## 2 Related Work
Moreover, [PERSON] and [PERSON] [26] matched learners' final grade in 23 Coursera MOOCs with the percentage of posts that these learners had submitted to the discussion forum, concluding that between 20% and 80% of the learners who obtained at least 60% of the final grade contributed through the forum. [PERSON] et al. [30] analyzed the role of \"top contributors\" (1% of MOOC learners with more posts submitted) to try to detect them early and assign them special permissions as community teaching assistants in the MOOC, finding a moderate positive correlation (r = 0.343) between the number of posts published and final grade in the MOOC. [PERSON] et al. [31] also studied the highest-volume forum contributors, named \"superposters\", concluding that \"superposters\" obtained higher grades than the average forum participants as well as replied faster and received more upvotes. In contrast, some other publications concluded that forum-related variables do not add much value when trying to predict learners' grades [32; 33], which makes this issue worth exploring even further. Nevertheless, it is not only important to consider the number of messages (quantity) sent by MOOC learners but also their relevance. This can be done by analyzing the characteristics of the message (e.g., length, number of replies, number of likes, etc.) or the content of the message (e.g., through text mining techniques) [16]. For example, [PERSON] et al. [34] developed a model to identify confused learners through expressions, such as \"I'm stuck\". [PERSON] et al. [35] proposed a model to identify urgent posts that needed immediate attention from instructors based on several linguistic features. [PERSON] et al. [36] explored the collective sentiment of learners in a MOOC based on forum posts, evaluating the impact of sentiment on attrition over time. Similarly, [PERSON] et al. [37] classified the posts obtained from a MOOC forum as positive, negative, or neutral according to a word dictionary and some grammar rules to detect moments of greater negativity throughout the course. In this same line, [PERSON] et al. [38] developed a weakly supervised system for detecting both sentiments and topics from the body of messages posted in 12 MOOCs. Furthermore, [PERSON] et al. [39] proposed a model for classifying threads in forums by ranking their relevance using 80 Coursera MOOCs. Finally, it is important to present the results of the analysis of the MOOC forum in a comprehensible way for the stakeholders (typically the instructors) so that these can make interventions or even improvements to the MOOC in future editions. Several dashboards have been developed to present visualizations with the information of the MOOC forums. For example, iForum [40] offers several visualizations regarding three interleaving aspects of MOOC forums: posts, users, and threads. VisMOOC [41] provides a visualization based on social network analysis that shows the shape of the social network created around the MOOC forum, including in one single chart both the learner's grade and the learner's activity level in the forum. MessageLens [42] provides several visualizations aimed at helping MOOC instructors to better understand forum discussions from three facets: discussion topic, learner attitude, and communication among learners. Finally, [PERSON] developed LAT3S [6], a tool that provides visualizations related to the use of the forum in edX MOOCs with a focus on three dimensions: number and type of social interactions, learners' sentiments, and learners' skills. All in all, this research work builds on the related literature by proposing an algorithm that can be used to automatically grade MOOC learners based on their contributions in the forum, taking into account not only the number of messages (quantity), but also their relevance, and presenting the results in the form of comprehensive visualizations; in addition, these visualizations are integrated into the tool LAT3S [6]. This is a novel research contribution to the best of the authors' knowledge.