to explore whether smartphone addiction has an impact on students' learning, thi...In the past few years, the phenomenon of smartphone addiction has become more serious especially among university students. In order to explore whether smartphone addiction has an impact on students' learning, this study examined the relationships between smartphone addiction and student engagement and academic performance among 157 English majors by One-way ANOVA. Results suggested that there was not any significant difference in student engagement and academic performance among students of different levels of smartphone addiction. The implications for prevention strategies of smartphone addiction and further research are discussed.展开更多
In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in t...In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in the Interactive-Constructive-Active-Passive(ICAP)framework,and applied hierarchical clustering to detect student engagement modes.A total of 840 learning rounds were clustered into four categories of engagement:passive(n=80),active(n=366),constructive(n=75)and resting(n=319).The results showed that there were differences in the performance of the four engagement modes,and three types of learning status were identified based on the sequences of student engagement modes:difficult,balanced and easy.This study indicated that based on the ICAP framework,the online learning platform log data could be used to automatically detect different engagement modes of students,which could provide useful references for online learning analysis and personalized learning.展开更多
Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied stud...Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied students’ engagementlevel of the Learning Management System (LMS) via a learning analytics tool,student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review(SLR) was employed for the selection, sorting and exclusion of articles fromdiverse renowned sources. The findings show that most of the engagement inLMS are driven by educators. Additionally, we have discussed the factors inLMS, causes of low engagement and ways of increasing engagement factorsvia the Learning Analytics approach. Nevertheless, apart from recognizing theLearning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of mergingthe learning analytics technique with the LMS engagement in every institution asbeing a direction for future research.展开更多
Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and p...Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and provides a rich understanding of students’experiences.The study utilized three self-designed scales-the Teacher Support Scale,Student Engagement Scale,and Student Learning Experience Scale-to gauge and examine the impact and relationship between perceived teacher support,student behavioral engagement,and the intermediary role of learning experiences.A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire.The results showed significant correlations between perceived teacher support,learning experience,and behavioral engagement.Perceived teacher support significantly predicted students’behavioral engagement,with socio-affective support exerting the most substantial predictive effects.All predictive effects were partially mediated by learning experience(learning mode,online resources,overall LMS-based learning,interaction with their instructor and peers,and learning outcome).The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive(vs.negative)learning experiences.Suggestions for further research are offered for consideration.展开更多
The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise f...The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise from learning struggles or failures.Effective transitions to engagement occur upon problem resolution,while unresolved issues lead to frustration and subsequent boredom.This study proposes a Convolutional Neural Networks(CNN)based approach utilizing the Multi⁃source Academic Affective Engagement Dataset(MAAED)to categorize facial expressions into boredom,confusion,frustration,and yawning.This method provides an efficient and objective way to assess student engagement by extracting features from facial images.Recognizing and addressing negative affective states,such as confusion and boredom,is fundamental in creating supportive learning environments.Through automated frame extraction and model comparison,this study demonstrates reduced loss values with improving accuracy,showcasing the effectiveness of this method in objectively evaluating student engagement.Monitoring facial engagement with CNN using the MAAED dataset is essential for gaining insights into human behaviour and improving educational experiences.展开更多
This review interrogates empirical and theoretical research on agentic engagement in foreign language(FL)learning.Through synthesizing peer-reviewed studies from Web of Science and CNKI databases,it maps the theoretic...This review interrogates empirical and theoretical research on agentic engagement in foreign language(FL)learning.Through synthesizing peer-reviewed studies from Web of Science and CNKI databases,it maps the theoretical evolution,methodological innovations,key influencing factors and proposed suggestion for further research on student agency.Future research should prioritize,longitudinal studies,culturally comparative designs,validity constructs and ethical evaluations of artificial intelligence’s impact on learner autonomy.This review calls for a holistic approach to FL education,where agentic engagement bridges individual initiative,pedagogical innovation,and sociocultural responsiveness to empower learners in multilingual global contexts.展开更多
Students＇ active engagement constitutes the core of the process of learning and teaching in the student-oriented classroom. The paper centers on a review of foreign researches on influential factors affecting student...Students＇ active engagement constitutes the core of the process of learning and teaching in the student-oriented classroom. The paper centers on a review of foreign researches on influential factors affecting students＇ engagement in English classroom. It is expected to figure out the relevant factors in order to promote students＇ active engagement.展开更多
The aim of the paper is to identify and explore leading development situation within the research field related to student classroom engagement.Using knowledge mapping tools,this paper conducted the visualization anal...The aim of the paper is to identify and explore leading development situation within the research field related to student classroom engagement.Using knowledge mapping tools,this paper conducted the visualization analysis on internationalliteraturein relation to student classroom engagementfrom the Web of Science databases.By combination of the statistical data and visualizationmapping,this paper studied on the research relationship networks and status for the co-authors'countries/institutions,co-citation documents/journalsand co-occurring keywordsbased on the sample datafromliterature.The results indicate that the number ofpublished papershasincreased obviously and expanded graduallysince 2000.Main countries and institutions of researches were the UnitedStates,Australia,United Kingdom,Canada and China in order,and these countries had intense collaboration.The theoretical basis of the research come from education and teaching,psychology.The hotspots of researches fromthe global literature coveredvarious fields including achievement,motivation,behavior,education,performance.As a result of the multidisciplinary integration,many relevant internationalresearch constantly expanded the research scopes,which promoted the combination and development of theories.展开更多
This study explores the integration of Generative AI(GenAI)tools and cooperative learning strategies to enhance writing proficiency among English as a Foreign Language(EFL)students in English-medium instruction(EMI)un...This study explores the integration of Generative AI(GenAI)tools and cooperative learning strategies to enhance writing proficiency among English as a Foreign Language(EFL)students in English-medium instruction(EMI)universities in China.Employing a quasi-experimental design,the study compared writing performance between an experimental group utilizing AI tools and a control group relying solely on traditional feedback methods.Quantitative findings revealed a significant improvement in writing scores for the experimental group(t=3.45,p<.01).A one-way ANOVA further demonstrated that higher engagement levels were positively associated with superior writing performance(F=4.32,p<.05),with post-hoc analyses highlighting peer collaboration and AI-driven personalized feedback as critical factors.Qualitative insights reinforced these findings,with students valuing the immediacy and specificity of AI feedback while still preferring the personalized nature of teacher input.However,the weak correlation between overall engagement levels and writing quality(r=.084)suggests that specific engagement dimensions,such as peer interaction and collaborative activities,warrant closer examination.The study contributes to the existing literature by bridging early constructivist learning theories with AI-driven pedagogical innovations,proposing a revised framework for understanding learning in technology-enhanced environments.Implications for educators include adopting blended approaches that leverage AI tools alongside traditional methods to foster active learning and improve writing outcomes in EFL contexts.展开更多
Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only...Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to 2020.To date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and instructors.Consequently,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notfirst identified.Therefore,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an LMS.The required data was collected from an LMS that is currently utilised by a local Malaysian university.The model was validated by a panel of experts as well as discussions with students.It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.展开更多
Students come to Australia for many and varied reasons, including the possibility of immigrating to Australia on completion of studies. Some programs of study are given higher priority for immigration than others and ...Students come to Australia for many and varied reasons, including the possibility of immigrating to Australia on completion of studies. Some programs of study are given higher priority for immigration than others and are thus popular among those hoping to immigrate. The master of professional accounting （MPA） is perhaps the most well-known of these programs, as the majority of its students are allegedly more interested in gaining permanent residency than becoming practicing accountants. Concerns over the quality of this program, its graduates, and its impact on the reputation of Australian higher education have been expressed in the media and in scholarly journals resulting in a stereotype of international postgraduate students as being motivated by immigration and without interest in accounting or engagement in learning. However, little has been done to investigate the experiences and perceptions of the students themselves. The objective of this paper is to more closely examine the motivations and learning behaviors of MPA students in order to test the accuracy of the stereotype. A population of postgraduate accounting students from an Australian university was invited to respond to an anonymous questionnaire survey adapted from the Australian Universities Survey of Student Engagement （AUSSE） to gain an insight into student engagement with learning. The results of this paper demonstrate that motivation is not relevant to learning engagement. The authors find a cohort of students spending many hours in study and facing barriers to learning because of poor English skills. Such findings do not accord with the stereotypical portrayal of international MPA students but lead to questions about the institutional motivations, the nature of accounting education, and English language entry standards and language support.展开更多
Most engineering courses require some level of work to be done by students using internet. A vast majority of material taught in classes is available online. Theoretically, a student could learn almost everything they...Most engineering courses require some level of work to be done by students using internet. A vast majority of material taught in classes is available online. Theoretically, a student could learn almost everything they want from the online resources. In this research, a comparative study is done between students learning and understanding when some basic aerospace concepts are taught in a traditional lecture based classroom versus when students are told to look for the same material on the internet. The results indicate that, although all the material taught in the classroom is available on the internet, students do not perform better when they exclusively use internet for learning. However a traditional lecture based class coupled with internet resources yields the favourable results.展开更多
Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their ...Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.展开更多
The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack sys...The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.展开更多
Teacher self-efficacy is an important predictor in teacher’s professional career for inquiry-based instruction.This study investigated the factors(gender,educational level,and teaching experience)associated with scie...Teacher self-efficacy is an important predictor in teacher’s professional career for inquiry-based instruction.This study investigated the factors(gender,educational level,and teaching experience)associated with science teachers’self-efficacy in instructional strategies,classroom management,and student engagement in teaching scientific inquiry.A questionnaire was used for data collection from 47 practicing teachers in state schools in 2018.A General Linear Model(GLM)Univariate analysis using SPSS 21.00 programme was used for data analysis.Fact findings revealed that mean perceived self-efficacy in inquiry-based instruction was considerably high irrespective of gender,education level,and teaching experience.Neither teacher self-efficacy in student engagement nor classroom management did not show statistically significant difference according to gender,education level,and teaching experience.However,results concluded that science teaching experience was a significant predictor of self-efficacy in instructional strategies of inquiry-based instruction.展开更多
文摘This study investigates whether and how exemplars can facilitate student engagement in self-assessment tasks.An intact class of 32 undergraduates majoring in Chinese-English translation participated in the study.After some preliminary training,the students performed three translation self-assessment tasks,each involving comparison with authentic exemplars of varying quality and filling out a structured self-assessment report.Our analysis of multiple sources of data reveals the multi-dimensional nature of student engagement in self-assessment activities and the potential for using authentic exemplars of different qualities to enhance students’cognitive and behavioral engagement and mitigate negative emotions in the process.Pedagogical implications for implementing exemplars and self-assessment to promote student engagement and support student learning are discussed.
基金supported by the 2024 Henan Province Philosophy and Social Science Planning Project(Youth Project)entitled“Research on the Mechanism and Intervention of Self-Regulated Learning in Promoting Children’s Chinese Reading Comprehension”(2024CJY070).
文摘This study examined the impact of teacher-student relationship quality on students’risk of bullying victimiza-tion and the mediating roles of student-student relationships and student engagement in this relationship.A total of 656 Chinese junior high school students(females=361,mean age=13.75,SD=0.98)completed validated measures of teacher-student relationship quality,student-student relationship quality,student engagement,and bullying victimization.Regression analysis results indicated that higher teacher-student relationship quality predicted a lower risk of student bullying victimization.Serial mediating effect testing of the student-student relationship quality and student engagement revealed that these factors fully mediated the relationship between teacher-student relationship quality and bullying victimization,resulting in a lower risk of bullying victimization.The results showed that student-student relationship quality had a more substantial mediating effect than student engagement.Thefindings support the Socio-Ecological Framework,suggesting that within the Microsystem,interactions between individuals and their immediate environments significantly impact their behavior.Specifically,thesefindings suggest that good teacher-student relationships can enhance the quality of student-student relationships and student engagement,thereby preventing and reducing the occurrence of bullying victimization.
文摘Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos.The evaluation of these models utilizes the DAiSEE dataset,a public repository capturing student affective states in e-learning scenarios.The initial model integrates EfficientNetV2-L with Gated Recurrent Unit(GRU)and attains an accuracy of 61.45%.Subsequently,the second model combines EfficientNetV2-L with bidirectional GRU(Bi-GRU),yielding an accuracy of 61.56%.The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory(LSTM)and bidirectional LSTM(Bi-LSTM),achieving accuracies of 62.11%and 61.67%,respectively.Our findings demonstrate the viability of these models in effectively discerning student engagement levels,with the EfficientNetV2-L+LSTM model emerging as the most proficient,reaching an accuracy of 62.11%.This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement,thereby contributing to advancements in online education quality.
文摘In the past few years, the phenomenon of smartphone addiction has become more serious especially among university students. In order to explore whether smartphone addiction has an impact on students' learning, this study examined the relationships between smartphone addiction and student engagement and academic performance among 157 English majors by One-way ANOVA. Results suggested that there was not any significant difference in student engagement and academic performance among students of different levels of smartphone addiction. The implications for prevention strategies of smartphone addiction and further research are discussed.
文摘In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in the Interactive-Constructive-Active-Passive(ICAP)framework,and applied hierarchical clustering to detect student engagement modes.A total of 840 learning rounds were clustered into four categories of engagement:passive(n=80),active(n=366),constructive(n=75)and resting(n=319).The results showed that there were differences in the performance of the four engagement modes,and three types of learning status were identified based on the sequences of student engagement modes:difficult,balanced and easy.This study indicated that based on the ICAP framework,the online learning platform log data could be used to automatically detect different engagement modes of students,which could provide useful references for online learning analysis and personalized learning.
基金supported by the University of Malaya,Bantuan Khas Penyelidikan under the research grant of BKS083-2017Fundamental Research Grant Scheme(FRGS)under Grant number FP112-2018A from the Ministry of Education Malaysia,Higher Education.
文摘Learning analytics is a rapidly evolving research discipline that uses theinsights generated from data analysis to support learners as well as optimize boththe learning process and environment. This paper studied students’ engagementlevel of the Learning Management System (LMS) via a learning analytics tool,student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review(SLR) was employed for the selection, sorting and exclusion of articles fromdiverse renowned sources. The findings show that most of the engagement inLMS are driven by educators. Additionally, we have discussed the factors inLMS, causes of low engagement and ways of increasing engagement factorsvia the Learning Analytics approach. Nevertheless, apart from recognizing theLearning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of mergingthe learning analytics technique with the LMS engagement in every institution asbeing a direction for future research.
基金Zhejiang Provincial Philosophy and Social Sciences Planning Project from Zhejiang Office of Philosophy and Social Science(21NDJC092YB)Zhejiang Provincial Educational Science Plan Project(2021SCG166)。
文摘Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and provides a rich understanding of students’experiences.The study utilized three self-designed scales-the Teacher Support Scale,Student Engagement Scale,and Student Learning Experience Scale-to gauge and examine the impact and relationship between perceived teacher support,student behavioral engagement,and the intermediary role of learning experiences.A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire.The results showed significant correlations between perceived teacher support,learning experience,and behavioral engagement.Perceived teacher support significantly predicted students’behavioral engagement,with socio-affective support exerting the most substantial predictive effects.All predictive effects were partially mediated by learning experience(learning mode,online resources,overall LMS-based learning,interaction with their instructor and peers,and learning outcome).The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive(vs.negative)learning experiences.Suggestions for further research are offered for consideration.
文摘The dynamics of student engagement and emotional states significantly influence learning outcomes.Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise from learning struggles or failures.Effective transitions to engagement occur upon problem resolution,while unresolved issues lead to frustration and subsequent boredom.This study proposes a Convolutional Neural Networks(CNN)based approach utilizing the Multi⁃source Academic Affective Engagement Dataset(MAAED)to categorize facial expressions into boredom,confusion,frustration,and yawning.This method provides an efficient and objective way to assess student engagement by extracting features from facial images.Recognizing and addressing negative affective states,such as confusion and boredom,is fundamental in creating supportive learning environments.Through automated frame extraction and model comparison,this study demonstrates reduced loss values with improving accuracy,showcasing the effectiveness of this method in objectively evaluating student engagement.Monitoring facial engagement with CNN using the MAAED dataset is essential for gaining insights into human behaviour and improving educational experiences.
文摘This review interrogates empirical and theoretical research on agentic engagement in foreign language(FL)learning.Through synthesizing peer-reviewed studies from Web of Science and CNKI databases,it maps the theoretical evolution,methodological innovations,key influencing factors and proposed suggestion for further research on student agency.Future research should prioritize,longitudinal studies,culturally comparative designs,validity constructs and ethical evaluations of artificial intelligence’s impact on learner autonomy.This review calls for a holistic approach to FL education,where agentic engagement bridges individual initiative,pedagogical innovation,and sociocultural responsiveness to empower learners in multilingual global contexts.
文摘Students＇ active engagement constitutes the core of the process of learning and teaching in the student-oriented classroom. The paper centers on a review of foreign researches on influential factors affecting students＇ engagement in English classroom. It is expected to figure out the relevant factors in order to promote students＇ active engagement.
基金This work was supported by the Research Project of Postgraduate Education Reform in Harbin Institute of Technology,Research Project of Postgraduate Education and Teaching Reform in Harbin Institute of Technology(Weihai).
文摘The aim of the paper is to identify and explore leading development situation within the research field related to student classroom engagement.Using knowledge mapping tools,this paper conducted the visualization analysis on internationalliteraturein relation to student classroom engagementfrom the Web of Science databases.By combination of the statistical data and visualizationmapping,this paper studied on the research relationship networks and status for the co-authors'countries/institutions,co-citation documents/journalsand co-occurring keywordsbased on the sample datafromliterature.The results indicate that the number ofpublished papershasincreased obviously and expanded graduallysince 2000.Main countries and institutions of researches were the UnitedStates,Australia,United Kingdom,Canada and China in order,and these countries had intense collaboration.The theoretical basis of the research come from education and teaching,psychology.The hotspots of researches fromthe global literature coveredvarious fields including achievement,motivation,behavior,education,performance.As a result of the multidisciplinary integration,many relevant internationalresearch constantly expanded the research scopes,which promoted the combination and development of theories.
文摘This study explores the integration of Generative AI(GenAI)tools and cooperative learning strategies to enhance writing proficiency among English as a Foreign Language(EFL)students in English-medium instruction(EMI)universities in China.Employing a quasi-experimental design,the study compared writing performance between an experimental group utilizing AI tools and a control group relying solely on traditional feedback methods.Quantitative findings revealed a significant improvement in writing scores for the experimental group(t=3.45,p<.01).A one-way ANOVA further demonstrated that higher engagement levels were positively associated with superior writing performance(F=4.32,p<.05),with post-hoc analyses highlighting peer collaboration and AI-driven personalized feedback as critical factors.Qualitative insights reinforced these findings,with students valuing the immediacy and specificity of AI feedback while still preferring the personalized nature of teacher input.However,the weak correlation between overall engagement levels and writing quality(r=.084)suggests that specific engagement dimensions,such as peer interaction and collaborative activities,warrant closer examination.The study contributes to the existing literature by bridging early constructivist learning theories with AI-driven pedagogical innovations,proposing a revised framework for understanding learning in technology-enhanced environments.Implications for educators include adopting blended approaches that leverage AI tools alongside traditional methods to foster active learning and improve writing outcomes in EFL contexts.
文摘Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to 2020.To date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and instructors.Consequently,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notfirst identified.Therefore,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an LMS.The required data was collected from an LMS that is currently utilised by a local Malaysian university.The model was validated by a panel of experts as well as discussions with students.It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.
文摘Students come to Australia for many and varied reasons, including the possibility of immigrating to Australia on completion of studies. Some programs of study are given higher priority for immigration than others and are thus popular among those hoping to immigrate. The master of professional accounting （MPA） is perhaps the most well-known of these programs, as the majority of its students are allegedly more interested in gaining permanent residency than becoming practicing accountants. Concerns over the quality of this program, its graduates, and its impact on the reputation of Australian higher education have been expressed in the media and in scholarly journals resulting in a stereotype of international postgraduate students as being motivated by immigration and without interest in accounting or engagement in learning. However, little has been done to investigate the experiences and perceptions of the students themselves. The objective of this paper is to more closely examine the motivations and learning behaviors of MPA students in order to test the accuracy of the stereotype. A population of postgraduate accounting students from an Australian university was invited to respond to an anonymous questionnaire survey adapted from the Australian Universities Survey of Student Engagement （AUSSE） to gain an insight into student engagement with learning. The results of this paper demonstrate that motivation is not relevant to learning engagement. The authors find a cohort of students spending many hours in study and facing barriers to learning because of poor English skills. Such findings do not accord with the stereotypical portrayal of international MPA students but lead to questions about the institutional motivations, the nature of accounting education, and English language entry standards and language support.
文摘Most engineering courses require some level of work to be done by students using internet. A vast majority of material taught in classes is available online. Theoretically, a student could learn almost everything they want from the online resources. In this research, a comparative study is done between students learning and understanding when some basic aerospace concepts are taught in a traditional lecture based classroom versus when students are told to look for the same material on the internet. The results indicate that, although all the material taught in the classroom is available on the internet, students do not perform better when they exclusively use internet for learning. However a traditional lecture based class coupled with internet resources yields the favourable results.
文摘Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.
基金funded by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University,grant number[R-2025-1637].
文摘The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.
文摘Teacher self-efficacy is an important predictor in teacher’s professional career for inquiry-based instruction.This study investigated the factors(gender,educational level,and teaching experience)associated with science teachers’self-efficacy in instructional strategies,classroom management,and student engagement in teaching scientific inquiry.A questionnaire was used for data collection from 47 practicing teachers in state schools in 2018.A General Linear Model(GLM)Univariate analysis using SPSS 21.00 programme was used for data analysis.Fact findings revealed that mean perceived self-efficacy in inquiry-based instruction was considerably high irrespective of gender,education level,and teaching experience.Neither teacher self-efficacy in student engagement nor classroom management did not show statistically significant difference according to gender,education level,and teaching experience.However,results concluded that science teaching experience was a significant predictor of self-efficacy in instructional strategies of inquiry-based instruction.
