**4.1 Survey findings**

The survey instrument was comprised of several overlapping sections with comparable metrics purposefully structured to elicit experiential feedback related to digital learning during COVID-19. The following sections subdivide this presentation into the core elements including the demographic overview and the core perceptions of the outcomes of digital ESL learning.

### *4.1.1 Demographic overview*

The first series of prompts focused on the demographic categorisation of the participants, targeting grouping variables that could serve as independent dimensions to weigh against other perceptual biases. Despite the large sample size (N = 1062), there was a relatively equitable grouping between male (51.9%) and female (48.1%) respondents. In contrast to this broad gender representation, **Figure 2** visualises a highly biased age range distribution that was based upon the sample targeting and selection procedure.

From a programme perspective, **Figure 3** visualises the distribution of the participants' enrolled status, with 63% in associates degree or higher diploma undertakings

**Figure 2.** *Participant age range distribution.*

**Figure 3.** *Current programme undertaking.*

**Figure 4.** *Personal technological status.*

and 34% pursuing a bachelor's degree. Through a Pearson's correlation analysis, a strong (PC = 0.167, P = 0.00), a positive correlation was observed between age and programme, suggesting that older participants were more likely to be pursuing higher level degrees. In fact, a crosstabular analysis revealed that 75% of the participants aged 31–45 were pursuing a Bachelor's degree or higher.

To classify the participants according to their technological acumen, **Figure 4** visualises their distribution between technological natives, immigrants, and unknown status. As predicted, there was a direct correlation between the participant age range and technological status (PC = 0.243, P = 0.000). A crosstabular analysis revealed that no participant over the age of 31 identified as a technological native and just 25% identified as a technological immigrant. The remainder were unsure about their classification. In contrast 72.8% of the participants aged 18–30 identified as a technological native which is appropriate for their Gen-Z and Millennial classification.

The subsequent prompts focused on student experiences in ESL learning, starting with contact hours, as visualise in **Figure 5**.

This model reveals that 92% of the participant sample received between 2 and 3 contact hours for English lessons each week (M = 2.85, SD = 0.633). Given the high degree of conformity, it can be generalised that most Hong Kong university students can expect between 2 and 3 hours of pedagogical contact each week.

*A Review of Digital Learning and ESL Online Classroom Experience in Higher Education DOI: http://dx.doi.org/10.5772/intechopen.107998*

**Figure 5.** *Contact hours for english lessons per week (#).*

#### **Figure 6.**

*Space occupied for online learning.*

To achieve their learning objectives, **Figure 6** highlights the spatial resources adopted by these participants with 62.1% indicating that they maintained a private space whilst 33.8% utilised a semi-private or shared space. This bias was expected as it reflects the use of a personal room or private office within a participant's household that can be dedicated to digital learning when needed.

Classifying these spaces according to their specific characteristics, **Figure 7** confirms that 62% of the students utilise their bedrooms, whilst 13% use sitting rooms and 11% use the dining room. Participants who had identified using public spaces were

#### **Figure 7.**

*Type of space used for online learning.*

#### **Figure 8.**

most likely to indicate a classroom or study area whilst other spaces such as sitting and dining rooms were equally distributed across public and private classifications.

Despite the majority of the participants indicating that they retain a private space, as visualised in **Figure 8**, just 41.8% of the respondents indicated that their learning space was ideal or adequate. There was a negative correlation between the type of space (public or private) and the perceived adequacy of the learning space (PC = -0.116, P = 0.000). Despite predicting that public spaces would be perceived as inadequate, the six participants who reported that their space was not adequate at all indicated that they learn in private spaces. Further, 57.8% of those identified that their space is inadequate to study in private spaces. Overall, however, just 26.7% of the respondents who study in semi-private spaces and 57.1% of those who study in public affirmed their spaces as ideal or adequate. Of the 48.5% of the respondents who had changed spaces during the past learning period, 90.7% indicated that they had moved more than twice, indicating that they were forced to move due to various causes or they valued mobility.

*Perceived adequacy of learning space.*

*A Review of Digital Learning and ESL Online Classroom Experience in Higher Education DOI: http://dx.doi.org/10.5772/intechopen.107998*

#### **Figure 9.** *Source of funding for equipment.*

A review of the existing equipment reported by these students for online learning revealed that more than 60% used some form of mobile computing device such as an iPad or laptop whilst the remaining students utilised a dedicated workspace in the form of a desktop computer. 61% of the sample did not need to acquire any additional equipment to participate in online learning. For the other 39% that did invest in new equipment, the responses indicated one of three primary resources including an iPad or tablet, a webcam, and/or a pair of headphones (with mic). As visualised in **Figure 9**, most of the participants who invested in new equipment (62%) were funded or sponsored by their families, whilst 37% were self-funded. Institutional sponsorships were entirely absent, supporting just six participants out of the total sample.

The participants were asked about the quality of training and support provided by their institution (**Figure 10**). A total of 34% of the 900 participants who answered this question felt that such support was good or exemplary, whilst just 8% felt that it was poor or not very effective. There was a strong statistical correlation (PC = 0.237, P = 0.000) between the experience of a problem (38.9% of the sample reported a problem) and the perception of university support/training. Problematically, the participants who had experienced problems were more likely

#### **Figure 10.**

*Quality of training or support for virtual classroom by institution.*

to report the support and training as poor or not very effective than those who had not experienced a problem. When assessing the range of problems experienced by the students, the core themes included network problems, delays in communication or responses, and camera/Zoom issues. From a more experiential perspective, many participants indicated that they missed the traditional interactions with their instructors and students during digital learning.
