e-learning
R. Nezhadsafar; N. Pourasghar; A. Rastgoo; Y. Namvar
Abstract
Background and Objectives: With the advancement of digital technologies and the emergence of new tools, the education system has undergone fundamental changes, and in the meantime, electronic education has become one of the essential pillars of this transformation. As the pioneer of these changes, higher ...
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Background and Objectives: With the advancement of digital technologies and the emergence of new tools, the education system has undergone fundamental changes, and in the meantime, electronic education has become one of the essential pillars of this transformation. As the pioneer of these changes, higher education plays a pivotal role in developing human resources skills and advancing societies. The role of higher education instructors in this transformation has changed significantly. They are no longer merely transmitters of knowledge but facilitators of the learning process and guides for creating knowledge through active student participation. To adapt to the changes of this era, higher education instructors need to review their teaching skills, requirements, approaches, and methods. Undoubtedly, empowering university professors and equipping them with the necessary knowledge and skills will not only improve the quality of education but will also have a direct impact on the sustainable development of society. Therefore, this research aims to identify and comprehensively analyze the key requirements of higher education instructors in the context of e-learning.Methods: The present research was conducted using content analysis with a qualitative approach. For this purpose, 16 professors and experts in the field of educational sciences and higher education were selected through semi-structured interviews and purposive sampling. Additionally, 29 articles and six books were reviewed using purposive sampling and note-taking from credible resources from 2000 to 2025. The content analysis method proposed by Graneheim and Lundman was used to analyze the data, and Lincoln and Guba's criteria were used to assess the validity and reliability of the study.Findings: The results of this study identified seven essential components as key requirements for e-learning instructors, which are: Personal requirements (Personal traits, Professional traits), Communication and interaction requirements (Constructive interaction ability, The ability to create motivational and encouraging activities, The ability to manage communications and participation strategies, The ability for grouping and team building), Educational and Professional requirements (Scientific and professional competence, Performance measurement and evaluation capability, Applying educational and learning strategies, Planning and management of e-Learning courses, The ability to enhance and develop the cognitive skills of students), Managerial and Organizational requirements (Support, Access to infrastructure and facilities, Educational policy-making, Empowerment and capability development), Cultural and Legal Requirements (e- learning culture, ethical-legal requirements), Technological Requirements (Technological knowledge, Technological skill), Health and Safety Requirements (Physical and mental health, Ergonomics and occupational health, Health policies).Conclusion: Research findings indicate that the success of higher education instructors in e-learning environments requires a combination of technical, professional, and personality skills. These results can be the basis for designing professional development programs to enhance the competence of instructors and improve the quality of e-learning in universities. The identified dimensions can help policymakers and instructors to identify the educational needs of professors and increase their ability to cope with the challenges posed by technological advances by continuously updating programs. The proposed framework provides a suitable basis for future research in instructors' evaluation and the development of e-learning systems, and helps to improve the skills and abilities of teachers in this field. It is recommended that macro and micro planning and policies be formulated in a way that, while keeping pace with the rapid changes of the present era, creates a suitable platform for innovation in education and dynamism in academic environments.
Educational Technology - Artificial Intelligence
M. Rezaei; E. Pazouki; R. Ebrahimpour
Abstract
Background and Objectives: Today, due to the increasing development of technology all over the world, e-learning systems are expanding rapidly. With the progress of electronic education, the movement from traditional education (the approach of providing one education for all) to personalized education ...
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Background and Objectives: Today, due to the increasing development of technology all over the world, e-learning systems are expanding rapidly. With the progress of electronic education, the movement from traditional education (the approach of providing one education for all) to personalized education began. Personalized education is an educational approach that aims to customize learning based on a learner's strengths, skills, interests, and needs. This method of education, like any other new method, has its strengths and weaknesses. In fact, increasing motivation and acquiring self-defense skills can be considered as one of the important benefits of this type of training. On the other hand, as the weaknesses of this method, we can mention the time-consuming training, the challenge in implementation, and the lack of clarity in the method of application. Due to the availability of many data from learners, the use of artificial intelligence to personalize education will both increase the quality and make education more attractive. Nowadays, one of the ways to personalize education is to provide it based on the preferences of learners. Learner preferences can be self-identified and explicitly identified and extracted by directly asking the learner or implicitly and collecting and monitoring data. Today, modeling user preferences is one of the most challenging tasks in e-learning systems that deal with a large amount of information. The aim of this research was to extract the implicit preferences of the learner by using an online interactive intelligent educational system that models the learner's preferences using conceptualization for learning objects through profile expansion and the use of artificial intelligence algorithms. The model was trained with the collected interactive data and provides new learning objects based on the learner's preferences. This research was practical in terms of purpose.Methods: In this research, according to the society available to us, 29 male and female undergraduate students of computer sciences, with an average age of 21.5 years, who had not taken the machine learning course, were included as the participants. After registration, the participants were randomly divided into two control and experimental groups. The experimental group was presented with personalized content that matched their preferences, and the control group was presented with content that did not match their preferences. After the training, the learning rate and cognitive load of the participants were measured by the designed performance test and the NASA workload index questionnaire. At the end, the significance level of the obtained results of the two groups was evaluated using the independent t-test.Findings: Based on the obtained results, the average performance test scores of the experimental group who received content matching their preferences had no significant difference compared to the average of the control group with a value of p=0.7 (while learning), but the cognition of the control group was significantly lower with p=0.00 compared to that of the experimental group.Conclusion: Based on the findings of the research, providing personalized educational content based on learners' preferences using the profile expansion technique significantly reduced the cognitive load during learning. So, Providing educational content based on learners' preferences, as one of the personalized educational methods in e-learning, plays an important role in reducing the cognitive load of learners.
Electronic learning- virtual
S. Azimpour; H. Vahedi
Abstract
Background and Objectives: In recent years, the use of technology and educational media in education has been one of the focus of studies. These media both improve the accuracy of the operation and increase the speed of learning and transfer of concepts. Considering the graphic and spatial nature of ...
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Background and Objectives: In recent years, the use of technology and educational media in education has been one of the focus of studies. These media both improve the accuracy of the operation and increase the speed of learning and transfer of concepts. Considering the graphic and spatial nature of the content of the geometry course and the problems related to accurate and correct drawing and visualization of shapes, it seems necessary to use new methods and tools in education. The aim of this study was to evaluate and compare the effectiveness of teaching the geometry lesson with electronic educational media methods (teaching with dynamic and static graphic images) and teaching in the traditional way , on the academic progress and geometrical academic self-efficacy of the students.Methods: According to the nature of the work, the research method was quasi-experimental. To conduct the research, a pretest-posttest with a control group design was used. The statistical population of this study included all male students in the tenth grade of mathematics in a District 1 of Tabriz in the academic year 2020-2021. The statistical sample consisted of 79 male students in the tenth grade of mathematics, including 3 classes, which were selected by availability sampling method and were randomly divided into three class groups. There were 26 people in the teaching group using dynamic graphic images electronic media class group, 26 people in the teaching group using static graphic images electronic media class group, and 27 people in the traditional teaching class group. The intervention tool of this research included the software for electronic teaching dynamic graphic images (Geo Gebra) and a set of educational slides designed by the researchers for teaching static graphic images. The study groups were trained for 6 weeks, 1 session per week and 90 minutes per session. In order to collect data related to academic progress, all three groups before and after the intervention, were assessed by using two parallel tests made by the researchers, including 14 questions, in the form of open-ended questions (explanatory) about the concepts of drawing and geometric reasoning from the geometry book of the 10th grade of the mathematics field. The first test was used as a pre-test and the second test was used as a post-test. Also, the participants answered the math self-efficacy questionnaire before and after the training. The data obtained from the pre-test and post-test stages were analyzed using one-way covariance analysis (ANCOVA) after checking the assumptions.Findings: The results showed that the studied teaching methods had different effects on academic progress. With regard to the academic progress, the difference between electronic educational groups (P<0.009), between electronic education group with dynamic graphic images and common education group (P<0.001) and between electronic education group with static graphic images and common education group (P<0.001) was significant. Also, the results showed that the studied teaching methods had different effects on geometry academic self-efficacy. Regarding academic self-efficacy, the difference between electronic educational groups (P<0.02), etween electronic education group with dynamic graphic images and common education group (P<0.05) and between electronic education group with istatic graphic images and common education group (P<0.001) was significant. Among the studied groups of this research, the best results of academic progress and academic self-efficacy were related to electronic education group with dynamic graphic images.Conclusion: The results of this study showed that the use of teaching methods with graphic images by teachers in teaching geometry can have a positive effect on students' academic achievement and academic self-efficacy. The use of these tools can play the role of an educational facilitator in improving the students’ academic performance.
Electronic learning- virtual
M. Rostami; S.S. Ayat; F. Saghari; F. Yaghoobi
Abstract
The purpose of this paper is to propose a method to anticipate students' proceed and to enhance their learning efficiency and success in a learning environment, using data mining. Based on library and survey searching methods, as well as consulting with experts, some effective features in students' learning ...
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The purpose of this paper is to propose a method to anticipate students' proceed and to enhance their learning efficiency and success in a learning environment, using data mining. Based on library and survey searching methods, as well as consulting with experts, some effective features in students' learning are identified and then using feature selection method, the most efficient ones are chosen. To clarify the relation between selected features, fuzzy clustering is applied to them. In the second phase of the research, scores of the students of Educational environment study, are predicted, using data mining. Variables taken are midterm and final scores and the average score of selected units in one semester by students studying there between 2006 (1385) and 2012 (1391). According to the achieved methods we can guide each student from the beginning of the semester in line with their effective features, and based on scores gained during the semester we can inform the student about his range of final score to receive an educational plan based on his/her abilities. These methods can be effective in streamlining learning procedure in a system. Test results show the desired accuracy (0.939) of the proposed method than previous methods (discovery of association rules, classification, and identifying the inconsistencies).
