Videos in Online Courses

The present study is aimed at taking a deep look at the differences between digital native and digital immigrant lecturers’ strategies in doing online learning in higher education. Online teaching is the teaching carried out not physically face to face between students and lecturer, rather by the use of any internet platform in distance. Digital native lecturers are lecturers born in the era of knowing the growth of technology and very familiar and skilled of operating any technological media of teaching (in this research, for those lecturers born after 1980). Digital immigrant lecturers are lecturers born in the era when the technology was not yet grown and popular and they still maintain to use conventional media of teaching (those lecturers born before 1980). The objectives of the present study are (1) to investigate how digital native lecturer is different from digital immigrant lecturer in the stage of implementation in teaching online courses for higher education student? This includes the problems such as their perception toward teaching online course, what platform they use for teaching online course, how they use the platform to implement and evaluate the online course; (2) to examine how digital native lecturer is different from digital immigrant lecturer in the stage of obstacles in teaching online courses for higher education student; (3) to elaborate how digital native lecturer is different from digital immigrant lecturer in the stage of overcoming obstacles in teaching online courses for higher education student. The findings of the present study provides insight on the practice of online teaching by both digital native and digital immigrant lecturers in higher education, then provides insight on the obstacles and strategies for solving them. Mostly (80%) of all lecturers have positive perception toward online teaching; merely five (mixed male and female lecturers) out of 21 lecturers who have negative perceptions and those are only digital immigrant lecturers (born before 1980) from different departments. Only few digital immigrant lecturers have negative perception toward online teaching. There are no disparity (no significant difference) between digital immigrant lecturers and digital native lecturers in terms of obstacles they face in online teaching as well as in solving the obstacles. Considering that there are many positive things in the use of technology in online teaching; in this case technology can optimize the quality of teaching, this can be the basis for universities broadly to make policies about the percentage of online teaching in the post-pandemic era with applicable terms and conditions. With background insight and the gap in technology literacy between digital native and digital immigrant lecturers, universities need to routinely provide training on the use of various technologybased platforms that support the optimization of teaching.

In this paper, a web-based teaching platform is constructed based on the background of big data, which is modeled by using a neural network algorithm. First, the neural network structure is output, and the error function is minimized by dynamic iterations using the activation function as neurons. Then, the error values were trained with implicit nodes, the activation function was modeled nonlinearly, and the sample set was extracted to define the cost function. Finally, the optimal particle is trained iteratively using the gradient descent method to derive the optimal solution, thus completing the construction of a web-based teaching platform based on the big data background. The experimental results show that after using this platform for online ideological education teaching, the percentage of students visiting online courses every day is 35%, which is 29% higher than that before using it. Therefore, to improve the construction level of ideological and political education online courses, it is necessary to strengthen the construction of ideological and political education online course platform resources, improve the informatization level of ideological and political education teacher teams, and promote the integration of ideological and political education online courses and classroom teaching.

Undergraduate Food and Resource Economics majors and those with different majors were surveyed to determine the perceived advantages and disadvantages of distance and online education courses from a student perspective. Specific objectives included determining if students who have been exposed to more online courses are more likely to rate online education as positive or negative relative to the traditional classroom setting. In general, Food and Resource Economics majors tended to view distance and online education courses less favorably than did the others.

Learning online could be an entirely new experience for students who are used to learning in face-to-face contact settings. Of importance are the ways course instructors and learners transition to online teaching and learning. Underpinned by the Community of Inquiry framework, the study sought to explore and understand the online learning experiences of participants on a 4-week long intensive training programme in a rural-based university in Eswatini. A purposive sample of eleven participants participated in the study by completing a programme evaluation form and participating in a virtual focus group discussion session. The thematic content analysis method was utilised to analyse qualitative data. The study found that while the participants found online learning exciting and flexible, they had different interpretations of their interactions with content, peers, course instructors and technology. The study concludes that effective online teaching and learning is dependent on the meaningful interaction of the learners with content, peers, course instructors and technology. There are important pedagogical implications on how online course instructors should establish and maintain the three ‘presences’ which are the cognitive, the social and the teaching in ways that impact positively on the online learners’ learning processes. Keywords: Online learning. Interaction. Presence. Online pedagogy DOI: 10.7176/JEP/13-20-02 Publication date: July 31 st 2022

This study evaluated online and traditional learning for effectiveness and student satisfaction. 500 university students evaluated both learning methods. Data was quantified. Age, gender, and major were collected for diversity. Two questionnaires assessed student satisfaction and performance with online learning platforms and traditional classrooms. Likert-scale items rated satisfaction and efficacy. Online and traditional classrooms had interesting variances in student enjoyment and efficacy. Students liked online learning. Most students said online and traditional classrooms were beneficial. Online learning platforms were better at facilitating self-paced learning, tailored instruction, and a wide selection of learning materials. The findings can help educators and policymakers create and implement online learning platforms. The study also emphasizes the importance of student preferences and needs when constructing instructional tactics and the potential benefits of blended learning approaches that combine both learning modalities.

The purposes of this research are 1) to identify the factors that influence students’ satisfaction with online courses at Guangxi Logistics Vocational and Technical College and 2) to provide guidelines for the development of online courses at the college based on the factors affecting students’ satisfaction. The population for this research primarily consists of 1600 university students enrolled at Guangxi Logistics Vocational and Technical College in China. The sample group, following Krejcie and Morgan (1970), comprised 310 students who were selected based on their participation in online courses and completion of an online survey. The research instruments used in this study include an online survey questionnaire that assessed students’ satisfaction levels across four dimensions: Course Content, Teaching Quality, Learning Experience, and Evaluation Mechanism. The statistics used to analyze the data were descriptive statistics, including mean values (M) and regression Analysis.The results of this research contribute to achieving the objectives as follows:1.Factors influencing students’ satisfaction with online courses: The study found that Teaching Quality received the highest average value (M) and ranked first, indicating that students were highly satisfied with the quality of teaching in online courses. Course Content ranked second, suggesting that it largely meets the professional development needs of students. The Learning Experience dimension ranked third, highlighting the importance of optimizing platform management and technical aspects to enhance students’ overall learning experience. The Evaluation Mechanism dimension ranked fourth, indicating that students were less satisfied with the current evaluation mechanism and desired a more scientifically designed and personalized evaluation system.2.Guidelines for the development of online courses: Based on the factors identified, the research provides valuable guidelines for the development of online courses at Guangxi Logistics Vocational and Technical College. These guidelines emphasize the importance of maintaining high teaching quality, addressing the professional development needs of students through course content, optimizing the platform management and technical aspects to enhance the learning experience, and implementing a scientifically designed and personalized evaluation system to improve student satisfaction.

This chapter addresses common concerns related to teaching online courses with laboratory components. The concerns align with three types of interactions central to student learning and engagement: student interactions with their instructor, student interactions with course content, and student interactions with their peers. Drawing on observations from instructors, strategies from an instructional designer, and student data from an online, asynchronous chemistry lab course for nonmajors, this chapter provides recommendations that promote student engagement and learning in online chemistry courses. These findings and recommendations may translate to other online and hybrid science courses. Specific suggestions for transitions back to in-person classes also are highlighted.

This paper presents the Massively Open Online Course for Educators (MOOC‐Ed) network dataset. It entails information on two online communication networks resulting from two consecutive offerings of the MOOC called The Digital Learning Transition in K‐12 Schools in spring and fall 2013. The courses were offered to educators from the USA and abroad. Though based on the same course, minor controlled variations were made to both MOOCs in terms of the course length, discussion prompts and group size. The dataset provides opportunities to examine how participants leverage online communication forums to support their learning. In particular, it allows modeling network mechanisms to better understand factors that facilitate or impede the exchange of information among educators.

The rapid expansion of distance education in higher education has left a high demand for faculty willing to design and teach online courses, often with little or no training. The path from face-to-face to online courses is not an easy one and can be filled with frustration and doubt for many faculty. Professional development often focuses on technology tools rather than pedagogical issues of online learning or course content. This chapter focuses on research that examines the experience of several faculty from the college of education at a state university in the Southeast United States, as they learned to teach online. It presents their negotiations of issues of online platform and pedagogy and their efforts to find professional development to meet their needs. The implications for institutions of higher learning are important as distance education continues to increase and more and more faculty are asked (or told) to transfer their courses online.

Given the special nature of online courses, an appropriate evaluation approach is needed to determine the effectiveness of the course and recommend the needed revision to make it more effective each time it is offered to a new group of students. This chapter covers the fifth D of the 5Ds Model for Planning and Teaching Online Courses, the determine stage. The determine stage constitutes the third phase of the 5Ds model, the evaluation phase. Primarily, the determine stage consists of three steps, formative evaluation, summative evaluation, and revision.

In the context of the new crown epidemic, online education courses have become mainstream. Along with the rapid development of the Internet and the rich reserves of educational resources, how to accurately understand the state of students to provide the required teaching courses, and how to better adapt students to online education to meet the differentiation between each other has become a mainstream problem and challenge at present. Analyze the user data, obtain the decision attributes therein, and explore the classification of online education courses; based on collaborative filtering algorithm, realize the accurate matching between students and courses. The intelligent recommendation strategy of online courses not only helps to improve the usage rate of online courses, but also helps to improve students' individuality, responding to the general policy of "stopping classes and not studying" in major universities, and carrying out more flexible teaching methods.

In recent years, millions of households have shifted from traditional TVs to smart TVs for viewing online videos on TV screens. In this article, we perform extensive analyses on a large‐scale online video viewing log on smart TVs. Because time influences almost every aspect of our lives, our aim is to understand temporal patterns of the online video viewing behavior of smart TV viewers at the crowd level. First, we measure the amount of time per hour spent in watching online videos on smart TV by each household on each day. By applying clustering techniques, we identify eight daily patterns whose peak hours occur in different segments of the day. The differences among households can be characterized by three types of temporal habits. We also uncover five periodic weekly patterns. There seems to be a circadian rhythm at the crow level. Further analysis confirms that there exists a holiday effect in the online video viewing behavior on smart TVs. Finally, we investigate the popularity variations of different video categories over the day. The obtained insights shed light on how we can partition a day to improve the performance of time‐aware video recommendations for smart TV viewers.

Little is known about the impacts of class attendance and learning preferences on academic performance in dermatology. This study was designed to examine the effects of medical student class attendance and learning preferences on students' academic performance in an introductory dermatology course. A total of 101 second-year medical students enrolled in a required introductory dermatology course were surveyed regarding their learning preferences. Records of class attendance and scores on the final examination were reviewed. The most frequently cited reason for attending classes was social expectation (96%), whereas the least cited was learning well in a classroom-type setting (65%). The top reasons cited by students for not attending classes were availability of lectures online (35%), preference for individual study outside the classroom setting (26%), and the inconvenience of traveling to class (24%). Multivariate analysis found no statistically significant relationship between class attendance and performance on the final examination (estimate -0.074, standard error 0.12; P=0.54) after adjusting for sex, age, Medical College Admission Test (MCAT) score, having children at home, and reason for attending class. Those who prefer to learn by watching online videos scored significantly higher on the final examination (prefer online videos: 87±5.5; neutral: 86±5.9; do not prefer online videos: 82±2.6 [P=0.049]). Class attendance was not associated with improved academic performance in a dermatology course. Those who preferred to learn by watching online videos demonstrated a higher level of performance than those who did not prefer to learn this way.

The perceived contribution of science education online course to pre-service students (N=121) from diverse backgrounds students with learning disabilities (25 LD students), 28 excellent students and 68 average students is presented in this five years research. During the online course students were asked to choose a scientific subject; to map it and to plan teaching activities; to carry out the proposed activities with students in a classroom experience; and to reflect the process. The assumption was that adapting the online course by using information and communication technology following formative assessment will improve students' self-learning ability as well as broaden their science knowledge, their lab performance and teaching skills. Data were collected using quantitative and qualitative tools including: pre and post questionnaires and nine (three students from each group) depth interviews upon completion of the course. Findings, based on students` perceived evaluation, pinpointed on the advantages of the online course for students of the three groups. LD students’ achievements were not inferior to those of their peers, excellent students and average students. Yet, it carefully reports on a slight but explicitly marginal perceived evaluation of the LD students in comparison to excellent students and average students regarding: forum participation, authentic task and water lab performance. The article discusses the affordance of the online course via additional features that can be grouped into two categories: knowledge construction and flexibility in time, interaction and knowledge. Further research is suggested to extend the current study by examine the effect of other courses and different contents and by considering various evaluation methods of online courses, such as: observation, the think aloud, text and tasks analysis, and reflection.

The advancement of modern multimedia and internet education systems has enriched the presentation and dissemination methods of educational resources and changed people’s learning methods and behavior habits. Online learning has become a way of life and a hotspot of the times, and optimizing the quality of online education services has gradually become a common demand of society. IoT is the sense of relating all things to the Internet. It is another type of network that has been extended and expanded by accelerated improvements in network infrastructure. It has powerful communication, data storage, modification, and computing functions for related logic and data. From the perspective of online course construction, this paper proposes a research direction of IoT online courses based on multimedia technology. Firstly, a brief introduction to the theoretical overview of online courses is given, and then, an in-depth study of the IoT technology is carried out. Then, through the theoretical analysis of the online course mode, combined with the characteristics and functions of the IoT of multimedia technology, the design method of the online course system is proposed. Finally, a comparative experiment is conducted between the IoT online course of multimedia technology and the traditional online course. The experimental results show that the overall mean of the students’ two test scores under the IoT online course teaching of multimedia technology is 6.675 points higher than that under the traditional online course teaching. It shows that the teaching of IoT online courses based on multimedia technology is more conducive to improving students’ learning ability and level, thereby improving the quality of teaching.