Is STEM a better adaptor than non-STEM groups with online education: an Indian peri-urban experience

Aakash Ranjan Das (Department of Sociology, Vidyasagar University, Midnapore, India)
Asmita Bhattacharyya (Department of Sociology, Vidyasagar University, Midnapore, India)

Asian Association of Open Universities Journal

ISSN: 2414-6994

Article publication date: 7 March 2023

Issue publication date: 31 May 2023

961

Abstract

Purpose

The existing literature contains few references on the better adaptors of online distance education amongst STEM (read as science, technology, engineering and mathematics) and non-STEM (composed of humanities, social science and commerce) study groups in an Indian peri-urban context. The study's objective is to determine the better adaptor amongst these two study groups in online distance learning in higher education systems in an Indian peri-urban context.

Design/methodology/approach

The investigation was carried out prior to COVID-19 and during the pandemic. The inquiry is triangulated in nature with a disproportionate stratified random sampling approach used to pick 312 post-graduate students (STEM = 135 and non-STEM = 177) from a peri-urban higher education institute in West Bengal, India, using the “Raosoft” scale. Given the prevailing social distance norms, 235 samples of respondents from 312 students were evaluated via telephonic/online interviews during the COVID-19 period. The data were analysed using SPSS 22.

Findings

This study's investigations reveal that the STEM respondents have better digital profiles, better basic computing and Internet knowledge and greater digital usage for academic purposes before the pandemic times than the non-STEM group. This prior digital exposure has enabled the STEM group to cope with regular online distance education during the pandemic more quickly than the non-STEM group, as evidenced by their regular attendance in online classes and their greater awareness of its utilitarian role than the other group.

Originality/value

The study offers a way forward direction to evolve with more inclusive online distance learning in peri-urban Indian regions.

Keywords

Citation

Das, A.R. and Bhattacharyya, A. (2023), "Is STEM a better adaptor than non-STEM groups with online education: an Indian peri-urban experience", Asian Association of Open Universities Journal, Vol. 18 No. 1, pp. 20-33. https://doi.org/10.1108/AAOUJ-07-2022-0092

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Aakash Ranjan Das and Asmita Bhattacharyya

License

Published in the Asian Association of Open Universities Journal. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

A key challenge for the 21st century is the use of technology in online learning approaches to support new pedagogies and teaching practices. Learning environments in higher education actually rely on their metamorphosis into a digital world, thus they must utilise the various learning instruments to the fullest extent possible (Cook and Thompson, 2014; Garrison and Kanuka, 2004). According to Lin et al. (2017), Jay Cross proposed digital learning (e-learning) in 1999. This new model substitutes an online learning environment for face-to-face instruction, allowing users to digitally interact with both students and teachers on a device's screen (The World Bank, 2020). Prior to the massive global transition into online learning platforms across all academic areas catalysed by the COVID-19 pandemic's emergence in 2020, this tendency had gradually transformed the style of academic courses (Rana, 2021; Bernard et al., 2014).

All of India's higher educational institutions were subsequently closed as a result of the general lockdown. Consequently, the traditional methods of teaching and learning had to undergo a change. The University Grants Commission (UGC) in India recommended that colleges and universities should maintain their educational practises while utilising technology and having access to academic opportunities. As a result, many higher education institutions have begun offering online distance courses in the majority of Indian states, including West Bengal, despite numerous obstacles since the lockdown (Rana, 2021).

This study was done earlier than the pandemic and covered a period during the pandemic. The study tends to propose pedagogical design through an in-depth analysis of students pursuing online distance education amongst the STEM and non-STEM students of a peri-urban region-based institution. “Science, technology, engineering and mathematics” are collectively known as STEM (Ng and UNESCO, 2019; Pagar, 2018; Breiner et al., 2012). “Three disciplines—Studies in Human Societies; Language, Communication, Culture and History and Archaeology”—combine to form the interdisciplinary department known as non-STEM (Uddin et al., 2021). In contrast to most non-STEM subjects, which are less likely to use technology in teaching and learning, STEM subjects incorporate technology into their curricula (Pagar, 2018). These two are the major disciplines in academics. In this digital transformation era, a solid comparison between the students of these two major disciplines regarding the application of digital technology in pursuing online distance education is required to understand the need for and make policy for their career advancement. Prior research has shown that general online learning techniques have a positive influence on academic performance in STEM courses (Vo et al., 2017). So, the concept of the “digital divide”—the disparity between those who have and those who do not have access to technology, and the “technology acceptance model” (TAM), which claims that a digital device's functionality and usability have an impact on how consumers feel about it, have been proposed in this paper. Thus, the study takes into account their digital profiles, their general knowledge of computing, and their aptness for the Internet of things, and deals with the specific digital skills and literary usages across STEM and non-STEM study groups.

1.1 STEM vs. non-STEM adaptors: a preview and theoretical support

There is a wide volume of literature documenting the effects of various online and blended learning programmes. Online and blended learning initiatives significantly improve academic performance in STEM courses compared to non-STEM courses (Vo et al., 2017). It has been demonstrated that students' self-efficacy is significantly influenced by their level of digital literacy, which contributes to the need for self-regulated learning in blended and online learning environments (Prior et al., 2016). “Self-regulated learning, epistemic cognition and digital literacy” have been linked to STEM learning (Greene et al., 2018) and to aiding learners' ability to organise their information (Demirbag and Bahcivan, 2021). In a recent study on undergraduate students in STEM (veterinary science) and non-STEM (psychology) groups during the lockdown period of COVID-19, it was observed that students with strong digital skills and self-instruction could maintain attention and engagement (Limniou et al., 2021). According to Babu et al. (2010), science (STEM) students are more likely to utilise the Internet than social science and humanities (non-STEM) students. The impact of information technology (IT) on non-STEM fields such as sociology and economics was investigated by Costa and Meadows (2000). According to Loan (2011), STEM students majoring in computer science used the Internet the most, followed by those majoring in non-STEM groups such as business and commerce, then STEM groups such as the general sciences and non-STEM groups such as humanities. Barboza's (2022) study provided details on ways of familiarising non-STEM students with data science so as to delimit the exclusivity of STEM specialists from entering the data science field. Shin et al. (2016) stated that role models do motivate students belonging to both STEM and non-STEM disciplines. But Fathulla and Kissoon (2022) observed that the general lack of relevant expertise among academicians in different non-STEM fields is a significant obstacle to the implementation of industrial digitalization (I4.0) technology. According to Tang and Chaw (2016), the students in the current generation are already familiar with online learning and social media, which include “digital access, content creation, and resource sharing”.

According to Centeio (2017), the “digital divide” is a lag that develops between people who have proper access to information and communication technology and those who have limited access to it. As per his study, the first wave of research on the digital divide focusses on variations in access to computers and the Internet, whereas the second wave of research has focussed on social and cultural variables, particularly skill disparities.

According to the TAM, a system's apparent value and usability have an impact on consumers' attitudes towards using it. Lin et al. (2011) are of the opinion that the TAM explains user gratification in three ways, which are, “perceived utility, ease of use and one's attitude towards its use”. The TAM may take external factors into account, such as “user training, system characteristics, user engagement in design and the nature of the implementation process”. Additionally, the stakeholders' behavioural objective to utilise the system is influenced by their attitudes towards using it and how convenient they perceive it to be. The proper use of the system is determined by the behavioural goal. The “technology acceptance model” no longer takes the user's disposition into account.

We discovered some studies on the aforementioned contexts in the western framework but very few within the Indian frameworks, and more specifically, in the peri-urban Indian perspectives phased in before and during the COVID-19 pandemic. The majority of research focusses on how technology is used in STEM fields, with less attention being paid to how it affects non-STEM fields of study. Thus, the present study features the following research questions to bridge the gaps in the previous literature by comparing the digital usage patterns of STEM and non-STEM departmental post-graduate students before and during the COVID-19 period. The research questions are as follows.

RQ1.

Is there any disparity in basic computer and Internet knowledge and digital skills in handling academic tasks amongst the STEM and non-STEM groups of the post graduate students in an Indian peri-urban region? If so, who is the better adaptor?

RQ2.

Is there any difference in the motivational factors, barriers, access points and frequency of using computers and the Internet amongst STEM and non-STEM post graduates of the same peri-urban area?

RQ3.

Is there any disproportion in the online learning experiences during the pandemic between STEM and non-STEM post graduates in the same study area?

2. The methods of the study

Cross-sectional triangulation methods were used to capture the study objectives. It is used to determine the predominant characteristics of a population at a given point in time when multiple research strategies are employed in a single study. In this study multiple research strategies, such as two different data collection techniques (face-to-face interview and telephonic/online interview) and two different time periods (before and during the pandemic), were used in this current study. Hence, the cross-sectional and triangulation strategies were applied to this research study.

2.1 The location of the research

In the peri-urban region, a higher education institution (HEI) in the Paschim Medinipur district of West Bengal, India, was selected. This peri-urban region was chosen for the study because it is an intermediary region between an expanding city and its rural neighbourhood (Dutta, 2012). It is neither an urban nor a rural area in its characteristics (Prakash, 2012), but it has mixed rural and urban characteristics (Fazal et al., 2015). Furthermore, the sampled HEI has been chosen for the study as this institute, even though located in a peri-urban region, has been providing high-speed Internet access since 2015. Hence, the post-graduate students of this institute were chosen for this study.

2.2 Research sample structure and unit of analysis

The study survey selected 312 respondents from a universe of 1,652 post-graduate students from the HEI using the “Raosoft” scale (95% significant value with 5% error). A disproportionate stratified random sampling method was used to collect samples from a higher education institution in the study area. These respondents were split into two study groups at the sampled HEI: STEM, where 135 students were picked from 16 departments, and non-STEM, where 177 students were included from 12 departments based on inclusion and exclusion criteria. A total of 235 student respondents were amongst them, with 105 STEM and 130 non-STEM students being interviewed during the pandemic times from those who had already been sampled during pre-pandemic times, i.e. 312 respondents, due to social distancing norms, time barriers, a lack of interest in providing answers, etc.

2.3 Data and instrument analysis

A relevant semi-structured questionnaire was developed for conducting interviews with participant samples, and data were collected in two phases. First, during the pre-pandemic time, face-to-face interviews were carried out between August 2019 to February 2020, with interviews focussing on digital competency, locations and frequency of computer and Internet use, motivating factors and obstacles they faced. The second phase of the telephonic or online interviews, which took place between July 2020 and June 2021 to cover pandemic time data, included 235 samples (105 STEM respondents and 130 non-STEM respondents) composed of the same respondents, who were respondents in the study in the pre-pandemic times and showed willingness to participate in this second phase. In the pandemic phase interviews, they discussed their prior online class experiences (if any) and the benefits of online learning. The majority of the responses were gathered using the Likert scale approach. The interview data were analysed using SPSS 22 (Windows 10 OS) version software to compare means and perform descriptive statistics. Additionally, qualitative responses were also documented along with the nature of the interviews, and their case studies were used to understand the patterns. The case studies of the respondents in this study are designated as “R”, followed by a number of identifiers.

3. Findings and interpretations

The findings and interpretation of the study from the collected data identified their digital skills, competency, access points and frequency of using computers and the Internet, as well as barriers and motivational factors they faced in the pre-pandemic era. They also explored their experiences with the online method of teaching and learning, the pros of online learning and their preferred education system for the future. The study provided some recommendations for the future learning process amongst the two STEM and non-STEM study groups.

3.1 Epochs preceding COVID-19

This part was dissected into three parts: demographic information, digital profiles of the respondents and their ABC knowledge of computing and the internet of things in general, along with specific digital skills and literary usages across STEM and non-STEM groups of students, with the help of a face-to-face interviews taken from August 2019 to February 2020.

3.1.1 Demographic information about students

Table 1 depicts the personal demographic information of the students. Amongst the 312 respondents to the survey, 50.3% were male students and 49.6% were female students. A significant percentage of the students were from rural areas, which made up 52.2%, followed by 26.9% from suburban areas and 20.5% from urban areas. In total, 43.3% belonged to 16 STEM departments, and 56.7% belonged to 12 non-STEM departments.

3.1.2 Digital profiles

The respondents' digital profiles deal with the ownership of various types of digital devices, identifying the access point of computer and Internet usages, motivational factors and barriers to using computing and the Internet of things amongst the respondents, largely hailing from rural places in West Bengal, India.

3.1.2.1 Possession of digital devices

Table 2 shows that the STEM group of students (44.4%) owns more computers or laptops than their non-STEM (19.2%) counterparts. However, all the respondents from both study groups owned smart phones. Due to preparation for scientific projects, the STEM group of respondents needs to own personal digital devices like personal computers or laptops, which is not a mandate for non-STEM students. Our study findings corroborate with Babu et al.’s (2010) study, which showed that students majoring in science (STEM) are more likely than those majoring in social science and humanities (non-STEM) to use the Internet, and also with a few other studies that illustrated that the STEM disciplines' curricula emphasise the use of technology. As a result, students in the STEM fields have a natural affinity for using desktops or laptops for educational purposes (Breiner et al., 2012; Pagar, 2018).

3.1.2.2 The access point for computing and Internet usage

Table 3 displays the multiple responses for the access categories for each respondent coded in the chart. It shows that most of the students from both the STEM and non-STEM groups use computers and the Internet at “cyber cafes” and then in their own institute. The only distinguishing feature is that more STEM students have computers in their homes than non-STEM students. One possible factor may be that STEM groups have more computer-based syllabi than non-STEM groups.

The compulsory inclusion of computer usage in practical/laboratory work and scientific analysis in the science and technology streams allows the STEM students to have higher computer/digital skills than the non-STEM streams in this HEI. However, some non-STEM departments, such as MBA, Commerce and Economics have statistical applications in their syllabus that allow these students to be more technologically proficient than the other non-STEM departments' students. Table 3 is the aggregate of responses where each respondent can select more than one category.

The case studies below reveal that most of them opt for cyber cafés and institutes instead of their home facilities. Most of the students using Internet facilities at home face cost factors for broadband connections with high-speed Internet facilities. The price hikes in a regular Internet pack are not affordable for all students. Alternatively, cyber cafes are accessed for their academic purposes only in times of need. But the higher education institution provides a free Internet connection to its stakeholders, including the students who are on campus, and who can enjoy the Internet connection 24/7.

R 78 (Male, 24 years, non-STEM department):

I don't own a computer machine at home. So I frequently access the computer at my institution or in cyber cafes when needed. However, we have a specified time limit in the computer lab of the institution for using computers and the Internet for students.

R 135 (Male, 23 years, STEM department):

I have a laptop but can't afford the high-speed internet price. Hence, I frequently access the Internet in cyber cafes and at my peers' homes. However, the institution has a limited number of desktop machines, and each student's time limit is restricted.

R 163 (Female, 23 years, STEM department):

I suggest increasing the number of desktop machines in the central library of the institute with a specific time limit. I'm particularly eager to use my digital device while on campus, as the high-speed internet facilities are available there.

The above case studies indicate the increasing necessity for hi-tech institutional support at an accessible cost to use digital technologies in their higher education institutions, which goes a long way in motivating students from a peri-urban region with a modest economic background. Moreover, they are queuing up for the increasing demand for desktops and delimiting the time slots for computer usage for students inside the institutional campus. This will have a positive impact on increasing digital usage.

3.1.2.3 Motivational factors for computer usage

Table 4 represents the calculation of the one-way ANOVA grouped by the STEM and non-STEM divisions of the students. The result reveals a significant association between the motivational factors and two study groups of the students for the fields “self-taught” (0.003*), “guidance by friends” (0.055*) and “computer training centre” (0.014*). The STEM group respondents are more self-taught than the non-STEM group. This study's result syncs with the research of Gibbons (2002), which explains that self-regulated learning propels knowledge enhancement, proficiency, digital ability, achievement or individual's progression that a person chooses and conveys through their efforts, which comes with self-teaching and also with the investigation of Greene et al. (2018), which found that learning in STEM fields has been linked to self-regulated learning and digital literacy. The STEM group respondents are more self-taught than the non-STEM group. The behaviourism aspect of learning theory, in which learners respond to environmental stimuli, is supported by “guidance by friends” and “computer training centres”.

The above results are well corroborated by the following case studies of the respondents, indicating how they started learning computer and with whose help they did. The studies indicate that most of them believe in self-learning, which they do with the help of their siblings or friends, rather than taking professional help.

R 8 (Male, 22 years, STEM department):

I took training on computer learning for the first time at school. Following my board exams, I used to enrol in a computer coaching centre for advanced level courses. I don’t have a computer machine at home. However, I concede that peer help and trial-and-error techniques have effectively enhanced my digital skills.

R15 (Female, 23 years, non-STEM department):

I used to take computer training from my brother with his desktop machine. I used to go to the computer coaching centre for the advanced level course during my college days. I feel that the trial-and-error techniques and help from siblings and peers simplify the learning process.

3.1.2.4     Barriers to compute and Internet usage

The calculation of the one-way ANOVA grouped by STEM and non-STEM students is depicted in Table 5. A significant association is seen between the different barriers and the two study groups in the arenas of “inadequate training in Internet use” (0.012*), “time barrier” (0.043*), and “obsolete equipment” (0.001*). “Inadequate training in Internet use” holds up the concept of the second wave of the digital divide, which is based on not having the proper skills to use digital devices despite having those. Various studies show that the STEM group is more technologically advanced than their non-STEM counterparts (Vo et al., 2017; Prior et al., 2016). Hence, “obsolete equipment”, related to the problem of old or not updated electronic gadgets, has created a barrier to using computers and the Internet for the STEM group.

The following case studies point out the barriers to using digital technologies and offer opinions on how to improve digital technology skills.

R 235 (Female, 23 years, non-STEM department):

Despite having a digital device, the price hike of the internet pack makes an issue while using digital equipment, and sometimes I face irregular power supply constraints. YouTube can be a good medium for learning and developing skills.

R 180 (Male, 22 years, STEM department):

I need a stable internet connection for studies. At first, I had a desktop machine at home, but the power supply problem was there. So I bought a laptop. But I face frequent internet price hikes and feel the electronic equipment is obsolete as the updated versions of electronic devices are available on the market.

R 101 (Female, 22 years, non-STEM departments):

Lack of digital literacy is a major barrier to me.

3.1.3 ABC knowledge of computers and the Internet of things with its academic applications

Here, the respondents' basic knowledge of computer and internet usage across the study groups is considered, along with their digital skills used exclusively for academic purposes. Also considered is the frequency of computer and Internet usage by groups for academic purposes. Finally, even their prior exposure to online education during pre-Covid-19 times is considered.

3.1.3.1 The students' basic computer and Internet knowledge

The students' computer and Internet knowledge are imperative to assessing their advanced digital skills. Table 6 shows the comparison of the mean values of STEM and non-STEM groups in computer and Internet knowledge. The findings of the study are consistent with previous research by Breiner et al. (2012) and Pagar (2018) that STEM subjects' curricula emphasise technology usage. Consequently, STEM department's students have a natural affinity for higher computer and Internet skills than non-STEM department students.

3.1.3.2 Knowledge of digital skills in handling academic tasks

Table 7 shows there is a significant association between two major study groups with “basic skills and using office packages” (0.010*), “using search engines and social media” (0.000*), “productivity and web navigation” (0.001*) and “downloading, saving and printing knowledge” (0.000*). The STEM group scores higher than the non-STEMs in all kinds of digital skills. The study's result is consistent with the investigation by Greene et al. (2018), which showed that learning in STEM fields has been linked to epistemic cognition, self-regulated learning and digital literacy, and has improved students' ability to organise relevant information (Demirbag and Bahcivan, 2021). The result is similar to the concept of the digital divide, especially the second wave of the digital divide, which propagates through social and cultural factors, particularly skill disparities.

Here the case studies depict the starting point for the users to use digital technologies for academic purposes on a regular/daily basis.

R 10 (Male, 23 years, STEM department):

I use search engines to get several pieces of information and examples on study topics, which helps me enhance my knowledge of the topic/subject. And also, use the internet for other purposes like banking, bill payment, trading, etc.

R 56 (Male, 23 years, non-STEM department):

I believe social media is a part of our lives to stay connected with people. I use search engines for searching and downloading applications for online trading, travel information, train timetables, banking, etc. I frequently post that I use it for academic purposes to prepare notes on the various topics and contents in the syllabi.

R 28 (Male, 23 years, STEM department):

As a newcomer in the city to achieving a post-graduate degree, the streets were unknown to me and I often took the help of a web navigation application.

R 6 (Female, 23 years, non-STEM department):

I have skill in downloading and saving it as a file on the machine, but didn't have printing skills until I understood the instructions given in the dashboard.

R 188 (Female, 23 years, STEM department):

I use the databases available on the departmental library's desktop machine and the access folder database I made for academic purposes.

3.1.3.3 Computer and Internet usage frequency

Table 8 explores the frequencies of using computers and the Internet by the STEM and non-STEM students. It shows a substantial difference between the frequencies of the STEM and non-STEM groups with “everyday” and “once a week”. STEM subjects have some technical content in their syllabi. In contrast, non-STEM subjects are mainly theoretical and have the fewest technical touch-ups on the syllabus, but the Commerce and MBA streams bear exception to this. So it is natural if respondents from “STEM” departments access the computer and the Internet “every day”, whereas non-STEM students access them “once a week”, basically for academic purposes. The study result is similar to the investigation by Babu et al. (2010), which found students studying science (STEM) are more likely to use the Internet than students studying social science and humanities (non-STEM).

3.1.3.4 Previous online course experience amongst students

The study in Table 9 also represents 63% of STEM students who have prior online course experience, which is much higher than that of the non-STEM respondents at 33.3%. This study's findings are consistent with an evaluation that found that online and blended learning experiences have a significantly stronger positive influence on STEM courses' academic accomplishments (Vo et al., 2017). The interviews with the respondent students revealed the reasons for the higher digital usage amongst the STEM group are related to their higher aspiration needs such as undertaking various online courses on varied software packages (e.g. JAVA, Python, etc.) or for short-term online courses on competitive examinations training (banking, railway, SSC, etc.), unlike that of the non-STEM respondents, who primarily pursued online courses on graphics, animation and basic MS Office packages and, barring a few, undertook short-term competitive examination training courses.

3.2 The COVID-19 times

During COVID-19 times, there has been a paradigmatic shift in the teaching and learning process where online deliberations have become the “new normal”, unlike in pre-COVID-19 times. Here, we discussed the online mode of attendance across the study groups and shared positive aspects of the online mode of education. This part was interviewed over the telephone and online from July 2020 to June 2021.

3.2.1 Types of attendance in online classes amongst the students

Table 10 shows the STEM respondents (66.6%) scored more in online classes' regular attendance than their non-STEM (60%) counterparts. Non-STEM respondents (40%) outperformed their STEM (33.3%) counterparts in terms of occasional attendance in online classes. High digital skills, updated digital devices and Internet access were the causes of STEM students' higher response in online classes, revealing the second wave of the digital divide between the two study groups based on device skills. Another way it almost supports the TAM for the non-STEM group who were active in online classes is that it almost supports the hypothesis that users' approaches towards utilising the system are controlled by its apparent usefulness and usability.

The following case studies try to reveal the probable reasons behind the disparities in attendance in online classes.

R 177 (Female, 23 years, non-STEM department):

I am not skilled in handling video-conferencing applications and have a limited data pack. Through an online teaching model, initially, I faced problems exchanging study materials with teachers and friends. But I have made the updates myself simultaneously.

R145 (Male, 22 years, STEM department):

Lack of concentration and poor internet issues are there in online classes.

3.2.2 The benefits of online classes

Table 11 displays how the STEM and non-STEM groups perceive online education in terms of the utilities of this mode compared to the previous offline mode, and it constitutes the summation of responses where each respondent is allowed to respond to more than one category. The advantages of online classes amongst these two study groups maintain the TAM, which proposes that the effectiveness and functionality of a system determine users' outlooks towards using it, and it is also a sign of reducing the digital divide based on digital skills (Lin et al., 2011).

3.2.3 The preferred education system for the future

Table 12 demonstrates that a significant number of student respondents from both STEM and non-STEM study groups preferred a “blended education system”, which is the amalgamation of online and conventional offline education patterns for the future education system. Thereafter, the STEM group preferred the online education system only, followed by offline only. But the non-STEM group preferred the offline education system, followed by the online education system only. The study's result is an indication of a move towards digitalisation of the education system by mitigating the digital divide and accepting the TAM.

4. Concluding remarks

The significant findings from the pre-pandemic era revealed that the STEM group had higher efficiency and skills in using digital technologies and handling academic tasks, as well as a higher frequency of using the computer and the Internet than the non-STEM group. The STEM group of respondents had the edge over the non-STEM group in terms of prior online course experience. The STEM group respondents' attendance in online classes was regularly compared to their non-STEM counterparts during the pandemic lockdown. Both groups have identified the benefits of online education, but the primary reasons vary. And in the future, both study groups would prefer a blended education system.

A significant way-forward recommendation for the non-STEM groups, especially the digitally backward students of any curriculum, is suggested herein. These respondents from peri-urban areas who come from rural areas face numerous barriers to acquiring and accessing digital skills. Despite these, we found the STEM group is a better adaptor than the non-STEM group. To make digital online teaching and learning more inclusive, we propose the following recommendations at various levels: The first thing to note is that all student respondents own smart phone devices but no other digital devices because of their modest economic condition. Thus, apps or applications need to be compatible with mobile devices.

Moreover, higher education institutions should provide accessible high-speed Internet facilities to allow them broader access to computers and the Internet. Further, the institution should increase the number of desktop machines and the timing of computer and Internet usage per student. The institution can initiate a basic computer training course for the digitally backward students so that the non-STEM group can become digitally competent. Here, the government can initiate a “students' Internet pack” at a cheap rate, and the Internet service providers should take care of slow web networks in those areas. Furthermore, software applications related to their subject should enable them to use computing and the internet for more profound interests and essential needs. We found the students from both the study groups are self-learners. Many websites, such as YouTube tutorials, may provide a crash course in various digital domains. Non-STEM individuals who are not digitally proficient enjoy the interactive aspect of online education and may benefit from audio and digital visualisation tools, graphical presentations and multimedia in virtual teaching and learning, which will help all students understand online lectures better than traditional presentation formats. If the blended learning pattern is to be permanently integrated into the education system, the institution will need a standard operating procedure. Students are showing an interest in pursuing courses both online and in traditional offline formats. The government and academic institutions are recommending a few strategies to make students more digitally efficient with their academic course work. As a result, making educators responsive to digitally backward students from any stream, planning educational materials to uplift digitally backward students, encouraging digitally backward students, particularly in non-STEM departments, in software usages, addressing self-censorship and making digitally backward students aware of their natural ability, making educators role models for the students and introducing digitally efficient peer groups can help the shy. Hence, the authorities can introduce the peer-to-peer teaching programme. Most importantly, it addresses time, proper training and family support to achieve the goals.

The study was designed on an institution-only basis in a peri-urban region. It is possible to design a study to gather information that will result in a larger, more diverse sample and include a greater number of higher educational establishments. The second segment of the interviews was conducted during the time of the pandemic, so all of the respondents from the first phase could not provide their responses, and only a few of them gave consent to answers. Due to the lock-down and social distancing norms, a telephonic/online interview was conducted where the body language of the respondents was not visible, and, due to time constraints, there was a lack of possibility to ask cross-check questions on interview topics. Future research could attempt to overcome all of these limitations and advance our understanding of how to improve the digital skills of tech-challenged students by presenting various solutions to more students from various diasporas.

Demographic information about students

Demographic informationFrequency (N = 312)Percentage (%)
SexMale15750.3
Female15549.6
Nature of hometownUrban6420.5
Suburban8426.9
Rural16452.5
Stream of educationSTEM13543.3
Non-STEM17756.7

Ownership of digital devices by the students

Personal devicesSTEM (N = 135)Non-STEM (N = 177)
FrequencyPercentage (%)FrequencyPercentage (%)
Desktop/Laptop6044.43419.2
Smart phones/Tablets135100177100
Both types devices6044.43419.2

Access point of computing and Internet usage by the students

Access pointSTEM (N = 135)Non-STEM (N = 177)
FrequencyPercentage (%)FrequencyPercentage (%)
Higher education institution8865.211062.1
Home6951.18447.5
Friend's/neighbour's home6346.69754.8
Cyber cafe11484.413174

Motivational factors for computer usage amongst the students

Motivational factorsSTEM (N = 135)Non-STEM (N = 177)F valueSignificance p value
Mean (±SE)Mean (±SE)
Self-taught1.326 (±0.04)1.181 (±0.03)8.9510.003*
Trial and error method1.541 (±0.04)1.497 (±0.04)0.5790.447
Help of school teacher1.415 (±0.04)1.395 (±0.04)0.1180.731
Help of parents and family members1.415 (±0.04)1.395 (±0.04)0.1180.731
Guidance by friends1.533 (±0.04)1.424 (±0.04)3.7130.055*
Reading computer books1.164 (±0.03)1.153 (±0.03)0.0770.781
Computer coaching centres1.941 (±0.02)1.853 (±0.03)6.1380.014*

Note(s): *p < 0.05 = statistical significance. The statistically significant values are marked in Italics only

Computer and Internet usage barriers for the students

BarriersSTEM (N = 135)Non-STEM (N = 177)F valueSignificance p value
Mean (±SE)Mean (±SE)
Not having advanced digital devices rather than the smart phone1.096 (±0.06)1.147 (±0.03)1.7910.182
Inadequate training in Internet use1.526 (±0.04)1.667 (±0.04)6.4430.012*
Time barrier1.452 (±0.04)1.339 (±0.04)4.1380.043*
Cost of utilisation1.556 (±0.04)1.475 (±0.04)2.0100.157
Obsolete equipment1.281 (±0.04)1.136 (±0.03)10.5160.001*
Digital device failures1.126 (±0.03)1.158 (±0.03)0.6430.423

Note(s): *p < 0.05 = statistical significance. The statistically significant values are marked in Italics only

Basic computer and Internet knowledge of the students

Computer and Internet knowledgeSTEM (N = 135)Non-STEM (N = 177)Significance p value
Mean (±SE)T-test valueMean (±SE)T-test value
1.819 (±0.05)35.7521.633 (±0.04)37.6360.000*

Note(s): *p < 0.05 = statistical significance

Knowledge of digital skills in handling academic tasks amongst the students

Digital skillsSTEM (N = 135)Non-STEM (N = 177)F valueSignificance p-value
Mean (±SE)Mean (±SE)
Basic skills and using office packages2.437 (±0.06)2.243 (±0.05)6.6870.010*
Using search engine and social media2.578 (±0.04)2.345 (±0.04)14.1960.000*
Productivity and web navigation2.496 (±0.04)2.294 (±0.04)11.0050.001*
Evaluating website relevance and using databases1.481 (±0.04)1.412 (±0.04)1.4780.225
Downloading, saving and printing knowledge2.222 (±0.07)1.819 (±0.06)18.2280.000*

Note(s): *p < 0.05 = statistical significance. The statistically significant values are marked in Italics only

Frequencies of computer and Internet usage by the students

Frequency of usageSTEM (N = 135)Non-STEM (N = 177)Chi-square valueSignificance p value
FrequencyPercentage (%)FrequencyPercentage (%)
Everyday4634.12815.814.1060.000*
2–4 days in a week3626.63529.90.4030.525
Once in a week4331.97542.43.6050.058*
Once in a month107.42111.91.7000.192

Note(s): *p < 0.05 is statistical significance

Previous online course experience amongst the students

Online course experience previouslySTEM (N = 135)Non-STEM (N = 177)
FrequencyPercentage (%)FrequencyPercentage (%)
Yes85635933.3
No503711866.7

Types of online class attendance amongst the students

CategorySTEM (N = 105)Non-STEM (N = 130)
FrequencyPercentage (%)FrequencyPercentage (%)
Regularly attend7066.67860
Occasionally attend3533.35240
Never attend

Benefits of online classes amongst the students

AdvantagesSTEM (N = 105)Non-STEM (N = 130)
FrequencyPercentage (%)FrequencyPercentage (%)
Interactive64619069.2
Repeat value2624.85139.2
Learn at his pace43415038.5
Learn on his own9590.57456.9
User friendly6864.87053.8

Preferred education system of the students for the future

Preferred systemSTEM (N = 105)Non-STEM (N = 130)
FrequencyPercentage (%)FrequencyPercentage (%)
Online education system only1514.2107.7
Offline education system only109.53526.9
Blended education system only8076.28565.4

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Corresponding author

Asmita Bhattacharyya can be contacted at: asmita.bhattacharyya@mail.vidyasagar.ac.in

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