The Effect of E-Learning as One of the Information Technology-Based Learning Media on Student Learning Motivation

Widiyanto, Andi; Prabowo, Nugroho Agung; Nuryanto, Nuryanto; Amarullah, Mochammad Ircham Nashif; Nuruzzaman, Arsyad Soni

doi:10.47738/ijiis.v4i2.108

Cited by 6 publications

(6 citation statements)

References 16 publications

(20 reference statements)

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“…For example, with the help of specific software (Hsueh, 2018 ; Hananto, 2021 ) or algorithms (Astuti and Handoko, 2018 ; Imron and Kusumah, 2018 ), educators could identify and target the students who hold high materialism through, as well as predicting the likelihood of them starting a business in the future through machine learning (Jen and Lin, 2021 ; Prayitno et al, 2021 ; Saputro and Nanang, 2021 ; Sugiyanto, 2021 ). And then, entrepreneurial education programs could provide them with decision support systems (Azis et al, 2020 ; Fujishima, 2022 ), e-learning classes (Widiyanto et al, 2021 ), and customized training plans (Thelen, 2021 ) to improve their achievement motivation and enhance attitudes and skills regarding entrepreneurship.…”

Section: Discussionmentioning

confidence: 99%

Materialism Predicts College Students' Entrepreneurial Intention: A Serial Mediation Model

Zhang

2022

Front. Psychol.

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Entrepreneurship is perceived as a critical pillar for unemployment alleviation and economic growth, especially in the era of COVID-19, which highlights the importance of the entrepreneurial potential of college students. The current research focused on the role of personal values in the entrepreneurial process and investigated the relationship between materialism and entrepreneurial intention among college students. Few studies have been examined this relationship, and the underlying mechanisms were also not identified. From the perspective of personal value, we hypothesized that materialism could positively predict entrepreneurial intention. Moreover, based on McClelland's theory of need for achievement and the theory of planned behavior, a serial mediation model, with achievement motivation and entrepreneurial attitude as the mediators, was proposed. We conducted a correlational study on a sample of 1,002 Chinese university students to examine our hypotheses. They participated in an online survey and completed the measurement of entrepreneurial intention, entrepreneurial attitude, materialism, and achievement motivation. The hypothesized models were examined through serial mediation bootstrapping procedures. The results showed that materialism positively predicted college students' entrepreneurial intention, and this relationship was serially mediated through achievement motivation and entrepreneurial attitude. Materialism boosted college students' achievement motivation, which in turn was associated with a more positive entrepreneurial attitude and subsequently stronger entrepreneurial intention. The present research is the first to empirically examine this association's mechanism and establish a serial mediation involving achievement motivation and entrepreneurial attitude. For the theoretical contribution, the present research provides a more comprehensive picture of the role of personal values in entrepreneurship by complementing the effect of materialism. And regarding the practical implications, the present research implies the silver lining of materialism and points out a possible way to enhance college students' entrepreneurial intention, i.e., entrepreneurial education could take advantage of the characteristics of materialism and transform the “harmful” value into socially beneficial entrepreneurial intentions through enhancing their achievement motivation and positive attitude toward entrepreneurship.

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Section: Discussionmentioning

confidence: 99%

Materialism Predicts College Students' Entrepreneurial Intention: A Serial Mediation Model

Zhang

2022

Front. Psychol.

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“…The results showed that both theoretical modules and workshops were effective in enhancing students' understanding of the Tripitaka teaching. The satisfaction level reached an average of 3.50, indicating the effectiveness of the online lessons in enhancing teachers' teaching skills [16].…”

Section: Conceptual Frameworkmentioning

confidence: 98%

Data Analytics of Online Lessons in Social Studies and Buddhism: Enhancing Dhamma Teaching and Tripitaka Understanding Among Teachers and Students

Luaensutthi

2023

J. Appl. Data Sci.

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The objectives were to (1) determine the effectiveness of online lessons of Social Studies and Buddhism on Dhamma's teaching regarding Tripitaka for teachers; (2) compare the pre-test and post-test achievements of teachers and primary school 6 (Grade 6) students; 3) examine the satisfaction of teachers and students using online lessons of Social Studies and Buddhism on Dharma's teachings according to the Tripitaka. The samples were 12 teachers, and 30 students studying primary school 6 (Grade 6) at Wat Proifon School. The instruments were online lessons of the Social Studies and Buddhism course on Buddha's Teaching Tripitaka, pre-test and post-test, and the questionnaire of teachers' and students' satisfaction towards studying the online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding the Tripitaka.Statistics used were percentage, mean, standard deviation, and t-test for dependent samples. The findings revealed that the efficiency of online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding Tripitaka was 81.92/80.83 on average based on the criteria. The teachers' learning achievements after using online lessons in the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test 11.40, SD.=1.51, while the average score of the post-test was 18.17, SD.=1.10, and the t-test between the pretest and post-tests was 6.77, which were significantly distinctive at the level of .05., and the students' learning achievements after using online lessons on the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test: 10.40, SD.=1.61, while the average score of the post-test was 16.17, SD.=1.11, and the t-test between the pre-test and post-tests was 5.77, which were significantly distinctive at the level of .05. Teachers' satisfaction was at high level with an average of 4.47, SD.=.55, and the students' satisfaction gained a very high level with an average of 4.50, SD.=.44.

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“…In addressing this issue, companies need to create engaging and interesting experiences for customers, through strategies such as loyalty programs, active communication, and personalization. In addition, problems with poor customer service can also be a contributing factor to churn [16][17] [18]. If customers face difficulties in contacting or get a slow response from the customer service team, they may be disappointed and decide to move to another company.…”

Section: Customer Churnmentioning

confidence: 99%

Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction

Lukita

2023

J. Appl. Data Sci.

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This research aims to predict and analyze customer churn using Data Mining and Machine Learning methods. The background of this research is based on the importance of understanding the factors that influence customer decisions to churn, as well as improving the effectiveness of customer retention strategies in a business context. The method used in this research involves the use of a customer bank dataset that includes information about customers who left in the past month, services registered by customers, customer account information, and demographic info about customers. The factors most influential to churn were identified through heatmap analysis, including MonthlyCharges, PaperlessBilling, SeniorCitizen, PaymentMethod, MultipleLines, and PhoneService. This research compares the performance of several machine learning algorithms, including Random Forest, Logistic Regression, Adaboost, and Extreme Gradient Boosting (XGBoost), to predict customer churn. Accuracy metrics and confusion matrix results are used to evaluate the performance of these algorithms. The results showed that XGBoost proved to be the best algorithm in predicting customer churn with high accuracy. The factors that have been correctly identified do not provide missed precision, showing a significant influence on customer churn decisions. The novelty and uniqueness of this research lies in focusing on the factors that have the most influence on customer churn and comparing the performance of machine learning algorithms. This research provides more specific and relevant insights for companies in developing effective customer retention strategies. However, this research has some limitations. One of them is the use of a dataset limited to a customer bank, so the generalizability of the findings of this research may be limited to that business context. In addition, other factors that are not the focus of this research may also contribute to the prediction of customer churn.

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The Effect of E-Learning as One of the Information Technology-Based Learning Media on Student Learning Motivation

Cited by 6 publications

References 16 publications

Materialism Predicts College Students' Entrepreneurial Intention: A Serial Mediation Model

Materialism Predicts College Students' Entrepreneurial Intention: A Serial Mediation Model

Data Analytics of Online Lessons in Social Studies and Buddhism: Enhancing Dhamma Teaching and Tripitaka Understanding Among Teachers and Students

Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction

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