Assessment Item 3: Due to be uploaded to Safe Assign by 5:00pm Friday 28th October 2016. Leveraging social media analytics to understand student engagement in large classes School of Management Assessment Item 3 BSB115 - Management Timothy Donnet 05402158 Word count: 1163 1. Introduction This report extends the analysis provided in the previously briefed issue of social media in the context of higher education classes. The report uses the Controlling management lens in an aim to demonstrate the utility of the data analytics captured by social media platforms; particularly with respect to enhancing the intelligence systems unit coordinators can use to enhance the understanding of how student engagement with resources influences student performance. Recommendations focus on the administrative development and implementation of social media monitoring routines that provide unit coordinators with timely student engagement metrics (a concurrent control system) that can be used to inform in-class and administrative teaching interventions to improve the quality of learning materials provided to students. 2. Defining and Framing the Issue Millennials and digital natives dominate the demography of undergraduate management classes (Duncan & Barczyk, 2015), making social media a valuable tool for educators seeking to enhance learning outcomes both within and beyond the physical classroom setting (Flanigan & Babchuck, 2015). While emerging research themes demonstrate the potential fit and benefits of using social media in higher education (Hamid, Waycott, Kurnia & Chang, 2015), the underlying problem for many unit coordinators is the paucity of information available for understanding student engagement with learning resources (Henrie, Halverson & Graham, 2015). Contemporary teaching strategies champion the use of student engagement analytics to improve classroom and assessment outcomes (Hamid et al., 2015), so the underlying issue is that many units, from a systems perspective, are not capturing enough information to effectively implement new teaching and learning strategies. Unit coordination has many similarities to the responsibilities of line managers – unit coordinators ensure their tutoring staff provide are consistent in the way they provide learning opportunities to their students; are responsible for ensuring the quality of information provided to students is up to date and consistent with the standards set by the university, higher education regulator, and accrediting peak bodies (such as AACSB and EQUIS); and provide performance feedback to the School for student progression and unit quality control purposes. These functional work tasks make management theories relevant to inform unit coordinator decision making for the adoption and use of social media in their units, and from an information systems perspective, makes Controlling the most relevant management function to frame the analysis presented below in Section 3. 3. Addressing the Issue As was highlighted in the Brief for this issue, Social media platforms, such as Twitter and YouTube, are useful for connecting to students in a resource efficient way. Within the Australian universities sector, YouTube is seen as particularly popular with students and educators alike (Henderson, Selwyn, Finger & Aston, 2015). Importantly, the analytics provided by YouTube and Twitter, with respect to how users engage with content, is rich and generated in real time. This access to real time usage and engagement analytics provides unit coordinators with a valuable source of information to inform their development of teaching and learning strategies to improve student outcomes (Ferguson, 2012). For example, questions posted by students on Twitter that become popular (via other students liking or retweeting) provides a concurrent mechanism (similar to Hepplestone, Holden, Irwin, Parkin & Thorpe, 2011) to allow the unit coordinator to increase classroom discussion for topics that help to address these popular concerns. By tapping into student engagement data, unit coordinators can systematise the tailoring of learning content for students, which is consistent with current best practices in the Flipped Classroom pedagogy (Findlay-Thompson & Mombourquette, 2014; Tay, 2016). As is indicated in Figure 1, below, YouTube’s engagement analytics are comprehensive, but stop short of identifying individuals within the data (data is presented as an aggregate of all viewers and views of each video resource). The audience retention analytics can be used to identify themes within a video’s content to act as a concurrent control mechanism – allowing unit coordinators to identify changes in student interest throughout the semester. For example, in Figure 1 the upswings in the blue audience retention line indicate areas that students watch more than others. In the week immediately following the video’s release, the blue line was quite flat, indicating students usually watched the video from start to finish without re-watching particular areas. Six weeks have since passed, and the increased number of upswings in the blue retention line indicates students have been reviewing themes that have become more relevant as the final assessment deadline approaches (i.e. referencing skills, as is highlighted in Figure 1).. Figure 1. Example of YouTube's engagement analytics (QUT Gateways, 2016) 4. Conclusion It is the recommendation of this report to encourage unit coordinators within the School of Management to implement social media to improve concurrent controls that inform unit content on a week-to-week basis. The above discussion demonstrates the utility of identifying popular topics via Twitter and YouTube’s analytics can be used to concurrently monitor changes in students’ use and focus within content to inform the application of blended learning strategies (as per Tay, 2016). However, if both of these alternatives were implemented in classes, they still (together) stop short of being able to determine whether students viewing the videos perform better than those who do not. That is, analysing student engagement with Twitter and YouTube helps to improve the overall information available to unit coordinators, but doesn’t provide a complete picture of student engagement and how this influences student performance. There may be other social media platforms, or other data capturing services, that could provide a more complete picture of student engagement and performance, and this should be investigated as part of an ongoing programme of improving teaching and learning within the School. The following recommendations provide a clear pathway for the next steps in implementing a social media platform to drive concurrent analytics into the unit coordination of units. Consistent with Henderson and colleagues (2015) findings, it is the view of this report that YouTube be implemented due to its existing prominence within Australia’s higher education sector. 5. Recommendations Before the end of November 2016, a full-day YouTube training program should be developed for unit coordinators, focusing on creating content and analysing audience retention data to support their teaching strategies and improve concurrent controls and flexibility in content delivery. At least half of the unit coordinators within the School should complete the training program prior to the end of January 2017 to ensure what they have learned can be implemented in Semester 1 2017. Before the end of January 2017, the School should acquire at least five video cameras and 20 licenses for Adobe Premiere (or other video editing software used in the YouTube training program) to provide the required resources for content creation for unit coordinators. An action item should be added to the School’s Teaching and Learning Committee Agenda for 2017 to find alternative data analytics platforms that enable the School to determine the links between student engagement and student performance; finding an alternative with this capability addresses a critical aspect of the control process for unit coordinators. 6. Reference List Duncan, D. G., & Barczyk, C. C. (2015). The Facebook effect in university classrooms: a study of attitudes and sense of community using an independent measures control group design. American Journal of Management, 15(3), 11-22. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304-317. Findlay-Thompson, S., & Mombourquette, P. (2014). Evaluation of a Flipped Classroom in an undergraduate business course. Business Education & Accreditation, 6(1), 63-71. Flanigan, A. E., & Babchuk, W. A. (2015). Social media as academic quicksand: A phenomenological study of student experiences in and out of the classroom. Learning and Individual Differences, 44(1), 40-45. Hamid, S., Waycott, J., Kurnia, S., & Chang, S. (2015). Understanding students' perceptions of the benefits of online social networking use for teaching and learning. The Internet and Higher Education, 26(1), 1-9. Henderson, M., Selwyn, N., Finger, G., & Aston, R. (2015). Students’ everyday engagement with digital technology in university: exploring patterns of use and ‘usefulness’. Journal of Higher Education Policy and Management, 37(3), 308-319. Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90(1), 36-53. Hepplestone, S., Holden, G., Irwin, B., Parkin, H. J., & Thorpe, L. (2011). Using technology to encourage student engagement with feedback: a literature review. Research in Learning Technology, 19(2), 117-127. QUT Gateways. (2016, October 7). BSB115 2016 S2: Week 4 FAQ vlog (2016 Semester 2) [video file]. Retrieved from youtu.be/TwupUxQg9_0 Tay, H. Y. (2016). Investigating engagement in a blended learning course. Cogent Education, 3(1), doi:10.1080/2331186x