Demographic characteristics
Table 1 provides information about the participants' demographics. Out of the 218 participants, 72.9% were females and 27.1% were males. Most participants were Malay (78.4%) and stayed inside the campus (95.4%). About 57.8% of the participants were from Year 4. Family income for most of the participants was less than RM4850 (42.7%). Most of the participants used Wi-Fi for the online classes (90.8%). The laptop is the most common digital tool used by participants (46.8%).
Table 1
Demographic characteristics of the participants (n = 218)
Characteristics | | Frequency (%) |
Gender | Female Male | 159 (72.9) 59 (27.1) |
Ethnicity | Malay Chinese Indian Arab Others | 171 (78.4) 15 (6.9) 19 (8.7) 6 (2.8) 7 (3.2) |
Academic years | 4th year 5th year | 126 (57.8) 92 (42.2) |
Family income (RM) | < 4850 4851–10970 > 10971 | 93 (42.7) 49 (22.5) 76 (34.9) |
Mode to access online classes (most of use) | Wi-Fi Mobile data Internet cafe Wifi and mobile data | 198 (90.8) 19 (8.7) 0 (0.0) 1 (0.5) |
Current accommodation | Inside campus Urban Rural | 208 (95.4) 6 (2.8) 4 (1.8) |
Digital tools | Laptop Mobile phone I pad/Tablet Desktop | 102 (46.8) 31 (14.2) 76 (34.9) 9 (4.1) |
LNT scale
Table 2 listed the summary of the nine items of LNT scale.
Table 2
Summary of nine item characteristics for LNT (n = 218)
Items | Score, n (%) |
| Never (1) | Hardly ever (2) | Sometimes (3) | Usually (4) | Always (5) |
Q1: The level of lighting in my study area allows me to see clearly what is around. | 0 (0.0) | 1 (0.5) | 33 (15.1) | 77 (35.3) | 107 (49.1) |
Q2: I can control the level of lighting in my study area when taking online classes | 0 (0.0) | 14 (6.4) | 23 (10.6) | 68 (31.2) | 113 (51.8) |
Q3: The level of lighting (from lamps, computer screen) in my study area allows me to have visual comfort | 2 (0.9) | 3 (1.4) | 25 (11.5) | 86 (39.4) | 102 (46.8) |
Q4: I have privacy in my study area when taking classes online | 19 (8.7) | 34 (15.6) | 64 (29.4) | 47 (21.6) | 54 (24.8) |
Q5: The noise level (coming from devices, people’s talks, external sources) in my study area allows me to concentrate | 10 (4.6) | 31 (14.2) | 73 (33.5) | 62 (28.4) | 42 (19.3) |
Q6: I can control the noise level in my study area | 17 (7.8) | 31 (14.2) | 58 (26.6) | 63 (28.9) | 49 (22.5) |
Q7: The temperature in my study area allows me to be comfortable and concentrate | 3 (1.4) | 11 (5.0) | 51 (23.4) | 91 (41.7) | 62 (28.4) |
Q8: I can control the temperature in my study area | 7 (3.2) | 19 (8.7) | 45 (20.6) | 78 (34.8) | 69 (31.7) |
Q9: The air quality in my study area is appropriate | 2 (0.9) | 7 (3.2) | 35 (16.1) | 91 (41.7) | 83 (38.1) |
The initial hypothesized model (model-1) estimated using MLR comprised of consisted of nine items with three factors. The results of model-1 showed good fit indices (Table 3). All factor loadings were between 0.74 and 0.82 (Fig. 1). The final model was then evaluated for CR and AVE. Lighting had a CR of 0.82, noise of 0.81 and temperature of 0.84. The AVE for lighting, noise and temperature were 0.61, 0.59 and 0.63, respectively. The r between lighting and noise was 0.51, p-value < 0.001, lighting and temperature was 0.65, p-value < 0.001, and noise and temperature were 0.74, p-value < 0.001. Although r was significant, it is less than 0.85, demonstrating that three factors have good discriminant validity. Table 4 displays the results for CR and AVE, while Table 5 displays the correlation between the LNT model's factors. .
Table 3
Model Fit Indices of the LNT measurement model
Model | CFI | TLI | SRMR | RMSEA (90% CI) |
Model-1 | 0.99 | 0.98 | 0.03 | 0.03 (0.00, 0.07) |
Table 4
Factor loadings, composite reliability and average variance extracted of the LNT measurement model
Factor | Item | Factor loading | CR | AVE |
Lighting | Q1 | 0.77 | | |
| Q2 | 0.74 | 0.82 | 0.61 |
| Q3 | 0.82 | | |
Noise | Q4 | 0.76 | | |
| Q5 | 0.75 | 0.81 | 0.59 |
| Q6 | 0.79 | | |
Temperature | Q7 | 0.82 | | |
| Q8 | 0.81 | 0.84 | 0.63 |
| Q9 | 0.74 | | |
Table 5
Factor correlation for LNT measurement model
Variables | Lighting | Noise | Temperature |
Lighting | 1 | 0.51 (< 0.001)* | 0.65 (< 0.001)* |
Noise | 0.51 (< 0.001)* | 1 | 0.74 (< 0.001)* |
Temperature | 0.65 (< 0.001)* | 0.74 (< 0.001)* | 1 |
*p-value |
Technology Scale
Table 6 listed the six items that were applied to assess the technical quality of online classes.
Table 6
Summary of item characteristics for technology (n = 218)
Items | Score, n (%) |
| Strongly disagree (1) | Disagree (2) | Neither agree nor disagree (3) | Agree (4) | Strongly Agree (5) |
Q10: The instructor’s voice is audible | 0 (0.0) | 5 (2.3) | 34 (15.6) | 135 (61.9) | 44 (20.2) |
Q11: Course content shown or displayed on the smart board is clear | 1 (0.5) | 4 (1.8) | 23 (10.6) | 135 (61.9) | 55 (25.2) |
Q12: The microphone is in good working condition | 0 (0.0) | 6 (2.8) | 29 (13.3) | 133 (61.0) | 50 (22.9) |
Q13: The video image is clear and comprehensive | 2 (0.9) | 9 (4.1) | 31 (14.2) | 130 (59.6) | 46 (21.1) |
Q14 Technical problems are not frequent, and they do not adversely affect my understanding of the course | 8 (3.7) | 23 (10.6) | 52 (23.9) | 106 (48.6) | 29 (13.3) |
Q15: The technology used for online teaching is reliable | 3 (1.4) | 2 (0.9) | 27 (12.4) | 133 (61.0) | 53 (24.3) |
The initial hypothesized model estimated using MLR comprised of six items with only one factor. The initial measurement model (model-1) robust fit indices of RMSEA were more than the maximum recommended value of 0.08, while TLI and CFI were less than the minimum recommended value of 0.95 as summarized in Table 7. All factor loadings ranged from 0.67 to 0.76 (Fig. 2).
Then, the items with correlated residuals for Q14 with Q11, Q13 with Q15, and Q13 with Q11 were later added in subsequent investigation to improve the initial model. The findings of the second model (model-2) revealed a good model fit based on all indices, except for the upper 90% CI of robust RMSEA = 0.15, with CFI = 0.98, TLI = 0.96, SRMR = 0.03, RMSEA = 0.07 (Table 7). All factor loadings between 0.64 and 0.77 (Fig. 3).
Further adjustment by adding items with correlated residuals for Q15 with Q12 was performed to enhance the second model. The results of the third model (model-3) revealed a good model fit based on all indices, except for the upper 90% CI of robust RMSEA = 0.16. The CFI = 0.99, TLI = 0.97, SRMR = 0.02, and RMSEA = 0.06 (Table 7). All factor loadings between 0.62 and 0.79 (Fig. 4).
The standardized item loading for the three Technology-M models is shown in Figs. 2, 3, and 4. All the parameter estimates were acquired from the original main hypothesized measurement models in Table 7. The standard item loading ranged from 0.67 to 0.76, 0.64 to 0.77, and 0.62 to 0.79, respectively, according to the results of Models 1, 2, and 3, which are considered to have a good to excellent factor loading. The three models were compared using AIC, BIC, and X2 difference. Model-3 was chosen as the best and final model based on smallest AIC, BIC, significant difference between model and its value for the better fit indices (Table 7).
The final model was then evaluated for CR and AVE. CR and AVE for technology was 0.84 and 0.51. The construct validity for the factor is considered good. The results for CR and AVE technology model are presented in Table 8.
Table 7
Model Fit Indices for technology measurement model
Model | CFI | TLI | SRMR | RMSEA (90%CI) | AIC | BIC |
Model-1 | 0.94 | 0.90 | 0.05 | 0.122 (0.066, 0.181) | 2438.4 | 2495.9 |
Model-2a | 0.98 | 0.96 | 0.03 | 0.073 (0.000, 0.157) | 2440.0 | 2494.1 |
Model-3b | 0.99 | 0.97 | 0.02 | 0.065 (0.000, 0.164) | 2438.4 | 2495.9 |
a Model with correlated item residual of Q14 with Q11, Q13 with Q15, and Q13 with Q11.
b Model with correlated item residual of Q14 with Q11, Q13 with Q15, Q13 with Q11, and Q15 with Q12
Table 8
Factor loadings, composite reliability and average variance extracted of technology measurement model
Factor | Item | Factor loading | CR | AVE |
Technology | Q10 | 0.73 | 0.84 | 0.51 |
| Q11 | 0.79 | | |
| Q12 | 0.67 | | |
| Q13 | 0.76 | | |
| Q14 | 0.65 | | |
| Q15 | 0.62 | | |