The LiT.RL News Verification Browser is a research tool for news readers, journalists, editors or information professionals. The tool analyzes the language used in digital news web pages to determine if they are clickbait, satirical news, or falsified news, and visualizes the results by highlighting content in color-coded categories. Although the clickbait, satire, and falsification detectors perform to certain accuracy levels on test data, during real-world internet use accuracy may vary. The browser is not a replacement for digital literacy and is not always correct. All processing is completed on the local machine-results are not sent to or from a remote server. Results may be saved locally to a standard SQLite database for further analysis.

Social media sites are increasingly being adopted to support teaching practice in higher education. Learning Analytics (LA) dashboards can be used to reveal how students engage with course material and others in the class. However, research on the best practices of designing, developing, and evaluating such dashboards to support teaching and learning with social media has been limited. Considering the increasing use of Twitter for both formal and informal learning processes, this paper presents our design process and a LA prototype dashboard developed based on a comprehensive literature review and an online survey among 54 higher education instructors who have used Twitter in their teaching.

As social media takes root in our society, more University instructors are incorporating platforms like Twitter into their classroom. However, few of the current Learning Analytics (LA) systems process social media data for instructional interventions and evaluation. As a result, instructors who are using social media cannot easily assess their students' learning progress or use the data to adjust their lessons in real time. We surveyed 54 university instructors to better understand how they use social media in the classroom; we then used these results to design and evaluate our own Twitter-centric LA dashboard. The overarching goals for this project were to 1) assist instructors in determining whether their particular use of Twitter met their teaching objectives, and 2) help system designers navigate the nuance of designing LA dashboards for social media platforms.

Offering one's perspective and justifying it has become a common practice in online text-based communications, just as it is in typical, face-to-face communication.Compared to the face-to-face communications, it can be particularly more challenging for users to understand and evaluate another's perspective in online communications. On the other hand, the availability of the communication record in online communications offers a potential to leverage computational techniques to automatically detect user opinions and rationales. One promising approach to automatically detect the rationales is to detect the common discourse relations in rationale texts. However, no empirical work has been done with regard to which discourse relations are commonly present in the users' rationales in online communications. To fill this gap, we annotated the discourse relations in the text segments that contain the rationales (N 5 527 text segments). These text segments are obtained from five datasets that consist of five online posts and the first 100 comments. We identified 10 discourse relations that are commonly present in this sample. Our finding marks an important contribution to this rationale detection approach. We encourage more empirical work, preferably with a larger sample, to examine the generalizability of our findings.

Designing a Learning Analytics Dashboard for Twitter-Facilitated Teaching Considering the increasing use of Twitter for both formal and informal learning, the primary goal of this project is to design a Learning Analytics (LA) dashboard to support instructors’ evaluation of Twitter-based teaching. To achieve this goal, we conducted an online survey involving 54 higher education instructors who have used Twitter in their past teaching. The main purpose was to identify why instructors use Twitter and what types of analytics they would consider valuable. The results of the survey evidence that instructors use Twitter to help students engage with class material, promote discussion, and build learning communities. Instructors expressed interest in analytical tools to help them quantitatively and qualitatively interpret Twitter data. Coupled with an in-depth literature review in this area, we relied on the survey data to prototype a Learning Analytics dashboard (https://dashboard.socialmediadata.org/educhat). Our online dashboard uses a simple, easy-to-read interface in accordance with previous successful dashboard implementations. Graphical visualizations allow instructors to monitor discussion patterns, such as the frequency and times of posting. Visual content breakdowns by number of retweets, original posts, and topics in the form of hashtags and named entities reveal the constituents of students’ posts. The dashboard provides additional analysis in the form of sentiment and subjectivity ranking as a way to contextually aid qualitative assessment. To support instructors’ awareness of class participation, we incorporated two visualizations that highlight the most active users and individuals who are most frequently mentioned in others’ tweets. Instructors can use the dashboard to gauge the participation at the individual- or classroom-level, and further discover what topics and links students discuss and share on Twitter. Three instructors piloted the LA dashboard over a 4-month semester in the Fall of 2017. Following their use, we conducted evaluation interviews with these instructors. Instructor evaluations confirmed that the proposed design is aligned with their pedagogical needs; they favored an intuitive interface that combined summative metrics for the entire class and personalized assessment of individual students. Based on instructors’ feedback, our future work will iteratively refine the design by integrating additional interactive features to adjust time scales of the output, investigate source data, collect data from lists of Twitter users (as opposed to a single hashtag), and further integrate the dashboard with other LMS (Learning Management System) data.