E-learning Behavior Research Articles

Spectral clustering algorithm based web mining and quadratic support vector machine for learning style prediction in E-learning platform

A learning system, which is composed of a computer and the internet as the major elements, is termed an e-learning platform. It also promotes the education standard with the utilization of modern technology and equipment. Meanwhile, to enhance the standard of education significantly, it is important to predict the learning style of the users with the assistants of feedback and supervision. Nevertheless, it will avert the inherent correlation among e-learning behaviors. Hence, to predict the learning style automatically we propose a novel Spectral Clustering algorithm based Quadratic Support Vector Machine (E-SVM) approach. Our proposed approach employs two phases: (i) Utilizing the Web usage mining approach the learning secrets are extracted from the log files of learners. (ii) The classification of learning styles of learners is effectuated with the proposed approach. Experiments are demonstrated with Python package and analyzed the performance. For simulation, we have utilized real-time dataset and compared the results with the state-of-art approaches. Our approach surpasses all the other approaches.

MOOCs: The Factors Impacting Learners’ Continuance Intention, the Intention to Complete or Cancel a Course

The growing popularity of massive open online courses (MOOCs), especially during the COVID-19 pandemic, has attracted significant attention from researchers and businesses. Though many studies have investigated what motivates learners’ continuance intention, it is no less important to reveal the factors that lead to course completion or cancellation. The aim of this study is to reveal the factors impacting three different e-learning behaviour intentions– continuance intention, the intention to complete, and the intention to cancel MOOCs – by applying the theory of planned behaviour (TPB) and the technology acceptance model (TAM). Based on a survey of 299 respondents, it was revealed that the TAM only explains continuance intention but cannot be fully employed to predict two other e-learning behavior intentions. Also, participants’ support and self-efficacy, being a part of the TPB model, had an influence on the intention to complete the course, while they did not affect continuance intention. Only participants’ support had a moderate positive impact on the intention to cancel it. Moreover, it was revealed that continuance intention positively impacted the intention to complete and negatively impacted the intention to cancel the course. This expands the body of knowledge about learners’ motivations for three different e-learning behaviour intentions and has managerial implications for their development in emerging economies.

E-Learning Behavior Categories and Influencing Factors of STEM Courses: A Case Study of the Open University Learning Analysis Dataset (OULAD)

With a focus on enhancing national scientific and technological competitiveness and cultivating innovative talents, STEM education has achieved remarkable results in developing students’ core quality and improving academic achievement. Online courses built for STEM education have attracted many learners. However, as the number of learners continues to grow, online STEM education faces problems such as difficulties in ensuring the quality of teaching and learning in STEM online courses and poor performance of students in online learning. An in-depth exploration of the correlations between learners’ E-learning behavior categories and learning outcomes in STEM education online courses will facilitate teachers’ precise interventions for students who are learning online. This study first predicts the E-learning performance of STEM course learners through machine learning and deep learning algorithms, then uses factor analysis methods to discover correlations between behavioral features, uses the random forest algorithm to explore the vital behavioral features that influence the E-learning performance of STEM courses, and finally performs a category classification of important characteristic behaviors based on the learning behavior category basis. The results show that the learning behavior classifications of learning preparation behavior, knowledge acquisition behavior, and learning consolidation behavior affect the E-learning performance of learners in STEM courses. Moreover, a series of characteristic behaviors strongly affect E-learning performance. In general, teachers can systematically intervene in time for at-risk students from the perspective of learning behavior categories and further improve the construction of STEM online courses.

Bayesian model selection for barriers in online learning behaviors

The study presents an overview of theories related to e-learning barriers and e-learning behavior. Research and synthesize relevant studies at home and abroad. From related studies, identify barrier factors affecting the online learning behavior of students. Then, the research model and hypotheses for the study are presented. In this study, the author identified 5 barriers affecting students’ online learning behavior in Ho Chi Minh City: economic barriers (ECOB), interaction barriers (IB), psychological barrier (PB), environmental barriers (ENI), and regulatory institutional barriers (RIB). Previous studies revealed that using linear regression. The paper uses the optimum selection by Bayesian consideration for e-learning barriers and e-learning behavior. Get the results, then make recommendations and solutions to help educational administrators reduce barriers to increase students’ effectiveness in online learning in a better way.

The Impact of Social Media on E-Learning Behaviors among University Students

This study aims to examine the relationships between the dimensions of Social Media and E-Learning Behaviors (SMELB) in education of Al Ain University Students in United Arab Emirates. A sample of 494 students completed a survey conducted at Al Ain University. The study adopts some methodologies such as Structural Equation Modeling (SEM) technique and Confirmatory Factor Analysis (CFA) to examine the reliability and validity of the measurement, and the structural equation modeling techniques (Amos 20.0) to evaluate the casual model. The findings of the study demonstrate the strong predictive power and explain of students’ Behaviors. They highlighted that the relationship between easy communication, improving confidence, lecture sharing, and increasing knowledge have a significant positive and direct impact on E-learning Behaviors (H1, H3, H4, and H5). While, reducing stress has insignificant positive and direct impact on E-learning Behaviors (H2).

Analysis of the Effect of Using E-Learning on the Learning Process Using the UTAUT Method

The purpose of this study is to conduct empirical testing of the influence of the use of the Moodle E-learning System in the Muhammadiyah Samarinda Junior High School environment in supporting the process of ICT-based activities in the teaching and learning process in order to develop ICT-based learning that is considered innovative under the standards of the 2013 Education Curriculum. This study's subjects were Muhammadiyah Junior High School students in Samarinda. The UTAUT model is used to determine the factors influencing E-Learning Behavior's purpose. The data collection survey was conducted by distributing questionnaires directly to 56 students, using Multiple Linear Regression Analysis by conducting a statistical T-test. The results of this study show the influence of expected variables in the performance of E-Learning (PE) technology, expectations on the business of using E-Learning (EE) technology, the influence of teachers and students (SI), the condition of school facilities (FC) and the suitability of learning (JF) with the behavioral intention of Muhammadiyah Junior High School students to use E-Learning learning technology by 83.4%.

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.

The association of self-control, self-efficacy, and demographic characteristics with home-based E-learning behavior in nursing and midwifery undergraduates: A cross-sectional study under the COVID-19 epidemic

BackgroundDuring the coronavirus disease 2019 (COVID-19) pandemic, nursing and midwifery undergraduate students' time spent in home isolation and being engaged in home-based e-learning was extended. Limited research has been conducted on home-based e-learning behavior during home isolation, and the relationships between individual factors, such as self-control, self-efficacy, and other demographic characteristics, and home-based e-learning behavior are unclear. ObjectivesThis study aimed to explore the associations between self-control, self-efficacy, and demographic information with home-based e-learning behavior among nursing and midwifery undergraduates during the COVID-19 pandemic. Design and methodsA cross-sectional study was employed, and an online survey was conducted with 3733 nursing and midwifery undergraduates across seven provinces and cities in mainland China. Data were collected using the Brief Self-control Scale, General Self-efficacy Scale, Undergraduate Home-based E-learning Behavior Questionnaire, and Demographic Information Questionnaire. A multiple linear regression analysis using the stepwise method was conducted to identify predictors of home-based e-learning behavior. ResultsUndergraduates achieved an overall mean score of 26.02 (SD = 4.20) for home-based e-learning behavior and an overall mean score of 42.54 (SD = 6.22) and 27.59 (SD = 3.89) for self-control and self-efficacy, respectively. Better self-control (β = 0.250, P < 0.001), higher self-efficacy (β = 0.169, P < 0.001), universities being located in non-Hainan provinces (β = 0.249, P < 0.001), being a sophomore or freshman (β = −0.255, P < 0.001), and good perceived health status (β = −0.044, P = 0.003) were identified as the predictors of better home-based e-learning behavior among nursing and midwifery undergraduates. ConclusionSelf-control, self-efficacy, and demographic characteristics including the province of the university, grade level, and personal perceived health status were associated with home-based e-learning behavior. Universities should help nursing and midwifery undergraduates improve self-control and enhance self-efficacy while taking measures to decrease the impact caused by demographic characteristic differences.

Design Principles for Integrating Gamification into Distance Learning Programs in Higher Education: A Mixed Method Study

This study aims to address the issue of low completion and high dropout rates in online and distance learning through play and gamification. The general aim of this study is to provide a set of gamification design principles for researchers and practitioners on how gamification can be used in online and distance learning programs in higher education. More specifically, the study intended to seek the answers whether student participation in the online and distance learning programs can be increased by the use of gamification, how the gamification influences the students’ academic performances in the online and distance learning programs and what learners think about integrating gamification into the open and distance learning programs in higher education. The study was designed as a sequential exploratory research, which is one of the mixed research methods. Findings indicate that integrating gamification into the online and distance learning programs has a significant impact on increasing students' visits to the learning environment. In addition, when the pre-gamification and post-gamification scenarios were compared in terms of students accessing to the content in the online and distance learning environment, it was discovered that there was a statistically significant increase. It can be also said that there is significant difference regarding to academic performances for gamified situations. Lastly, gamification is said to be fun by students and to contribute positively to their motivation. According to this, gamification has a positive effect on learners' e-learning behaviors and should be used more in online and distance learning programs.

E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior.

Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners’ academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers.

The Role of Mental Health and Sustainable Learning Behavior of Students in Education Sector Influences Sustainable Environment.

Mental health has been declared as the essential component of overall human wellbeing. However, there has been a very steep rate of depression and anxiety in students that exhibit their social and personal burdens. It has been widely accepted that the wellbeing and mental health of individuals are a mix of psychological, genetic, social, lifestyle factors, and environmental exposure. Due to the pandemic, the shift from traditional classroom learning to e-learning has also disturbed the mental health of students, which consequently affects environmental stability. The current study has also measured the effect on the mental health of e-learning behaviors (psychological motivation, peer collaboration, cognitive problem-solving, interaction with the instructor, community support, and learning management). The population of the study was the undergraduate students enrolled in the colleges of China, and they were chosen via convenient sampling. The findings of the study show that mental health has a significant positive effect on the e-learning behavior of the students and consequently affects environmental sustainability. Educational institutions are improving their e-learning programs by understanding the preferences and challenges of students regarding online learning. Educational institutions should revise their policies on online education and teaching methodologies. Furthermore, the current study has taken undergraduate students as the sample. In future studies, these relationships can be checked in higher education as well.

Influence of Online Learning Behavior and Video Playing Questions on Students’ Learning Effect

The interactive mode in e-learning can effectively stimulate students' learning motivation, especially the way of video questioning as the mainstream interactive mode of e-learning video, which is conducive to improve students' enthusiasm to participate in learning and enhance learning thinking. This study constructs a theoretical model of the impact of E-learning behavior on Students' learning effect, and analyzes the impact of video bullet problem as an intermediary variable on E-learning behavior on students' learning effect. The results show that the overall Cronbach of the questionnaire α the coefficient is 0.883 and Kaiser – Meyer – Olkin value is 0.822, indicating that the questionnaire has good reliability and validity. The research results of this study provide reference suggestions for exploring the main influencing factors of learning behavior, improving the effect of students' online learning, revealing the optimization strategy of video bullet problem feedback under the network teaching environment, and the production of bullet problem interactive video.

The Influence of E-learning Behavior on Students’ Learning Performance of Disaster Emergency Knowledge

E-learning has advantages of huge learning resources, enthusiastic learners, friendly interaction and flexible space-time, especially, the large-scale application of many new educational technologies makes e-learning possible. Based on the theory of deconstruction program behavior and task technology fit model, four assumptions of e-learning behavior’s influence on learning performance of students’ disaster emergency knowledge were put forward from two aspects, namely the individual factors (self-efficacy), information literacy and environmental factors (technical characteristics, and promote conditions), the PLS (partial least squares) method was used to carry out regression analysis of affecting factors. Results show that the Cronbach’s ɑ values of the questionnaire are all greater than 0.8, indicating that the measurement indexes have high reliability. AVE (average variance extracted) values of all latent variables are greater than the threshold level of 0.5, showing good aggregation validity. The square root of AVE value is greater than correlation coefficient of each latent variable, and the discrimination validity is good. Information literacy, technical characteristics and facilitation conditions have a positive and significant impact on learning performance of students adopting e-learning of disaster emergency knowledge. The conclusions provide practical suggestions and opinions for enriching the analysis of learning behavior under the e-learning environment, depicting the characteristics of students’ e-learning behavior.

Research on E-learning Behavior Evaluation of Students Based on Three-way Decisions Classification Algorithm

With the popularization and application of E-learning platform, it is feasible to collect learning behavior data of students, which provides data basis for analyzing the knowledge and rules contained in learning process of students. At present, the common e-learning platform only realizes the simple statistics and display function for the data, and does not do further in-depth calculation. The data observed by educators are still superficial learning phenomena, and they cannot see the learning rules behind the appearance. It is difficult to effectively guide students, change their learning routes and feedback teaching strategies. In view of this, the main characteristics of learning behavior deeply mined from learning activities and learning evaluation, and the important data feature items affect E-learning effect obtained. Three decision classification algorithms applied to establish prediction and evaluation model for students with learning behavior risk. The overall accuracy of model test is in line with the expectation. This provides a practical application modeling method for further implementation of personalized learning service.

P0675 / #906: PEDIATRIC CRITICAL CARE FELLOW ENGAGEMENT WITH AN OPEN-ACCESS E-LEARNING RESOURCE

Aims & Objectives: Pediatric critical care medicine (PCCM) fellows must provide acute, complex clinical care while addressing numerous knowledge gaps, and so may particularly benefit from online educational support. Limited data illustrate the e-learning behaviors or interests of PCCM fellows specifically. Such information may help guide design and targeting of educational interventions. To explore this, we characterized PCCM fellow usage of OPENPediatrics, a worldwide open-access e-learning resource, from June 2018 through June 2019. Methods: IRB-approved retrospective analysis of automatically collected activity log data from users who self-identified as PCCM fellows. The most popular resources (based on percent of fellows who accessed the resource) are reported. Timestamps were used to calculate the duration of time spent by individual users on the website. Results: 299 fellows from 39 countries were included. The distribution of time spent on the site was bimodal, with 105/299 (35%) of users logging <=60 minutes on the site in the analyzed interval, and 100/299 (33%) logging >=240 minutes (Figure 1). These users accessed an average of 9 resources on the site (range 1-89). Table 1 reports the most frequently accessed resources by this cohort of users.Conclusions: The majority of PCCM fellows spent at least 30 minutes on the site. Over 33% of included fellows spent >=4 hours using the site in one year. Courses and simulators were especially popular. Specific popular items included PCCM fellowship-specific curricula, materials for cardiology and congenital heart disease, and interactive online simulators teaching mechanical ventilation and hemodialysis. Our findings can help shape educational interventions and strategies for PCCM fellows.

Influence of Attitudinal belief on e-learning behavior Among Academics in State Universities of Sri Lanka

The objective of this paper is to investigate the influence of attitudinal belief of “Decomposed Theory of Planned Behavior” (DTPB) on actual e-learning behavior among university academics in Sri Lanka. The study applied a quantitative method by using questionnaire to collect data among university academics. Explorative and Confirmatory Factor analysis was used to examine the reliability and validity. And this study carried out Structural Equation Modeling to assess model fit and through path analysis, the association between variables was investigated. The study resulted that perceived ease of use, perceived usefulness, compatibility, attitude, behavioural intentions and actual behaviour met the indices for model fit. The findings showed perceived usefulness and perceived ease of use associated with attitude while compatibility is not. Attitude significantly and positively associated with behavioural intention. While behavioural intention positively associated with the actual usage this mediates the relationship between attitude and actual behaviour. The e-learning usage of academicians will help to enhance the quality of higher education system in Sri Lanka as well as to magnify the number of university intake as the country seeks to achieve its vision to be “the knowledge hub in the region and a leader in higher education in Asia by 2020”. Though the e-learning adoption studies are growing trend of education and system researchers as evidenced by extant literature, studies on attitude and intention of academicians especially in an emerging economy like Sri Lanka is still lacking. This study attempts to establish the attitudinal factors that contributes actual e-learning behavior of academicians.

Research trend on the application of “E-learning adoption theory” : A scientometric study during 2000-2019, based on Web of Science and SCOPUS

Bibliometric study of the research trend on e-learning adoption theory is essential to investigate the existing literature to identify a suitable theory for new research. Objective of this study is to explore the research trend in scientific literature on e-learning adoption theory. Search of Web of Science and SCOPUS were carried out to find papers dealing with the contours of e-learning adoption theory. This study retrieved 84 research papers from Web of Science and 324 from SCOPUS on the “e-learning adoption theory.” USA and Taiwan accounted for the highest number of publications with 39 papers (12.03%) and 19 papers (22.62%) in SCOPUS and Web of Science respectively. Computer Science has the highest number of papers in SCOPUS whilst Education and Educational Research is the top in Web of Science. China Agricultural University and Universiti Teknologi Malaysia have the highest number of publications four and seven in Web of Science and SCOPUS respectively. National Central University and China Agricultural University are the most productive organization as they have received 321 and 69 citations on e-learning adoption theory in Web of Science and SCOPUS respectively. Among the authors, Tarhini Ali of Brunel University, London has published the highest number of papers (five) on e-learning adoption theory in Web of Science and received 124 citations. Despites, Giannakos, M.N. and Pappas, I.O. have published the same quantity of papers (five) in SCOPUS, they have received only 47 and 22 citations respectively. Journal namely Computers and Education have published the highest number of papers in both databases at 13 (16.05%) and 14 (4.32%) received the highest number of citations at 996 (53.04%) and 1487 (39.37%) for those papers in Web of Science and SCOPUS respectively. Among the technology adoption theories, the Decomposed Theory of Planned Behaviour is more suitable to study e-learning behavior of individuals.

A New Intelligent Optimization Network Online Learning Behavior in Multimedia Big Data Environment

In the era of multimedia big data, the online learning behavior of users becomes rich and colorful. This paper proposes a hybrid clonal selection differential evolution optimization algorithm based on clonal selection algorithm and differential evolution in multimedia big data environment. The proposed intelligent optimization algorithm treats each e-learning behavior of the user as an antibody, and gets the best results of multimedia big data mining by a number of iterative searches. Experimental results show the feasibility and effectiveness of the proposed intelligent optimization algorithm.

Research on College Students' Application Behavior Based on Blackboard System - A Case Study on Business College of Beijing Union University

Blackboard system is a network platform that is widely used by Universities and Colleges both at home and abroad. Its role is to broaden students' learning time, space and to assist the students' learning in their class hours and also after their class. Based on the data of a questionnaire on local college students’ attitude and behavior on the elearning platform of Blackboard, this thesis presents a generalization and analysis the characteristic of students’ e-learning behavior by addressing their three behaviors: Information retrieval and processing behavior, social interaction behavior, and problems solving behavior. The behavioral characteristics of students’ application of Blackboard were analyzed in detail. The results showed that: overall, there is no great gender difference in the three aspects, but there are significant differences in different grades. Finally, according to the results of the questionnaire and statistical analysis, several suggestions and advices of curriculum construction based on Blackboard were put forward.

Attributes of Affective E-learning Behavior: Emerging Issues Beyond Teaching and Learning

Attributes of Affective E-learning Behavior: Emerging Issues Beyond Teaching and Learning