Learning Analytics Dashboard for Twitter

Abstract

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.