Holistic Evaluation and Support of Remote, Adjunct Faculty: Strategies to Foster Teaching Effectiveness

Teaching Analytics

A challenge for many institutions lies in the time, cost, and resources to support a large (and growing) population of remote, adjunct faculty. To address this challenge, data analytics have become a key component of institutional decision making in higher education (Desouza & Smith, 2016). Teaching analytics can be used proactively by using the LMS to track instructors’ activities in the online classroom (Tobin Mandernach, & Taylor, 2015). Teaching effectiveness can be measured by many different activities in both the online classroom and traditional face-to-face classroom. These include active participation, timely quality feedback, student engagement, and classroom management. While the principles of effective teaching are the same between online classes and traditional face-to-face classes, they can look much different. One advantage that online instruction has is the instructional footprint that each activity and interaction leaves behind (Mandernach & Palese, 2015). Online teaching can be monitored by identifying and assessing each of these instructional footprints (Mandernach & Palese, 2015).

Many different online teaching activities may be monitored using data analytics (Tobin, et al., 2015), including notifications, facilitations of discussion forums, synchronous interactions, grading and feedback, and engagement (Mandernach, 2017). The instructional footprints in the online classroom create an ability to collect robust data to inform decisions about instructional quality in the online classroom. Teaching analytics captured by most LMSs include: