This study aimed to explore the online learning experiences of audiology students at a South African university.

An exploratory qualitative research design was employed to understand the experiences of audiology students regarding online learning. The participants’ features for students between the second and fourth years of study were described as guided by the research objectives and based on the study’s inclusion criteria (Leedy & Ormrod, 2021).

The study site was the Department of Speech Pathology and Audiology, Audiology Division, at the University of the Witwatersrand. This context was chosen because of its feasibility, convenience and easy access to participants at the University of the Witwatersrand. The audiology department (study site) employed online learning, which was readily phased in as per the university’s implementation teaching and learning plan for 2020–2024 (University of the Witwatersrand, 2020). Online platforms, including Microsoft Teams (MS Teams) and Big Blue Button (BBB), are LMS that have been used since 2020. Prior to the COVID-19 pandemic, audiology students used an LMS called Sakai and later the introduction into Ulwazi (canvas) in 2021. In 2021, the university introduced a blended learning approach and lecturers designed their blended learning courses as Ulwazi courses to ensure access to course materials, lecturers for consultations and course assessments.

In this study, participants were selected using a purposive sampling method (Showkat & Parveen, 2017). Audiology students from the second to fourth year who were registered during the period from 2020 to 2022 were invited to participate in the study. The researchers followed two steps to reach the participants: (1) the administrator of the audiology department distributed the participant information sheet to the participants via email and (2) potential participants interested in participating in this study were asked to directly contact the researcher. Subsequently, the snowball sampling technique was used to attract additional participants (Naderifar et al., 2017). At the point of data saturation, a total of 10 (n = 10) participants were reached. Data saturation was determined by the repetitiveness of the participants’ responses and the researchers’ belief that after coding the eighth interview, no new codes emerged from additional interviews (Saunders et al., 2017).

Table 1 illustrates the participants’ demographic information. Nine of the 10 participants were females, with the age range between 19 and 29 years. The majority of our participants were in their fourth year of study, which is the final year of an undergraduate professional degree. All our participants were students who were commuting daily from home to the university.

The data were collected using a self-designed interview guide (Appendix 1) that was informed by the literature and the study aim. This tool was pre-tested before being used in the main study. Semi-structured interviews were conducted with the 10 participants. These semi-structured interviews that lasted approximately 35 min, were conducted on Microsoft Teams. While six participants agreed to use the video option, four chose to keep their videos off during the interviews. The interviews were audio recorded using the recording feature on MS Teams, to allow the researcher to review and replay the interviews, transcribe data and identify key information during data analysis (Cook, n.d.). The researcher (A.A.) maintained a reflexive journal to document their position as an audiology student while collecting data for the study (Haynes, 2012).

Interview transcriptions were analysed using thematic analysis, which facilitated the identification, organisation, and description of themes. These themes were deductively coded based on the main study aim (Bingham & Witkowski, 2021). This provided valuable insights into the students’ experiences, which were essential for the final interpretations of the study’s findings (Braun & Clarke, 2006). Finally, member checking was employed to ensure consistency, transparency and truthfulness of the data collected (Birt et al., 2016; Cypress, 2017; Schwandt et al., 2007).

Before conducting data collection for the study, non-medical ethical clearance to conduct this study was obtained from the University of the Witwatersrand School of Human and Community Development Ethics Committee (no. STA_2022_23), and then participants were invited to participate. Participants who consented to participate in the study. During data collection, participants’ online interviews that were collected, were protected. In addition, access to interviews during data collection was controlled by using the waiting room feature to vet participants. The researcher (A.A.) employed the use of a reflexive journal to avoid interview bias (Haynes, 2012).

Table 2 indicates that participants could engage in online learning effectively at home but learning at home may have led to a lack of motivation. Furthermore, participants had access to and used computers, smartphones, and the internet for online learning.

As reflected in Table 3, online learning allowed participants to spend more time learning and less time travelling to the university to attend lectures. Furthermore, online learning was overall cost-effective and less tiring for participants, as they did not need to travel to attend lectures. Learning online also allowed participants to learn in their own spaces, at their own pace, at times that were suitable to them, and learn more independently (Table 3).

As indicated in Table 4, participants experienced challenges with online learning. They reportedly had limited input from instructors. They also encountered connectivity issues because of load-shedding and had difficulty maintaining a stable connection to LMS, which impacted their interactions with course content and lecturers. Participants also had difficulty managing their time. Learning online also affected participants getting to know their peers and it impacted their preparedness for clinical practicals.

As reflected in Table 5, participants used these strategies as facilitators for online learning: self-regulated learning, consulting additional resources, instructor presence, provision of internet data bundles, integrated asynchronous and synchronous modes of learning and using a blended approach to online learning.

As implied by the students’ responses, it seems that when engaging in online learning they did so at their homes. The students indicated that their home environment is an optimal learning space for online learning, the space enabled effective and efficient learning at home, thus providing a conducive infrastructure that facilitated online learning. Our result was consistent with Gao et al. (2021) findings. Nevertheless, our finding seems to differ from those reported by Laher et al. (2021) who found that students had difficulties engaging in online learning at home because of having multiple responsibilities at home, such as family responsibilities, which resulted in a lack of motivation to engage in online learning. The participants in our study also reported a lack of motivation but theirs stemmed from the repetitive nature of the course setup for online learning. Our findings and those from Laher et al. (2021) emphasise differences in experiences among students who attended the same university and highlight the need to incorporate accommodations for diverse student needs during infrastructure planning for online learning (Bharuthram & Kies, 2013; Mupawose et al., 2021).

Online learning as a pedagogical approach requires an efficient information communication technology (ICT) infrastructure to ensure quality learning and teaching and to improve students’ experiences of learning. Similar to the audiology students who participated in Kim’s (2021) study, our study participants favoured engaging in online learning at home because this was a more time and cost-effective mode of learning that offered more learning opportunities. As a result, the participants found online learning to be less exhausting than in-person learning sessions and were thus able to better prepare for assessments and organise their days efficiently. This finding may elaborate on the advantages of online learning (Gajura, 2021).

Online learning also seemed to allow for flexible students’ learning. The students were able to learn at their own pace, unpressurised, and at times that were most suitable to them. Therefore, these results support the findings obtained by Bączek et al. (2021) who also found that students deemed online learning to be advantageous as they were able to learn at their own pace and under comfortable conditions. Furthermore, our study findings and those from Bączek et al.’s (2021) study may be integrated into the expanded description of flexible learning, which supports the notion of providing students with the choice of where and when to learn (Lee & Hannafin, 2016). In addition, flexible online learning encourages self-discipline (Bączek et al. 2021), which are potential attributes that support students’ independence in their learning (Winne & Marzouk, 2019). Therefore, the benefits of online learning bode well with higher-order thinking necessary for a self-regulated who is motivated to improve their learning experiences.

However, challenges associated with time management during online learning were noticed. Participants in our study found online learning to be challenging as they had to manage their own study time, which contradicts the benefit of time effectiveness as reported by the same participants. The participants attributed their difficulties with time management to procrastination, which is associated with planning or lack thereof. According to Laher et al. (2021) and Rawashdeh et al. (2021), the authors suggested that increased workloads, difficulties structuring the day, multiple responsibilities, a lack of concentration, and unstructured lesson plans consequently lead to poor time management, particularly when engaging in online learning at home (Rawashdeh et al., 2021; Laher et al., 2021). Therefore, the flexibility and independence brought by the online learning approach may not always result in improved preparedness and planning even with adequate infrastructure to support online learning experiences for students.

The active presence of a course instructor aids in students’ active engagement with the course content and allows for student-instructor engagement (Kim et al., 2021). However, some of our participants reported a seemingly limited experience with their course instructors. Laher et al. (2021) found that limited instructor presence resulted in students not understanding the course content. Our findings supported Laher et al.’s (2021) findings, as the students seemed to struggle to engage with the course content in the absence of the course instructors. Furthermore, students reported limited opportunities for peer interactions during online learning, these seemed to be restricted to course-related work, with few opportunities to get to know their peers or form friendships. Laher et al. (2021) found the same among undergraduate psychology students and concluded that this limited peer interaction may lead to students experiencing isolation. Thus, limited instructor presence and peer-to-peer interactions were barriers to building a sense of community among students to encourage collaborative work with the hope of improving students’ experiences with online learning.

Encountering connectivity issues also resulted in online learning being challenging for the students. Our study participants reported that load shedding led to unstable internet connection and failure to power up their devices; similar findings have been reported previously (Azionya & Nhedzi, 2021; Barber & Sher, 2022; Laher et al., 2021). Students also reported experiencing connectivity issues to the LMS and ICT; these disrupted their focus and limited their engagement with synchronous online learning activities, which may have resulted in them missing out on learning opportunities.

Students also felt unprepared for clinical practicals when engaging in online learning, a similar finding was reported by audiology students studying in the United States of America and Turkey (Ölçek et al., 2021; Thome et al., 2020). The students whose responses are reflected in our study seemed to attribute their lack of preparedness for clinical practicals to the limited understanding of theoretical course content. In their strategies for improved clinical training, the participants in our study alluded to in-person clinical training being the most effective means to develop clinical competency, which is similar to the findings obtained by Thome et al. (2020). Seventy-two per cent of the audiology students who participated in the study conducted by Thome et al. (2020) in their survey reported that in-person clinical training was more effective than receiving clinical training through simulations and telepractice. However, traditional in-person clinical training and simulations were complementary learning approaches that improved clinical competency and students’ clinical training experience (Nagdee et al., 2022).

To facilitate online learning and possibly reap the benefits and overcome the challenges experienced with online learning, the students seemed to have employed strategies that were centred around self-regulated learning (SRL) (Dörrenbächer & Perels, 2016; Panadero, 2017). The participants reported that the following strategies such as consulting supplementary material, instructor presence, and provision of internet data bundles for free and accommodations, combining synchronous and asynchronous modes of learning, and blended learning improved their online learning experiences. Furthermore, Tugtekin (2022) and Barber and Sher (2022) suggested that these strategies were commonly employed by students in higher education to facilitate their ability to manage and comprehend extensive coursework. This was observed during online learning when students engaged independently with content and consulted supplementary materials such as watching videos to increase their learning (Kim et al., 2021). Similar findings were reported by Queiros and De Villiers (2016) that supplementing course material with video clips, allowed students to better understand the course content. In our findings, students reported that the application of SRL strategies, which allowed them to own their learning and accommodate variations in students’ learning strategies. However, theories and models of SRL were not integrated into our study; thus, there is a need to consider these in future research.

A combination of synchronous and asynchronous modes of learning is student-centred and encourages students’ engagement with learning (Lee & Hannafin, 2016). According to the students’ reports in our findings, the use of multiple modes of learning that supplement and complement each other provided them with opportunities to reinforce their understanding of the course content and improve their learning experience. This finding differs from the findings obtained by Singh et al. (2022) who found that when using synchronous and asynchronous modes of online learning audiology students experienced online learning fatigue. However, Singh et al. (2022) attributed this to students’ varying educational levels and their limited experience with online learning. In addition and supporting Singh et al. (2022); and Kim et al. (2021) findings, the students in our study preferred the blended learning approach (Dziuban et al., 2018), which allowed for flexible and independent learning and provided students with opportunities to engage with the course content and co-create with their peers. Our findings highlighted the need to explore the application of student-centred learning theories and practices in online learning for students registered in audiology programmes.

There is a growing interest in understanding students’ experiences as independent life-long learners at institutions of higher learning in South Africa; thus, this study presents findings from students at different educational levels. In addition, the qualitative interviews conducted with the students provided a deeper insight into students’ experiences with online learning. Furthermore, this study employed member checking to ensure participants’ experiences were reflected accurately and truthfully.

Our use of qualitative interviews as the only data collection tool possibly restricted our findings. While the themes of this study managed to explore and understand the audiology students’ experiences of online learning, only one site was used. Yet, it would be beneficial to understand the experiences of audiology students from other universities across South Africa and to also include postgraduate students registered in the same programme.