Massive Open Online Courses Discussion Research Articles

Fail or pass? Investigating learning experiences and interactive roles in MOOC discussion board

In massive open online course (MOOC) discussion board, students' learning experience, reflecting implicit cognitive and affective states, is related to their learning outcomes and course's completion rates. The majority of researches about learning experience identification in MOOCs depend on post-hoc questionnaires, which may encounter issues such as personal biases, hazy memories, or time constraints, and distribution difficulty in MOOCs. Moreover, learning experience is influenced by students' interactions during learning but their relationship has not been thoroughly explored. This study aimed to address these issues. Firstly, it proposed an artificial intelligence-based text analysis approach for automatically identifying patterns of learning experiences from the large-scale students' posts in MOOC discussion board. It had performance advantage in terms of accuracy when compared with the other competing approaches. Secondly, this study defined students' interactive roles from both social relations and interaction behaviors in MOOC discussion board, and analyzed learning experiences corresponding to the different interactive roles. For students with high participation and low influence in interactions, flow and boredom were prone to happen, while for students with low participation and high influence in interactions, anxiety and apathy were easy to generate. Finally, this study revealed the effect of learning experience on learning achievement with respect to interactive role. For students with high participation characteristics, their learning achievements were less affected by learning experience, while for students less active in interaction, flow was related with good learning achievements. In summary, this study had significant methodological implications for automated learning experience identification. Moreover, this study revealed importance of interactive role in describing the interplay between learning experience and learning achievement, and provided suggestions for the improvement of MOOCs.

Attention-based contextual local and global features for urgent posts classification in MOOCs discussion forums

The Massive Open Online Courses (MOOCs) platform offers communication channels for students to share concerns about the educational process. Due to the large number of students compared to the instructors’, it is challenging to identify urgent forum posts that require attention and prompt response from the instructor. This paper presents an innovative automated classification model called the “Attention Based on Contextual Local and Global Features (AT-CX-LGF)” classifier to identify MOOCs’ urgent posts. It can aid instructors in managing many posts and prioritizing their responses, allowing them to respond more quickly to student questions and reduce dropout rates while increasing completion rates. The suggested model obtains word embedding to represent the context information using BERT (Bidirectional Encoder Representation from Transformer). It depends on several phases. First, it extracts local and semantic (or global) contextual features using multi-layer CNN and Bi-LSTM. Then, two attention layers parallelly identify the most significant local and global features. After that, the outputs of the attention layers are concatenated and normalized. Finally, fully connected, and sigmoid layers are used for the classification process. On three groups (A, B, C) gathered from the Stanford MOOC Posts dataset, the AT-CX-LGF classifier obtained urgent posts recall of 87%, 87.1%, and 90.6% with 5.5%, 2.4%, and 7.5% improvements over the most recent algorithms, respectively. Furthermore, the model outperformed the state-of-the-art method in the weighted F1-score with handling the concept drift of the dataset.

CNN-Bi-LSTM Model for MOOC Forum Post Classification

The discussion forum of the massive open online course (MOOC) is a platform for students to communicate with teachers, teaching assistants, and platform managers. It is one of the important factors related to course quality. A reasonable classification of discussion posts in the forum will help students better communicate and solve problems, so as to improve the quality of teaching. Aiming at the classification of discussion forum posts, this paper proposes a text classification model integrating convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM). Firstly, the user types and behavior characteristics are analyzed to build the taxonomy. The taxonomy includes three categories: course related, teacher related and platform related. Then, a text classification model is constructed based on CNN and Bi-LSTM. In order to verify the effectiveness of the proposed model, it is applied to the classification of 19285 discussion posts from the MOOC platform of icourse163.org. The overall classification accuracy of the proposed model is 93.6%, which is 12%, 10%, and 8% higher than traditional machine learning methods, CNN and Bi-LSTM, respectively. The model is used for automatic text classification in MOOC discussion forum, which can provide effective help and support for learners, teachers and platform managers, and improve the automation level of MOOC platform.

Mind the Gap: Gender Disparity in Online Learning Platform Interactions

Problem definition: Education technology innovations, such as massive open online course (MOOC) platforms, could potentially enable a more inclusive learning environment by delivering education to traditionally disadvantaged learners, like women. However, inclusivity does not necessarily translate into equal treatment on the platform. We investigate whether female and male learners benefit equally from forum discussions—typically the only form of interaction available—in online learning platforms. Methodology/results: Utilizing a large-scale, interaction-level data set on 174 courses on Coursera, we uncover an economically sizable and statistically significant disparity between male and female learners in receiving responses to their posts in MOOC discussion forums. On average, female learners’ questions are 3.11 percentage points less likely to receive responses from teaching staff than male learners’ questions, which equals 15.2% of the female group average. We also find significant gender disparity in staff response quality and sentiments. We investigate possible mechanisms behind the gender disparity using new techniques, including textual analysis tools. We show that the disparity is not because of content differences in male and female learners’ posts, nor is it attributable to their linguistic styles or the reputation of the posters. Instead, our results are most consistent with a male-driven gender homophily mechanism; although female staff members are gender neutral in their interactions with learners, male staff members systemically prefer responding to posts from male learners. We additionally show that receiving staff response leads to significant improvement in course passing rates, particularly for female learners. Therefore, the unequal access to information through course forums unfavorably hinders female learners’ performance. Managerial implications: Our results provide operational and organizational suggestions to platforms and content providers, including degendering user identifiers, implementing a content-focused post recommendation system, incorporating a gender-neutral user reputation system, promoting the recruiting of female teaching staff, and providing staff training that highlights the importance of gender-neutral interactions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0426 .

Sentiment Analysis on Massive Open Online Courses (MOOCs)

Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented study, the authors aim to explore learner sentiment expressed in their comments using machine learning and multi-factor analysis methods. They address several research questions on sentiment analysis on educational data. A total of 3311 messages, posted on a MOOC discussion forum, were analysed and categorized using machine learning and data analysis. The results obtained in this study show that it is possible to perform sentiment analysis with very high accuracy (94.1%), and it is also possible to periodically supervise the variations in learners' sentiments. The results of this study are very useful. In the context of online learning, it is very beneficial to have information about learner sentiment.

Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners' cognitive presence and learning achievements.

The MOOCs (Massive Open Online Courses) forum carries rich discussion data that contains multi-level cognition-related behavior patterns, which brings the potential for an in-depth investigation into the development trend of the group and individual cognitive presence in discourse interaction. This paper describes a study conducted in the context of an introductory astronomy course on the Chinese MOOCs platform, examining the relationship between discussion pacings (i.e., instructor-paced or learner-paced discussion), cognitive presence, and learning achievements. Using content analysis, lag sequential analysis, logistic regression, and grouped regression approaches, the study analysed the online discussion data collected from the Astronomy Talk course involving 2603 participants who contributed 24,018 posts. The findings of the study demonstrated the significant cognitive sequential patterns, and revealed the significant differences in the distribution of cognitive presence with different discussion pacings and learning achievement groups, respectively. Moreover, we found that the high-achieving learners were mostly in the exploration, integration, and resolution phase, and learner-paced discussion had a greater moderating effect on the relationship between cognitive presence and learning achievements. Based on the findings and discussion, suggestions for improving the learners’ cognitive presence and learning achievements in the MOOC environment are discussed.

A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed?

Automatic analysis of the myriad discussion messages in large online courses can support effective educator-learner interaction at scale. Robust classifiers are an essential foundation for the use of automatic analysis of cognitive presence in practice. This study reports on the application of a revised machine learning approach, which was originally developed from traditional, small-scale, for-credit, online courses, to automatically identify the phases of cognitive presence in the discussions from a Philosophy Massive Open Online Course (MOOC). The classifier performed slightly better on the MOOC discussions than similar previous studies have found. A new set of indicators to identify cognitive presence was revealed in the MOOC discussions, unlike those in the traditional courses. This study also cross-validated the classifier using MOOC discussion data from three other disciplines: Medicine, Education, and Humanities. Our results suggest that the cognitive classifier trained using MOOC data in only one discipline cannot yet be applied to other disciplines with sufficient accuracy.

Cross Validating a Rubric for Automatic Classification of Cognitive Presence in MOOC Discussions

As large-scale, sophisticated open and distance learning environments expand in higher education globally, so does the need to support learning at scale in real time. Valid, reliable rubrics of critical discourse are an essential foundation for developing artificial intelligence tools that automatically analyse learning in educator-student dialogue. This article reports on a validation study where discussion transcripts from a target massive open online course (MOOC) were categorised into phases of cognitive presence to cross validate the use of an adapted rubric with a larger dataset and with more coders involved. Our results indicate that the adapted rubric remains stable for categorising the target MOOC discussion transcripts to some extent. However, the proportion of disagreements between the coders increased compared to the previous experimental study with fewer data and coders. The informal writing styles in MOOC discussions, which are not as prevalent in for-credit courses, caused ambiguities for the coders. We also found most of the disagreements appeared at adjacent phases of cognitive presence, especially in the middle phases. The results suggest additional phases may exist adjacent to current categories of cognitive presence when the educational context changes from traditional, smaller-scale courses to MOOCs. Other researchers can use these findings to build automatic analysis applications to support online teaching and learning for broader educational contexts in open and distance learning. We propose refinements to methods of cognitive presence and suggest adaptations to certain elements of the Community of Inquiry (CoI) framework when it is used in the context of MOOCs.

Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums

Discussion forums are a valuable source of information in educational platforms such as Massive Open Online Courses (MOOCs), as users can exchange opinions or even help other students in an asynchronous way, contributing to the sustainability of MOOCs even with low interaction from the instructor. Therefore, the use of the forum messages to get insights about students’ performance in a course is interesting. This article presents an automatic grading approach that can be used to assess learners through their interactions in the forum. The approach is based on the combination of three dimensions: (1) the quality of the content of the interactions, (2) the impact of the interactions, and (3) the user’s activity in the forum. The evaluation of the approach compares the assessment by experts with the automatic assessment obtaining a high accuracy of 0.8068 and Normalized Root Mean Square Error (NRMSE) of 0.1799, which outperforms previous existing approaches. Future research work can try to improve the automatic grading by the training of the indicators of the approach depending on the MOOCs or the combination with text mining techniques.

E-learning Text Sentiment Classification Using Hierarchical Attention Network (HAN)

Massive Open Online Courses (MOOCs) have recently become a very motivating research field in education. Analyzing MOOCs discussion forums presents important issues since it can create challenges for understanding and appropriately identifying student sentiment behaviours. Using the high effectiveness of deep learning, this study aims to classify forum posts based on their sentiment polarity using two experiments. The first use the three known sentiment labels (positive/negative/neutral) and the second one employs sevens labels. The classification method implemented the Hierarchical Attention Network (HAN) algorithm; it combines the attention mechanism with a hierarchical network that simulates the same hierarchical structure of the document. The analysis of 29604 discussion posts from Stanford University affirms the effectiveness of our model. HAN achieved a classification accuracy of 70.3%, which surpassed the other prediction results using usual text classification models. These results are promising and have implications on the future development of automated sentiment analysis tool on e-learning discussion forum.

Twelve tips for engaging learners in online discussions

Educators play a critical role in designing, facilitating and delivering an online medical education experience. Their teaching decisions and practices shape learners’ experiences and affect their achievements. This is true even in large-scale, open educational contexts such as Massive Open Online Courses (MOOCs), which are designed based on self-regulated and collaborative learning. Thus, it is particularly important to explore the ways in which educators can effectively support online or blended learning through their interactions with learners. The 12 tips in this article aim to provide medical educators with guidelines for creating engaging online discussions that both support and challenge learners’ understanding. The advice draws on a recent study examining the experiences and activities of 24 educators in MOOC discussion areas, and how their learners engaged with them. It provides practical recommendations on facilitating online discussions, producing engaging discussion tasks, and creating a balanced educator presence.

MOOC discussion forums: The interplay of the cognitive and the social

Massive Open Online Course (MOOC) platforms capture the digital traces of millions of learners and generate an avalanche of “numbers” on learner behavior in MOOCs. Yet little is known about the dynamics through which MOOCs can support individual learning as the cognitive and social constituents of this complex process and their interplay within this process do not clearly surface in this large mass of “numbers”. This study analyzed the content generated by learners in a MOOC discussion forum with a particular focus on the still under-explored cognitive dimension of learning in MOOCs and demonstrated how certain levels of cognitive engagement relate to learning. It further examined the interplay between the cognitive and social aspects, revealing the moderating role of the social aspect in the association between the lowest level of cognitive engagement and learning in a MOOC environment. The study concludes with discussing the theoretical and practical implications of the findings and with highlighting the need to consider the interdependencies between the cognitive and social variables and learning when designing and evaluating MOOCs.

Influence of MOOC learners discussion forum social interactions on online reviews of MOOC

Although some studies have explored massive open online courses (MOOCs) discussion forums and MOOC online reviews separately, studies of both aspects are insufficient. Based on the theory of self-determination, this paper proposes research hypotheses that MOOC learning progress has a direct impact on MOOC online reviews and an indirect influence on MOOC online reviews through social interactions in discussion forums, as well. Coursera the largest MOOC platform, is selected as the empirical research object, and data from learners who participated in the MOOC discussion forum and provided MOOC online reviews from August 2016 to December 2019 are obtained from the most popular course, “Machine Learning”. After processing, data from 4376 learners are obtained. Then, according to research hypotheses, multi regression models are constructed accordingly. The results show that the length of MOOC online review text is affected by the MOOC learning progress, the number of discussion forum posts, the number of follow, the online review sentiment and MOOC rating. This study highlights the main factors that affect MOOC online reviews. As a result, some suggestions are put forward for the construction of MOOC.

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

AbstractThe purpose of this study was to explore the relationship between clout and cognitive processing in massive open online course (MOOC) discussion forum posts. Cognitive processing, a category variable generated by the automated text analysis tool, Linguistic Inquiry Word Count (LIWC), is made up of six sub‐scores (insight, causation, discrepancy, tentativeness, certainty and differentiation). Clout is a nontransparent summary variable in LIWC that can be used to understand the level of confidence conveyed in the text. Because clout is nontransparent, we do not know the algorithm used to calculate its value. To better understand this variable, this study examined cognitive processing alongside clout. In this study, a series of linear mixed models were fitted to evaluate, after controlling for gender, degree and type of post, if the focal relationship between each sub‐score of cognitive processing as the predictor variables and clout as the dependent variable changed across courses with different pacing (self‐paced or instructor‐paced). Next, the focal relationship between each predictor and clout was examined with or without regard to pacing. Findings showed words classified as showing discrepancy, certainty or differentiation were negatively associated with clout scores.

Exploring the relationship between social presence and learners’ prestige in MOOC discussion forums using automated content analysis and social network analysis

Research has repeatedly proven the importance of social interactions in online learning contexts such as Massive Open Online Courses (MOOCs), where learners often reported isolation and a lack of peer support. Previous studies of social presence suggested that the ways learners present themselves socially online affect their learning outcomes. In order to further understand the role of learners' social presence, this study attempts to examine the relationship between social presence and learners' prestige in the learner network of a MOOC. An automated text classification model based on the latest machine learning techniques was developed to identify different social presence indicators from forum posts, while two metrics (in-degree and authority score) in social network analysis (SNA) were used to measure learners' prestige in the learner network. Results revealed that certain social presence indicators such as Asking questions, Expressing gratitude, Self-disclosure, Sharing resources and Using Vocatives have positive correlations with learners' prestige, while the expressions of Disagreement/doubts/criticism and Negative emotions were counterproductive to learners' prestige. The findings not only reinforce the importance of social presence in online learning, but also shed light on the strategies of leveraging social presence to improve individual's prestige in social learning contexts like MOOCs.

Is critical thinking happening? Testing content analysis schemes applied to MOOC discussion forums

AbstractLearners’ progress within computer‐supported collaborative learning environments is typically measured via analysis and interpretation of quantitative web interaction measures. However, the usefulness of these “proxies for learning” is questioned as they do not necessarily reflect critical thinking—an essential component of collaborative learning. Research indicates that pedagogical content analysis schemes have value in measuring critical discourse in small scale, formal, online learning environments, but research using these methods on high volume, informal, Massive Open Online Course (MOOC) forums is less common. The challenge in this setting is to develop valid and reliable indicators that operate successfully at scale. In this study, we test two established coding schemes used for the pedagogical content analysis of online discussions in a large‐scale review of MOOC comment data. Pedagogical Scores are derived from manual ratings applied to comments by raters and correlated with automatically derived linguistic and interaction indicators. Results show that the content analysis methods are reliable, and are very strongly correlated with each other, suggesting that their specific format is not significant in this setting. In addition, the methods are strongly associated with the relevant linguistic indicators of higher levels of learning and have weaker correlations with other linguistic and interaction metrics. This suggests promise for further research using Machine Learning techniques, with the goal of providing realistic feedback to instructors, learners, and learning designers.

Using the Internet: Nutrition Information-Seeking Behaviours of Lay People Enrolled in a Massive Online Nutrition Course.

People’s accessibility to nutrition information is now near universal due to internet access, and the information available varies in its scientific integrity and provider expertise. Understanding the information-seeking behaviours of the public is paramount for providing sound nutrition advice. This research aims to identify who learners in a nutrition-focused Massive Open Online Course (MOOC) turn to for nutrition information, and how they discuss the information they find. A multi-methods approach explored the information-seeking and sharing behaviours of MOOC learners. Summative content analysis, and an exploratory, inductive, qualitative approach analysed learners’ posts in MOOC discussion forums. From 476 posts, the majority (58.6%) of nutrition information sources learners reported were from websites. Providers of nutrition information were most commonly (34%) tertiary educated individuals lacking identifiable nutrition qualifications; 19% had no identifiable author information, and only 5% were from nutrition professionals. Qualitative themes identified that learners used nutrition information to learn, teach and share nutrition information. Consistent with connectivist learning theory, learners contributed their own sources of nutrition information to discussions, using their own knowledge networks to teach and share information. Nutrition professionals need to understand the principles of connectivist learning behaviours in order to effectively engage the public.

Machine Learning for Knowledge Construction in a MOOC Discussion Forum

discussion forums are spreadly employed as learning tools in online courses, particularly in the Massive open online course (MOOC). Learners share opinions, express needs, and seek tutoring, and participate in discussions in the online forum. However, learner’s workstation generates massive information due to the number of MOOC participants, making it difficult to identify relevant information that can help and answer questions during the MOOC. Identifying and extracting knowledge from a MOOC discussion forum requires learner’s engagement in a collaborative and informative learning environment that enables knowledge exchange and information sharing. In this article we offer a new approach to explore forums, interactions and collaboration of learners online, in a knowledge building process, by an extraction framework and presentation of knowledge based on the characteristics of the text written in the learners' messages during the training. Our proposal consists in combining the pretreatment of the natural language by the TF-IDF metric, and the embedding of the words by Word2Vec, and then we will use the machine learning algorithm SVM for a semantic classification according to the analysis interactions model. Thus, we will apply the transformations and pretreatments on the messages posted in the forums by the participants in the MOOC, then the Word2Vec to represent each word as a vector, which will be concatenated to the features of the context TF-IDF. These vectors will form the data input of our Learning SVM machine algorithm, which aims to establish semantic relationships between concepts. The knowledge is then expressed as ontology for a representation of knowledge and an enrichment of our model.

Setting the pace: examining cognitive processing in MOOC discussion forums with automatic text analysis

ABSTRACTLearning analytics focuses on extracting meaning from large amounts of data. One of the largest datasets in education comes from Massive Open Online Courses (MOOCs) that typically feature enrollments in the tens of thousands. Analyzing MOOC discussion forums presents logistical issues, resulting chiefly from the size of the dataset, which can create challenges for understanding and adequately describing student behaviors. Utilizing automatic text analysis, this study built a hierarchical linear model that examines the influence of the pacing condition of a massive open online course (MOOC), whether it is self-paced or instructor-paced, on the demonstration of cognitive processing in a HarvardX MOOC. The analysis of 2,423 discussion posts generated by 671 students revealed the number of dictionary words used were positively associated with cognitive processing while analytical thinking and clout was negatively associated. We found that none of the student background information (gender, education), status of the course engagement (explored or completed), or the course pace (self-paced versus instructor paced) significantly influenced the cognitive processing of the postings.

On the Efficiency of Online Social Learning Networks

A social learning network (SLN) emerges when users exchange information on educational topics with structured interactions. The recent proliferation of massively scaled online (human) learning, such as massive open online courses (MOOCs), has presented a plethora of research challenges surrounding SLN. In this paper, we ask: how efficient are these networks? We propose a method in which the SLN efficiency is determined by comparing user benefit in the observed network to a benchmark of maximum utility achievable through optimization. Our method defines the optimal SLN through utility maximization subject to a set of constraints that can be inferred from the network, and given multiple solutions searches for the one closest to the observed network so as to require the least amount of change to user behavior in practice. Through evaluation on four MOOC discussion forum data sets and optimizing over millions of variables, we find that the SLN efficiency can be rather low (from 76% to 90% depending on the specific parameters and data set), which indicates that much can be gained through optimization. We find that the gains in global utility (i.e., average across users) can be obtained without making the distribution of local utilities (i.e., utility of individual users) less fair. We also propose an algorithm for realizing the optimal network through curated news feeds in online SLN.