ACM Logo  An ACM Publication  |  CONTRIBUTE  |  FOLLOW    

A Messaging Framework for Online Educators

Special Issue: Paradigm Shifts in Global Higher Education and eLearning

By Karen Gebhardt, Kelly N. McKenna / May 2019

Print Email
Comments Instapaper

In online education, the three types of interactions known as student–teacher, student–student, and student–content [1] are well established as essential components of any course to create deep and meaningful learning [2] and increase student learning outcomes [3]. One important subcomponent of student–teacher interaction is communication in the form of instructor–initiated messaging. Messaging, such as emails, announcements, alerts, or phone calls, are used as an intervention to inform or clarify course content or expectations [4], remediate or encourage student performance [5], or to promote a sense of community [6].

For the purpose of this framework, messaging is defined as a deliberate form of instructor initiated one–way communication that is not included in the body of the course content when the course was designed. Messaging does not require, but can prompt, interaction with students. Additionally, messaging can be triggered by a student–teacher, student–student, or student–content interaction. To understand what is and what is not messaging, some examples are useful. Messaging occurs when an instructor clarifies previously posted course content. For example, an instructor providing assignment instructions within a module is not messaging, whereas an announcement clarifying the required formatting of an assignment is messaging. Messaging is outside the typical grading activities. For example, if an instructor grades and provides feedback to a student on a quiz, this is not messaging. If an instructor sends a group email highlighting and explaining the most often missed questions on the quiz, this is messaging.

There is a deep body of research related to messaging and their impacts on student outcomes such as the frequency of messaging [7], the effectiveness of the types of language used in messages [8], or the effect on learner’s satisfaction level when interpersonal communication needs are met [9]. Although messaging research is robust, a theoretical framework for messaging has not been developed. This article provides that theoretical framework. Instructors can use this framework to implement and more precisely target messaging to improve student learning outcomes. 


In order to develop a theoretical framework for messaging in online courses, two methods were adopted: a literature review of recent research related to messaging and an exploration of our own experiences as online instructors. Since theoretical framework literature is relatively thin in this area, it was important for us to develop our knowledge and understanding of practice through our own interactions in online classes.

Messaging Framework

Two dimensions of messaging inform this framework:

  1. Motivation of the message: Is the message reactive or not reactive to an individual student or all students’ actions or course expectation outcome?
  2. Content of message: Is the message content unique to an individual student or common to all students?

Reactive means the message was motivated in response to an action (or lack of action) by a student or the class or a class expectation was (or was not) met. In other words, this type of message would be initiated in response to an individual student or class-wide (i.e., collective) behavior. Reactive messages can be generated because a student or the class requires remediation or to let students know they did an exceptional job. Messages that are not reactive are often motivated by the need to clarify existing course content, inform about upcoming coursework, or as a way to build community through sharing news stories or opportunities for extracurricular activities. Unique means the content of the message was crafted with a particular student in mind (i.e., personalized). Common means the content of the message may be specific to a behavior or expectation, but not an individual student.

Using these dimensions, messaging falls into six categories (see Figure 1) that reflect the motivation and the content of the message:

  1. Messages that are in reaction to an individual student’s behavior and are common to students with this behavior (RI–C).
  2. Messages that are in reaction to an individual student’s behavior and the content is unique to the student (RI–U),
  3. Messages that are in reaction to a collective behavior and the content is common to all students (RC–C).
  4. Messages that are in reaction to a collective behavior and the content is unique to the student (RC–U).
  5. Messages that are not in reaction to behavior and the content is common to all students (NR–C).
  6. Messages that are not in reaction to behavior and the content is unique to the student (NR–U).

It is important note this framework is independent of message delivery mode.

Figure 1. Messaging framework.

[click to enlarge]

There is no “best” type of message and often several categories of messages or delivery modes are appropriate. Research has shown that each type of messaging improves outcomes and combinations of message types and delivery can sometimes result in the greatest gains in learning outcomes. When creating messages, an instructor should first identify the motivation for the message, determine if personalization is appropriate, and then identify the delivery mechanism.

Selection of message category.  Not every category of messaging is appropriate for all scenarios and instructors should carefully evaluate the scenario in order to identify the best type of message. In current practice, the instructor initiates most messaging in online courses, but with the increased use of learning analytics, automated messages or “alerts” generated in response to data has become more prevalent [10].

 Modes for message delivery.  The advancement of technology allows delivery of text, audio, or video messaging through a variety of modes. Many online courses are developed and delivered via a learning management system (LMS) affiliated with the educational intuition (e.g., Canvas, Blackboard, Moodle, or Desire2Learn), which often include embedded digital communication tools that are text based (e.g., email or messaging), audio-based (e.g., audio feedback for an assignment), and video-based (e.g., video announcements). Outside of an LMS, instructors can choose tools like email, phone calls, text messaging, video conferencing (e.g., Skype or Google Hangouts), or social media (e.g., Facebook or Twitter) to deliver text, audio, or video messages.

Using the messaging framework.  To illustrate how the messaging framework may be used to select the category of message, three scenarios are given and research-based examples of messages and the efficacy of these messages through various delivery modes is presented.

Scenario 1: An instructor has discovered that a student has missed an assignment.

The motivation for this message is in reaction to the individual student behavior (i.e., missing the assignment), therefore an instructor would choose between RI-U or RI-C. Research suggests both of these types of messages can be effective. For example, the instructor could message via RI-U through personalized email message or voicemail. In their synthesis of literature on methods of student engagement, Angelino, Williams, and Natvig conclude faculty-initiated contact by phone could reduce attrition by deepening the integration of students into the online learning community [11]. On the other hand, the instructor could use an early-alert system that automatically sends the student a reminder. This message would be categorized as RI-C. A study related to the efficacy of automatic messaging in an early-alert system based on student characteristics suggested these types of messages resulted in higher student end-of-semester course grades [12].

Scenario 2: A student emailed the instructor seeking clarification about the exam proctoring requirements.

In this scenario an instructor responds to a proactive interaction by the student (i.e., clarification of exam requirement). It is important to first respond to the student-initiated inquiry in a timely manner [11] and because the interaction was communicated over email the personalized response will also likely be over email. However, because the content of the inquiry is about clarifying an existing assignment and is relevant to the collective group, the messaging may lead to an instructor-initiated NR-C message, possibly an announcement. The announcement isn’t reactive to a behavior by the course participants, but the clarification may be beneficial to all participants and should likely be communicated to the entire class to alleviate any concerns of fairness or equity [13]. 

Scenario 3: The quality of student discussion postings has fallen in recent weeks. 

In studies related to RC or NR messages sent to all students, researchers identify these messages commonly provide encouragement and performance updates [13] or assignment-specific advice prior to assignment due dates [14], and these types of messages can lead to engagement and diminish isolation [11]. The decline in discussion post quality is a collective behavior (i.e., overall class performance has fallen), thus messaging regarding this change in behavior is reactive to a collective behavior and the message is a common message sent to all participants, suggesting RC-C would be the most appropriate category of message. The communication may come in the form of an announcement or email. This common messaging is a way for the instructor to engage with the class participants. In the online classroom successful communication is important to the creation of community and it should be consciously initiated and created by the instructor [15].


A successful learning environment is enhanced through social interaction and communication [15]. Instructor initiated messaging is one aspect of the communication exchange essential in the online learning environment and should incorporate both common and unique messages that are reactive and not reactive to student behaviors. Despite this important role, messaging cannot take the place of good course design. The effectiveness of messaging can be negatively impacted if students receive a confusing or overwhelming number of messages [7]. Oversaturation may be an issue of particular concern for messaging to the course as a whole.

Another consideration is the potential benefit of messaging leading to two-way interactions. Messages that offer flexible response options and provide practical advice may encourage greater student engagement with the online learning community and help develop trust [8]. Additionally, it can be beneficial if messaging can lead to both synchronous and asynchronous two-way interactions as the synchronous communication is motivating and preferable for complex ideas [16].

Despite evidence of success of messaging, it is important to be sensitive to student feelings about messaging because students report some ambivalence about personal contact (such as a phone call) compared to alerts or announcements. According to results from focus groups at a large public university, performance messages, encouragement messaging, and messaging related to links to additional resources should be made available to all students in a class in the form of announcements or alerts to avoid providing an advantage to some students over others (e.g., equity concerns) or “singling out” at-risk students (e.g., privacy concerns) [13].

Messaging is a significant element in the online learning environment. For students to succeed in distance education there is a need for greater interaction and communication [17]. Considering the messaging framework and ensuring inclusion of all messaging types allows for increased engagement and opportunities for instructors to improve communication for online learners and thus their success with this delivery format.


[1] Moore, M. G. Three types of interaction. American Journal of Distance Education 3, 2, (1989), 1–6.

[2] Anderson, T. and Garrison, D. R. Learning in a networked world: New roles and responsibilities. In C. Gibson (Ed.), Distance Learners in Higher Education. Atwood Publishing, Madison, WI, 1998.

[3] Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A.,  Tamim, R. M., Surkes, M. A., and Bethel, E. C. A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research 79, 3 (2009), 1243–1289.

[4] Anderson, T. Modes of interaction in distance education: Recent developments and research questions. In M.G. Moore & W.G. Anderson (Eds.), Handbook of Distance Education. Lawrence Erlbaum Associates, Inc., Mahway, NJ, 2003.

[5] Rau, P.-L. P.,  Gao, Q., and Wu, L. Using mobile communication technology in high school education: Motivation, pressure, and learning performance. Computers & Education 50, 1 (2008), 1–22.

[6] Aragon, S. R. Creating social presence in online environments. New Directions for Adult and Continuing Education 2003, 100 (2003), 57–68.

[7] Arnold. K. E., and Pistilli, M. D. Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the Second International Conference on Learning Analytics and Knowledge. ACM, New York, 2012. 267–270.

[8] Dwyer, N. and Marsh, S. 2016. How students regard trust in an elearning context. In Proceedings of the 2016 14th Annual Conference on Privacy, Security and Trust (PST 2016). IEEE, Washington D. C., 682–685.

[9] Dennen, V. P.,  Darabi, A. A. , and Smith, L. J. Instructor–learner interaction in online courses: The relative perceived importance of particular instructor actions on performance and satisfaction. Distance Education 28, 1 (2007), 65–79.

[10] Hanover Research. Early alert systems in higher education. Hanover Research, Washington, D.C. 2014.

[11] Angelino, L. M. , Williams F. K., and Natvig, D. Strategies to engage online students and reduce attrition rates. Journal of Educators Online 4, 2 (2007), 1–14.

[12] Jayaprakesh, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R. ,and Baron, J. D. Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics 1, 1 (2014), 6–47.

[13] Roberts, L. D., Howell, J. A., and Seaman, K. Give me a customizable dashboard: Personalized learning analytics dashboards in higher education. Technology, Knowledge and Learning 22, 3 (2017), 317–333.

[14] Nichols, M. Student perceptions of support services and the influence of targeted interventions on retention in distance education. Distance Education 31, 1 (2010), 93–113.

[15] McInnerney, J. M. and Roberts, T. S.  Online learning: Social interaction and the creation of a sense of community. Educational Technology & Society 7, 3 (2004), 73-81.

[16] Hrastinski, S. The potential of synchronous communication to enhance participation in online discussions: A case study of two e-learning courses. Information & Management 45, 7 (2008), 499-506.

[17] Rovai, A. P., Ponton, M. K., and Baker, J. D. Distance Learning in Higher Education: A Programmatic Approach to Planning, Instruction, Evaluation, and Accreditation. Teachers College Press, New York, 2008.

About the Authors

Dr. Karen Gebhardt is the Director of the Online Economics Program at the University of Colorado Boulder. Her research focuses on using learning analytics to improve student learning outcomes and increase grades and completion rates in online courses with an emphasis on economics courses and programs. She is an early adopter of teaching with technology and advocates strongly for it because she sees the difference it makes in student engagement and learning.

Dr. Kelly McKenna is an assistant professor in the Adult Education and Training master's degree program in the School of Education at Colorado State University. Her teaching experience includes face-to-face, hybrid, and online instruction. Her research interests lie in the field of adult education, with research objectives aimed to support adult learners in their educational and occupational endeavors by creating optimal learning environments and facilitating successful student experiences. She focuses on distance education with attention to technology enhanced teaching and learning and online learning communities.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Copyright © ACM 2019 1535-394X/2019/07-3331175 $15.00


  • There are no comments at this time.