Using VoiceThread as a discussion platform to enhance student engagement in a hospitality management online course

Journal of Hospitality, Leisure, Sport & Tourism Education 26 (2020) 100236

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Using VoiceThread as a discussion platform to enhance student engagement in a hospitality management online course

T

Cynthia Mejia University of Central Florida, Rosen College of Hospitality Management, 9907 Universal Boulevard Orlando, FL, 32819, USA

ARTICLE INFO

ABSTRACT

Keywords: Hospitality management Online learning VoiceThread Community of inquiry Student engagement Mixed methods

Video- and voice-enabled discussion platforms have the potential to increase student engagement, leading to student success. The cloud-based discussion platform, VoiceThread, was used with undergraduate students in an online hospitality facilities management course. The findings revealed that students’ use of the audio function, both for posting their own responses and for listening to others, was a statistically significant predictor of student engagement with their classmates. The results from this mixed mode exploratory study offer theoretical implications for the CoI framework, as well as best practices for instructors interested in using VoiceThread, including discussion design and grading strategies.

1. Introduction Online courses represent the fastest growing instructional modality in tertiary education (Shea & Bidjerano, 2009), and in recent years have transitioned into the notion of simply ‘learning’ (Cavanagh, 2012), as the previous long-held boundaries between online learning and traditional face-to-face modalities have become indistinguishable. Recent patterns in online enrollments have demonstrated a steady upward trend for well over a decade (Seaman, Allen, & Seaman, 2018). For example, in the U.S. from Fall 2015 to Fall 2016, the number of higher education distance learning students increased by 5.6%, reaching 6.3 million students enrolled in at least one distance course. In 2016, students enrolled entirely in online courses comprised 14.9% (3,003,080) of all U.S. students, while 16.7% (3,356,041) were enrolled in a combination of face-to-face and online courses (Seaman et al., 2018). Convenience is one of the most influential factors of asynchronous online learning, as students may participate in a course from anywhere in the world, at any time. However, once enrolled in an online class, one challenge can be the isolation or the silent participation within the course. A traditionally designed online course can reside mostly in the heads of students, even when instructors “engage” students with voiceless written discussions, quizzes, term papers, and games on the computer. The concept of “humanizing” an online class (Dupin-Bryant & DuCharme-Hansen, 2005) is important to consider so that students feel more connected to the instructor and to each other, and therefore are more engaged and motivated to complete the course. The incorporation of voice and video instruction is a strategy toward humanization. Supported by social constructivism (Vygotsky, 1978) and the Community of Inquiry (CoI) framework in the online context (Garrison, 2007), this mixed methods exploratory study set out to determine the role that “learning out loud” (Pacansky-Brock, 2013) might play via video, voice, and texting contributions to an online VoiceThread discussion, on student engagement outcomes. Germane to the hospitality industry and to the increasing numbers of Generation Z students entering college, the amplification of online learning must contend with student engagement in order to achieve student satisfaction and graduate a competent workforce.

E-mail address: [email protected]. https://doi.org/10.1016/j.jhlste.2019.100236 Received 23 July 2019; Received in revised form 16 November 2019; Accepted 19 December 2019 1473-8376/ © 2019 Elsevier Ltd. All rights reserved.

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2. Literature review For all of the promise that online learning affords, including convenience and flexibility, this learning modality can also present unintended challenges to students (Brunvand & Byrd, 2011). Prior research has elicited several factors which lead to a high rate of dropout from online courses in higher education, and include feelings of isolation and disconnectedness (Willging & Johnson, 2009). In a study at the University of West England, Croft, Dalton, and Grant (2010) determined that physical and temporal separation can lead to feelings of isolation among distance learners, but could be ameliorated through e-mentoring, building a sense of community, mapping student locations, and generally ‘humanizing’ the online course. Results from an empirical study in of distance learning students in South Korea revealed that social presence was a significant predictor of online learning satisfaction, attenuating feelings of disconnection (Kim, Kwon, & Cho, 2011). Finally, Kurucay and Inan’s (2017) quasi-experimental study of undergraduates in an online course in the Southwest region of the U.S. revealed that online group collaborative activities increased student satisfaction, promoting feelings of community and connectedness, compared with students who did not engage with their peers, thus diminishing the learner's online experience. When parsed out into the top three factors, Lee and Choi (2011) identified course design and interactivity, administrative and institutional support of students, and the lack of student-to-student interactions, all contributing to student attrition in the online space. Other attenuation factors can be attributed to students often forgetting about assignments in an online course leading to multiple missed deadlines and overwhelm, which create a sense of isolation and loss of confidence in the course (Zhan & Mei, 2013). Prior research has demonstrated that social interaction in the online environment is an essential component for learning (Uijl, Filius, & Cate, 2017), building community (Boling, Hough, Krinsky, Saleem, & Stevens, 2012), resulting in student satisfaction, all contributors to course completion (Bickle & Rucker, 2018). 2.1. Theoretical underpinnings The ubiquity of online teaching and learning commands a constant inquiry into the role students play in their own learning, engagement, and course completion. Students in the online modality generally interact with the course on an individual basis, alone and mostly silent. However, given the social constructivist nature of learning, students perform better and learn more when they work collaboratively with their peers (Vygotsky, 1978). Social constructivism in the online environment maintains the necessity of meaningful interactions and engagement in a community of learners, requiring a sense of belonging and accomplishment both on and offline (Hrastinski, 2009; Wang, 2008; Woo & Reeves, 2007). These mindful interactions between students foster engagement and help students to attain contextual knowledge, a necessity in the online environment (Garrison, Anderson, & Archer, 2001). Ideally, online learning should be designed to stimulate a sense of community by creating meaningful interactions between students and the instructor, and under the Community of Inquiry framework (CoI), social constructivism provides the mechanism of “presences” conducive to the online learning environment (Dixson, 2015; Garrison, Cleveland-Innes, & Fung, 2010). The Community of Inquiry framework (CoI) consists of three elements: social presence, cognitive presence, and teaching presence (Garrison, 2007). Social presence, a key factor in student engagement (Dixson, 2015), can be described at the ability for students to establish personal and purposeful relationships in the online space, promoted by three aspects: effective communication, open communication, and group cohesion (Garrison, 2007). Cognitive presence refers to how learners construct meaning through reflection and discourse in a learning community (Garrison, Anderson, & Archer, 1999). Teaching presence describes the course design and how the instructor directs the cognitive and social processes toward meaningful outcomes (Garrison & Arbaugh, 2007). 2.2. Generation Z and hospitality management The majority of students currently enrolled in tertiary education represent a highly socially connected generational cohort (Mintz, 2019). Generation Z (Gen Z), aged 7 to 22 in 2019 and born between 1997 and 2012 (Fry & Parker, 2018), are replacing the Millennial generation, aged 23 to 38 in 2019 and born between 1981 and 1996 (Dimock, 2019), in college enrollments (Loveland, n. d.). Gen Z is more dependent on smart devices and social media than previous generations, and has revealed a preference for selfdirect learning, immersive educational experiences, and technology-mediated instruction (Mintz, 2019). Having used technology beginning in primary school through to 12th grade in high school, Gen Z is accustomed to individualized instruction and collaboration with their peers (Zorn, 2017). This generational cohort prefers text messaging versus communication via phone call, shows a preference for interacting with friends online, is highly dependent on smart devices, and is accustomed to 24/7 data access (Seemiler & Grace, 2016). While Gen Z students prefer to do their work on an individual basis, they also suffer from “Fear of Missing Out” anxiety, as a result of intensive social media use, leading to increased levels of depression and anxiety (Hogan, 2015; Seemiler & Grace, 2016). In the online course context, the learning and working preferences of Gen Z undergraduate students present a challenge for instructors. Given that this generational cohort prefers individualized instruction, the online learning environment offers up isolation as a set of circumstances, promoted by asynchronous teaching design, and potentially damaging to students once they enter the workforce (Bickle & Rucker, 2018). Physical and emotional isolation in the online learning environment increases the likelihood that students will not engage with the course, the instructor, nor their peers (Herrington, Oliver, & Reeves, 2003). This lack of engagement not only hampers learning and delays successful course completion, but it is of particular significance in the services industries, where service quality is valued, and the interactions between and among customers and peers fuel competitive advantage (Jeong & Oh, 1998; Parasuraman, Zeithaml, & Berry, 1988). A characteristic of Gen Z worth examining is that they gravitate toward human 2

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connections in the workplace, seeking out positive and mentoring relationships when looking for a job (Colletta, 2018). These favorable Gen Z attributes combined, when mobilized in an online learning environment, have the potential for rich and meaningful interactions, lending themselves toward engagement and student success. Interactive online learning technologies supported by LMS-based discussion boards, wikis, and third-party cloud-based software, are transforming the learning experience and encourage collaboration and engagement among students (Panettieri, 2013). Empirical research has revealed that online discussions improve students’ perceived learning (Wu & Hiltz, 2004) and enhance critical thinking (MacKnight, 2000). In the hospitality education context, online discussions have been found to improve critical thinking skills (Sigala, 2004), activate a global workforce mindset (Cho, Schmelzer, & McMahon, 2002), and inspire entrepreneurial thinking (Ahmad, Bakar, & Ahmad, 2018). 2.3. Learning out loud and VoiceThread “Learning out loud” and “humanizing online learning” are tag lines associated with the cloud-based discussion software, VoiceThread, and its use championed by Pacansky-Brock (2013) and others. VoiceThread has been shown to enhance social presence in the online teaching environment, which has directly impacted student engagement. VoiceThread is a multimedia slideshow tool in which the instructor and/or student communicates via video recording, voice recording, and/or text (https://voicethread.com/). In addition to user-generated content, students and instructors may upload YouTube videos, PowerPoint slides, or any other electronic documentation. In addition, VoiceThread software allows for user annotations with a drawing tool, or with voice/video overlay. Users post (upload) their responses over the Internet through a smartphone, tablet, desktop or laptop computer. Audio and video recordings are saved to the cloud server, and available for display to those enrolled in the course on the platform (Bickle & Ryan, 2018). Prior research has shown that the use of VoiceThread in online learning environments promotes a sense of community (Delmas, 2017; Kirby & Hulan, 2016), requires greater preparation of students before posting leading to increased learning (Kirby & Hulan, 2016), and encourages collaborative learning in group environments (Fox, 2017). VoiceThread is a tool uniquely positioned to enrich online course discussions, particularly targeting Gen Z students who seek individualized learning opportunities, yet want to be part of a group. VoiceThread has also been demonstrated as effective across other generational cohorts (Brunvand & Byrd, 2011; Delmas, 2017; Kirby & Hulan, 2016). VoiceThread also has the potential to enliven online learning structures, of utmost importance in the services industries. Due to the continuing upward trend of online curricula in hospitality management education, total silence in the online learning space is contradictory to the discipline, and to the hospitality and tourism industries. The purpose of this mixed methods exploratory study was to determine the relationship between student engagement with their peers in an undergraduate hospitality online course and the use of VoiceThread for audio and video recorded asynchronous discussions. 3. Methodology An online course was taught for 6 weeks at a Southeastern University in the U.S.A. during June and July 2018. Throughout the course, students were required to participate in 5 VoiceThread-enabled discussions, each with an introductory video recorded by the instructor which detailed the discussion criteria, due dates, and discussion prompts. The introductory video was followed by one to two YouTube videos which provided background context. For each VoiceThread discussion, the students were required to upload their initial post (either by voice, video, or text) one week prior to the due date of two mandatory follow-up posts (also by either voice, video, or text). Students accessed the discussions through the cloud-based third party VoiceThread software (https:// voicethread.com), which was linked out of the institution's learning management system (LMS). An assignment page for each discussion was also created within the LMS, with written instructions duplicating the video instructions available within each of the VoiceThread discussions, along with a corresponding rubric to facilitate grading directly connected to the LMS gradebook. The instructor purchased a single site VoiceThread user license for US$99 which included access for 50 students. As seventy-two (72) were enrolled in the course, the instructor purchased an additional 22 student access codes for US$2 each, a total of US$143 for access to the software. In the first week of the course, the students received an access code via email from the instructor with instructions for setting up their individual VoiceThread profiles. The instructor monitored student sign-up activity, and organized small groups of 5–6 students each on the VoiceThread platform, for a total of 12 groups. All groups participated simultaneously with the same discussion material within their small groups, and followed identical due dates throughout the course. The sample consisted of 72 undergraduate students enrolled in a hospitality facilities management course. The semester this class was taught would be the first in the full online modality, with previous semesters delivered in the mixed mode, and typical prior enrollments ranging from 30 to 45 students. Near the completion of the online course at the fifth week, the instructor emailed each student a link to Qualtrics-based survey. As an incentive to complete the survey, students were offered 5 points of extra credit, which accounted for approximately 1% of the total cumulative points possible in the course. The survey was composed of 28 questions: 5 were demographic in nature, 9 regarding course and instructor satisfaction, and 14 VoiceThread-related questions regarding technical issues, engagement in the learning community, and enjoyment with the VoiceThread platform's functions (see Appendix). Noncategorical questions were organized according to a 7-point Likert scale of agreement (i.e. Highly Disagree = 1 to Highly Agree = 7). Two additional short answer questions were included in the survey from which to extract qualitative data, and these questions referred to what the participants liked most and least about the use of VoiceThread for the discussions. The survey was open for 2 weeks, and all but one student elected to participate for a total of 71 surveys returned. Of these, one 3

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Table 1 Demographics of the sample (n = 70). Variable Year in College (according to credit hours) First Year (Freshman) Second Year (Sophomore) Third Year (Junior) Fourth Year (Senior) More than Four Years (Senior+) Gender Male Female Declared Major Hospitality Management (HM) Event Management (EM) Restaurant & Foodservice Management (RFM) Double Major (n = 3; 4.3%) HM & RFM HM & EM Course Required for Major Yes No (n = 24; 34.3%) Full online delivery Taken all other electives Last choice Other reason Student Circumstances While Enrolled in the Course Worked full time Worked two jobs Enrolled in one other course Enrolled in two or more other courses Travelled during the course

n

%

– 1 9 57 3

– 1.4 12.9 81.4 4.3

15 55

21.4 78.6

60 1 6 1 2

85.7 1.4 8.6 1.4 2.9

46 19 5 1 6

65.7 26.6 7.0 1.4 8.4

49 7 23 21 27

38.6 5.5 18.1 16.5 21.3

survey was incomplete due to half the responses missing and so it was deleted, yielding a total of 70 useable surveys. The quantitative data were analyzed utilizing the SPSS v.24 statistical software package, and the analysis methods included demographics, means, frequencies, and simple regression. The qualitative data were analyzed using MAXQDA v.11 qualitative data analysis software. 4. Results According to their earned credit hours, the majority of undergraduate hospitality students self-identified as fourth year students (i.e. seniors) (81.4%), followed by third year students (i.e. juniors) (12.9%), seniors in their fifth year of college (4.3%), and second year students (i.e. sophomores) (1.4%). No first year (i.e. freshmen) were enrolled in the course (see Table 1). The majority of participants were female (78.6%), which was consistent with the demographics of the college from which the data were collected. The vast majority of students in the course declared Hospitality Management (HM) as their primary major (85.7%), followed by Restaurant & Foodservice Management (RFM) (8.6%), and Event Management (EM) (1.4%). Three students declared double majors: one was an HM and RFM double major (1.4%), and two others were HM and EM double majors (2.9%). The online course was required in the degree program for 46 (65.7%) participants, while the remaining 24 (34.3%) students offered a variety of reasons for enrolling in the course even though it was not required, including that it was offered in the full online mode (26.6%), they had extinguished all other options (7.0%), it was their last choice among the courses offered that semester (1.4%), and other reasons (8.4%) which encompassed career interests, instructor preference, and guessing it might be an interesting course. Students identified with several situational factors while enrolled in the full online course: 49 students (38.6%) worked full time, 7 students (5.5%) worked two jobs, 23 students (18.1%) were concurrently enrolled in one other course, 21 students (16.5%) were concurrently enrolled in two or more other courses, and 27 students (21.3%) travelled while enrolled in the course. 4.1. Students’ satisfaction with the online course The participants were asked a series of questions pertaining to how challenging they found the course, how much they learned, how reasonable the workload was, and their overall satisfaction with the course. The majority of participants did not express strong views either way when asked if the course was challenging (M = 3.92, SD = 1.04) and somewhat agreed they learned a lot as a result of completing the online course (M = 5.29, SD = 1.33) (see Table 2). The majority of participants believed the course workload was reasonable (M = 6.20, SD = 1.15) and were overall satisfied with the course (M = 6.14, SD = 0.82).

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Table 2 Results pertaining to students’ satisfaction with the course (n = 70). Variablea

M

SD

The course was challenging I learned a lot in this course The workload was reasonable Satisfaction with the course

3.92 5.29 6.20 6.14

1.04 1.33 1.15 0.82

a

Based on a 7-point Likert scale of agreement (1 = Highly Disagree to 7 = Highly Agree).

4.2. Students’ use and perceptions of VoiceThread (VT) The participants were asked a series of questions specifically related to their experiences using the VoiceThread (VT) platform throughout the online course (see Table 3). When asked about their technical experiences, the majority of students reported being able to register on the VT platform without problems (M = 6.16, SD = 1.59), and once they were comfortable with VT, the students did not experience any additional technical difficulties (M = 5.63, SD = 1.69). The majority of participants did not find VT to be confusing, nor frustrating to use (see Table 3). When asked about engagement with their classmates in the course as a result of using VT, the students felt engaged (M = 5.49, SD = 1.63) and reported feeling connected to their respective small groups (i.e. learning communities) while using VT (M = 5.44, SD = 1.70). Finally, the students agreed that they learned a lot about the course content while in the VT discussions (M = 5.78, SD = 1.43). The participants did not enjoy videoing themselves during course discussions in the VT platform (M = 3.39, SD = 2.18), and in this study, they demonstrated a preference for the audio and texting capabilities of the VT software when uploading their discussion responses to the platform. Students somewhat enjoyed recording audio of themselves (M = 4.60, SE = 1.96) while in the VT discussions, and similarly, somewhat agreed to a preference for using the texting function in the VT discussions (M = 4.50, SE = 1.91). In contrast to their negative feelings about recording their own videos for inclusion in the VT discussions, the participants enjoyed viewing their classmates’ responses in the video format (M = 4.97, SE = 1.63). Similarly, the participants enjoyed listening to the VT audio responses of their classmates in the discussions (M = 5.46, SE = 1.53). 4.3. Students’ recording format preference in VT and regression analysis Over half of the participants (56.5%) revealed a preference to record themselves in audio format for upload to the VT discussion, followed by a preference for texting their responses in the platform (39.1%). Few students (3) preferred the use of the video recording function for their contribution to the online VT discussions (see Table 4). A regression analysis was performed to test which VoiceThread functions significantly predicted participants' engagement with their peers in the online course (see Table 5). The results of the regression indicated that five predictors explained 59.6% of the variance (R2 = 0.596, F(5,64) = 18.852, p < .001, f2 = 1.29). Of the variables tested, only two (‘I enjoyed recording audio of myself in VT’ (β = 0.259, p < .01) and ‘I enjoyed listening to the audio responses of my peers in VT’ (β = 0.641, p < .001)) significantly predicted participants' feeling of engagement with their peers in the online course. Given the relatively small sample size, the adjusted R2 was used to calculate the effect size for this analysis (f2 = 1.29), and was interpreted as a very large effect (Coe, 2002; Rosenthal, 1996). Table 3 Results pertaining to students’ use and perceptions of VoiceThread (n = 70). Variablea

M

SD

I registered for VT without problems. Once I was comfortable with VT, I did not have any problems. I found VT to be confusing. I found VT to be frustrating.

6.16 5.63 3.00 2.84

1.59 1.69 1.68 1.56

I felt engaged with classmates using VT. I felt connected to the learning community using VT. I learned a lot about the course content while in the VT discussions.

5.49 5.44 5.78

1.63 1.70 1.43

I enjoyed recording video of myself in VT. I enjoyed recording audio of myself in VT. I preferred using the text function in VT.

3.39 4.60 4.50

2.18 1.96 1.91

I enjoyed viewing the video responses of my classmates in VT. I enjoyed listening to the audio responses of my classmates in VT.

4.97 5.46

1.63 1.53

a

Based on a 7-point Likert scale of agreement (1 = Highly Disagree to 7 = Highly Agree). 5

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Table 4 Students’ preference of recording format in VoiceThread (n = 70). Recording Format

n

%

(1) Audio (2) Text (3) Video

39 27 3

56.5 39.1 4.4

Table 5 Regression analysis for five variables predicting student engagement with their peers in the online course (n = 70). Variable

B

SE B

β

I enjoyed recording video of myself in VT. I enjoyed recording audio of myself in VT. I preferred to use the text function in VT. I enjoyed viewing the video responses of my peers in VT. I enjoyed listening to the audio responses of my classmates in VT. R2 .596 Adj. R2 .564 F 18.852**

.028 .245 .045 -.050 .685

.068 .081 .074 .106 .118

.038 .259* .053 -.050 .641**

*p < .01; **p < .001.

4.4. Results of the qualitative analyses from the short-answer questions The survey included two short-answer questions regarding which aspects the participants liked most and least about the use of VoiceThread for the discussions. The data collected from these questions were subject to qualitative analysis, and emergent themes were identified based on a coding schema using a grounded theory approach (Creswell, 2014). The associated thematic descriptions were interpreted and organized according to frequency and percent of mentions by the participants. 4.4.1. Liked most about the use of VoiceThread for online discussions According to the frequency of mentions in the data, the participants liked the technical aspects of the VoiceThread software most, such as being able to hear how they sounded prior to posting, the ease of use and access, and the ease of navigation within the software (57.4%) (see Fig. 1). One student mentioned the accessibility, “I liked how [VT] was easy to access, and I used the app on my phone so I could listen to my peers in the discussion on the go”. The students also liked how VoiceThread enabled them to become more engaged and interactive with their peers in the discussions (19.4%). For example, “I felt more engaged with my fellow classmates and also more connected to the learning community.” “I liked getting to see and speak to classmates I otherwise wouldn't have.” “I liked the connectivity it gave me to my classmates.” “It made me feel a connection to my classmates and compare responses to different posts and opinions.” The words, “connected”, “connectivity”, and “connection” revealed a noticeable theme in which the students expressed engagement and interactivity with their peers, underscoring how the use of VT for the discussions facilitated feelings of interconnection

Fig. 1. Aspects liked most about the use of VoiceThread in the online discussions. 6

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Fig. 2. Aspects liked least about the use of VoiceThread in the online discussions.

within the asynchronous environment. Students both voiced and inferred that the VT discussions were the next best alternative to an in-person discussion in a face-to-face classroom, compared with the typical text-based discussion experiences they encountered in past online courses. The participants expressed that they enjoyed listening to their peers in the discussions, rather than reading the text responses (8.3%). Some students liked the flexibility of format choice, either using video, voice, or text, and also that VoiceThread provided a different approach to a typical online discussion (3.7%). Students enjoyed seeing the instructor (2.8%), and felt that using VoiceThread enabled them to increase their ability to learn (1.9%). Finally, one student mentioned that using VoiceThread provided an ancillary benefit (0.9%), “Using [VT] made me come out of my comfort zone, and that is what [college] classes should do for students”. 4.4.2. Liked least about the use of VoiceThread for online discussions The participants were asked what they liked least about the use of VoiceThread in the online course discussions (see Fig. 2), and according to the data, the students did not like the technical glitches which frequently occurred when using the VoiceThread cloudbased software. Students mentioned the software was not compatible with some browsers, would crash occasionally, and for some students, the software was not compatible on their smart devices (47.6%). Students also explained that the VoiceThread interface was confusing to navigate at first. Some participants reported they did not like recording themselves at all (11.9%), and others did not like hearing their own voices on the audio function (11.9%). Some students did not like using the video recording function (7.1%), and others did not like that the instructor restricted their ability to edit a response after posting (7.1%). One student expressed frustration, “I didn't like when I messed up my thoughts during an audio upload, and I had to restart the entire audio recording”. A few participants did not like interacting with their peers (4.8%) and offered, “I don't like having to listen to others talk. Most people are better at putting their thoughts on paper rather than in words because it allows them to review and edit what they wish to say.” Two participants thought their peers took too long to respond (4.8%), for example, “People take forever to get to their point”. One student was opposed to using the text box function (2.4%). 5. Discussion The majority of participants at the time of this study were in their senior year of college (85.7%), and consistent with the use of online discussions at the institution where the data were collected, students most likely participated in online discussions throughout their degree program requirements, whether enrolled in a face to face, mixed mode, or full online course. While the majority of participants (65.7%) enrolled in the course because it was a requirement for their major, over one quarter of participants (26.6%) enrolled because the course was offered in the full online modality. Finally, the participants were overall satisfied with the online hospitality course (M = 6.14, SD = 0.82). The quantitative results indicated that the students, once comfortable with the software, did not experience any technical difficulties using VoiceThread (M = 5.63, SD = 1.69), nor did they find VoiceThread to be confusing, or frustrating. This finding was in direct contrast to the results from the qualitative component of this study, where students expressed displeasure with the technical glitches encountered during the use of VoiceThread, representing the highest frequency of aspects liked least among participants (47.6%). This contrast in findings could be explained by the prevalence of software incompatibilities students increasingly encounter across browsers and learning management systems, third-part software, and cloud-based software. The dominance of online course delivery has led instructors to exploit ancillary cloud software, social media, and other Internet-based media, often challenging compatibility on several levels (Yang, 2013). Also, given the variety of hardware (i.e. desktop and laptop computers) and smart devices on which students use to access course learning management systems, there are bound to be a number of non-supported platforms students face throughout a course. In this study, students were eventually able to gain access to the VoiceThread platform 7

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when confronted with incompatibility, while still experiencing technical glitches when solving the work-around. Consistent with previous research (Gillett-Swan, 2017), this finding demonstrates that while students are resilient in overcoming technical glitches (Mintz, 2019), they most likely encounter these as a regular occurrence and have grown used to solving technical work-arounds, perhaps imposed unknowingly by their instructors. Other quantitative results in this study revealed that the participants felt engaged with their peers when using VoiceThread in the discussions (M = 5.49, SD = 1.63) and felt connected to their learning community (M = 5.44, SD = 1.70). The qualitative results aligned with this finding, as students expressed an appreciation for the novelty of improved interactivity with VoiceThread over traditional text-only discussion platforms, more commonly found in tertiary education. 5.1. Conflicting format preferences: posting vs. viewing/listening Both the quantitative and qualitative results from this study revealed that the participants did not like posting videos of themselves to the VoiceThread discussions. Over half (56.5%) of the participants preferred posting to the discussion in the audio format, followed by text postings (39.1%), and video postings (4.4%). Contrary to their own posting preferences, the participants did enjoy viewing their classmates' video responses (M = 4.97, SE = 1.63). In addition, the participants enjoyed listening to their peers' audio responses (M = 5.46, SE = 1.53), although some students expressed displeasure, which were offered in their qualitative responses (i.e. having to listen to their peers’ audio responses taking too long). Five predictor variables were tested in a regression analysis to determine which posting formats in VoiceThread led to students feeling engaged with their peers in the online discussion. Of the five variables tested in the model, only two emerged as statistically significant and accounted for 59.6% of the variance (R2 = 0.596, F(5,64) = 18.852, p < .001, f2 = 1.29). ‘I enjoyed recording audio of myself in VoiceThread’ as a predictor of student engagement among their peers was a statistically significant result (β = 0.259, p < .01), while ‘I enjoyed listening to the audio responses of my classmates in VT’ was highly statistically significant (β = 0.641, p < .001). The remaining variables tested were not statistically significant and also resulted in low standardized beta coefficients: ‘I enjoyed recording video of myself in VT’ (β = 0.038); ‘I preferred to use the text function in VT’ (β = 0.053); and ‘I enjoyed viewing the video responses of my peers in VT’ (β = −0.050). The interpretation of these results demonstrated students' affinity for audio capabilities in the VoiceThread software as a means to engage with each other in the discussions. Creating their audio posts and listening to the audio responses of their peers, together, accounted for over half of the variance (59.6%) in the model and very large effect size (f2 = 1.29), making this a substantial finding new to the literature. 5.2. Liked most technical vs. liked least technical The results of the qualitative analyses revealed a dichotomy between what students liked most and liked least about the use of the VoiceThread software for course discussions. While students liked most the utilitarian technical aspects, including ease of use and ease of access to the VoiceThread platform, they also expressed concurrent problems with software unreliability and intermittent technical glitches. Discussion formats offer a popular mechanism for students to contextualize and assess theory and concepts learned via course content (Kent, Laslo, & Rafaeli, 2016). The majority of asynchronous online discussions are text-based, and perhaps that VoiceThread was a novelty for the participants in this study, the technological capabilities of the software may have been appealing enough and therefore, regarded highly as a “most liked” aspect. However, as with all emergent technology, social media, or interactive software platforms, technical glitches are prevalent until the platform becomes more widely used and more fully integrated across a variety of browsers, devices, and learning management systems (Rogers, 2010). 6. Conclusion The purpose of this mixed methods exploratory study was to determine if the use of audio or video recorded online discussions in VoiceThread enhanced student engagement with their peers in an undergraduate hospitality course. Using a quantitative approach to attain means, frequencies, and regression, the data collected was further enhanced by two open-ended questions subject to qualitative analysis for emergent themes. The results generated from this study shed light on the potential for “learning out loud” in an online course environment. While anecdotally, video embedded in social media appears to be of intense interest among college students, according to the results in this study, in the online learning space it is voice recordings that capture students' interests, engaging them with their peers. This finding supports the preference of Gen Z's tendency toward individualized, personal learning, yet uniquely appeals to their “Fear of Missing Out” (bib_Strong_2016 Strong, 2016; Zorn, 2017) by participating in an engaging format, without the full exposure of a video recording. 6.1. Theoretical implications The results of this study further support the significance of the social presence in CoI, a key factor in student engagement (Dixson, 2015; Garrison, 2007). The social presence of the CoI framework in the online environment promotes effective communication, open communication, and group cohesion (Garrison, 2007) in order for students to effectively realize the outcomes of CoI. The significance of the audio preference findings from this study demonstrate students’ impressions of what they determined to be “effective communication” in the online space. Being available in a transparent manner to all in the small groups, the VoiceThread discussion were also a vehicle for “open communication”. Finally, the regression results of the study demonstrated that their preference for using 8

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audio recordings was a statistically significant predictor of engagement, the precursor toward “group cohesion”. Theoretical implications from this study offer a unique vision as to what “effective communication” will mean to online learning of the future. Presently, Gen Z (and some Millennials) envision audio communication on cloud-based discussion software as effective communication, leading to engagement. 6.2. Practical implications The results from this study offer several practical implications for online teaching strategies regarding the use of discussions to foster student engagement. As a matter of funding, if VoiceThread (or other cloud-based discussion software) is not supported by the institution, then the onus of payment falls upon the student or instructor. This might be untenable for large classes. However, if affordable or supported by an institution, VoiceThread seamlessly integrates with most LMS platforms. Other tools could be employed in place of VoiceThread, given financial constraints, and might include the use of voice-enabled annotated PowerPoint presentations, uploaded MP3 or MP4 files, or uploaded short video files incorporated into the typical learning management system discussion platform. Some learning systems, such as Canvas, Blackboard, and Moodle, offer embedded voice and video capability, with the added benefit of Moodle (https://moodle.org/) as a free and open-sourced learning management system. The results from this study revealed that students encountered some confusion upon first contact with the software, which was mostly alleviated after they became used to the platform. Best practices call for utilizing the software in a low-stakes, introductory manner to mitigate frustrations and the confusion that typically accompanies new software. In addition, this study revealed that while students may encounter periodic discrepancies with software compatibility, they may not voice those instances to the instructor. Online instructors should attempt multiple communications with students throughout the course to elicit responses from students related to problematic browser interfaces or software incompatibilities. One point of advice related to this study, is to prepare for a large amount of video and voice data generated by students. Without constraints, students might speak or video themselves for 5 min or longer. In a small discussion group of 5–6 students, each generating an initial post followed by two response posts, an instructor might need to watch or listen to 90 or more minutes of content. In large classes of 50 + students, this factor increases five-fold, requiring a substantial amount of time to grade. Additionally, in contrast to a text-based discussion, when the instructor checks in to a VoiceThread discussion periodically for multiple groups, comprehensive grading at the end may be difficult to keep track of. Careful planning and audio/video length parameters within the student instruction will alleviate grading challenges. 6.3. Limitations and future research This study was subject to limitations. As an exploratory investigation, the sample size was small (n = 70), and although appropriate for the number of variables tested in the regression (6 variables x 10 participants = 60) (VanVoorhis & Morgan, 2007), future studies would benefit from a larger sample size, perhaps inclusive of several more institutions, enhancing the generalizability of the findings. Future studies could also test the use of VoiceThread and its relationship with student engagement across face-to-face and mixed modalities. Additional variables should be included in future studies in order to investigate which other factors predict student engagement in an online course in conjunction with VoiceThread. Future studies could employ an experimental design, testing the differences between groups regarding the presence or absence of VoiceThread as a discussion platform, and its relationship to student satisfaction. Future research should also test other discussion platforms with similar voice and video capabilities, whether present within available learning management systems, or as third-party software. Finally, as the instructor restricted the students’ ability to edit or revise their posts, future studies should allow students to revise the video or audio files prior to upload to determine if this restriction affected student engagement or satisfaction with the course, or with their posting preference modality (i.e. video or voice). Appendix. Survey questions for the study Category

Survey Question

General Demographics

1. Year in college according to credit hours: 2. Gender: 3. Declared major in the hospitality management program: 4. During this course, the following applied to me (check all that apply): 5. Was this course required for your major? 6. What inspired you to enroll in this course? 7. Overall, how satisfied or dissatisfied were you with this course? 8. On a scale of 1–10, how likely are you to recommend this course to a friend or classmate? 9. How challenging was this course? 10. How much did you learn from this course? 11. How reasonable or unreasonable was the workload for this course? 12. How reasonable or unreasonable were the instructor's expectations 13. How knowledgeable was the instructor of the material presented in the course? 14. How well did this course meet your expectations?

Course & Instructor

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VoiceThread: Qualitative

15. 16. 17. 18. 19. 20. 21.

What did you like most about this course? What did you like least about this course? How could this course be improved? Format used in VT (video and/or audio and/or text function) Ranked preference of format Explain ranked preference (qualitative) Level of agreement (Highly Agree = 5 to Highly Disagree = 1) regarding VT I was able to join and register for VT without any problems Once I became comfortable with VT, I did not have any technical problems I found VT to be confusing I found VT to be frustrating VT allowed me to feel more engaged with my fellow classmates VT allowed me to feel more connected to a learning community I learned a lot about different aspects of facilities management while participating in the VT discussions I enjoyed recording video of myself in VT I enjoyed recording audio of myself in VT I preferred to use the text function in VT I enjoyed the video responses of my peers in VT I enjoyed the audio responses of my peers in VT 22. What did you like the most about VoiceThread? 23. What did you like least about VoiceThread? 24. What recommendations could you make to improve the use of VoiceThread in this course in future semesters?

• • • • • • • • • • • •

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