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Linguistic Reflections of Student Engagement in Massive Open Online Courses

AAAI Conferences

While data from Massive Open Online Courses (MOOCs) offers the potential to gain new insights into the ways in which online communities can contribute to student learning, much of the richness of the data trace is still yet to be mined. In particular, very little work has attempted fine-grained content analyses of the student interactions in MOOCs. Survey research indicates the importance of student goals and intentions in keeping them involved in a MOOC over time.  Automated fine-grained content analyses offer the potential to detect and monitor evidence of student engagement and how it relates to other aspects of their behavior. Ultimately these indicators reflect their commitment to remaining in the course.  As a methodological contribution, in this paper we investigate using computational linguistic models to measure learner motivation and cognitive engagement from the text of forum posts.  We validate our techniques using survival models that evaluate the predictive validity of these variables in connection with attrition over time. We conduct this evaluation in three MOOCs focusing on very different types of learning materials. Prior work demonstrates that participation in the discussion forums at all is a strong indicator of student commitment. Our methodology allows us to differentiate better among these students, and to identify danger signs that a struggling student is in need of support within a population whose interaction with the course offers the opportunity for effective support to be administered.  Theoretical and practical implications will be discussed.


From the User to the Medium: Neural Profiling Across Web Communities

arXiv.org Artificial Intelligence

Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.


From the User to the Medium: Neural Profiling Across Web Communities

AAAI Conferences

Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.


Exploiting Crowd-Based Labels for Domain Focused Information Retrieval

AAAI Conferences

Information search and retrieval from online sources or social forums is often performed with term based boolean queries. Such queries can produce low relevance documents in situations where the user is interested in retrieving in- formation related to a concept, or belonging to a specific domain. In this work an approach for concept-based infor- mation retrieval is presented, which exploits word and doc- ument distributions derived from topic modeling performed on data from online sources. Documents acquired from the Reddit and Stack Exchange online social forums are used for extracting concepts, and subsequently training and testing a detector that aids in identifying and retrieving documents associated with the concept of interest. The selection of training sets for our concept based detector is aided by pre-partitioning of documents by online users (or crowd) into concept focused sub-forums, such as sub-reddits. Topics derived from a sample of the overall document set are taken to represent concepts. These topics then form the basis for identifying sub-forums that have a strong correspondence with the concept of interest, and documents within are assigned (noisy) binary labels. The applicability of our approach is demonstrated by creating a domain focused detector for Cyber Security content from Reddit data. The cross utility of this detector is demonstrated by success- fully retrieving relevant Cyber Security documents from an alternate test online source: Stack Exchange. Document classification results of the proposed approach are compared favorably with classifications performed by human analysts.


Learning Latent Engagement Patterns of Students in Online Courses

AAAI Conferences

Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement will help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interaction with the MOOC open up avenues for studying student engagement at scale. In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues. Our first contribution is the abstraction of student engagement types using latent representations and using that in a probabilistic model to connect student behavior with course completion. We demonstrate that the latent formulation for engagement helps in predicting student survival across three MOOCs. Next, in order to initiate better instructor interventions, we need to be able to predict student survival early in the course. We demonstrate that we can predict student survival early in the course reliably using the latent model. Finally, we perform a closer quantitative analysis of user interaction with the MOOC and identify student activities that are good indicators for survival at different points in the course.