Analysis of Factors Affecting Behavioral Intention to Use E-Learning Uses the Unified Theory of Acceptance and Use of Technology Approach

The purpose of this study isto analyze the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived creadibility, and anxiety on e-learning behavioral intention to use who are moderated by experience and voluntariness of use.The study population was 215 students who used e-learning in the Accounting Department of SMK N 1 Karanganyar. The sample selection using Slovin method with an error rate of 5% and sampling area technique obtained by respondents as many as 140 students. The technique of collecting data using a questionnaire. Data analysis techniques used descriptive statistical analysis and SEM-PLS. Data analysis tool using WarpPLS 5.0.The results of the descriptive statistical analysis show that the behavioral intention to use e-learning, performance expectancy, effort expectancy, social influence, facilitating conditions, perceived creativity, anxiety, experience and voluntariness of use are in the sufficient category. Hypothesis test results show the influence of performance expectancy on e-learning behavioral intention to use, effort expectancy does not affect the behavioral e-learning intention to use, social influence has an effect on behavioral e-learning intention to use, facilitating conditions have no effect on behavioral intention to Using e-learning, perceived creativity does not affect e-learning behavior, anxiety influences the behavioral intention to use e-learning, voluntary moderating negative social influences the behavioral e-learning intention to use, experience moderates the effect of effort expectancy on The behavior of e-learning intention to use, experience does not moderate the influence of social influence on the behavioral e-learning intention to use, experience does not moderate the effect of facilitating conditions on e-learning behavioral intention to use e-learning the conclusion of this study states that of the ten hypotheses proposed there are five types of hypotheses accepted. Keywords: E-learning, Behavioral Intention, UTAUT

Fuzzy Logic A Soft Computing Approach For E-Learning: A Qualitative Review

E-learning model has been developed rapidly because of development in technology, mobile platform such as smart phone and pad etc. But due to low rate of completion of e-learning platform it is necessary to analyze behavior characteristics of online learners which enhance the quality of learning. This can be achieved by recommending suitable e-contents available in learning servers that are based on learning style, learning pattern, time, environment, psychology and mood of learners. All these factors are uncertain. In such case fuzzy logic and neural network approach of soft computing is desirable to use and helps to take decision for prediction of e-learning. The aim of this paper is to study development and work in e-learning, adaptive learning and web-based learning globally. Also study for to develop reliable and efficient solution for e-learners and e-content provider. This paper represent studies of learning style prediction, learning style model, learning system and analysis of related work in e-learning and web environments. This is review of previous research in e-learning prediction

Determinants of Freshmen’ Behavioral Intention and Use Behavior of Ubiquitous Learning in Chengdu, China: A Case of Three Universities

Purpose: This study aims to explore the factors that influence first-year students’ behavioral intention and use behavior when using ubiquitous learning in Chengdu, Sichuan Province. The key variables are understanding u-learning, assimilating u-learning, applying u-learning, perceived usefulness, e-learning motivation, social influence, behavioral intention, and use behavior. Research design, data, and methodology: Quantitative methods and questionnaires were used to collect sample data from the target population. The sampling methods are purposive, quota, and convenience sampling. The index of item-objective congruence and Cronbach's Alpha pilot tests were used to test the validity and reliability of the content before the questionnaire was distributed. Confirmatory factor analysis and structural equation model were used to analyze the data, verify the model's goodness of fit, and confirm the causal relationship between variables for hypothesis testing. Results: The findings indicate that the conceptual model can effectively predict behavioral intention and usage behavior. Assimilating u-learning, applying u-learning significantly influence perceived usefulness. Perceived usefulness, e-learning motivation, social influence significantly influences behavioral intention towards use behavior. In opposite, understanding u-learning has no significant influence on perceived usefulness. Conclusions:  It is found that the conceptual model of this study can predict and explain the behavioral intent and usage behavior of college students when using u-learning

Penerimaan dan Penggunaan E-Learning pada Masa Pandemi Covid-19: Aplikasi Model UTAUT2

Abstrak: Penelitian ini bertujuan untuk memperoleh dan menganalisis bukti empiris faktor-faktor yang mempengaruhi penerimaan dan penggunaan e-learning oleh peserta didik pada jenjang SLTA dan Perguruan Tinggi di Tasikmalaya dengan total subjek penelitian sebanyak 289 responden.  Teknik analisis data dilakukan dengan Partial Last Squares – Structural Equation Modeling (PLS – SEM) melalui software SmartPLS versi 3.2.9. Berdasarkan hasil pengujian pada sampel siswa SLTA diketahui bahwa Social influence dan Habit berpengaruh positif terhadap Behavioral intention, adapun Use behavior e-learning pada siswa SLTA secara signifikan dipengaruhi oleh Habit dan Behavioral intention. Pengujian pada kelompok sampel mahasiswa menunjukkan bahwa facilitating condition, hedonic motivation, dan habit berpengaruh signifikan terhadap behavioral intention, adapun Use behavior e-learning pada mahasiswa hanya dipengaruhi oleh Habit. Melalui penelitian ini akan dapat diketahui faktor-faktor determinan yang mempengaruhi penggunaan e-learning pada peserta didik.Abstract: This study aims to obtain and analyze empirical evidence of the factors affecting the acceptance and use of e-learning by students at the high school and college students in Tasikmalaya with a total number are 289 research objects. The data analysis technique was done by using Partial Last Squares - Structural Equation Modeling (PLS-SEM) through SmartPLS version 3.2.9 software. Based on the test results showed in a sample of high school students, it is known that social influence and Habit gave a positive effect on Behavioral intention, while the Use behavior of e-learning on high school students was significantly influenced by Habit and Behavioral intention. Then the Testing on a sample group of college students showed that facilitating conditions, hedonic motivation, and habits gave a significant effect on behavioral intention, while the use of e-learning behavior on students only influenced by Habit. Through this research, it will be able to know the determinant factors that affect the use of e-learning in students

Big Data Reference Architecture for e-Learning Analytical Systems

The recent advancements in technology have produced big data and become the necessity for researcher to analyze the data in order to make it meaningful. Massive amounts of data are collected across social media sites, mobile communications, business environments and institutions. In order to efficiently analyze this large quantity of raw data, the concept of big data was introduced. In this regard, big data analytic is needed in order to provide techniques to analyze the data. This new concept is expected to help education in the near future, by changing the way we approach the e-Learning process, by encouraging the interaction between learners and teachers, by allowing the fulfilment of the individual requirements and goals of learners. The learning environment generates massive knowledge by means of the various services provided in massive open online courses. Such knowledge is produced via learning actor interactions. Also, data analytics can be a valuable tool to help e-Learning organizations deliver better services to the public. It can provide important insights into consumer behavior and better predict demand for goods and services, thereby allowing for better resource management. This result motivates to put forward solutions for big data usage to the educational field. This research article unfolds a big data reference architecture for e-Learning analytical systems to make a unified analysis of the massive data generated by learning actors. This reference architecture makes the process of the massive data produced in big data e-learning system. Finally, the BiDRA for e-Learning analytical systems was evaluated based on the quality of maintainability, modularity, reusability, performance, and scalability

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Learning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model

Critical Success Factors on Gamification to Support the Learning Process in University

Gamification is the process of implementing a game strategy in the learning process. Gamification in the lecture environment increases creativity and interactivity and provides students with a sensation of accomplishment (Sense of accomplishment). The development of communication technology increased rapidly, which impacted the development of the games industry, which attracted Indonesian people in this case. Therefore, gamification can be an alternative to represent innovative and exciting learning for students in Indonesia. In this Research, the authors tried to analyze the determinants of the effectiveness of learning using gamification Keywords: Gamification; Learning; Critical Success Factors; University Students; Learning Process eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by E-International Publishing House, Ltd., UK. This is an open-access article under the CC  BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under the responsibility of AMER (Association of Malaysian Environment-Behavior Researchers), ABRA (Association of Behavioral Researchers on Asians), and cE-Bs (Centre for Environment-Behavior Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia

Investigating Mutual Adaptation Process between Users and E-Learning System: A Knowledge Access Efficiency Approach

For its advantages in being able to provide rapid and comprehensive access to knowledge, e-learning system has been widely adopted in education to improve learning processes. However, such IT-mediated processes involving both users and system are subject to changes. There exists adjustment in browsing behavior on user side and web structure refinement on system side. It would impose uncertainty and influence on learning quality and thus create a strong need for rapid adaptation between users and the e-learning system. Focusing upon knowledge organization in terms of web structure management, this study intends to find out an effective way to speed up the adaptation process. It consists of two stages. In the first stage, we would like to study the general adapting behaviors of e-learners, and analyze how they are affected by the web structure. Through analytical modeling, Knowledge Access Efficiency (KAE), which is capable of reflecting the quality of adaptation, is conceptualized mathematically. In the second stage, the dynamics of KAE during the whole adaptation process will be carefully investigated. It is hoped that this study can give us useful clues regarding knowledge organization of e-learning system, and by improving web structure management it can elicit better performance of elearners

Design and evaluation of a gamified e-learning system for statistics learning activities

Researchers in statistics learning have made an effort to develop technological solutions to support students in this field. However, such solutions must involve and engage students to perform learning activities to develop their statistical thinking. In this scenario, this study proposes the design of a gamified e-learning system to involve students when they perform learning activities. We analyze students’ behavior towards statistics activities when using the environment. To achieve these goals, this project focuses on a gamified structure for the development of learning activities with a reward system. The learning activities are based on question and answers developed by teachers to measure the students’ understanding of different course contents and also provide them with a problem-based approach. The project was applied in a Probability and Statistics course during 30 days. The results suggest a positive outcome mostly because the designed gamification elements achieved their desired role inside the environment.info:eu-repo/semantics/publishedVersio