The influence of interdisciplinary literacy on learning effects among college students: the mediating effects of online physical education on learning behavior

IntroductionThe development of high-quality physical education curriculums is required in the information age. Interdisciplinary literacy and student learning behavior are two significant factors that affect the quality of teaching and learning. This study explores the relationship between interdisciplinary literacy (IDL) and learning effects (LE) among Chinese college students during the COVID-19 pandemic, as well as the mediating effects of online physical education learning behaviors (OPELB). This research aims to provide a reference for the development of high-quality online physical education. MethodsThe study involved 691 college students from 10 general universities in Shaanxi Province as research subjects. Descriptive statistics, Pearson correlation analysis, multiple regression analysis and Bootstrap testing were used to evaluate the mediating effects. ResultsThere was a significant positive relationship between the three variables of IDL, OPELB, and LE (p < 0.001). Multiple regression analysis found that IDL significantly and positively predicted LE and OPELB (p < 0.001), and OPELB predicted LE (p < 0.001). IDL among college students had a total effect of 0.816 on LE, with OPELB accounting for 22.67% of the mediated effect. DiscussionThis study demonstrates that OPELB has a partial mediating effect on IL and LE, and stable IDL and OPELB improve LE. Therefore, teachers should pay attention to improving students' IDL while encouraging them to develop better OPELB to achieve satisfactory learning outcomes.

Blended Learning in a Higher Education Context: Exploring University Students' Learning Behavior

Blended learning is a growing phenomenon in higher education after the COVID-19 pandemic (the educational process moved entirely online), and the way is prepared for blended education mode in universities. Although blended learning research is on the rise, fewer studies regard university students' learning behavior in blended learning environments. This study aims to investigate university students' blended learning behavior perceptions shortly after the pandemic. A 19-item questionnaire was administered to 176 university students in Greece. Students, in general, expressed positive blended learning behavior perceptions. Higher percentages of agreement were associated with the role of audio-visual online resources in facilitating and supporting independent learning and with student motivation in blended education. Students expressed lower percentages of agreement, and some uncertainty, with regard to involvement in small group work with their peers. Implications for students, educators, as well as university policy and practice are discussed. © 2023 by the authors.

Research on the Sustainable Development Strategy of Online Learning: A Case Study of YouTube Users

Amid the COVID-19 pandemic and the widespread adoption of mobile devices, video-based online learning has emerged as a critical mode of education. However, empirical research on the determinants of online learning behavior and intention among video users remains scarce. To explore the factors influencing the continuous intention of users to engage in YouTube video-based online learning, the present study drew on the perceived value theory and the ECM perspective to construct a model. This study is a quantitative study in which 669 valid data were collected from online users of online learning and communication communities through online questionnaires distributed by non-probability sampling, and the constructed model was tested using SPSS 27.0 and AMOS 27.0. The results revealed that perceived value had a positive direct effect on continuous intention and an indirect effect through satisfaction on continuous intention. Therefore, to effectively and sustainably promote video-based online learning, measures should be taken to enhance users' continuous intention and retention. Thereafter, suggestions for further research were proposed.

Teacher perceptions of the role of challenging student behavior and social-emotional learning

This study aims to explore teachers' perceptions of the role of challenging student behavior and social-emotional learning (SEL). The problem addressed was that challenging student behavior interferes with teachers' ability to teach, and children's academic achievement. The conceptual framework that underpinned this study is the concept of social and emotional learning. The Collaborative for Academic, Social, and Emotional Learning's evidence-based five core competencies were analyzed and utilized as the model for this study. A qualitative case study was chosen as the research method for this study. Four elementary schools within the same district in California were identified, and seven heterogeneous participants were selected, including probationary or permanent teachers with various levels of experience in teaching. Data collection took place following the approval of the Institutional Review Board and consisted of a demographic questionnaire, semi-structured interviews, and artifacts. The two qualitative questions that guided this research were: How do elementary school teachers in a suburban southern California school district describe their understanding of social and emotional learning? The findings revealed that teachers had a basic to advanced level of understanding of the benefits of social and emotional learning in the classroom. How do teachers describe their use of SEL to support their work with students exhibiting challenging student behavior? Findings support that teachers need knowledge and skills to understand student behavior. Emergent themes revealed that SEL must be explicitly taught, relationship is part of the SEL process, teachers' buy-in and leadership in the SEL process are necessary, teachers need support and professional development to increase SEL implementation, SEL increases engagement with students who exhibit challenging behavior, SEL supports building relationships between teachers and students with challenging behaviors, and SEL increases collaboration with school, community, and home. There is a need to expand a qualitative case study with a larger sample size. It might be even more beneficial to explore the effects of the COVID-19 pandemic on these perceptions compared to pre-pandemic perceptions. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

SA-FGDEM: A Self-adaptive E-Learning Performance Prediction Model

With the rapid development of computer computing power and the severe challenges brought by the COVID-19, e-learning, as the optimal solution for most students and other learner groups, plays an extremely important role in maintaining the normal operation of educational institutions. As the user community continues to expand, it has become increasingly important to guarantee the quality of teaching and learning. One way to ensure the quality of online education is to construct e-learning behavior data to build learning performance predictors. Still, most studies have ignored the intrinsic correlation between e-learning behaviors. Therefore, this study proposes an adaptive feature fusion-based e-learning performance prediction model (SA-FGDEM) relying on the theoretical model of learning behav-ior classification. The experimental results show that the feature space mined by fine-grained differential evolution algorithm and the adaptive feature fusion combined with differential evolution algorithm can support e-learning performance prediction more effectively and is better than the benchmark method. © 2022 IEEE.

The relationship between student motivation and academic performance: the mediating role of online learning behavior

Purpose: This paper aims to use a quantitative approach to explore the role of online learning behavior in students' academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context. Design/methodology/approach: Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis. Findings: The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly. Originality/value: Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students' online learning behavior to boost blended learning effects during the COVID-19 pandemic. © 2022, Emerald Publishing Limited.

Academic performance prediction associated with synchronous online interactive learning behaviors based on the machine learning approach

With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students participating in online class. Five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output. The analysis shows the gradient boosting regression model achieves the best performance with the highest correlation coefficient (0.7558), and the lowest RMSE (9.3595). Intellectual education score (IES) is the most important factor of comprehensive performance while the number of completed assignment (NOCA), the live viewing rate (LVR) and the replay viewing rate (RVR) of online learning behaviors are the most important factors influencing FESs. Students with higher IES are more likely to achieve better academic performance, and students with lower IES but higher NOCA tend to perform better. Our study can provide effective evidences for teachers to adjust teaching strategies and provide precise assistance for students at risk of academic failure in advance.

Academic performance prediction associated with synchronous online interactive learning behaviors based on the machine learning approach

With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students participating in online class. Five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output. The analysis shows the gradient boosting regression model achieves the best performance with the highest correlation coefficient (0.7558), and the lowest RMSE (9.3595). Intellectual education score (IES) is the most important factor of comprehensive performance while the number of completed assignment (NOCA), the live viewing rate (LVR) and the replay viewing rate (RVR) of online learning behaviors are the most important factors influencing FESs. Students with higher IES are more likely to achieve better academic performance, and students with lower IES but higher NOCA tend to perform better. Our study can provide effective evidences for teachers to adjust teaching strategies and provide precise assistance for students at risk of academic failure in advance. [ FROM AUTHOR]

Learners' characteristics and the mastery of digital education during the COVID-19 pandemic in students of a medical faculty in Germany.

BACKGROUND: In response to the spread of the coronavirus, educational institutions have been closed and digital education has become a new teaching method to ensure the continuity of medical education. Since this format was a new form of learning for students at medical faculties in Germany, little is known about the perception of it and the factors that contribute to successful mastery. The current study aimed to analyze students' learning experiences during the first online semester and to identify associations between learners' characteristics and enjoyment, mastery experiences, as well as the perceived stress level. METHODS: In this cross-sectional study, students of a medical faculty from Germany answered an online questionnaire including information about perceptions towards digital education and learners' characteristics (study skills and dispositions). Data were analyzed using multivariate linear regression analysis. RESULTS: In total, 383 students responded to the online survey. A majority of students felt at least somewhat worse about their studies compared to before the pandemic. Success of study tasks was related to preferences for cooperative learning (B = - 0.063, p < .001) and success of study organization was associated to the use of metacognitive learning strategies (B = 0.019, p = .04). Enjoyment of studying in times of digital education was positively related to the use of metacognitive strategies (B = 0.049, p = .04) and self-efficacy (B = 0.111, p = .02). The perceived stress was influenced by cognitive strategies (B = 0.401, p = .02) and test anxiety (B = 0.466, p < .001). CONCLUSIONS: Although students perceive digital teaching as a good alternative for big courses, those with low self-efficacy beliefs and low self-regulation have problems in coping with the demands of this learning format and need further support.

Predicting User Dropout from Their Online Learning Behavior

The Covid-19 pandemic, which required more people to work and learn remotely, emphasized the benefits of online learning. However, these online learning environments, which are typically used on an individual basis, can make it difficult for many to finish courses effectively. At the same time, online learning allows for the monitoring of users, which may help to identify learners who are struggling. In this article, we present the results of a set of experiments focusing on the early prediction of user drop out, based on data from the New Heroes Academy, a learning center providing online courses. For measuring the impact of user behavior over time with respect to user drop out, we build a range of random forest classifiers. Each classifier uses all features, but the feature values are calculated from the day a user starts a course up to a particular day. The target describes whether the user will finish the course or not. Our experimental results (using 10-fold cross-validation) show that the classifiers provide good results (over 90% accuracy from day three with somewhat lower results for the classifiers for day one and two). In particular, the time-based and action-based features have a major impact on the performance, whereas the start-based feature is only important early on (i. e., during day one). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Achievement Motivation and Learning Behavior of Students During COVID-19 Pandemic: Gender Differences

This study aimed to describe and compare the achievement motivation and learning behavior based on male and female students in Indonesia. Respondents have involved 902 female high school students and 637 male high school students using the cluster random sampling technique. Data were collected using a scale of achievement motivation and learning behavior with has a validity coefficient of items in the range of 0.362 to 0.724, and each had Cronbach’s alpha reliability of 0.811 and 0.866;data analyzed by descriptive and Mann-Whitney tests. The results showed significant differences in the level of achievement motivation and learning behavior of male and female students. Female students showed better achievement motivation and learning behavior than male students. This study discussed by compare between several countries and recommends the need for guidance and counseling services to increase achievement motivation and student learning behavior based on gender differences. © 2022, Pegem Egitim ve Ogretim Dergisi. All Rights Reserved.

SA-FEM: Combined Feature Selection and Feature Fusion for Students' Performance Prediction.

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.

DATA PREPARATION AND EXPLORATION FOR DESCRIPTIVE MODELING IN EDUCATION

Education domain generates huge amounts of data, especially in online teaching and learning. Data analysis enables detecting patterns of students' learning behavior which leads to personalized attention and adaptive feedback. Learning management system (LMS) data are in the focus of this paper. The LMS logs store information about students' login frequency, time of visits, number of downloading different resources, time and frequency of various activities. Within this research, log file analysis is performedfrom a Business decision making course at the University of Zagreb. Raw LMS data were extracted from Moodle, data was prepared, explained and explored in order to detect patterns in student's behavior in the online environment. Research results provide a basis for teachers' interventions on one hand, and serve as an input into predictive models' development, on the other hand.

The relationship between student motivation and academic performance: the mediating role of online learning behavior

Purpose: This paper aims to use a quantitative approach to explore the role of online learning behavior in students’ academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context. Design/methodology/approach: Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis. Findings: The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly. Originality/value: Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students’ online learning behavior to boost blended learning effects during the COVID-19 pandemic. © 2022, Emerald Publishing Limited.

Caregivers' perceived changes in engaged time with preschool-aged children during COVID-19: Familial correlates and relations to children's learning behavior and emotional distress.

The COVID-19 pandemic and its resulting containment measures have forced many children and their caregivers around the world to spend unprecedented amounts of time at home. Based on a sample of 764 households with preschool-aged children in Wuhan, China, where the pandemic began, this study examined how primary caregivers perceived changes in the amount of time spent engaging with their children (i.e., engaged time) from the start of the pandemic and whether these changes were associated with children's learning behavior and emotional distress. The results showed that primary caregivers generally perceived increases in the amount of engaged time spent on indoor activities with their children but decreases in the amount of engaged time spent playing with their children outdoors. A bigger family size and greater loss of family income during the pandemic were associated with bigger perceived increases in caregivers' engaged time spent on indoor activities, whilst a higher level of parental education was associated with bigger perceived decreases in engaged time spent playing with children outdoors. The family's poorer physical health and higher levels of chaos during the pandemic were related to smaller perceived increases in caregivers' engaged time spent on educational activities. Finally, although bigger perceived increases in caregivers' indoor engaged time (e.g., time spent on educational activities) were associated with higher levels of positive learning behavior and fewer symptoms of anxiety and withdrawal in the children, bigger perceived decreases in outdoor play time were associated with fewer symptoms of anxiety and withdrawal. These findings offer valuable insights into caregivers' allocation of engaged time with their preschool-aged children during the COVID-19 pandemic.

Motivation contribution to religious learning behavior during the covid pandemic

The Indonesian Minister of Education designed the 2013 school curriculum (K13) to activate students’ learning behavior but there is low-intensity research in it. Hence, this study aims to explain the contribution of achievement, affiliation, power, and religious motivation to learning behavior in Islamic Religion that applies K13 during the pandemic in adolescent students. It employed a causal relationship-explanation design involving 201 samples selected through random stratification representing 795 student population aged 13-16 years, grades 7th, 8th, and 9th from 26 parallel classes. Data were collected through a five scales test for item validity ≥ 0.3 and Cronbach Alpha reliability by 0.6-0.904, and then analyzed via multiple regression. The results showed that the theoretical regression model was empirically fit (sig F (201) = 0.000 < 0.05). The contribution of the four predictor motivations in the model together was 72.9 percent on learning behavior. Achievement, affiliation, and religious motivation could contribute in increasing learning behavior, but power motivation demonstrated otherwise. Consequently, teachers need to guide adolescent students to increase achievement motivation, religion, and affiliation but reduce power motivation at an ideal level to improve student learning behavior. © 2022, author.

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model.

With the spread of COVID-19 worldwide, online education is rapidly catching on, even in some underdeveloped countries and regions. Based on Bandura's ternary learning theory and literature review, this paper takes online learning of college students as the research object and conducts an empirical survey on 6,000 college students in East China. Based on literature review and factor analysis and structural equation model, this paper discusses the relationship among learning cognition, learning behavior, and learning environment in online learning of college students. The learning behavior includes interactive communication, self-discipline mechanism, classroom learning, and study after class. The learning environment includes teaching ability, knowledge system, platform support, process control, and result evaluation; learning cognition includes learning motivation, information perception, and adaptability. It is found that the learning environment has a significant positive impact on learning behavior, and learning cognition has a significant positive impact on learning behavior. It is uncertain whether the learning environment significantly impacts learning cognition. At the learning environment level, the teaching ability (0.59) has the most significant impact on the learning environment, followed by result evaluation (0.42), platform support (0.40), process control (0.33), and knowledge system (0.13). In terms of the influence on learning behavior, classroom learning has the most significant impact (0.79), followed by self-discipline mechanism (0.65), study after class (0.54), and interactive communication (0.44). In terms of learning cognition, information perception (0.62) has the most significant influence, followed by learning motivation (0.50) and adaptability (0.41). This paper suggests strengthening the construction of platforms and digital resources to create a more competitive learning environment. Improve process management and personalized online services, constantly stimulate students' enthusiasm for independent online learning, and cultivate students' online independent learning ability to promote the sustainable and healthy development of online education.

Experiences with alternative online lectures in medical education in obstetrics and gynecology during the COVID-19 pandemic-possible efficient and student-orientated models for the future?

PURPOSE: The onset of the COVID-19 pandemic posed an eminent challenge for medical teachers worldwide. Face-to-face lectures and seminars were no longer possible, and alternatives had to be found. E-learning concepts quickly emerged as the only practicable solutions and also offered the opportunity to evaluate whether traditional face-to-face lectures could be translated into an online format, independent of the COVID-19 pandemic. METHODS: We offered an e-learning program consisting of lecture notes, screencasts with audio narration, and online webinars that covered topics normally taught in traditional lectures and seminars. To evaluate the learning behavior and quality of our e-learning program, we drafted a questionnaire that students completed at the end of the 2020 summer semester that had been designed to enable a comparative analysis of the different e-learning modules. RESULTS: Voluntary participation in the online courses was high. Survey analysis revealed high satisfaction with and a distinctive preference for the format, even under regular, COVID-19-independent conditions. In general, a positive appraisal of e-learning-especially as a substitute for regular lectures-was found. Students also reported higher studying efficiency. Exam results were equal to those of previous semesters. CONCLUSION: Both acceptance of and satisfaction with our e-learning modules were high, and students displayed increased demand for this kind of e-learning format. We, therefore, conclude that e-learning offerings could serve as reasonable, efficient, student-orientated substitutes for certain medical courses, especially lectures. These curricular adaptations would correlate with the high digitalization seen in students' everyday lives. This correlation may also hold true independent of the ongoing COVID-19 pandemic.

Editorial Note: Understanding and Bridging Gap in Multi-mode Digital Learning during Post-Pandemic Recovery

COVID-19 pandemic had changed the world-wide education landscape as the whole society is adapting to the “new normal.” We orgainised a special issue collecting research papers that shed insights on how teaching and learning designs will be affected, and how novel educational technologies will help in a fast post-pandemic recovery. 26 papers were received but only 11 papers were finally selected to publish, after two rounds of rigorous reviews. This editorial note discusses the background, quality management and thematic topic groups of the papers. © 2022,Educational Technology and Society. All rights reserved

Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms. © 2022, North Atlantic University Union NAUN. All rights reserved.