Intervening with Students
It is important to contact students when their observed behaviors within the course indicate cause for concern (e.g., disengaged or at-risk of not succeeding). In addition to the obvious behavior of not submitting assignments, checking the Learning Management System’s (LMS) “Student Tracking” tool frequently (sorting by last access) allows the instructor to identify students who have not logged in to the LMS within a specified time period (e.g., more than one week). Monitoring the grade book at key intervals (e.g., one-quarter of the way through the course or at mid-term) allows the instructor to identify students whose grade average places them at risk of not succeeding in the course (e.g., less than a C average). Some contemporary learning management systems include within their “learning analytics” features semi-automated tools for identifying students who have exhibited behaviors of concern and facilitating the sending of LMS-based messages to these students. As Cornell and Martin (1997) conclude about similar behaviors, “these are signs that something is amiss with the student and his or her progress through the class.”
Methods of contacting students should align with the cause for instructor intervention. For instance, sending a learning management system (LMS)-based message to a disengaged student who isn’t logging in may be less productive than sending a message to that student’s “real” email address. Email messages (whether within the LMS or external) have the advantage of being easily retrievable for documentation purposes. However, a telephone call from the instructor may make more of an impact on the student.
Depending on the severity of the situation (and the instructor’s time/inclination) it may be advisable to schedule a one-on-one synchronous consultation with the student (via telephone, Skype, instant messaging, etc.) to discuss course expectations, current student strategies, and necessary changes in behavior. Of course, these proposed tactics outside of the LMS presuppose that the instructor has solicited personal contact information at the beginning of the term and is willing to intervene with the student as soon as possible (Cornell & Martin, 1997).
How to Intervene
A student invention is appropriate after key course events such as the first week of class, a major exam/test, a milestone assignment/project, or the week of the withdraw deadline. Determine performance criteria to group students into sub populations. Customize a message based on the group’s performance. For a low performing group, send information on how to receive assistance in the course whether it is office hours, tutorials, or other resources. It is important to address the population which did not complete the particular task and determine why they did not complete the coursework. (Conversely, for a well performing group, sending positive reinforcement is also an option.)
This strategy can be used in isolation or in sequence with the following strategies to more effectively engage with your students outside of class:
Create a Personalized Student Intervention
- Reach More Students with Targeted Office Hours
- Managing Student Interaction with Sign Up Sheets
- Use Web Conferencing Tools for Office Hours
Intervention Tools Available in Learning Management Systems
- Canvas: Message Students Who
- Blackboard: Early Warning System
- Moodle: Engagement and Learning Analytics Module
- Desire2Learn: Intelligent Agents
Link to example artifact(s)
From UCF’s Dr. Kelvin Thompson (graduate course in educational technology)
Several different LMS-based messages [Word doc; size = 64kb] sent to students who scored above or below certain standards
- Dr. Massiah describes the large undergraduate course in which she uses intervention messages [mp3 audio file; 45 seconds]
- Dr. Massiah describes her implementation of the intervention messages linked above [mp3 audio file; 1 min. 49 seconds]
Link to scholarly reference(s)
Cornell, R., & Martin, B. L. (1997). The role of motivation in web-based instruction. In B. Khan (Ed.), Web-based instruction (pp. 95-96). Englewood Cliffs, NJ: Educational Technology Publications.
Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30-32. http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education