Students online learning experience during emergency remote teaching in higher education

Abstract

For actionable insight, this study explored computer science and information technology students’ online learning experience in an emergency remote teaching during the COVID-19 pandemic in a public technical university in Ghana. A mixed method research of both quantitative and qualitative design was used to expand the evidence base. The inherent set of factors of the learning experience were determined by an exploratory factor analysis and confirmed with a multiple regression. Six factors comprising sense of concern, institutional support, effective communication, connectedness, mediated technologies, and stress levels explained 66.5% of the variance in the patterns of relationship. All factors had positive regression weights and added significantly to the prediction. Potentials identified comprised positive reactions to institutional support, improved lecturer-student interaction, active participation, accessible course contents, and moderately high future online learning expectations. The challenges included unpreparedness, unreliable Internet, high cost of data, and incidental learning design and pedagogy. Evidently, the higher education landscape shifted during the pandemic in obvious ways to merit further studies and reflective online learning administration to inform robust technology-oriented post-pandemic practices.

Keywords

Covid-19 emergency remote teaching higher education online learning technology

Introduction

The lockdown policy during the COVID-19 pandemic forced a shutdown of all social gatherings, including mandatory closures of schools worldwide (BBC, 2020; WHO, 2020). Ghana reported its COVID-19 first case on March 12, 2020 (Ghana Health Services, 2020a; MoE, 2020), hence, locked down all social gatherings and closed its educational institutions on 16th March 2020, in compliance with the WHO directives. The closure affected almost 9.2 million pre-K12 students and 500,000 in higher education. The Coronavirus (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus with the first case reported in the Republic of China (WHO, 2020).

The mandatory lockdown policy necessitated changes in pedagogy and student learning at all levels of education globally. Various emergency responses were adopted by higher education institutions as alternative methods, including online learning to engage students for continuous education (Aguilera-Hermida, 2020; Anaba, 2020a; Bonney, 2020; Mahyoob, 2020; Van Lancker and Parolin, 2020; Yan et al., 2021). This global decision to engage teaching faculty in technology-mediated remote teaching through various media could reverberate for years to come, particularly in developing countries.

Ghana inclusive, academic calendars were very much disrupted during the mandatory closures, and institutions of higher learning in Africa had trouble implementing online learning options. Unquestionably, the Association of African Universities reported that most higher education institutions in Africa were unable to provide continuity and equitable learning for students, nor implement sustainable processes of teaching, research, collaboration, and administrative functions, 19 months after COVID-19 was declared a pandemic, hence, the call to African universities to build technology-based resilient institutions (AAU, 2021).

Before COVID-19, reports on Africa had indicated a slow adoption of eLearning for several reasons (e.g., Hollow & ICWE, 2009; Kamba, 2009; Mpofu et al., 2012; Mutisya and Makokha, 2016). Universities in Ghana were challenged and had minimally adopted eLearning as fully or supplementary education delivery option (AEITG Press Release, 2020). No exigency plans existed to deal with the crisis, until the government through the Minister of Education directed all educational institutions to roll out [“emergency”] distance education and remote learning (MoE, 2020). As a reactionary measure, linear eLearning through television broadcast became the main source of distribution of learning materials for the K-12 systems. And the universities adopted various eLearning and digital communication models to ameliorate the mandatory lockdown policy (Agormedah et al., 2020; Anaba, 2020a).

Migrating classes hastily online with limited experience in online learning design, development, management, and evaluation systems meant playing into vulnerabilities with major consequences on pedagogical performance and learning outcomes. The contexts and conditions under which online learning was implemented in Ghana seemed to mimic the emergency remote teaching explained by Hodges et al. (2020), Zagkos et al. (2022), and Oliveira et al. (2020) during disasters.

Contrasting a well-planned online learning ecosystem, emergency remote teaching (ERT) was viewed as a temporary shift of instructional delivery to an alternative delivery mode during crisis or disaster (Hodges et al., 2020; Iglesias-Pradas et al., 2021; Zagkos et al., 2022). In such ERTs, many faculty, staff, and students have been observed to respond with little or no background in remote education (Jandric et al., 2020).

With inadequate knowledge and competencies, students could experience increased stress levels, frustrations, high attrition rates, a feeling of isolation, and low academic performance. Lecturers were bound to experience extreme pressures to reform conventional curriculum and instruction for emergency delivery. Accordingly, faculty members needed about a second and third iteration to feel comfortable teaching in an online learning environment (Hodges et al., 2020). Without a precedence, the adoption of ERT in Ghana during the pandemic raised lots of concerns about quality of instruction and delivery strategies, technology access, student participation, mode of faculty and student interactions, assessment, and data protection. Since social isolation was significantly correlated with distance students’ attrition rates (e.g., Ali and Leeds, 2009; Link and Scholtz, 2000), student isolation during the lockdown policy also became a major concern, likewise organizational factors (Iglesias-Pradas et al., 2021) and support systems.

Students’ online learning during the pandemic was influenced by several factors, including context, input, process, and products (Bacus et al., 2022), ICT usage and personal adaptation (Oliveira et al., 2020), Internet connections (Means and Neisler, 2020), psychological pressure and anxiety (Bozkurt and Sharma, 2020), instructional strategies and digital competences (Bacus et al.), student connectedness (Blaich and Wise, 2020), previous online learning experience, and change management.

Of major importance to this study were questions such as: in what ways have the educational landscape shifted in obvious and not-so obvious ways to merit considerations? And how could researchers, educators, and policy makers gain insights and situated examples to improve pedagogy and learning outcomes in a post-pandemic educational landscape? Thus, focusing on computer science and information technology students in a technical public university in Ghana, this exploratory study examined students online learning experience (SOLE) in an emergency remote teaching (ERT) during the COVID-19 pandemic. Specifically, the researchers sought to

• Explore students support to cope with the effect of COVID-19 pandemic on academic work.

• Identify key technology-mediation tools deployed during the ERT.

• Determine instructional considerations during the ERT.

• Gain actionable insights into students learning experience during the ERT.

In this study, actionable insights meant applicative information and data to inform further decisions. Lecturer and faculty had been used interchangeably to refer to academic instructors with pedagogical responsibilities. Technology-mediated platforms denoted digital technologies deployed for disseminating course materials and facilitating interactivities between faculty and students. This initial study posited that though computer science and information technology students might demonstrate a comparative advantage of digital literacy or technical knowledge, the same might not apply when they learn through computers.

Literature review

The onset of COVID-19 pandemic put a lot of pressure on conventional systems to transit onto online learning (Aguilera-Hermida, 2020; Van Lancker and Parolin, 2020) after global and nationwide closures of educational institutions (BBC, 2020; UNICEF, 2020). According to UNESCO (2020a), over 190 countries had closed schools by mid-April 2020. Many institutions migrated fully to online learning in developed countries because of previous exposure, but a lot more in developing countries adopted an emergency response teaching to remain relevant and productive. Reviewed on differentiated responses during the pandemic for insights and relationships, available studies showed that within an emergency remote teaching, defining factors, lessons learned, and recommendations could be distinctive and contextually situated.

Emergency remote teaching

Emergency remote teaching (ERT), defined as a temporary shift of instructional delivery to an alternative delivery mode during crisis or disaster could be differentiated from well-planned online learning experiences (Hodges et al., 2020). Supportive of Hodges et al. assertion, equating ERT to online learning could be problematic and exacerbate the erroneous impression that the latter was inferior to in-person teaching though available research showed otherwise (ibid.). Again, the differences in responses suggested that variables for assessing students’ online learning and emergency remote teaching experiences could be applied within situated contexts with varied factors, results, and solutions.

For instance, Bacus et al. (2022) applied Stufflebeam’s CIPP (context, input, process, and product) evaluation model in teachers and administrators’ emergency remote instructional strategies in higher education institutions (HEI) during the pandemic. They found context was influenced by leadership, materials and connectivity for learning continuity, and technology infrastructure capabilities. Input evaluation reflected teachers’ digital competence, support and resources, and curriculum and instruction. Process evaluation called for action and culture of change, and product evaluation was determined by flexibility, resilience, self-helpfulness, and interdependence.

Oliveira et al. (2020) recounted mixed results for both teachers and students on personal adaptation and usage in a study on how learning was mediated by technology in a fast-paced instructional ERT during the pandemic. Using ICT platforms for mediated teaching, contributed positively to students’ learning experience, yet personal adaptation had a negative influence. Likewise, Iglesias-Pradas et al. (2021) provided evidence in a mixed method case to suggest significant increase in students’ academic performance, and no significant difference across courses with different class sizes on delivery modes during the pandemic.

The lack of digital equipment and underdeveloped technological infrastructure contributed to students’ inabilities to perform adequately in an ERT environment, in which case, students strongly agreed that face-to-face engagements could not be replaced by distance learning, especially in laboratory training (Zagkos et al., 2022). A systematic review of ERTs across descriptive and cross-cultural analysis of graduate students from a broad range of countries, concluded that most institutions used combinations of mainly synchronous collaboration educational technologies and text-based tools (Bond et al., 2021), suggesting inadequate considerations for meaningful integration of asynchronous digital media, as well as carefully designed and developed instruction for flexibility and best practices.

Considered, unplanned pedagogy through emergency remote teaching could be associated with dramatic changes, increased stress levels, and low academic achievements (Bond et al., 2021; Hodges et al., 2020; Iglesias-Prada et al., 2021). Teachers became learners during the ERT (Bacus et al., 2022), and those with previous knowledge in educational technology became more productive (Naykki et al., 2021).

Online learning

Hodges et al. (2020) argued for a differentiated approach to evaluating online learning and ERT because, unlike ERT, online education including online teaching and learning has been based on proven models, theories, research studies, standards, and evaluation criteria. Among others, effective online learning has been associated with careful systematic design and development (Branch and Dousay, 2015) in a well-defined ecosystem of learner support, quality instruction (Vai and Sosulsky, 2015), technology-mediation, meaningful interactions and engagements, and learner-controlled systems.

Students assume greater responsibilities in their studies in the online learning environment with additional skills or competencies (Zawacki-Richter, 2004) and therefore needed various support systems to perform adequately. For instance, in Pakistan, online learning could not produce the desired impact during COVID-19 because of financial constraints and inability to access the Internet (Adnan and Anwar, 2020). Students’ experiences also differed in digital proficiencies, suffered resource deficiencies and tools overload as faculty and institutions attempted to engage them in various technology media and platforms (Hershock & LaVaque-Manty, 2012).

Contrary to Mahyoob’s (2020), the interactions of today’s learners might not be translated into active recipients of eLearning because they engaged in different technologies for various purposes. Students’ interactions with computers as learning tools could differ in Ghana because of lack of exposure to educational technology and online learning compared with the findings of Ko and Rosen (2017), Mohalik and Sahoo (2020), and Mahyoob. According to Agormedah et al. (2020), higher education students in Ghana had positive response to online learning during the pandemic but lacked formal orientation and training to engage effectively.

Challenges

Factors militating against online learning included negative attitudes (Mutisya and Makokha, 2016) resistance to change by institutions and faculty, inability to shift from in-person to digital engagement and redesign “static” courses to dynamic and interactive formats, skill deficits in delivery options, high cost of data, limited Internet connectivity (e.g., Hallow & ICWE, 2009; Kamba, 2009; Mpofu et al., 2012; Thomas et al., 2018), digital divide (World Bank, 2020a, 2020c); and infrastructure deficit (O’Doherty et al., 2018).

Notwithstanding, the online learning environment has been linked with problem-based learning, interactivity, and active participation as against the traditional in-person teaching approach where the lecture method dominates in higher education (Spencer and McKenzie, 2014). Short of well-planned learning environments, students in an emergency remote teaching or online learning could experience cognitive overload and low learning outcomes resulting from incidental instruction and applications of undefined multifaceted mediated technologies.

Method

Study design and setting

This exploratory mixed method study of both quantitative and qualitative design was conducted to examine computer science and information technology students online learning experience (SOLE) in an emergency remote teaching during the COVID-19 pandemic in Koforidua Technical University (KTU). KTU offers bachelor’s in technology (BTech) programs, higher national diploma (HND), other diplomas, and certificate courses in business, engineering, and sciences. It has five Faculties of Business and Management Studies, Applied Science and Technology, Engineering, Built and Natural Environment, and Health and Allied Sciences, and an Institute of Open and Distance Learning. The total student population is about 8000.

Survey instrument

Using Likert scale formats, a modified survey from Blaich and Wise (2020) was used with a focus on learning experience instead of students’ retention, since the latter did not seem to be very critical in the provision of higher education in Ghana. SOLE was measured on summated scores of supports to cope with academic work, communication, concerns about taking online courses, mediated technologies, instructional delivery methods, connectedness, social isolation, previous online learning experience, online learning expectations, and perceived stress levels. A total of 30 self-reported structured quantitative and qualitative items applied for the initial descriptive analysis. The instrument was verified for reliability and construct validity through expert reviews and Cronbach alpha tests. The reliability of an instrument determines its consistency, stability, and dependability of the scores (McMillan, 2007).

Data collection and analyses

The data were collected voluntarily in Google Form online survey and filtered for valid responses. A total of 235 respondents, representing 52% of administered questionnaire were analyzed with MS Excel and SPSS v. 27 packages. Majority (94.3%) were males. Graduates represented (19.7%), undergraduate (56.9%) and HND - higher national diploma (23.4%). An exploratory factor analysis (EFA), a statistical analysis, was conducted on the quantitative items for construct validity, increased reliability, and removal of redundant variables for a further predictive regression model. Through the EFA, the appropriateness of the items was determined through descriptive analysis and further confirmed by normality distribution. The Kaiser-Meyer-Olkin (KMO) Sampling Adequacy test and Bartletts’ Tests of Sphericity were used to verify sampling adequacy and correlations between items, respectively.

Based on the results of the EFA, a further multiple regression was run for confirmation. The multiple regression model allowed for more efficient simultaneous examination of the influence of multiple factors on the dependent variable (Cohen et al., 2003). Kindle (2017) also argued in favor of EFA of data collected in a new setting to confirm or disconfirm the factor structure of the construct, and to significantly strengthen the findings of the regression by potentially eliminating items that were irrelevant. For instance, through the EFA, instructional delivery methods and program regarding computer and information technology were excluded.

The general multiple regression model of SOLE_ij of the ith of the independent variables was represented as:

SOLEij = α + β_{1 j} G_{ij} + β_{2 j} {PL}_{ij} + β_{3 j} {POLE}_{ij} + β_{4} j {SC}_{ij} + β_{5 j} SS + β_{6 j} {SEC}_{ij} + β_{7 j} {SeC}_{ij} + β_{8 j} {MT}_{ij} + β_{9 j} {PSL}_{ij} + e_{ij},

Where, SOLE represented students online learning experience (dependent variable), α was the constant to be estimated, β - the estimated coefficient, G was gender, PL – the program levels, POLE – previous online learning experience, SeC - sense of concern, SEC - sense of effective communication, SS – sense of support, MT – applied mediated technologies, PSL – perceived stress levels, and e − the random error term with standard assumptions.

A priory, it was predicted that none of the specified independent variables would significantly be associated with SOLE during the ERT, all things being equal. Qualitative data collected were analyzed on selected responses for patterns of shared meaning (Braun et al., 2018; Creswell, 2009; Denzin and Lincoln, 2005, 2011; Evans et al. 2016; Hill et al., 2015; Lincoln and Guba, 2007).

Results

Presented under institutional support, applied mediated technologies, instructional considerations, and actionable insights into the online learning experience, the results are as follows:

Institutional support during COVID-19

Rated from strongly agree to strongly disagree, the results were moderately mixed. Of the total responses (n = 229), 61.7% agreed that the university adequately protected them from the effect of COVID-19 and 13.1% disagreed. About 64% agreed on administrative support to manage the online learning transition effectively and 22.8% remained neutral. Almost a half (54.2%) agreed with institutional support to addressing students’ learning needs, and 21.1% disagreed. On the adoption of alternative instructional strategies, 42.1% agreed that the lecturers performed better, 29.6% remained neutral and 28.3% disagreed. Nearly, 39% suggested no major changes in academic plan and 34.1% disagreed.

Applied mediated technologies

Though the university had deployed a Moodle-based learning management system (LMS) referred to as KTU VLE, faculty deployed eclectic technologies for the remote teaching. Key among the six applied mediated technologies were Zoom (93.91%), Google Classroom (71.74%), and WhatsApp (26.09%). Only 8.6% responded to using the LMS. Further responses showed that the technologies perceived to have worked best for students were Google Classroom (73.2%), Zoom (71.5%), and WhatsApp (37.7%). Those perceived to have not worked well were the VLE-LMS (46.27%), Zoom (41.3%), and other LMS (39.80%). The results are presented in Figure 1.

Figure 1.

Key mediated technology platforms.

Instructional considerations

Figure 2 presents the results of the seven methods considered and adopted by the university to engage the students.

Figure 2.

Adopted instructional delivery methods. Note: A-VP = Audio and visual presentations, ISVL = interactive simulation and virtual laboratories, OQA = online quizzes and assignments, and OLSP = online lecture and slide presentations.

The three most adopted instructional methods were Zoom conferences (76.2%), online quizzes and assignments (56.4%), and online lecture and slide presentations (51.1%). Further responses showed methods perceived to have worked best for respondents under the circumstances were Zoom conferences (62.8%), online quizzes and assignments (54.7%), and online lecture with slide presentations (52.5%). Interactive simulation and virtual laboratories (42.4%), virtual group discussions (37.1%), and audio and video presentations (36.6%) seemed not to have worked for the students. The least used method was interactive simulations and virtual laboratories (4.8%).

Actionable insights into students online learning experience

Students concerns, connectedness, communication, stress levels, previous and online learning expectations were examined for further actionable insights.

Student concerns

From “never to always,” students’ concerns were measured on how often they worried about the transition to online learning. The results showed that almost 36% worried about the number of course migrated online and 39% were never worried. While 47.4% worried about the mediated technologies, 35% worried sometimes, and the rest did not. A little over a half (58.4%) expressed concerns about coping with online learning effectively and 29.3% worried sometimes. In addition, 21.8% worried about writing online examinations effectively, 18.3% were often worried, and 13.1% were never worried. Thirty-two percent were concerned about completing their online courses successfully, 22.3% were often worried as against 7.4% never worried. The majority (74%) showed greater concern about cost of data, and only a total of 7.3% worried occasionally and never. About a third (37.1%) worried about losing physical contacts with faculty, friends, and other students, with a total of 25.8% worried occasionally and never.

Communication

Effective communication was rated on students’ level of satisfaction. Under a third (24.9%) were satisfied with communication to support smooth transition to ERT compared with dissatisfied respondents (41.5%). A third apiece were either satisfied (30.6%), dissatisfied (37.6%) or neutral regarding communicating the requirements and expectations of online learning effectively. Only 10.6% were satisfied with communication on changes in their financial obligations such as tuition, hostel and feeding fees during the pandemic, while 41.3% were dissatisfied.

Stress levels

Regarding psychological pressure and anxiety on the learning experience, students’ responses on perceived stress levels or difficulties in engaging in online learning ranged from “none to very stressful.” Of the total respondents (n = 226), 21.7% intimated a very stressful experience, 40.3% responded to somehow stressful, 29.6% implied it was of little stress. The rest was no stress.

Connectedness

Students’ connection to the university and faculty during the pandemic was rated from “very strong connection to no connection.” Only a total of 15.1% felt very strongly and strongly connected to faculty compared to 13.6% to the university. A half (50.7%) felt somehow connected to the university in contrast to 47.3% to faculty. A third (30. 5%) felt little connection to faculty relative to 28.4% connection to the university.

Expectations

The question on whether students would recommend continuation of online learning after COVID-19, showed mixed results with 35.1% yes, 31.6% no, and 33.3% maybe. Further responses showed that about 63% had previous online learning experience prior to the pandemic.

Exploratory factor analysis

Table 1 shows the factor loadings of the 21 items underlying SOLE with the percentage total variances extracted, means, standard deviations, and reliability scores. All items had high reliabilities, at or on Cronbach’s α > 0.829 and above 0.70 (Kaiser, 1960).

Table 1.

Factor loadings for exploratory factor analysis (EFA) with varimax rotation of students online learning experience (SOLE) in emergency remote teaching.

Factor scales	M	SD	Factor loadings	Cronbach alpha (α)	% Total variance extracted (TVE)
Sense of concern (SC)					24.435
Completing online course successfully	3.39	1.44	.784	.831
Writing online exams	2.94	1.45	.784	.835
Coping with online teaching and assignments	3.18	1.34	.764	.829
Cost of data to access online courses	3.98	1.41	.678	.833
Technology access	3.18	1.38	.665	.834
Migrated online courses	2.88	1.29	.505	.836
Sense of support (SS)					16.774
Support for change management	3.51	1.10	.806	.833
Mitigate adverse COVID-19 health effects	3.48	1.07	.796	.833
Academic plan maintained - unaltered	2.99	1.18	.777	.836
Learning needs addressed by institution	3.25	1.18	.760	.836
Adopted teaching strategies	3.09	1.16	.746	.833
Sense of effective communication (SEC)					8.042
Changes in financial obligations	2.63	1.27	.839	.834
Online learning requirements and expectations	2.85	1.23	.831	.837
Smooth migration to online learning	2.71	1.26	.822	.836
Sense of connectedness (SeC)					6.849
Connection to institution	2.65	.94	.853	.838
Connection with faculty	2.65	.94	.852	.839
Mediated technologies (MT)					5.541
Technology deployed by institution	3.20	1.39	.794	.831
Technology deployed by faculty	3.20	1.35	.789	.833
Perceived stress levels (PSL)					4.895
Future online learning expectations	1.92	.88	.734	.832
Past online experience before COVID-19	1.33	.54	.660	.843
Online learning anxiety	1.91	1.27	.520	.825

Note: Extraction method - principal component analysis (PCA) with Varimax Rotation.

Six factors explained the variance (66.54%) in the patterns of relationship among the 21 items with eigenvalues >1. The Keiser-Mayer-Olkin (KMO = 0.822 measured the sampling adequacy of the factors, and was considered above the recommended threshold of 0.6 (Kaiser, 1974). The significant Bartletts’ Test of Sphericity, ꭓ² (210) = 1922.38, p < .000, also indicated that the correlations were sufficiently large for the EFA. Hence, the factors underlying students online learning experience (SOLE) with their percentages, were determined as sense of concern (SC – 24.43%), sense of support (SS – 16.77%), sense of effective communication (SEC – 8.04%), sense of connectedness (SeC – 6.85%), mediated technologies (MT – 5.54%), and perceived stress levels (PSL – 4.90%).

Multiple regression

A multiple regression analysis confirmed the relationship between SOLE on gender, program levels, previous online learning experience, sense of concern, sense of support, sense of effective communication, sense of connectedness, mediated technologies, and stress levels. The model statistically significantly predicted SOLE, F (9, 200) = 761.855, p < .001, R² = 0.972. All the six factors derived from the EFA (SC, SS, SEC, SeC, MT, and PSL) had positive regression weights and added significantly to the prediction, p < .05, suggesting that addressing these issues adequately could affect students’ online learning experiences positively. For example, for every unit conditional change in students’ sense of concern, there was 0.612 (61%) change in SOLE, and for a unit conditional change in a sense of support, SOLE was increased by 38.6% all other predictors held constant in each case. The regression coefficient, R = 0.986 indicated a strong correlation among SOLE and the predictors. Except for gender, program levels, and previous online learning which did not contribute to the multiple regression; the hypothesis of no influence was rejected. The results are summarized in Table 2.

Table 2.

Predictors of student online learning experience (SOLE) in emergency remote teaching.

Variables	Coefficients			t
	Unstandardized coefficient		Standardized coefficient
	B	SEB	β
(Constant)	−1.228	1.136	.004	−1.081
Gender	.176	.496	.004	.355
Program levels	.050	.158	.019	.315
Previous online learning experience	.400	.253	.612	1.579
Sense of concern (SC)	.994	.022	.386	44.687***
Sense of support (SS)	.990	.033	.340	30.255***
Sense of effective communication (SEC)	1.040	.040	.173	25.906***
Sense of connection (SeC)	1.091	.080	.235	13.654***
Mediated technology (MT)	.952	.055	.166	17.399***
Perceived stress levels (PSL)	.996	.077		12.972***
R² = .972
Durbin-watson = 2.078
Max VIF ≤ 1.321

Note: ***p, <.001. Dependent variable = Student Online Learning Experience (SOLE).

Qualitative results

For patterns of shared meaning, the qualitative data covered instructional effectiveness, why selected technologies did not work, future online learning expectations, and students’ general impression about the online learning experience. The results are as follows:

Effectiveness of instructional strategies

Instructional strategies perceived to have worked best for the respondents were Zoom conferences, online quizzes and assignments, online lecture with slide presentations, and online discussions because they afforded students the opportunity to participate actively and interact better with lecturers and peers. The shy students could easily ask questions, which to them, would have been impossible during in-person classes. Those with fast connectivity, found the course materials easily accessible and convenient. With Zoom conferences and online lectures, students seemed to suggest no difference compared to face-to-face lectures since the experience felt the same. Examples of response:

“Video (zoom) conferencing gives us the opportunity to have the feeling of student and lecturer interaction. Because seeing the lecturer in the video equally works like the lecturer standing before you in the classroom.”

“Because we easily have access to them and mostly get the means to visit these instructional delivery methods. We can keep and watch later for more understanding.”

“They are effective and preferred because, we get to understand everything just as we are in class. Also, there is no disturbance since you are in the comfort of your home and so you can have all the concentration since you can hear the lecturers speak, show, or share the slides on the screen for us to see. We have the privilege to ask questions without any fear or shyness.”

Why selected technologies did not work

Emerged major themes on why self-selected mediated technologies did not work for students included unreliable Internet, ineffective communication, unfamiliar learning management solutions, inadequate support, and user preferences.

Almost a half of the students (49%) had various challenges with Internet connectivity regarding inaccessibility and unaffordable data cost. Citing locations, students in rural communities were the most affected. The pattern of responses included:

“Expensive Internet data and bad connectivity.”

“Zoom consumes lots of data. Sometimes we don't have money to buy data to join class.”

“Poor network connectivity and high cost of data bundle”.

“Requires a lot of data to stay connected. It is more expensive to use.”

“Internet runs very slow and consumes lots of data.”

“Due to the hours of lectures, it consumes lots of data which I cannot afford.”

“The network is unstable in my area which causes these technologies not to work for me.”

“Zoom needs a very strong network before one can smoothly do video calls.”

“The VLE platform requires the use of fast network, which I don’t have in my area sometimes.”

Moreover, respondents complained of unfamiliar mediated technologies. Responses included:

“We do not understand their use and is very stressful.”

“They are not working for me because of unfamiliarity with using such technologies.”

“It's not easy to have access to the technologies and they are not familiar in our system”.

“Simply because we've not used that before.”

“Complex and unfamiliar.” I have never used them, yet to try.”

“Because we have not used them before.”

Problems with communication included inadequate announcements or notifications and miscommunication. For example:

“We mostly don't receive notifications on LMS when assignments are posted.”

“Some of the technologies are not working for me because when you are not online and there are messages from the lecturer you will definitely miss them.”

“Lecturers don’t make things clear to us.”

“I’m not online sometimes because classes are held without notice.”

“The codes given to us by lecturers to access the online courses are sometimes incorrect”.

Other notable responses included personal exclusion due to lecturers or students’ preferences, unreliable and obsolete technological devices, and inadequate support to address network issues.

Continuity of online learning

Responding to whether students would continue with online learning after COVID-19, the results were mixed with 35.1% yes, 31.6% no, and 33.3% maybe. The “yes” responses were supported with reasons related to global adoption of educational technology in higher education, easy access to education without geographical boundaries, convenience relative to cost of commuting to campus, flexibility and time saving, usefulness, prevention of overcrowded classes, and improvement in interactivity. A student who found online learning useful wrote:

“I know this is the best way to adjust our learning methods, it can't always be physical. I believe technology has made things easier for us, so why are we making things difficult for ourselves. Online learning, I vouch for because it is very useful for our lives, socially, educationally, health wise during covid, entertaining, and research. I believe, it is here to help not to disrupt!”

Respondents with “maybe” advocated for a blended approach because of the practical delivery considerations in their subject or program areas. Responses included:

“Some courses require in-person teaching, not online; 50/50, both should be employed because face to face interaction with the lecturer could go a long way to help us grasp more of the concepts”.

“Maybe because even though most of us are on the various online platforms, some students are still missing out. I will be glad if they sometimes teach in person and sometimes teach online too, that would be great.”

Reiterated, lack of Internet connectivity, unaffordable data, and inadequate support reverberated as the major hindrances to continuing with online learning after the pandemic. Very few suggested the experience was stressful, ineffective, and unreliable.

• Generality

In general, respondents perceived the learning experiences as moderately positive with the majority suggesting improvement in technology infrastructure, Internet connectivity, and data access. To the government, they suggested an improvement in ICT infrastructure nation-wide and in all institutions to support the online learning ecosystem. Many requested adequate institutional supports relative to increased bandwidth, partnerships with the Internet service providers for lowered data cost for educational purposes (e-rates), better implementation strategies, digital competency development for both faculty and students, and provision of adequate digital learning resources. They commended lecturers for making course materials available throughout the COVID-19 period, yet students felt the lecturers were too rigid and inexperienced and could be supported with professional interventions. Following are sample responses.

“Even though online learning can be a better substitute for physical class. It is not implemented the right way. The mindsets of students are not well prepared for online learning experience. The research team can help students to enhance online learning experience and improve the level of commitment and competence of lecturers in online studies. That will help.”

“Yes! I want the researchers to be interested in the status of student. Many at times, we go to school stressed out, depressed, full of complaints but no one seems to care or think about the status of student. Let school be made easier to go so we can immerse our whole self into it. Making school difficult to complete will not in any way benefit us. Let faculty consider migrating to online (FAST). We are computer science students, how best are we using the technology? If you ask me, we are wasting resources. Technology is here to help; allow us to make good use of it. Even business-related institutes are making good use of it. They should not only think about the school but also the students. Together we make an institution. Thank you!”

Discussions

The study showed that the computer science and information technology students online learning experience during the ERT was moderately positive with mixed outcomes. For instance, many respondents agreed that they were adequately protected from the adverse effect of COVID-19 on personal and academic work, but the results were mixed with about two-thirds assenting to adequate support for smooth transition to online learning as against a little over a third on adoption of effective instructional strategies. Reactions were also mixed for major change, no change, and neutral responses on support to maintain academic plans.

Contrasting the support for protection, students were dissatisfied about communication regarding the transition to online learning. Many were not aware of what was required nor expected, and rather found themselves in unfamiliar learning territories. Similarly, the reactions were mixed for communication about changes or otherwise in their financial obligation. Often, many students believed that online learning should be relatively cheaper than face-to-face classes and expected a reduction in their financial commitments regarding tuition, feeding, and hostel facilities since they resided off-campus during the pandemic.

Like other studies during the pandemic (see Agormedah et al., 2020), lecturers in the university studied, adopted various technology-mediated platforms to communicate, interact, and distribute learning materials to students. Key among them were Zoom, Google Classroom, and WhatsApp because of familiarity and personal preferences, and for mostly synchronous engagement for which students’ reactions were mixed.

A very few lecturers used the existing LMS deployed by the university before the pandemic, possibly because of unpreparedness and inadequate knowledge and skillset. Technologically, students were challenged because of network issues, communication gaps, and inadequate support to own and manage learning in the relatively unique environment. Like the findings of Oliveira et al. (2020) and Zagkos et al. (2022), students were particularly constrained by unreliable networks. Inaccessible technology, unaffordable data and compatibility gaps were found to be very critical for learning continuity during the ERT. Some students were excluded because of location and obsolete mobile devices. WhatsApp was reasonably accessible for communication and collaboration, but ineffective as a full-fledged course management solution. With Google Classroom, students were able to reference course materials with some level of convenience and flexibility.

Distinct from the lecture method in conventional practices, faculty adopted other digital instructional delivery strategies of which Zoom conferences, online lectures and slide presentations, quizzes and assignments, and online discussions dominated. Requiring major planning, production and reviews, instructional delivery methods such as interactive simulations, virtual laboratories, and audio and video presentations were least used. The absence of germanely designed and developed instructional strategies for online teaching was obvious since the practice seemed to mimic the familiar face-to-face lecture experience. The emergency response to teaching did not allow adequate preparation in the short-term. Students however, appreciated the convenience and interactivity with which they accessed course materials and connected with faculty and peers, respectively.

Students felt more connected to the university rather than to faculty, which could be attributed to the extension of the formal, rigid inter-personal relationships between lecturers and students in a typical conventional system. It seemed the lecturers tried to maintain the status quo without allowing for the flexibility associated with active participation, interactivity, and learner control in an online learning environment. Nevertheless, students showed greatest concerns about the number of courses migrated online, writing online assignments and examinations, and completing their online courses successfully. Social isolation was of a little concern probably because students could easily interact informally among themselves through social media and could not be bothered with physical contacts during the lockdown. They found the experience moderately stressful, suggesting that with adequate planning and implementation, students could relate to online learning positively.

The predictors of students online learning experience comprised sense of concern, sense of support, sense of effective communication, sense of connectedness, mediated technology, and perceived stress levels. Sense of concern was influenced by completing online courses, writing online examinations successfully, coping with online teaching and assignments effectively, accessing data, mediated technologies, and number of courses migrated online. Sense of support was determined by institutional support to address the adverse effects of COVID-19 on academic work, maintenance of academic plans, students’ learning needs adequately, and adoption of alternative instructional methods. Effective communication depended on abilities to address issues regarding changes in financial obligations, online learning and expectations, and migration to online learning smoothly. Sense of connectedness was associated with connection to the institution and academic faculty. Mediated technologies consisted of technologies deployed by the university and faculty. Perceived stress levels were determined by online learning anxiety, past online learning experience before COVID-19, and future online learning expectations.

The results on whether students would continue with online learning after the pandemic were mixed, yet the relationship between students with previous experience in online learning (63%) and those who responded to yes and maybe (68%) was very close, suggesting continuity of online learning in the university could be influenced by increased exposure and experience.

Conclusion

The COVID-19 pandemic led to unprecedented changes in the higher educational landscape. Without prior experience in online learning and teaching, many universities in Ghana adopted ERT under the directives of the Ministry of Education. This study examined students online learning experience in a public technical university during the ERT. The results considered, improving technological infrastructure can help engage students in blended or hybrid learning environments. Professionally designed online learning environments and development of academic staff must be considered for students to learn with educational technologies. Students’ concerns can be addressed adequately to minimize their anxieties and perceived sidelining with clearer and supportive communication. Lecturers can allow some flexibility when they engage students in online learning and remote teaching to promote self-directed learning experience. Planning towards emergencies can help minimize their adverse effects on academics, particularly with learning design, delivery modes, and media applications.

Evidently, differences existed between well-planned meaningful online learning experiences and courses offered online during crisis (Hodges et al., 2020). Arguably, higher education in Ghana shifted during the pandemic in obvious ways to merit further studies, reflective online leadership and administrative functions, and policy considerations, not only during crisis management, but for a robust technology-oriented post-pandemic education landscape.

Limitations

The study was situated in a relatively new online learning context where a temporarily shift to remote teaching was implemented because of mandatory responses to crisis management. The experience was divorced from the typical online learning ecosystem associated with well researched and advanced evaluation criteria. The approach was more pragmatic and informed by the possibilities for creative solutions and might not apply in all settings. The results were specific to an institution and a discipline and must be replicated for validity.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

JA Larbi-Apau

Author Biographies

Dr. JA Larbi-Apau, is the executive director of AAL-Global Consultants, Ltd., the past President of AEITG, a consultant and private practitioner in higher education. She is focused on transformative research, interactive instructional design, education and instructional technology, eLearning management, and related fields. She can be reached at jlarbiapau@gmail.com

Dr. B Kwofie is a lecturer in Computer Science and Head of Innovation with a focus on educational technology, information systems, and information technology entrepreneurship. His work experience spans from policy review and development, faculty chair and vice-dean of applied sciences and technology, and head of department. He can be reached at benjaminkwofie@gmail.com

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