Abstract
Educational institutes across the world have closed due to the COVID-19 pandemic jeopardizing the academic calendars. Most educational institutes have shifted to online learning platforms to keep the academic activities going. However, the questions about the preparedness, designing and effectiveness of e-learning is still not clearly understood, particularly for a developing country like India, where the technical constraints like suitability of devices and bandwidth availability poses a serious challenge. In this study, we focus on understanding Agricultural Student’s perception and preference towards the online learning through an online survey of 307 students. We also explored the student’s preferences for various attributes of online classes, which will be helpful to design effective online learning environment. The results indicated that majority of the respondents (70%) are ready to opt for online classes to manage the curriculum during this pandemic. Majority of the students preferred to use smart phone for online learning. Using content analysis, we found that students prefer recorded classes with quiz at the end of each class to improve the effectiveness of learning. The students opined that flexibility and convenience of online classes makes it attractive option, whereas broadband connectivity issues in rural areas makes it a challenge for students to make use of online learning initiatives. However, in agricultural education system where many courses are practical oriented, shifting completely to online mode may not be possible and need to device a hybrid mode, the insights from this article can be helpful in designing the curriculum for the new normal.
1. Introduction
With the COVID-19 -a novel corona virus disease spreading across the globe, many countries have ordered closure of all educational institutes. Educational institutions have come to a functional standstill since they had to protect their students from viral exposures, which are likely in a highly socializing student community. In the beginning of February 2020, schools only in China and a few other affected countries were closed due to the proliferating contamination. However, by mid-March, nearly 75 countries have implemented or announced closure of educational institutions. As on 10th March, school and university closures globally due to the COVID-19 has left one in five students out of school. According to UNESCO, by the end of April 2020,186 countries have implemented nationwide closures, affecting about 73.8% of the total enrolled learners (UNESCO, 2020). Even though the lockdown and social distancing are the only ways to slowdown the spread of the COVID-19 by breaking the chain of transmission, closure of educational institutions has affected large number of students.
As the schools and colleges are shut for an indefinite period, both educational institutions and students are experimenting with ways to complete their prescribed syllabi in the stipulated time frame in line with the academic calendar. These measures have certainly caused a degree of inconvenience, but they have also prompted new examples of educational innovation using digital interventions. This is a silver lining on a dark cloud considering the sluggish pace of reforms in academic institutions, which continues with millennia-old lecture-based approaches in teaching, ingrained institutional biases and obsolete classrooms. Nevertheless, COVID-19 has been a trigger for educational institutions worldwide to pursue creative approaches in a relatively short notice. During this time, most of the universities have shifted to online mode using Blackboard, Microsoft Teams, Zoom, or other online platforms.
The educational institutions in affected areas are seeking stop-gap solutions to continue teaching, but it is important to note that the learning quality depends on the level of digital access and efficiency. The online learning environment varies profoundly from the traditional classroom situation when it comes to learner’s motivation, satisfaction and interaction (Bignoux & Sund, 2018). The Community of Inquiry (COI) framework offers a convenient baseline for intervening in online teaching and learning (Garrison et al., 2001). According to COI framework, success of web-based instruction is determined by creating a learners’ group. In this group (analogous to the traditional classroom situation), learning happens through three interdependent elements: (1) social presence, (2) cognitive presence, and (3) teaching presence. Study by Adam et.al. (2012) argued that there was no significant difference between online learning and face to face class with regard to their satisfaction and also, they supported the fact that online class will be as effective as traditional class if it is designed appropriately. These facts clearly show us that online learning is a perfect substitute for the traditional classroom learning if they are designed suitably.
Educational institutions in India have also made a transition to online teaching environment soon after Union Government’s decision to impose nation-wide lock-down for 21 days from 25th March, 2020 which was later extended for 19 more days. However, the major concern is about the quality of learning which is closely related with how well the content is designed and executed. Effectiveness of learning also depends on how the content is curated to online environment and also in understanding and addressing the constraints faced by students. The study is even more relevant considering that in India the system of online education has never been tried at this scale and this is like a massive social experiment. Further, in agriculture education sector, the curriculum of agriculture gives a lot of importance to practical aspects and adopting it to online platform can decide the effectiveness. In this line, we have examined Indian agricultural students’ perception regarding online education and various attributes which could make the online learning more effective and successful.
The results of the study are important for educational institutes in Agriculture for two main reasons. Firstly, the shift to online mode has been an abrupt one due to unprecedented lockdown imposed to manage the COVID-19, and the institutes did not had time to design and adopt the course contents for online mode. In this context, experience of students and the learnings can be incorporated to make online learning easy, efficient and productive. Second, even after lockdown is revoked, life after the COVID-19 pandemic will not be like before and online learning is here to stay, though in combination with regular offline classes. There is uncertainty about the length of the pandemic and chances of reinfections, the social distancing can become a new normal. So, all the educational institutes need to be prepared to shift majority of the course content to e-learning platforms and modify the course structure and curriculum suitably. The results of our study can be important input in deciding on the learning environment in online platform to promote effective learning. In the next section, we provide a brief review of literature followed by data and methods section where we describe the methodology used in the study. Then, we discuss the results and the implications followed by concluding remarks of the study.
2. Review of literature
The current technological advancements allow us to employ several ways to design the online content. It is very important to consider the preferences and perception of learners while designing the online courses to make the learning effective and productive. Preference of the learner is related to the readiness or willingness of the learner to participate in collaborative learning and the factors influencing the readiness for online learning. In the section to follow, we summarie the learnings from the review of related literature.
Warner et al. (1998) proposed the concept of readiness for online learning in the Australian vocational education and training sector. They described readiness for online learning mainly in terms of three aspects:(1) the preference of student’s for the way of delivery opposed to face-to-face classroom instruction; (2) student’s confidence in the utilising the electronic communication for learning which includes competence and trust in the use of the Internet and computer-based communication; and (3) capability to engage in autonomous learning. The concept was further refined by several researchers like McVay (2000, 2001) who developed a 13-item instrument which measured student behaviour and attitude as predictors. Subsequently, Smith et al. (2003) conducted an exploratory study to validate the McVay’s, (2000) questionnaire for online readiness and came up with a two-factor structure, “Comfort with e-learning” and “Self-management of learning”. Later, several studies were taken up for operationalising the concept of readiness for online learning (Evans (2000); Smith (2005)).The factors that influenced the readiness for online learning as put forth by researchers were self-directed learning(Guglielmino (1977); Garrison (1997); Lin and Hsieh (2001); McVay (2000, 2001)), motivation for learning (Deci and Ryan (1985); Ryan and Deci (2000); Fairchild et al. (2005), learner control (Hannafin (1984); Shyu and Brown (1992); Reeves (1993)),computer and internet self-efficacy ((Bandura (1977,1986 1997); Compeau and Higgins (1995); Eastin and LaRose (2000); Tsai and Tsai (2003); Tsai and Lin (2004); Hung et al. (2010)), online communication self-efficacy (Palloff and Pratt (1999); McVay (2000); Roper (2007)).
Any efforts to strengthen the effectiveness of online learning needs to understand the perception of the users. Studies have documented both favourable and unfavourable perceptions by students on online learning. Several studies indicate that the instructor’s interaction with students has considerable impact on the student’s perceptions of online learning. Consistency in course design (Swan et al. 2000), the capability of the interaction with course instructors to promote critical thinking ability and information processing (Duffy et al. (1998, pp. 51–78); Picciano (2002); Hay et al.(2004)) rate of interactivity in the online setting (Arbaugh (2000); Hay et al. (2004)), the extent of instructional emphasis on learning through interaction, the flexibility of online learning (Chizmar and Walbert (1999); McCall (2002); National Centre for Vocational Education Research (2002); Petrides (2002); Schrum (2002); Klingner (2003); Kim et al. (2005)), chances of engaging with teachers and peers in online learning settings (Soo and Bonk (1998); Wise et al. (2004); Kim et al. (2005)), social presence (Barab and Duffy (2000); Kim et al. (2005); Jonassen (2002)),academic self-concept (Trautwein et al. (2006); Lim et al. (2007)), competencies required to use the technology (Wagner et al. (2000) were identified as the perceived strengths of online learning. Hence an effective online class depends upon well-structured course content (Sun and Chen (2016)), well-prepared instructors (Sun and Chen (2016)), advanced technologies (Sun and Chen (2016)), and feedback and clear instructions (Gilbert, 2015).
However, several weaknesses related to online learning were also described in the literature. Delay in responses (Hara and Kling (1999); Petrides (2002); Vonderwell (2003), scepticism of their peers’ supposed expertise(Petrides (2002)); lack of a sense of community and/or feelings of isolation (Woods’, (2002); Vonderwell (2003); Lin & Zane, (2005)); , problems in collaborating with the co-learners, technical problems Piccoli et al.(2001); Song et al.(2004)), issues related to instructor (Muilenburg & Berge, 2005) higher student attrition rates (Frankola (2001); Ryan (2001); Laine (2003)), the need for greater discipline, writing skills, and self-motivation; and the need for online users to make a time commitment to learning (Golladay et al. (2000); Serwatka (2003) are considered to be barriers or weakness of online learning.
Several researchers compared the efficacy of online or web-based tutorials with conventional teaching in classrooms. The types of possible encounters that might occur online as compared to conventional classrooms differ substantially, and the impact of communicating within one setting or another can have a direct effect on attitudes of the students and faculty. The studies explored perceptions of online learning experiences vs. conventional classroom experiences by students and faculty and reported mixed findings that demand further studies. Some of those areas include analysing the nature and amount of interactions that is available online (Moore and Kearsley (1995)), flexibility and accessibility of web – based instructions (Navarro and Shoemaker (2000)),the skills, motivations, time and perception of learner and instructor(Yong and Wang (1996); Shih, Ingebritsen, Pleasants, Flickinger, & Brown, 1998; McIsaac et al. (1999); White (2004) and whether some or all of these aspects are linked to academic achievement (Brewer and Erikson (1997)).It was also found that there was no significant difference between online learning and face to face class with regard to their satisfaction and also in terms of their academic performance (Hara and Kling, 1999).Studies also supported the fact that online class will be as effective as traditional class if it is designed appropriately (Nguyen, 2015).
The literature has highlighted different models which provides the basic framework to understand the students perception regarding online education. Papers have also highlighted potential bottlenecks for success of the online learning. However, not many papers have attempted to understand the students perception and preference in Indian context. It is understandable that only limited number of distance education platforms were using online mode of education before the Covid-19 pandemic. Further, to the best of our knowledge, study on these lines has not been attempted in the field of agricultural education, where online learning initiatives are even lesser probably because of higher share of practical learning aspects in curriculum. We try to fill this gap with our study, drawing insights from the literature in conceptualizing the problem, exclusively focusing our attention on online learning in agricultural education.
3. Data and methods
3.1. Participants
Agricultural graduates were chosen as the respondents for this study as agriculture is the most diverse subject that includes subjects ranging from life sciences to social sciences where students work from lab to land. The participants were 307 agricultural graduates from different universities of National Agricultural Research System (NARS). It included 136 Under Graduates, 84 Postgraduates and 87 students pursuing their Ph.D. Among them 172 were female and 135 were male.
3.2. Procedure
A structured and unstructured preliminary questionnaire was designed with the help of literature survey and informal discussions with the students who are currently attending the online classes. Pre-testing was done with 12 respondents and their feedbacks were considered for designing the final questionnaire.
3.3. Domain of the study
First of all, we identified key-informants among different agricultural universities for online survey. The link for Google form was sent to the key-informants through the WhatsApp. After submitting their responses, they circulated the questionnaire among other university students like snowball sampling. We have disabled the link after 10 days of circulating the Google forms. In this way, responses from a total of 307 students were obtained from different universities of the NARS.
3.4. Data analysis
Data were collected on demographic features, followed by learners’ preferences, perception, advantages, constraints and suggestions. The statements were prepared based on extensive review of literature and discussion with experts to minimize researchers bias. To analyze and summarize the perception, statements were rated on a five-point continuum scale (five being most effective and 1 being the least effective). Frequency and percentage were calculated for most of the questions to summarize the data. Apart from calculating the percentage table for the perceptions, we used a measure of consensus for each of the statements. The consensus was calculated by the formula suggested by Tastle and Wierman (2007).cons(X)=1+∑i=1npilog2{1−(|Xi−μX|/dX)}
pi = probability or frequency associated with each Likert attribute Xi ; i ranges from 1to 5
dX = width of X
μX = mean of X.
Further, each statement regarding perception of respondents based on effectiveness of online learning in comparison to classroom teaching was ranked based on mean rank obtained by Friedman’s test. Formula used for calculating mean rank in Friedman’s test is as followsMeanrank=12nrk(k+1)∑Ri2−3nr(k+1)Where, k = number of columns(treatments); nr = number of rows(blocks); Ri =Sum of the ranks.
To identify the most important benefits and constraints of online learning, Garret ranking technique was used. For this, 5 benefits and 8 constraints were given to the respondents and they were asked to rank it based on their opinion. As a first step these ranks were be converted into percent positions based on the following formulaPercentposition=100(Rij−0.5)/NjWhere.
Rij = Rank given for the ith Benefit/constraint by jth respondents
Nj = Number of Benefits/constraints ranked by jth respondents
As a second step these percent position of each rank was converted into scores using the table given by Garrett and Woodworth (1969). And then for each factor, scores of individuals were added and divided by the total number of respondents to get the mean score of each factor. The Benefit and Constraint with the highest mean score was considered as the most important.
The perception study detailed above has a limitation that the responses are dependent on how the questions are framed. Insights can be drawn only on statements for which answers are recorded. In this context, to broaden the perception of students regarding the online course and factors determining the success, we have used content analysis. To analyze the open-ended questions conventional content analysis was done. Content analysis is defined as a generic name for a variety of textual analyses that typically involves comparing, contrasting, and categorizing a set of data (Schwandt, 1997). We tried to perform content analysis to identify the trends in learners’ perspective regarding online classes. As a foremost step, two authors after looking into all the responses of the open-ended questions, created the themes and sub-themes which was checked for inter-rater reliability using Kappa Co-efficient with the help of the other two authors. The estimated Kappa co-efficient was found to be 0.72 which denotes substantial agreement between the two rater’s.
