MOOC Videos Research Articles

Metadiscourse in MOOC Video Lectures: Comparison with University Lectures and Disciplinary Variation

Abstract Based on Hyland’s (2018) interpersonal model of metadiscourse, this study explores MOOC video lecturers’ use of metadiscourse and compares it to traditional university lecturers’. Disciplinary variation in metadiscourse in the MOOC video lectures has also been investigated. The MOOC corpus consists of sixteen MOOCs across three disciplinary areas, including Arts and Humanities, Engineering and Physical Sciences, and Social Sciences. The results suggest that despite slight differences, the most frequently appearing metadiscourse in the MOOC corpus resembles the patterns found in university lectures, which reveals the remarkable similarities in using metadiscourse between MOOCs and traditional face-to-face lectures. However, different instructional contexts and teacher-student relationships indeed contribute to the variance in the employment of metadiscourse. Overall, MOOC video lecturers employed more interactive metadiscourse but less interactional metadiscourse than university lecturers. Cross-disciplinarily, the use of metadiscourse in MOOC video lectures is considerably influenced by disciplinary features. MOOCs containing more discipline-specific content and requiring intensive cognitive effort tend to use more interactive metadiscourse and engagement markers; while for MOOCs that may include more perspectives than basic facts, boosters and attitude markers seem to be employed more frequently. Possible reasons and purposes of the differentiated use of metadiscourse across disciplines are discussed in detail.

A Deep Dive into the Disparity of Word Error Rates across Thousands of NPTEL MOOC Videos

A Deep Dive into the Disparity of Word Error Rates across Thousands of NPTEL MOOC Videos

Research on Reading Habits of College Students Based on Fuzzy Set Theory and Intelligent Transformation of Libraries

Abstract This article analyzes the impact of the Internet on college students’ reading habits and preferences, emphasizing that short reading, micro-reading, and miscellaneous reading have become trends. The article discusses the intelligent transformation of libraries, proposes strategies such as enhancing resource publicity, strengthening academic resource construction and providing personalized services, and analyzes the effect of brilliant reading promotion assessed by WeChat Communication Index (WCI). The results show that reading speed in the virtual reality environment is significantly faster than in the traditional environment, but deep comprehension takes longer. The time spent by mobile college students on entertaining videos, WeChat public numbers, etc. reached a ratio of 0.6740 and 0.5637. PC students spent more time on professional academic journals, academic MOOC videos, etc. The results of the WCI index for March-September 2022 showed that the WCI before the beginning of the school year was 176.23, and the highest in the middle of the semester reached 387.63, which proved the effectiveness of the intelligent transformation of the Library. The article demonstrates college students’ new trend of reading behavior in the Internet era and the effectiveness of Library coping strategies.

Development and Data Storage of Sports Teaching Live Broadcast Platform Based on Mobile Device Edge Computing

Aiming at the problems of low computing power and limited storage capacity of mobile terminals, a lightweight identity authentication protocol suitable for mobile edge computing environment is proposed. This protocol combines cryptography with the security protection technology of the physical layer. The system is a web software system based on the Internet network, which provides both teachers and students with the necessary network teaching environment and related tools for online teaching. To integrate the text, graphics, voice and video document systems required for the course, relying on Yu Classroom, Xuetangyun, Chuangao APP, WeChat group and other platforms to create "theoretical learning + live teaching + MOOC video + online Q&A" four the integrated teaching mode has achieved teachers' teaching, students' independent learning and multi-channel interaction between teachers and students.

Detecting the Confusion of Students in Massive Open Online Courses Using EEG

Confusion among students hinders learning and contributes to demotivation and disinterest in the course materials. However, it takes a lot of time and resources to identify confused pupils in extensive courses. Using LSTM and Attention, we suggest a deep learning model for monitoring students' confusion by EEG signals from students when they watching MOOC videos. The model obtained an accuracy of 0.82 on the EEG data, exceeding the previous experimental results for this dataset. Experiments show that the attention mechanism picks up on the significance of various features on prediction results. It can effectively solve the overfitting problem and improve the model classification effect.

Video Features, Engagement, and Patterns of Collective Attention Allocation

Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.

Attitude toward the Use of Dual Subtitles by JapaneseEnglish Learners

In recent times, a lot of Massive Open Online Courses (“MOOC”) contents are offered with dual subtitles in the native and target languages. However, there is little research that examines the relationship between their learning behavior and the role of subtitles. This study investigates the effects of subtitles in dual language (“Dual subtitles”) English and Japanese through a MOOC video, a platform that allows students to watch academic topics and contents provided in English (mainly academic lectures delivered in English at prestigious universities in the United States) on MOOCs with dual subtitles. According to the findings, more than 70% of the participants used dual subtitles most frequently and more than 95% of the participants affirmed that dual subtitles were useful or preferable to other subtitle modes (i.e., either Japanese only or English only). Although it is often stated that subtitles may place a cognitive load on language learners, the finding demonstrates that most participants had relatively positive feedback or attitude toward the use of dual subtitles and that some of them watched the video by rewinding or pausing the video. The findings revealed that a dual subtitle mode helped participants learn about the video in a variety of ways.

Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Due to the epidemic, online course learning has become a major learning method for students worldwide. Analyzing its massive data from the massive online education platforms becomes a challenge because most learners watch online instructional videos. Thus, analyzing learners’ learning behaviors is beneficial to implement personalized online learning strategies with sentiment classification models. To this end, we propose a context-aware network model based on transfer learning that aims to predict learner performance by solving learners’ problems and improving the educational process, contributing to a comprehensive analysis of such student behavior and exploring various learning models in MOOC video interactions. In addition, we visualize and analyze MOOC video interactions, enabling course instructors and education professionals to analyze clickstream data generated by learners interacting with course videos. The experimental results show that, in the process of “massive data mining,” personalized learning strategies of this model can efficiently enhance students’ interest in learning and enable different types of students to develop personalized online education learning strategies.

A Video Production Method of Microclass Combined with MOOC

In recent years, video production has become one of the key challenges for researchers in the area. In the face of the poor video effects of earlier video production methods and approaches, this study has considered the MOOC-based microclass combined with the MOOC video production method for improving the video production effect. According to the idea of MOOC, based on the early design stage and shooting preparation stage of video development, the video development team prepares the text, manuscript, and course type according to the initial design stage of video development and formulates the recording process. In the video recording stage, all activities of classroom teaching are recorded through the multislot mode for enhancing the sense of scene of the video picture. In the later stage of video production, video editing and feature processing are implemented by means of film and television to improve the effect of video production. The video production method based on the Hidden Markov model is used to ensure the continuity of front and back shots and complete online open video production and release to the cloud. The experimental results of the study show that the application effect of this method is effective, and the average satisfaction is as high and measured as 91.36%.

The Reform of Blended Courses for Software Project Management

Blended teaching is a teaching method integrating classroom teaching and online learning. Based on OBE (Outcome-based education), this paper proposes some reform ideas for blended teaching by software project management curriculum. Project driven, flipped classroom, vivid animations, MOOC video and cases study etc. are adopted to improve the self-study ability and team work of students. Although it takes more time to study, it gain high praise and enhance the teaching quality.

Examining Mooc Videos in Terms of Learning Theories

The purpose of this study is to reveal the learning theories based on the videos on different MOOC platforms. Videos in this context were examined in terms of the nature of the information, the role of the teacher, the role of the student, learning, learning and teaching type, teaching strategies, educational environments and evaluation variables. The data were analyzed by descriptive analysis method and quantitative research method was used to analyze MOOC video contents. The behavioral learning theory was found to be predominant in the videos in the five MOOC sites examined. In addition, some variables were found to be based on cognitive learning theories.

Automatic Online Lecture Highlighting Based on Multimedia Analysis

Textbook highlighting is widely considered to be beneficial for students. In this paper, we propose a comprehensive solution to highlight the online lecture videos in both sentence- and segment-level, just as is done with paper books. The solution is based on automatic analysis of multimedia lecture materials, such as speeches, transcripts, and slides, in order to facilitate the online learners in this era of e-learning - especially with MOOCs. Sentence-level lecture highlighting basically uses acoustic features from the audio and the output is implemented in subtitle files of corresponding MOOC videos. In comparison with ground truth created by experts, the precision is over 60 percent, which is better than baseline works and also welcomed by user feedbacks. On the other hand, segment-level lecture highlighting works with statistical analysis, mainly by exploring the speech transcripts, the lecture slides and their connections. With the ground truth created by massive users, an evaluation process shows that general accuracy can reach 70 percent, which is fairly promising. Finally, we also attempt to find potential correlation between these two types of lecture highlights.

How Do English Language Learners Interact with Different Content Types in MOOC Videos?

English Language Learners (ELLs) are a substantial portion of the students who enroll in MOOCs. In order to fulfill the promise of MOOCs – i.e., making higher education accessible to everyone with an internet connection – appropriate interventions should be offered to students who struggle with the language of course content. Through the analysis of clickstream log data gathered from two MOOC courses deployed on Coursera, Introduction to Psychology and Statistical Thermodynamics, we show that compared to native English speakers, ELL students have distinct behavioral patterns in how they engage with MOOC content including increased interaction with content that contains text, increased seeking away from content without visual support, and decreased video play rates. These patterns are expressed differently in response to different types of course content and domains. Our findings not only suggest more fine-grained methods for automatically identifying students who need language interventions, but also have further implications for the design of language support interventions and MOOC videos.