Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and students may lose the motivation to continue in the course. To help address this problem, we build predictive models that automatically determine the urgency of each forum post, so that these posts can be brought to instructors' attention. This paper goes beyond previous work by predicting not just a binary decision cut-off but a post's level of urgency on a 7-point scale. First, we train and cross-validate several models on an original data set of 3,503 posts from MOOCs at University of Pennsylvania. Second, to determine the generalizability of our models, we test their performance on a separate, previously published data set of 29,604 posts from MOOCs at Stanford University. While the previous work on post urgency used only one data set, we evaluated the prediction across different data sets and courses. The best-performing model was a support vector regressor trained on the Universal Sentence Encoder embeddings of the posts, achieving an RMSE of 1.1 on the training set and 1.4 on the test set. Understanding the urgency of forum posts enables instructors to focus their time more effectively and, as a result, better support student learning.
more »
« less
#lets-discuss: Analyzing student affect in course forums using emoji.
Emoji are commonly used in social media to convey attitudes and emotions. While popular, their use in educational contexts has been sparsely studied. This paper reports on the students’ use of emoji in an online course forum in which students annotate and discuss course material in the margins of the online textbook. For this study, instructors created 11 custom emoji-hashtag pairs that enabled students to quickly communicate affects and reactions in the forum that they experienced while interacting with the course material. Example reporting includes, inviting discussion about a topic, declaring a topic as interesting, or requesting assistance about a topic. We analyze emoji usage by over 1,800 students enrolled in multiple offerings of the same course across multiple academic terms. The data show that some emoji frequently appear together in posts associated with the same paragraphs, suggesting that students use the emoji in this way to communicating complex affective states. We explore the use of computational models for predicting emoji at the post level, even when posts are lacking emoji. This capability can allow instructors to infer information about students’ affective states during their ”at home” interactions with course readings. Finally, we show that partitioning the emoji into distinct groups, rather than trying to predict individual emoji, can be both of pedagogical value to instructors and improve the predictive performance of our approach using the BERT language model. Our procedure can be generalized to other courses and for the benefit of other instructors.
more »
« less
- Award ID(s):
- 1915724
- NSF-PAR ID:
- 10374291
- Editor(s):
- Mitrovic, A.; Bosch, N.
- Date Published:
- Journal Name:
- Proceedings of the 15th International Conference on Educational Data Mining
- Page Range / eLocation ID:
- 339–345
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Online forums are an integral part of modern day courses, but motivating students to participate in educationally beneficial discussions can be challenging. Our proposed solution is to initialize (or “seed”) a new course forum with comments from past instances of the same course that are intended to trigger discussion that is beneficial to learning. In this work, we develop methods for selecting high-quality seeds and evaluate their impact over one course instance of a 186-student biology class. We designed a scale for measuring the “seeding suitability” score of a given thread (an opening comment and its ensuing discussion). We then constructed a supervised machine learning (ML) model for predicting the seeding suitability score of a given thread. This model was evaluated in two ways: first, by comparing its performance to the expert opinion of the course instructors on test/holdout data; and second, by embedding it in a live course, where it was actively used to facilitate seeding by the course instructors. For each reading assignment in the course, we presented a ranked list of seeding recommendations to the course instructors, who could review the list and filter out seeds with inconsistent or malformed content. We then ran a randomized controlled study, in which one group of students was shown seeds that were recommended by the ML model, and another group was shown seeds that were recommended by an alternative model that ranked seeds purely by the length of discussion that was generated in previous course instances. We found that the group of students that received posts from either seeding model generated more discussion than a control group in the course that did not get seeded posts. Furthermore, students who received seeds selected by the ML-based model showed higher levels of engagement, as well as greater learning gains, than those who received seeds ranked by length of discussion.more » « less
-
Lynch, Collin F. ; Merceron, Agathe ; Desmarais, Michel ; Nkambou, Roger (Ed.)Discussion forums are the primary channel for social interaction and knowledge sharing in Massive Open Online Courses (MOOCs). Many researchers have analyzed social connections on MOOC discussion forums. However, to the best of our knowledge, there is little research that distinguishes between the types of connections students make based upon the content of their forum posts. We analyze this effect by distinguishing on- and off-topic posts and comparing their respective social networks. We then analyze how these types of posts and their social connections can be used to predict the students’ final course performance. Pursuant to this work we developed a binary classifier to identify on- and off- topic posts and applied our analysis with the hand-coded and predicted labels. We conclude that the post type does affect the relationship between the students and their closest neighbors or community members clustered communities and their closest neighbor to their learning outcomes.more » « less
-
This Innovate Practice full paper presents a cloud-based personalized learning lab platform. Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. With that in mind, ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoTh Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes.more » « less
-
Nicewonger, Todd E. ; McNair, Lisa D. ; Fritz, Stacey (Ed.)https://pressbooks.lib.vt.edu/alaskanative/ At the start of the pandemic, the editors of this annotated bibliography initiated a remote (i.e., largely virtual) ethnographic research project that investigated how COVID-19 was impacting off-site modular construction practices in Alaska Native communities. Many of these communities are located off the road system and thus face not only dramatically higher costs but multiple logistical challenges in securing licensed tradesmen and construction crews and in shipping building supplies and equipment to their communities. These barriers, as well as the region’s long winters and short building seasons, complicate the construction of homes and related infrastructure projects. Historically, these communities have also grappled with inadequate housing, including severe overcrowding and poor-quality building stock that is rarely designed for northern Alaska’s climate (Marino 2015). Moreover, state and federal bureaucracies and their associated funding opportunities often further complicate home building by failing to accommodate the digital divide in rural Alaska and the cultural values and practices of Native communities.[1] It is not surprising, then, that as we were conducting fieldwork for this project, we began hearing stories about these issues and about how the restrictions caused by the pandemic were further exacerbating them. Amidst these stories, we learned about how modular home construction was being imagined as a possible means for addressing both the complications caused by the pandemic and the need for housing in the region (McKinstry 2021). As a result, we began to investigate how modular construction practices were figuring into emergent responses to housing needs in Alaska communities. We soon realized that we needed to broaden our focus to capture a variety of prefabricated building methods that are often colloquially or idiomatically referred to as “modular.” This included a range of prefabricated building systems (e.g., manufactured, volumetric modular, system-built, and Quonset huts and other reused military buildings[2]). Our further questions about prefabricated housing in the region became the basis for this annotated bibliography. Thus, while this bibliography is one of multiple methods used to investigate these issues, it played a significant role in guiding our research and helped us bring together the diverse perspectives we were hearing from our interviews with building experts in the region and the wider debates that were circulating in the media and, to a lesser degree, in academia. The actual research for each of three sections was carried out by graduate students Lauren Criss-Carboy and Laura Supple.[3] They worked with us to identify source materials and their hard work led to the team identifying three themes that cover intersecting topics related to housing security in Alaska during the pandemic. The source materials collected in these sections can be used in a variety of ways depending on what readers are interested in exploring, including insights into debates on housing security in the region as the pandemic was unfolding (2021-2022). The bibliography can also be used as a tool for thinking about the relational aspects of these themes or the diversity of ways in which information on housing was circulating during the pandemic (and the implications that may have had on community well-being and preparedness). That said, this bibliography is not a comprehensive analysis. Instead, by bringing these three sections together with one another to provide a snapshot of what was happening at that time, it provides a critical jumping off point for scholars working on these issues. The first section focuses on how modular housing figured into pandemic responses to housing needs. In exploring this issue, author Laura Supple attends to both state and national perspectives as part of a broader effort to situate Alaska issues with modular housing in relation to wider national trends. This led to the identification of multiple kinds of literature, ranging from published articles to publicly circulated memos, blog posts, and presentations. These materials are important source materials that will likely fade in the vastness of the Internet and thus may help provide researchers with specific insights into how off-site modular construction was used – and perhaps hyped – to address pandemic concerns over housing, which in turn may raise wider questions about how networks, institutions, and historical experiences with modular construction are organized and positioned to respond to major societal disruptions like the pandemic. As Supple pointed out, most of the material identified in this review speaks to national issues and only a scattering of examples was identified that reflect on the Alaskan context. The second section gathers a diverse set of communications exploring housing security and homelessness in the region. The lack of adequate, healthy housing in remote Alaska communities, often referred to as Alaska’s housing crisis, is well-documented and preceded the pandemic (Guy 2020). As the pandemic unfolded, journalists and other writers reported on the immense stress that was placed on already taxed housing resources in these communities (Smith 2020; Lerner 2021). The resulting picture led the editors to describe in their work how housing security in the region exists along a spectrum that includes poor quality housing as well as various forms of houselessness including, particularly relevant for the context, “hidden homelessness” (Hope 2020; Rogers 2020). The term houseless is a revised notion of homelessness because it captures a richer array of both permanent and temporary forms of housing precarity that people may experience in a region (Christensen et al. 2107). By identifying sources that reflect on the multiple forms of housing insecurity that people were facing, this section highlights the forms of disparity that complicated pandemic responses. Moreover, this section underscores ingenuity (Graham 2019; Smith 2020; Jason and Fashant 2021) that people on the ground used to address the needs of their communities. The third section provides a snapshot from the first year of the pandemic into how CARES Act funds were allocated to Native Alaska communities and used to address housing security. This subject was extremely complicated in Alaska due to the existence of for-profit Alaska Native Corporations and disputes over eligibility for the funds impacted disbursements nationwide. The resources in this section cover that dispute, impacts of the pandemic on housing security, and efforts to use the funds for housing as well as barriers Alaska communities faced trying to secure and use the funds. In summary, this annotated bibliography provides an overview of what was happening, in real time, during the pandemic around a specific topic: housing security in largely remote Alaska Native communities. The media used by housing specialists to communicate the issues discussed here are diverse, ranging from news reports to podcasts and from blogs to journal articles. This diversity speaks to the multiple ways in which information was circulating on housing at a time when the nightly news and radio broadcasts focused heavily on national and state health updates and policy developments. Finding these materials took time, and we share them here because they illustrate why attention to housing security issues is critical for addressing crises like the pandemic. For instance, one theme that emerged out of a recent National Science Foundation workshop on COVID research in the North NSF Conference[4] was that Indigenous communities are not only recovering from the pandemic but also evaluating lessons learned to better prepare for the next one, and resilience will depend significantly on more—and more adaptable—infrastructure and greater housing security.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
