Massive Open Online Courses Videos Research Articles

MRMLREC: A Two-Stage Approach for Addressing Data Sparsity in MOOC Video Recommendation (Student Abstract)

With the abundance of learning resources available on massive open online courses (MOOCs) platforms, the issue of interactive data sparsity has emerged as a significant challenge.This paper introduces MRMLREC, an efficient MOOC video recommendation which consists of two main stages: multi-relational representation and multi-level recommendation, aiming to solve the problem of data sparsity. In the multi-relational representation stage, MRMLREC adopts a tripartite approach, constructing relational graphs based on temporal sequences, courses-videos relation, and knowledge concepts-video relation. These graphs are processed by a Graph Convolution Network (GCN) and two variant Graph Attention Networks (GAT) to derive representations. A variant of the Long Short-Term Memory Network (LSTM) then integrates these multi-dimensional data to enhance the overall representation. The multi-level recommendation stage introduces three prediction tasks at varying levels—courses, knowledge concepts, and videos—to mitigate data sparsity and improve the interpretability of video recommendations. Beam search (BS) is employed to identify top-β items at each level, refining the subsequent level's search space and enhancing recommendation efficiency. Additionally, an optional layer offers both personalization and diversification modes, ensuring variety in recommended videos and maintaining learner engagement. Comprehensive experiments demonstrate the effectiveness of MRMLREC on two real-world instances from Xuetang X.

ConceptThread: Visualizing Threaded Concepts in MOOC Videos.

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this paper, we propose ConceptThread, a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, speech recognition and topic modeling, to extract core knowledge concepts and construct the hierarchical and temporal relations among them. Then, by using a metaphor of thread, we present a novel visualization to intuitively display the concepts based on video sequential flow, and enable learners to perform interactive visual exploration of concepts. We conducted a quantitative study, two case studies, and a user study to extensively evaluate ConceptThread. The results demonstrate the effectiveness and usability of ConceptThread in providing online learners with a quick understanding of the knowledge content of MOOC videos.

Multimodal Discourse Analysis of MOOC Videos

Massive Open Online Courses (MOOC), as a newly developed teaching mode, is just stepping into the classes of universities and the view of researchers. There are more and more professors and famous teachers in the universities beginning to make their own MOOC, which includes recording videos, designing questions and arranging discussions through MOOC platform, so as to develop rich teaching contents and vivid teaching mode, which contributes to attracting the learning interests of many learners. Especially during the outbreak of the new crown virus, online teaching has become an essential means of teaching in contemporary colleges and universities. In this thesis, the characteristics of MOOC videos and MDA method are combined, that is to say, it chooses one MOOC-related to English teaching as the research object and used MDA to study the videos in it，so as to present the distinguished characteristics of MOOC.

Effect of video styles on learner engagement in MOOCs

ABSTRACT Video lectures in massive open online courses (MOOCs) provide an opportunity to not only deliver instructional content but also engage learners. While there are many different styles of video lectures, it is not clear how video styles affect learner engagement. This study analysed and critiqued different typologies of video styles and classified MOOC video styles on a speaker-centric to media-centric spectrum. A total of 1372 survey responses were used for data analysis. The findings indicated that the ‘media-centric’ and ‘balanced’ video styles enhanced learner engagement to varying degrees in MOOCs of different study areas. In contrast, the ‘speaker-centric’ video style offered no advantages for promoting engagement in any MOOC study area. Effect sizes ranged from .03 to .07, indicating that video styles had a small to medium effect on engagement. These findings can provide new insights into the design of video lectures for different study areas in MOOCs.

Using Learner Reviews to Inform Instructional Video Design in MOOCs.

Videos are arguably the most important and frequently used instructional resource in massive open online courses (MOOCs). Recent research has explored learners' perceptions and preferences regarding MOOC instructional videos. However, these studies are often limited to a small number of specific courses, and few grounded theory studies have been undertaken to investigate this topic. In the present study, a multiple-coder research methodology was adopted to analyze 4534 learner reviews of MOOCs in 14 categories. The study aimed to identify key characteristics associated with learners' favorable perceptions of MOOC videos, types of supplemental or in-video resources learners perceive helpful to support MOOC video use, and video production features learners value. Results revealed that (a) "organized", "detailed", "comprehensible", "interesting", and "practical" were the top five important characteristics associated with learners' favorable perceptions of MOOC videos; (b) learners perceived "presentation slides", "reading materials", "post-video assessments", "embedded questions", and "case studies" as helpful resources to support their utilization of MOOC videos; and (c) learners found "duration" a more salient production feature than "editing", "resolution", "subtitles", "music", or "voice". The findings present implications for MOOC video design and foundations for future research avenues.

Google Translate in Massive Open Online Courses: Evaluation of Accuracy and Comprehensibility among Female Users

The increasing production of audiovisual texts online has led to the growing use of Machine Translation (MT) in its raw form to facilitate multilingual access. In this study, we examine the accuracy of Google Translate’s English–Arabic subtitles and their comprehensibility among female users through a case analysis of Massive Open Online Courses (MOOCs). We also seek to contribute to the research on MT by providing empirical evidence on the use of MT in audiovisual contexts, which is a relatively new and under-researched study area. The research questions considered in this study are: (i) What types of accuracy errors can be identified in Google Translate’s English–Arabic translation of MOOC subtitles? (ii) What effects do machine-translated MOOC subtitles have on female users’ comprehension? The study used a mixed-methods approach. First, 147 machine-translated subtitles of five MOOC videos were annotated by six evaluators based on five accuracy error types: mistranslation, terminology, omission, addition, and untranslated-segment errors. Second, the comprehensibility of the machine-translated subtitles was examined through a quasi-experiment. Sixty-two female participants were divided into two groups. Each group was presented with MOOC videos with either machine- or human-translated subtitles. Then, the participants completed a comprehension test. The qualitative analysis of the accuracy errors showed the occurrence of all five accuracy error types. The quantitative analysis of users’ comprehension revealed a lack of statistically significant differences between the examined groups. The study’s results suggested that MT can be useful in facilitating users’ access to MOOC video content.

A multi-dimensional analysis of English-medium massive open online courses (MOOCs) video lectures in China

Massive open online courses (MOOCs) have widely transformed teaching and learning of higher education worldwide. Based on Biber’s (1988) multi-dimensional analysis framework, this study uncovers linguistic features of English-medium MOOC video lectures in China and reveals linguistic variation across different disciplinary areas. The results suggest that the video lecture language in English-medium MOOCs provided by Chinese universities tends to be highly informational, non-narrative, explicitly referential, low in persuasion, abstract, and formally planned. Different from traditional university lectures, the closest registers to the target MOOC video lectures in China include academic prose, official documents, and press reportage. Significant differences also exist across disciplinary areas regarding all six functional linguistic dimensions. Findings of this study may provide insights into how language is used in the emerging spoken register, which may further be taken into consideration by instructional designers and instructors of MOOCs. For countries where English is used as a second or additional language, such as China, the results may be of particular interest when designing English-medium MOOC video lectures.

A novel cluster-based approach for keyphrase extraction from MOOC video lectures

Massive open online courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify the right knowledge they want to learn and thus expedite their learning process. In this paper, we propose SemKeyphrase, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. SemKeyphrase incorporates a new semantic relatedness metric and a ranking algorithm, called PhraseRank, that involves two phases on ranking candidates. We conducted experiments on a real-world dataset of MOOC video lectures, and the results show that our proposed approach outperforms the state-of-the-art keyphrase extraction methods.

Gestatten: Estimation of User's Attention in Mobile MOOCs From Eye Gaze and Gaze Gesture Tracking

The rapid proliferation of Massive Open Online Courses (MOOC) has resulted in many-fold increase in sharing the global classrooms through customized online platforms, where a student can participate in the classes through her personal devices, such as personal computers, smartphones, tablets, etc. However, in the absence of direct interactions with the students during the delivery of the lectures, it becomes difficult to judge their involvements in the classroom. In academics, the degree of student's attention can indicate whether a course is efficacious in terms of clarity and information. An automated feedback can hence be generated to enhance the utility of the course. The precision of discernment in the context of human attention is a subject of surveillance. However, visual patterns indicating the magnitude of concentration can be deciphered by analyzing the visual emphasis and the way an individual visually gesticulates, while contemplating the object of interest. In this paper, we develop a methodology called Gestsatten which captures the learner's attentiveness from his visual gesture patterns. In this approach, the learner's visual gestures are tracked along with the region of focus. We consider two aspects in this approach -- first, we do not transfer learner's video outside her device, so we apply in-device computing to protect her privacy; second, considering the fact that a majority of the learners use handheld devices like smartphones to observe the MOOC videos, we develop a lightweight approach for in-device computation. A three level estimation of learner's attention is performed based on these information. We have implemented and tested Gestatten over 48 participants from different age groups, and we observe that the proposed technique can capture the attention level of a learner with high accuracy (average absolute error rate is 8.68%), which meets her ability to learn a topic as measured through a set of cognitive tests.

MOOC Video Personalized Classification Based on Cluster Analysis and Process Mining

In the teaching based on MOOC (Massive Open Online Courses) and flipped classroom, a teacher needs to understand the difficulty and importance of MOOC videos in real time for students at different knowledge levels. In this way, a teacher can be more focused on the different difficulties and key points contained in the videos for students in a flipped classroom. Thus, the personalized teaching can be implemented. We propose an approach of MOOC video personalized classification based on cluster analysis and process mining to help a teacher understand the difficulty and importance of MOOC videos for students at different knowledge levels. Specifically, students are first clustered based on their knowledge levels through question answering data. Then, we propose the process model of a group of students which reflects the overall video watching behavior of these students. Next, we propose to use the process mining technique to mine the process model of each student cluster by the video watching data of the involved students. Finally, we propose an approach to measure the difficulty and importance of a video based on a process model. With this approach, MOOC videos can be classified for students at different knowledge levels according to difficulty and importance. Therefore, a teacher can carry out a flipped classroom more efficiently. Experiments on a real data set show that the difficulty and importance of videos obtained by the proposed approach can reflect students’ subjective evaluation of the videos.

Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

AbstractThe rapid advancement in information and communication technology has made e‐learning an alternative learning method for many learners. In the last few years, a huge number of learners around the world have registered in massive open online courses (MOOCs) provided by various online learning platforms. However, MOOC platforms have a vital task for the online course provider to provide enhanced students' learning experiences and satisfaction. In this work, we developed a brain–computer interface for gathering data and detecting a learner's mental situation by observing MOOC videos and electroencephalogram (EEG) devices based on John Sweller's Cognitive Load Theory. The acquired EEG signals are preprocessed with two different normalization methods to scale signals. To validate the introduced framework, the system adopted three machine learning algorithms (random forest using non‐Markovian model, support vector machine, and k‐nearest neighbors) to develop a model with preprocessed training data and test the classifiers to validate their ensemble classifiers' performance. Finally, experimental analysis showed that the random forest classifier with the non‐Markovian approach achieved more than the other two techniques in the form of overall accuracy (99.15%) and F‐measures (99.21%).

Videos as learning objects in MOOCs: A study of specialist and non‐specialist participants' video activity in MOOCs

AbstractDespite the emergence of massive open online courses (MOOCs) and the field of MOOC research, we have a limited understanding of the specific needs of different learner groups and how MOOCs can successfully address those needs. Video lectures and demonstrations are a central learning component of MOOCs. This paper contributes to the research community by examining the use of MOOC videos for two groups of learners. In particular, we explore whether there is an observable difference between specialists' and non‐specialists' video‐watching activity. We analyse data collected from three MOOCs on the edX platform. Our findings indicate that while age and educational background impacts the level of video activity, there is no significant difference between specialists and non‐specialists. We conclude that the MOOC format may be suited to non‐specialist groups, allowing them to self‐direct their learning and utilise videos as educational resources.

PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses.

Massive open online courses (MOOCs) aim to facilitate open-access and massive-participation education. These courses have attracted millions of learners recently. At present, most MOOC platforms record the web log data of learner interactions with course videos. Such large amounts of multivariate data pose a new challenge in terms of analyzing online learning behaviors. Previous studies have mainly focused on the aggregate behaviors of learners from a summative view; however, few attempts have been made to conduct a detailed analysis of such behaviors. To determine complex learning patterns in MOOC video interactions, this paper introduces a comprehensive visualization system called PeakVizor. This system enables course instructors and education experts to analyze the "peaks" or the video segments that generate numerous clickstreams. The system features three views at different levels: the overview with glyphs to display valuable statistics regarding the peaks detected; the flow view to present spatio-temporal information regarding the peaks; and the correlation view to show the correlation between different learner groups and the peaks. Case studies and interviews conducted with domain experts have demonstrated the usefulness and effectiveness of PeakVizor, and new findings about learning behaviors in MOOC platforms have been reported.