Undergraduates’ Email Response Expectations and Instructor Responsiveness: Traditional Versus Online Courses

Instructors and students routinely communicate through email technologies, in both online and traditional college courses. A primary form of electronic communication, proper email messaging structure is essential to ensure that the receiving party understands the message (Thill & Bovee, 2017). Few current research studies examine expectations and perceptions regarding specific elements of faculty-student email communication. Understanding students’ communication expectations may help instructors achieve more effective e-communication, resulting in a greater likelihood of shared meaning of messages, improved student course evaluations, and a more satisfactory learning experience.

Social presence is “the ability to perceive others in an online environment” (Richardson et al., 2017, p. 402) and includes three components: cognitive presence, social presence, and teaching presence. Social presence is performative and “demonstrated by visible activity; posting messages, responding to others, and participating in the activities of the groups” (Kehrwald, 2008, pp. 94–95). Instructors’ frequency of interaction, responsiveness, message style, and nonverbal channels play an important role in the level of presence achieved (Russo & Campbell, 2004, p. 224). Prompt personal instructor-student responses maintain social presence in an online environment (Russo & Campbell, 2004, p. 226). Social presence influences student learning experiences, student participation, motivation to participate, and course/instructor satisfaction (Richardson et al., 2017), resulting in a “strong, positive relationship” between social presence and student satisfaction (Richardson et al., 2017, p. 409).

Learner-instructor interaction is essential (Moore, 1989, “Learner-Instructor Interaction,” para. 1). Transactional distance is “a psychological and communications space to be crossed” and may result in misunderstanding between instructor and learner (Moore, 1993, p. 23). A prong of transactional distance theory, structure involves the rigidity/flexibility of course design (e.g., course objectives, activities, media choices, and content) (Moore & Kearsley, 2013). Dialogue involves purposeful constructive interactions based on active listening and respect of the instructor and student for the goal of a student’s understanding (Moore, 1993). Dialogue and structure are inversely related (Moore & Kearsley, 2013); the more dialogue, the less transactional distance is felt by learners (Gorsky & Caspi, 2005). In today’s distance learning environment, web-based courses are asynchronous (Schulte, 2004), and lack direct, face-to-face interaction between instructor and student (Bergstrand & Savage, 2013). Effective online instructor-student interaction contributes to a sense of affiliation to the faculty, removes loneliness associated with being isolated, and gives a student a sense of belonging to a learning community (Morgan & McKenzie, 2003). High perceived levels of instructor interaction also result in high levels of perceived learning (Fredericksen et al., 2000). Primarily textbased, online communication’s lack of social cues makes it less personalitycentered (Smith et al., 2001–2002), complicating the “interpersonal context” (Onwuegbuzie et al., 2007, p. 146). Distance education students have higher expectations of instructor availability (Kitsantas & Chow, 2007); consequently, faculty may have difficulty maintaining high levels of online student interactions due to other competing responsibilities (Morgan & McKenzie, 2003).

The turn of the 21st century represents much of the initial research comparing various facets of online versus traditional with face-to-face course delivery; much of which concluded with mixed results. Distance education and traditional classroom instruction are similar in terms of student outcomes, attitudes, and satisfaction (Phipps & Merisotis, 1999), student satisfaction and learning (Arbaugh, 2000; Spooner et al., 1999), methods of instruction (Smith et al., 2000), and student achievement (van Schaik et al., 2003; Waschull 2001). However, Beard and Harper (2002) found that online course participants expressed concern about the lack of teacher-student interactions.

Toda/s college students are part of the “Net Generation” (“digital natives”), composed of Gen Y (“Millennials”)—born between 1977 and 1997 (Portillo, 2011; Tap-scott, 1998)—and Generation Z (the “iGeneration”)—born post-1997. The Net Generation is characterized by “a high level of technology use (computer, internet, mobile phone), which is “firmly embedded into their lives” (Andone et al., 2007, p. 44). Accessing technology from childhood (Gibson & Sodeman, 2014), Millennials work well collaboratively, and prefer a keyboard instead of a paper-based notebook (Chicioreanu & Amza, 2018). Millennials’ adapt quickly to technological changes, multitask, use different technologies simultaneously (Gibson & Sodeman, 2014), and across various devices and applications (Howe & Nadler, 2010). Millennials rely on Wikipedia and Google Search in their studies (Nicholas, 2008), and prefer online methods to process materials at their own pace and in the order they prefer (Paechter & Maier, 2010). Email, SMS, and instant messaging are part of their daily routine (Andone et al., 2005b). Asynchronous communication is preferred with professors; in particular, email communication (Jensen et al., 2005). Information should be “presented fast, visually and interactively” (Andone et al., 2005a, “Control” para. 2). The Net Generation expects quick responses (Frand, 2000), a considerable amount of student-faculty contact, and firm deadlines (Bradford et al., 2006–2007). Accustomed to instant gratification, they “have less patience for delay of any kind” (Miller 1994, cited by Tapscott, 1998, p. 109). Generation Z (the “iGeneration”) grew up connected to smart mobile devices, Wi-Fi and high-bandwidth cellular service (Dimock, 2019, para. 12); they spend less time in-person with friends and “allocate an extremely high amount of time to social media and to texting” (Chicioreanu & Amza, 2018, p. 14). Gen Y and Gen Z “consume massive amounts of media” and “spend nearly all their waking hours using media and technology” (Rosen, 2011, p. 13). Unlike Gen Y, Gen Z is unfamiliar with “the concept of struggling” (Bencsik et al., 2016, p. 93), are less patient than predecessors, and look for solutions to problems on the Internet (Tari, 2011). While both generations use social media, Gen Z uses it as an “avenue to build trust and create a sense of community” (Smith & Cawton, 2017, p. 102).

Traditional course instructors use e-mail to answer questions about assignments and concept understanding (Gahungu et al., 2006). Email was the primary communication choice with online instructors (Chang et al., 2015), where “teacher–student interaction is predominantly dyadic and private, supported by individual emails with feedback and evaluation” (Russo & Campbell, 2004, p. 228). Reading and responding to student e-mail messages is time-consuming for instructors (Roberson & Klotz, 2002); however, facilitation of discourse can lead to higher teaching presence in online courses (Wisneski et al., 2015).

Email communication lacks the nonverbal cues of face-to-face communication (Foral et al., 2010). Research regarding student expectations of instructor email response times is somewhat dated, given recent technological advancements. Past studies show that students expect a timely response to email (Dahl, 2004; Vonderwell, 2003; Wilkie, 2016), and expect email to be answered and assignments returned more quickly in online-only courses (Carswell et al., 2000; Wilkie, 2016). Instructor responsiveness is important to help students feel individualized within an online class (Cunningham, 2015). Jensen et al. (2005) found that 87% of participants expected responses within 24 hours during weekdays; 81.9% of instructors responded to student email messages within 24 hours. Also, almost half of students expected instructors to respond within 25 to 48 hours on weekends. Ragan (n.d.) cites industry standards of one business day (24 hours) during weekdays and 48 hours on weekends for online instructors; studies generally support the 24-hour instructor response timeframe (Argon, 2003; Foral et al., 2010; White & Weight, 1999; Zhang et al., 2016).

Few current research studies examine specific elements of faculty-student email communication in online and traditional course delivery, given the recent advancements of mobile technologies. The present study examines specific categories of email messages, and compares undergraduates’ email response expectations versus instructor response rates in both online and traditionally delivered courses. The following hypotheses were developed:

• H1: There is no significant relationship between instructor email message response expectations or reported response rates in traditional versus online college courses with regard to general messages, grade/assignment-related messages, weekend messages, or messages sent when the instructor is out of town.

• H2: There is no significant relationship between expected versus reported instructor-student email message response rates in traditional college courses with regard to general, grade/ assignment-related, weekend messages, or messages sent when the instructor is out of town.

• H3: There is no significant relationship between expected versus reported instructor-student email message response rates in 100% online college courses with regard to general, grade/ assignment-related, weekend messages, or messages sent when the instructor is out of town.

The sample consisted of 2,000 undergraduate students from a midsized eastern university. University policies restricted the use of emails to 2,000 randomly assigned email addresses provided by the university. The survey was administered via Qualtrics; participation was voluntary and confidential. An approved informed consent form was completed by all student participants prior to advancing through the online survey. The Statistical Package for the Social Sciences 14.0 (SPSS) was used to analyze the data.

Based on a review of literature and theoretical standpoints, the survey was developed and pilot-tested prior to final survey development for this study. The survey utilizes Likert scaling (Nachmias & Nachmias, 1987) to measure participants’ email message response rate expectations and perceived instructor response rates in both online and traditional courses.

The study followed a descriptive research design using survey methods with statistical treatments. The design was a cross-sectional survey, the most frequently used study design (Babbie, 1990, p. 65). Descriptive statistics, such as frequency distributions, means, and standard deviations, were utilized to analyze demographic characteristics; correlation tests (Cohen, 1988), and chi-square analysis were performed to identify significant relationships between each of the categorical variables (expected response rates and reported response rates).

Internal consistency reliability analysis was performed on the Likert subscales of the measure to provide a reliability measurement. Results revealed an internal consistency of α =.907. Tukey’s test for additivity was not significant (F = 1.458, p = .227, α = .05), revealing that the scale items are independent from one another.

Out of 2,000 invitations to participate, 224 people responded; incomplete responses and responses of participants that have never completed an online course were eliminated, leaving 91 respondents.

In terms of sample demographics, most participants were female ( n = 58.4%), 18–24 years of age ( n = 90.1%), and junior or senior class standing ( n = 37.4% and n =39.6%, respectively). Most participants’ majors were housed in the colleges of health and human services (n = 26.4%), natural science and math (n = 23.4%), or business (n = 22%).

Prior to chi square statistical tests, two responses (“wouldn’t send an email message” or “have never sent a course-related email message”) were eliminated. The data set was further refined to reduce the total number of cells in each contingency table to increase statistical accuracy of the results. Statistical analysis was performed on the data collected with a significance level set to 95% (p > .05).

H1: There is no significant relationship between instructor email message response expectations or reported response rates in traditional versus online college courses with regard to general messages, grade/assignment-related messages, weekend messages, or messages sent when the instructor is out of town.

The H1 hypothesis is rejected; statistically significant positive relationships exists between expected response rates for general email messages in online courses compared to traditional courses (p .0001, two-tailed; Fisher’s exact test [FET]). Most participants in both online and traditional courses expect a response within 8 to 24 hours; approximately 12% expect a 24-hour response in a traditional course expect a response within 8 hours in an online course (Table 1).

Statistically significant positive relationships exists between expected response rates for grade/assignment-related email messages in online compared to traditional courses (p .0001, two-tailed; Fisher’s exact test). Most participants expect a response to a grade/assignment related message within 8 to 24 hours; also, 8% of students that expect a 24 hour or 48– 72 hour response in traditional course(Table 1).

Statistically significant positive relationships exist between expected response rates for weekend messages in traditional versus online courses (p .0001, twotailed; FET). More than 93% of participants expect weekend email messages responses, regardless of delivery method; almost 1/3 of online course participants expect a response within 24 hours, and more than 13% of traditional course participants that expect a response within 48–72 hours expect a response within 24 hours in an online course (Table 1).

Statistically significant positive relationships exist between expected response rates for email messages when the instructor is out of town in traditional versus online courses (p .0001, two-tailed; FET). n both traditional and 100% online courses, 75% of participants expect a response when the instructor is out of town, regardless of delivery method. More than a third of online course participants expect a response within 24 hours, and 16.5% of traditional course participants that expect a 48–72 hour response in a traditional course expect a response within 24 hours in an online course when the instructor is out of town (Table 1).

Statistically significant positive relationships exist between reported response rates for general email messages in online courses and reported response rates in traditional courses (p .0001, two-tailed; FET). No matter the delivery method, approximately 10% of both online and traditional course participants that expect a response report receive no instructor response to general email messages (Table 2).

Statistically significant positive relationships exist between perceived grade/ assignment message response rates in traditional versus online courses (p .0001, two-tailed; FET). More than half of both online and traditional course participants received a response in either 8 or 24 hours; 21% of traditional course participants that receive grade/assignment-related responses within 8 or 24 hours report longer response rates in an online course, and 5% of traditional course participants report receiving no response to a grade/assignment-related message in an online course (Table 2).

Statistically significant positive relationships exist between perceived weekend response rates in traditional versus online courses ( p .0001, two-tailed; FET). Approximately 65% of participants reported that instructors responded to weekend email messages within 24 to 72 hours; more than one third received no response (Table 3).

Statistically significant positive relationships exist between perceived out-of-town response rates in traditional versus online courses (p .0001, two-tailed; FET). In both traditional and online courses, 42.7% of participants received an instructor response when the instructor was out of town, 44% received no response, and 13.3% that reported a response in a traditional course, reported no response in a 100% online course (Table 3).

H2: There is no significant relationship between expected versus reported instructor-student email message response rates in traditional college courses with regard to general, grade/ assignment-related, weekend messages, or messages sent when the instructor is out of town.

The H2 hypothesis is rejected; statistically significant relationships exist in expected versus reported instructor response rates to general email messages in traditionally delivered courses ( p = .001, two-tailed; FET). Approximately 25% of traditional course participants’ general email response expectations were not met (Table 4).

Statistically significant relationships exist in expected versus reported instructor response rates to grade/assignment-related messages in traditional courses (p .0001, two-tailed; FET). Approximately 25% of traditional course participants’ grade/ assignment email response expectations were not met (Table 4).

Statistically significant relationships exist in expected weekend instructor response rates versus reported weekend instructor response rates in traditional courses (p .0001, two-tailed; FET). More than 78% of traditional course participants expect a response from the instructor on a weekend, but more than 35% of those participants did not receive a response from the instructor (Table 5).

Statistically significant relationships exist in expected instructor out-of-town response rates versus reported instructor out-of-town response rates in traditional courses (p = .010, two-tailed; FET). Per Table 5, 78% of traditional course participants expect an email message response when the instructor is out of town; how-ever, 43% of expectations were not met, and more than one third of participants that expected a response received no response.

H3: There is no significant relationship between expected versus reported instructor-student email message response rates in 100% online college courses with regard to general, grade/ assignment-related, weekend messages, or messages sent when the instructor is out of town.

The H3 hypothesis is rejected; statistically significant relationships exist in expected versus reported instructor response rates to general messages in online courses ( p = .028, two-tailed; FET). Approximately 37% of participant expectations were not met (Table 6).

Statistically significant relationships exist in expected instructor response rates versus reported instructor response rates to grade/assignment-related messages in online courses (p .0001, two-tailed; FET). Approximately 30% of online course participants’ grade/assignment related response rate expectations are not met (Table 6).

Statistically significant relationships exist in expected weekend instructor response rates versus reported weekend instructor response rates in online courses (p .0001, two-tailed; FET). More than 93% of online course participants expect a response from the instructor on a weekend, almost one third of participants’ expectations were not met, and almost one quarter of participants expecting a response received no response (Table 7).

Statistically significant relationships exist in expected instructor out-of-town response rates versus reported instructor out-of-town response rates in online courses (p = .001, two-tailed; FET). Per Table 7, 83% of online course participants expect an email message response when the instructor is out of town; however, 54% of expectations were not met, and 42% of participants that expected a response received no response.

College students have high expectations of instructors. We found that 70% of respondents in both online and traditional courses expect a general email message response within either 8 or 24 hours (41.9% < 8 hours; 28% < 24 hours), lower than Jensen et al.’s (2005) study. Also, around 12% of participants expect a faster response in an online course than a traditional course. Almost three fourths of both traditional and online course participants expect a grade/assignment-related response within 8 or 24 hours (40.8% < 8 hours; 32.1% < 24 hours), and 8% of participants expect a faster response in an online course than in a traditional course. We also found that 93% of participants in both online and traditional formats expect a weekend instructor response, higher than Zhang et al.’s (2016) weekend response findings. While the industry standard reasonable weekend response timeframe is 48 hours (Ragan, n.d.), our study found that almost 1/3 of both online and traditional course participants expect a 24hour response on a weekend, with 40% of online participants expecting a weekend response within 24 hours. Also, 13% of participants expecting a 48–72 hour response in a traditional course, expect a 24-hour response in an online course. When an instructor is out of town, 75% of both traditional and online participants expect an instructor to respond, more than one third of online course participants expect a response within 24 hours. Also, 16.5% of participants expecting a 48–72 hour response in a traditional course expect a 24-hour response in an online course. While previously cited studies cite 24-hour instructor response expectations, our study differentiated response expectations based on message categories to further define students’ response expectations. Based on our results, online instructors should expect that approximately 10–15% of online class participants expect a faster message response than their traditional course peers.

Our findings support Foral et al.’s (2010) study that faculty perceive themselves as more accessible to students than students perceive. Undergraduate instructors need to be more responsive to students’ email messages in both traditional and online courses, even while out of town. Placing reminders on the course home page or sending reminders may help to alleviate frustration experienced by students due to response delays (Wilkie, 2016). Also, adjust assignment due dates so they do not coincide with the out of town unavailability of the instructor.

Overall, the results of the present study are not entirely bad news, as the sample found that 75% of traditional course participants’ and 63% of online course participants’ general email response expectations were met or exceeded. In our study, approximately 78% of instructors responded to general messages within 24 hours, slightly lower than Jensen et al.’s (2005) study. Also, 75% of traditional course participants and 60% of online course participants’ grade/ assignment related email response expectations were met or exceeded. However, our study also found that, regardless of delivery method, one quarter to one third of students may be dissatisfied with instructor response rates with regard to general or grade/assignment related email messages. In terms of weekend responses, 68% of traditional course participants and approximately 63% of online course participants’ weekend email response expectations were met or exceeded. However, almost one fourth of online participants and more than one third of traditional participants expecting a response received no response at all. In terms of out-of-town responsiveness, 57% of traditional course participants and approximately 31% of online course participants’ out-of-town email response expectations were met or exceeded. However, 42% of online participants and more than one third of traditional participants expecting a response received no response.

Contrary to findings of Foral et al. (2016), our study found that online course participants are generally less satisfied with instructor email responsiveness compared to traditional course participants, particularly on weekends or when the instructor is out of town. Online instructors of college courses should engage in active social presence demonstrated by visible activity per Kehrwald (2008) and frequent interaction and responsiveness per Russo and Campbell (2004). Prompt personal responses from instructor to student with regard to questions, assignments, and discussion postings are important in both online and traditional courses (Russo & Campbell, 2004).

Reading and responding to student email messages is time-consuming (Roberson & Klotz, 2002) and faculty may feel compelled to be available at any time (Ryan et al., 2004). However, perception of social presence is a strong predictor of student course satisfaction (Gunawardena & Zittle, 1997; Swan, 2003). Enhancing social presence overcomes the distance, both real and perceived, in online environments (Richardson & Swan, 2003), and instructors’ prompt responses to email messages reduces inhibitors of social presence such as immediacy, salience, and time. Further, Millennials’/Gen Z’s expectations and digitalness must be considered when teaching both online and traditional college courses.

It might also be helpful to follow the suggestion that “if an immediate response isn’t possible, to respond with an email stating, ‘I received your email and will get back with you as soon as I can’” (Zhang et al., 2016, p. 8). Finally, to mitigate email response expectations, include online communication guideline statements in the syllabus, in an introductory email, or in an announcement (Coyle, 2014).

This study’s findings reinforce prior research findings that Gen Y and Z expect a quick response from instructors (Frand, 2000; Wilkie, 2016), a considerable amount of student-faculty contact (Bradford et al., 2006–2007), and that student course satisfaction is affected by how fast the instructor responds to communications (Jensen et al., 2005). The present study also confirms that students expect email to be answered and assignments returned quickly in an online course (Carswell et al., 2000; Elbeck & Song, 2011; Wilkie, 2016; Zhang et al., 2016), and we would argue that those expectations also apply to traditionally delivered college courses, as well, given the prevailing use of web-supported course management tools in traditional courses. To communicate with Gen Z in particular, Popova (2017) recommends that teachers “consider interesting and prospective facets of this student’s personality, notice what he/she is keen on and what areas or topics he/she is interested in” (p. 42) to motivate them. Also, just saying “good job” may be inadequate; “to make the praise effective and sincere for the students of Generation Z, you simply need to give a short description of what you are praising them for” or focus on specific examples of achievement (Popova, 2017, p. 42).

Further research should be conducted to determine the role of increased usage of text messaging, smart phones, and social media in terms of immediacy expectations in both 100% online, blended, and traditionally delivered college courses. According to (Dahl, 2004; Vonderwell, 2003), students are interested in timely response to email. However, based on this study, further research should also be conducted to determine if differences exist between students’ definition of timely and instructors’ definition of timely.

In conclusion, the results of this exploratory study are preliminary, and additional factors may exist which further explain email message response expectations versus perceived instructor response rates in traditional versus online college courses.