Online Learning Behavior Research Articles

Use of Online Learning Media and Learning Behavior as Predictors of Student Accounting Understanding

The Covid-19 pandemic has an impact on all fields, especially education, which requires learning to be done online. Therefore the use of online learning media and learning behavior is important to study to determine the effect on learning outcomes in the form of understanding. The purpose of this study was to determine the effect of the use of online learning media and the learning behavior students on the level of accounting understanding in the Accounting Introduction course. The sample in this study was 63 undergraduate students of Accounting Education class 2020. The data collection technique used was a questionnaire, while data analysis using multiple regression. The results: (1). The use of online learning media has an effect on the level of accounting understanding with the t-count> t-table, namely 3,345>1,67022, (2). Learning behavior affects the level of accounting understanding as evidenced by t-count> t-table, namely 2,995>1,67022 and (3). The use of online learning media and student accounting learning behavior simultaneously affects the level of accounting understanding as evidenced by the value of F-count>F-table, namely 23,179>3,15. The conclusion of this study is the use of online learning media and student learning behavior affects the level of accounting understanding.

Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach

ABSTRACT Online and open learning has recently been made prevalent in many regions in order to mitigate educational inequality and to enhance students’ learning experiences and outcomes. Previous studies showed that students perform differently in the learning process, where cultural differences matter. However, little is known about how cultural differences affect students’ learning behavioral patterns. This study applies a lag sequential analysis approach to understand the behavioral patterns in an online six-week course of 262 students from three cultures, namely Confucian (for Chinese students), Arab (for Tunisian students), and Serbian (for Serbian students). This study then discusses the different learning behavior patterns based on the theoretical framework of Hofstede’s National Cultural Dimensions (NCD). The obtained results highlighted that students from each culture behave differently due to several interconnecting factors, such as educational traditions. The results also showed that some of the learning behaviors were not in line with their students’ cultures based on NCD, calling for further investigation in this regard. Finally, the results pointed out that culture is a complex dimension, and further investigation is needed to understand the other dimensions that may affect online and open learning behaviors.

Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success

Predictive modelling of academic success and retention has been a key research theme in Learning Analytics. While the initial work on predictive modelling was focused on the development of general predictive models, portable across different learning settings, later studies demonstrated the drawbacks of not considering the specificities of course design and disciplinary context. This study builds on the methods and findings of related earlier studies to further explore factors predictive of learners' academic success in blended learning. In doing so, it differentiates itself by (i) relying on a larger and homogeneous course sample (15 courses, 50 course offerings in total), and (ii) considering both internal and external conditions as factors affecting the learning process. We apply mixed effect linear regression models, to examine: i) to what extent indicators of students' online learning behaviour can explain the variability in the final grades, and ii) to what extent that variability is attributable to the course and students' internal conditions, not captured by the logged data. Having examined different types of behaviour indicators (e.g., indicators of the overall activity level, those indicative of regularity of study, etc), we found little difference, if any, in their predictive power. Our results further indicate that a low proportion of variance is explained by the behaviour-based indicators, while a significant portion of variability stems from the learners' internal conditions. Hence, when variability in external conditions is largely controlled for (the same institution, discipline, and nominal pedagogical model), students’ internal state is the key predictor of their course performance.

Research on the Effect of Online Service by Logistic Regression Model and Remote Video Technology

At present, online teaching is developing rapidly, and with it is how to improve the effectiveness of online teaching. This research constructs learning behavior indicators from the online learning process, and uses logistic regression to analyze the learning data in online teaching, and explores the impact of learners’ online learning behavior on academic performance. The correlation between indicators such as the time lag of course registration, the number of login courses, the number of submitted homework tests, the average of the number of saved exercises, and the degree of completion of video viewing and other indicators and scores were discussed. The study found that the submission of homework tests can be used as key indicators for online teaching and learning performance prediction.

Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses.

Perceived Stress Among Chinese Medical Students Engaging in Online Learning in Light of COVID-19.

PurposeAfter a year of the COVID-19 pandemic, countries have repeatedly imposed strict quarantine regimes as the virus mutates and becomes more contagious. Medical undergraduate education has been disrupted and transformed into prolonged home isolation and online learning. Although studies have reported that the COVID-19 pandemic tends to increase perceived stress (PS) and affect the mental health of medical students, the influencing factors are unclear. Therefore, based on the stress process model, this study will comprehensively evaluate the distribution of stressors of medical students and explore the personal and environmental predictors of PS during the epidemic.Participants and MethodsAn online survey was conducted among medical students (n=369) from three medical universities in western China who engaged in online learning. A stress process conceptual framework was formed to explore the influencing factors of PS. The survey items contained four sections: (a) the potential stressors derived from academic, psychosocial and health-related demands; coping resources such as (b) online learning environment support and (c) personal resilience, including online learning behavior and individual characteristics; and (d) PS, perception of imbalanced demands and coping resources.ResultsThe mean PS score was 17.39 (SD=4.58), and over four-fifths (82.3%) of the students had moderate to high levels of stress. The average item scores of academic, psychosocial and health-related stressors were 2.72 (SD=0.55), 2.31 (SD=0.55) and 2.07 (SD=0.50), respectively. Gender, grade, psychosocial stressors, health-related stressors, specific online learning behavior (persistence, attitude and flexibility), and the online learning environment (teaching, social and cognitive presence) were predictors of PS.ConclusionOur results specify that a reduction in psychological and health-related stressor stimulation, specific online learning behavior promotion, and well-established online learning environment support could be considered essential for alleviating the negative impacts of COVID-19 on the psychosocial health of medical undergraduates.

Web Based Academic Monitoring and Analysis Dashboard

Web Based Academic Monitoring and Analysis Dashboard is a newly developed learning support tool that is believed to allow students and teachers to review their online learning behavior patterns intuitively through the provision of visual information. The purpose of this study was to provide virtual experience to users using business intelligence (BI) for better decision making relation to academics and To optimize the work and to bring efficiency in data analysis and make it more convinent to understand. Owing to the multitude of benefits, Web Based Academic Monitoring integrated with Analytics Dashboard System has become a buzz amongst top-notch educators these days. It can handle everyday tasks of institutions & generate various reports with high accuracy that can minimize the workload of faculty. However, education experts state that tracking student’s progress is an integral part of institutional growth. So, educators must choose an efficient ERP Software that can monitor & track student’s progress. Faculty members can note down the weak areas & ask students with low grades to attend extra-classes. Teachers can monitor their weekly & monthly performance simply by logging in their dashboard portal at regular intervals to understand whether their hard work is paying off or not. Sometimes what happens is, a faculty member better at teaching Mathematics may not be able to deliver good performance while teaching English. So, with the help of analytics dashboards reports, educators can analyze the forte of teachers and assign them subjects where they can excel. It will make the task of teachers much simpler & help them monitor their students with ease.

Curriculum Innovation in Times of the COVID-19 Pandemic: The Thinking-Based Instruction Theory and Its Application.

At the beginning of 2020, to stop the spread of the coronavirus disease (COVID-19) to the campus, the Ministry of Education of China launched a policy “Suspension of classes without suspending schooling” for the spring semester of 2020. However, the drawbacks of online teaching (e.g., students’ inadequate autonomous learning, the lack of effective online instruction) forced us to modify teaching strategies during this special period, especially developing courses that are suitable for student learning at home and improving their key competencies. In order to solve these problems, this study introduces some theoretical exploration and practical work of curriculum design under the guidance of thinking-based instruction theory (TBIT) during the pandemic. We firstly introduce TBIT, and elaborate on the curriculum design under the TBIT theoretical frame. Then we describe a series of TBIT-based micro-courses with the pandemic as background. A descriptive study is reported to illustrate the effects of three micro-courses. Results showed that, compared to national curricula, the TBIT-based micro-courses not only improved the course quality but also enhanced students’ motivation and facilitated their online learning behavior (such as interactive communication) for the online courses. The current study has important implications for how to design effective and interesting online courses suitable under pandemic and capable of improving students’ thinking abilities and key competencies.

Investigation and Analysis of College Students’ Online Learning Behavior in the Post-epidemic Era—Take Suqian College as an Example

The COVID-19 epidemic broke out in the world in 2020. With the continuous efforts of people all over the world for a year, the development trend of the epidemic was gradually restrained, and countries began to enter the post-epidemic period one after another. With the students from all over the country returning to school gradually, how to strengthen the normal study of young college students and how to restore the normal teaching order in schools is a huge challenge to both schools and students during this special period of epidemic prevention and control. This paper starts with the phenomenon that the learning effect of online education for college students in Suqian University is not ideal, and carries out on-the-spot questionnaire survey to explore the problem. According to the research, some students have cultivated their good learning habits in the home-school environment, but there are still some problems, such as the majority of students lack of inertial thinking of online learning, lack of attention to online learning and unable to find a suitable mixed online teaching model. Schools should select appropriate teaching models to guide students to participate in online learning. Students should also actively cooperate with teachers to carry out educational work, restrain themselves and devote themselves to study within the stipulated time. Finally, I hope that this paper can effectively promote the development of online e-learning by putting forward some countermeasures and suggestions for the construction of high-quality e-learning model in schools.

Analysis of Online Learning Behavior of College Students during the Anti-epidemic Period—Take Suqian College as an Example

Affected by the COVID-19 epidemic, in order to prevent the virus from spreading again caused by students’ off-line learning and realize the normalization of anti-epidemic, colleges and universities have implemented the relevant requirements of the Ministry of Education on “suspension of classes and non-stop learning” during the epidemic, and college students across the country have started large-scale on-line learning. Facing this reality, teachers and students actively cooperate with online learning. However, with the development of online learning, college students are gradually exposed to low learning efficiency. Taking Suqian University as an example, this paper conducts an empirical study to analyze college students’ online learning behavior and its influencing factors, and puts forward an improvement path to optimize college students’ online learning behavior and improve online learning efficiency.

Exploring Perceived Fits, Attitudes, and Self-Efficacy: A case of Digital Natives’ Online Learning Behavior

Some pre-service teachers who are studying give less effective perceptions regarding the implementation of online learning during the Pandemic period. Pre-service teachers today belong to a generation of digital natives (DNG) who are very familiar with technology and have unique characteristics, so it is very important to analyze their online learning behavior to design effective online learning. The purpose of this study was to analyze the online learning behaviors of digital natives reviewed from perceived fits, attitudes, and self-efficacy. Survey research was used to analyze the phenomenon of DNG’s online learning behavior and identify the factors that influence it. The instrument in the form of a questionnaire consists of 49 statement items on a scale of 5. Of the 152 polls collected, only 148 were valid. Quantitative and qualitative descriptive analysis was used for data analysis. The results of this study showed that all components of DNG’s online learning behavior had relatively high average scores except the interaction component through online discussion forums. The causes of low interaction through online discussion forums were DNG less active, lack of enthusiasm, easy to give up, difficult to move on, and network constraints. The conclusion of this study was an in-depth analysis related to perceived fits, attitudes, and self-efficacy producing phenomena and are variables capable of explaining the variables of DNG online learning behavior. The results of this study can be used as the basis for further research, namely related to the development of online learning models for the DNG.

A Qualitative Study on the Perceptions and Learning Behavior of Medical Students in Online Classes

Since the emergence of coronavirus disease 2019 (COVID-19), medical schools have experienced a sudden, full-scale transition to online classes. As the COVID-19 pandemic continues, it is important to evaluate current educational programs and to assess their implications. This study explored perceptions of online classes and learning behavior among medical students. Twenty preclinical medical students were interviewed in focus groups for 2 months. They generally expressed positive perceptions about online classes, and in particular, positively assessed the ability to lead their individual lifestyles and study in comfortable environments with fewer time and space constraints. Students thought that the online environment provided a fair chance of facilitating positive interactions with the professor and considered communication with the professor to be an important factor only when it was related to the class content or directly helped with their grades and careers. Students also had negative views, such as feeling uncertain when they could not see their peers learning progress and assess themselves in comparison and feeling social isolation. Learning behaviors have also changed, as students explored their learning styles and adapted to the changed learning environment. Students expanded their learning by using online functions. However, students sometimes abused the online class format by “just playing” the lecture while not paying attention and relying on other students’ lecture transcripts to study. The results of this study are hoped to provide a useful foundation for future research on online class-based teaching and learning.

대학생의 온라인 강의 학습행태, 온라인 강의 시 학습자 특성, 학습만족도, 교수-학생 상호작용에 따른 온라인 강의 집중도

대학생의 온라인 강의 학습행태, 온라인 강의 시 학습자 특성, 학습만족도, 교수-학생 상호작용에 따른 온라인 강의 집중도

Investigation and Research on College students' online Learning behavior and its influencing factors -- Taking Northeastern University at QinHuangdao as an example

Since the outbreak of the new crown epidemic, at the request of the Ministry of Education, colleges and universities in China have carried out many online teaching activities of different scales. Because of the repetition of COVID-19's epidemic situation, it is very necessary to promote and improve the online teaching mode and distance education in our country. It is of great significance to the development of higher education in our country to study college students' online learning behavior and its influencing factors, and put forward solving measures and behavioral countermeasures on this basis. To this end, a number of network teaching activities have been carried out to conduct research and development from the perspective of students. This paper analyzes the influencing factors of college students' online learning behavior, and puts forward some feasible suggestions to improve the quality of online teaching.

A Study on Online Learning Behaviors of the Private University Students Based on SPOC Mode—Take Zhejiang Yuexiu University as an Example

With the rapid development of MOOCs and SPOC in higher education under the wave of education informatization, research on online learners’ behavior has received more and more attention from university researchers. However, there are just a few studies on online learning behaviors of students in private universities. Relying on Zhejiang Province’s first-class undergraduate course (online and offline hybrid)—A Survey of Major English-speaking Countries, this paper conducted a study on the analysis of students’ online learning behavior based on SPOC mode, by collecting data on the online learning behavior process of 224 junior students from Zhejiang Yuexiu College of Foreign Languages, including students’ weekly login times to the platform, video viewing hours, total online learning hours, unit quiz scores, forum posting and final test scores. These data were statistically and analytically analyzed using SPSS.20, and correlation analysis was conducted using the Pierce correlation coefficient. The results show that students’ participation in online learning is satisfactory, the teacher’s ability to guide learning directly affects students’ online learning logins, and students’ learning performance is positively correlated with the number of course logins, video viewing hours, unit test scores and the number of forum posts, especially with unit test scores, but there is little student-student interaction in forum discussions. It can be seen that SPOC mode can organically combine traditional classroom with online classroom, especially visualize the characteristics of students’ online learning behavior, help teaching administrators and teachers in private colleges and universities accurately grasp the learning status of learners, thus promoting them to provide strong support in the process of course reform, strengthen the ability and responsibility of information technology, integrate online and offline teaching more efficiently, and at the same time make students to pay more attention to the usual grades, to focus on the cultivation of learning ability, thus giving students a complete and in-depth learning experience, improving the completion rate of the course and promoting the improvement of teaching quality.

A study of ISEC students' online learning behaviour in ethnic universities and colleges

Studies on e-learning behaviours of undergraduates are of great significance to improve the quality of e-learning. With e-learning behaviours in the course cross-cultural communications offered for students majoring in International Economy and Trade of the International Scholarly Exchange Curriculum (ISEC) program launched by an ethic university in China as the object, this paper collects students' e-learning behaviour data during the learning process and concludes that there is strong utilitarian element and weak interactivity with e-learning. Students lack the awareness of reflective learning. There is significant difference in learning hours among students, with a considerable portion of the hours being allocated to homework. Content of courses on the e-teaching platform is not pertinent. This study provides suggestions to improve e-learning behaviours of undergraduates in terms of course resources, external and internal monitoring of e-leaning, teacher-student interaction and assessment system optimisation. E-learning environments should be improved and more online courses should be provided. External and internal monitoring over e-learning should be enhanced to foster independent learning capabilities. Communications between students and teachers should be encouraged to better the e-learning quality.

Exploration of data mining algorithms of an online learning behaviour log based on cloud computing

In this research, the learning patterns and behavioural data of learners' online learning in a cloud computing environment are analysed, and the learners' learning progress, learning rules, and learning effects through data mining algorithms are studied to promote learners' self-learning consciousness and self-learning ability and achieve the learner-centred application goal. This study is aimed at an online learning course, randomly selected eight learners as research subjects, and analysed their online learning behaviour through data mining algorithms, including establishing learning behaviour models, behavioural data feature analysis and behaviour evaluation, and finally realise personalised recommendation of learning content based on data mining results. It finds that through the data mining algorithm to analyse the learning behaviour of online learners, the learner's learning state and learning effect can be intuitively understood, and the individual's learning behaviour characteristics can be roughly defined, then guiding suggestions can be given to help them complete learning goals and improve learning efficiency. Due to the small sample size of the survey, the results obtained are relatively one-sided. In future research, attention should be paid to the data collection and collation work.

A Study of ISEC Students’ Online Learning Behavior in Ethnic Universities and Colleges

Studies on e-learning behaviours of undergraduates are of great significance to improve the quality of e-learning. With e-learning behaviours in the course cross-cultural communications offered for students majoring in International Economy and Trade of the International Scholarly Exchange Curriculum (ISEC) program launched by an ethic university in China as the object, this paper collects students' e-learning behaviour data during the learning process and concludes that there is strong utilitarian element and weak interactivity with e-learning. Students lack the awareness of reflective learning. There is significant difference in learning hours among students, with a considerable portion of the hours being allocated to homework. Content of courses on the e-teaching platform is not pertinent. This study provides suggestions to improve e-learning behaviours of undergraduates in terms of course resources, external and internal monitoring of e-leaning, teacher-student interaction and assessment system optimisation. E-learning environments should be improved and more online courses should be provided. External and internal monitoring over e-learning should be enhanced to foster independent learning capabilities. Communications between students and teachers should be encouraged to better the e-learning quality.

Exploration of Data Mining Algorithm of Online Learning Behavior Log based on Cloud Computing

In this research, the learning patterns and behavioural data of learners' online learning in a cloud computing environment are analysed, and the learners' learning progress, learning rules, and lea...

Evaluation of learners' online learning behaviour based on the analytic hierarchy process

Evaluation of learners' online learning behaviour based on the analytic hierarchy process