Factors influencing student transition to online education in the COVID 19 pandemic lockdown: evidence from Romania

Figures & data

Figure 1. Research Model of Student transition to online education (STOE).

Source: From literature.

Table 1. Factor analysis of academic experience items (Principal Axis Factoring).

Rotated factor matrix^a
Item	Factor
	1	2	3	4	5
Internet speed and coverage in Romania					.718
Equipment/device availability					.771
Student-student interaction in online mode			.598
Student-content interaction during online courses			.581
Student self-confidence in adapting to online mode				.713
Student self-confidence in operating with technological tools or software				.791
General Communication with colleagues			.720
Student-instructor interaction			.839
University and administrative support for online mode	.621
Instructors’ performance/knowledge	.851
Instructors’ empathy	.809
Instructors’ evaluations	.742
Instructors’ digital skills	.706
Instructors’ skills to adapt teaching material to online format	.648
Instructors’ feed-back	.714
Student online Class participation with webcam and microphone
Online working groups
Course material availability		.529
Clarity of course content		.786
Online suitability of course structure		.769
Course objectives	.408	.706
Self confidence in passing over online evaluation				.460
Connectivity difficulties
Extraction Method: Principal Axis Factoring; Rotation Method: Varimax with Kaiser Normalization.

Source: Own research.

Notes. Rotation converged in 6 iterations. Kaiser-Meyer-Olkin Measure of Sampling Adequacy: 0.894; Bartlett’s Test of Sphericity-Sig.: 0.00. Cronbach Alpha: >0.70 for all factors and between factors.

^aRotation converged in 6 iterations.

Table 2. Logistic regression-student transition to online education (STOE) as dependent variable.

Variable	B	S.E.	Wald	df	Sig.	Exp (B)	95% C.I. for Exp (B)
							Lower	Upper
F1 (Instructors’ and administrative staff support)	.532	.154	11.881	1	.001	1.703	1.258	2.304
F2 (Course structure)	.580	.144	16.239	1	.000	1.786	1.347	2.369
F3 (Lack of interaction)	–1.245	.190	42.819	1	.000	.288	.198	.418
F4 (Student’s lack of self-confidence)	–1.418	.168	71.338	1	.000	.242	.174	.337
F5 (Technology availability)	.406	.144	7.923	1	.005	1.501	1.131	1.991
University online platform functionality (Poorly)			71.332	2	.000
Most often adequately	1.277	.205	38.876	1	.000	3.585	2.400	5.356
Very well	2.067	.286	52.268	1	.000	7.899	4.511	13.832
Enrolment level (Bachelor)			5.648	2	.059
Master	.701	.320	4.803	1	.028	2.016	1.077	3.775
Doctorate	1.118	1.096	1.041	1	.307	3.060	.357	26.205
Place of residence (Urban)			7.780	2	.020
Rural	–.714	.295	5.871	1	.015	.490	.275	.873
Another country	1.057	.899	1.380	1	.240	2.877	.494	16.765

Source: Own research.

Notes. Hosmer and Lemeshow Test-non-significant value (p > 0.05)-adequate level of data fitting; Chi-square = 375.265 (p < 0.001); Nagelkerke R Square = 0.647; correctly classifying 83% of the cases. Logistic regression assumptions met (according to Haydam et al., 2017).

Haydam, N., Purcarea, T., Edu, T., & Negricea, I. C. (2017). Explaining satisfaction at a foreign tourism destination–An intra-generational approach. Evidence within generation Y from South Africa and Romania. Amfiteatru Economic, 19(45), 528–528.

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