The Impact of Socialization on Collaborative Learning Method in E-Learning Management System (eLMS)

Muhisn, Zahraa Abed Aljasim; Ahmad, Mazida; Omar, Mazni; Muhisn, Sinan Adnan

doi:10.3991/ijet.v14i20.10992

Cited by 15 publications

(9 citation statements)

References 28 publications

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“…In this review, we found that the role of instructor was still irreplaceable, because the instructor's involvement existed in most of the AI-STEM research. Even though AI can free instructors from redundant tasks in STEM education, it still lacks the human ability to convey social emotion, solve critical problems, and implement creative activities (Collinson, 1996;Gary, 2019;Muhisn et al, 2019). Therefore, although AI techniques can bring opportunities to develop STEM education (Hwang et al, 2020), we cannot overstate the role of technology and overlook the essential role of instructor (Selwyn, 2016).…”

Section: Educational Implicationsmentioning

confidence: 99%

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Ouyang

2022

IJ STEM Ed

129

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Background The application of artificial intelligence (AI) in STEM education (AI-STEM), as an emerging field, is confronted with a challenge of integrating diverse AI techniques and complex educational elements to meet instructional and learning needs. To gain a comprehensive understanding of AI applications in STEM education, this study conducted a systematic review to examine 63 empirical AI-STEM research from 2011 to 2021, grounded upon a general system theory (GST) framework. Results The results examined the major elements in the AI-STEM system as well as the effects of AI in STEM education. Six categories of AI applications were summarized and the results further showed the distribution relationships of the AI categories with other elements (i.e., information, subject, medium, environment) in AI-STEM. Moreover, the review revealed the educational and technological effects of AI in STEM education. Conclusions The application of AI technology in STEM education is confronted with the challenge of integrating diverse AI techniques in the complex STEM educational system. Grounded upon a GST framework, this research reviewed the empirical AI-STEM studies from 2011 to 2021 and proposed educational, technological, and theoretical implications to apply AI techniques in STEM education. Overall, the potential of AI technology for enhancing STEM education is fertile ground to be further explored together with studies aimed at investigating the integration of technology and educational system.

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Section: Educational Implicationsmentioning

confidence: 99%

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Ouyang

2022

IJ STEM Ed

129

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“…For much achievement data, association rules in data mining technology can be used to obtain the association relationship between subjects and find the neglected content in learning, which can provide targeted help and academic early warning for each student, and provide teaching guidance for teachers and administrators. Data mining is regarded as a set of technologies that allow automatic or semi-automatic extraction of much useful information, models and trends from many datasets, such as "clustering", "classification", "association" and "regression"; intelligent artificial algorithms such as Apriori algorithm, Bayesian algorithm, and neural network will be used to extract patterns from data, and these patterns are realized to interpret and predict their behaviors [4][5][6][7][8]. Association rule is an important task to discover frequent patterns in data mining.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Development of College and University Education by use of Data Mining Methods

Wang

Chung

2021

Int. J. Emerg. Technol. Learn.

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To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.

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“…Online learning provides a change in the world of Education that provides opportunities for students to receive and share information easily (El-Seoud, 2014). Using the internet is not only effective but also students are also faster to access various learning materials (Muhisn, 2019).…”

Section: Tabel 4 Comparison Of the N-gain Score Of The Learning Outcomesmentioning

confidence: 99%

The Effect of Online Learning System Through Blended Learning Model toward Higher Student’s Academic Achievement

Nurdiyanti

Rajab

Sudarsono

et al. 2019

JBE

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Era industry 4.0, especially in the field of education, emphasizes the use of digital technology and the internet which develops rapidly in the learning process. Online learning is one form of implementation of learning in industry 4.0 era. The purpose of this study is to determine the effect of the implementation of the learning system on the higher students’ academic achievement. This research is experimental research, with a pretest-posttest control group design. The purpose of this design is to reveal whether online learning systems can affect students' academic achievement or not that's measured through the learning outcomes of biology education students at the concept of structure and transport of cell membranes in the University of Muhammadiyah Makassar. Based on analyzed data, it shows the difference in the increase of students' learning outcomes taught by the online learning system and without using an online learning system. Based on the findings of this study, students who are taught by online learning system get a higher score of learning outcomes. It can be said that there is an effect of the online learning system toward students’ academic achievement.

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The Impact of Socialization on Collaborative Learning Method in E-Learning Management System (eLMS)

Cited by 15 publications

References 28 publications

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Sustainable Development of College and University Education by use of Data Mining Methods

The Effect of Online Learning System Through Blended Learning Model toward Higher Student’s Academic Achievement

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