Study on Factors Influencing Effectiveness of Learning during Covid 19
Dr Pradeep Kumar Ca, Nimal CNb, Renjith KRc
aProfessor and Director, Jai Bharath Arts and Science College, Perumbavoor
bAssociate Professor, Adi Shankara Institute of Engineering and Technology, Kalady
cAssistant Professor, Adi Shankara Institute of Engineering and Technology. Kalady
Article History: Received: 11 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published
online: 23 May 2021
Abstract: This paper examined the factors influenced the effectiveness of learning during COVID 19 pandemic. It is based on
an empirical survey conducted during Nov–Dec, 2020. Students from Ernakulam district, state of Kerala was issued with online questionnaire. Response rate was satisfactory. Factor analysis was conducted and five prominent factors influencing learning effectiveness were used for further model building and inferences. We found factors like Instructor-Learner Connect, Course Coherence, Interactive Learning were more statistically significant than Proactive & Appropriate Pedagogy and Prompt Feedback.
Keywords: COVID 19, Pedagogy, Learning effectiveness 1. Introduction
Covid 19 has disturbed all walks of life and affected education in a very serious way. The pandemic has damaged/ delayed many student’s future. According to UNESCO (2020), over 1.5 billion learners in 195 countries are affected by the COVID-19 lockdown and subsequent closure of educational institutions. Which in effect translates to roughly 87% of the world’s student population. This resulted in a paradigm shift in the way teaching is done. This study was aimed to understand the effectiveness of the new teaching and learning systems that was put in place on account of Covid 19.
2. Background of the study
With the pandemic and its effect getting extended the educational institutions had to find a way to engage the students and keep the learning processes going. With contact classes being impossible in this situation the teaching methodology had to undergo a seminal change. Enter the concept of online learning. The pandemic has forced the higher education stake holders to revise the strategy and forced then to adopt the online mode teaching and learning. Though in initial days it was difficult to implement due to various factors prolonged the new normal forced both faculty members and students to accept it.
According to Jacobson et al (2017) online learning is learning with the support of communication and information technology by using web and internet. Even within the online mode different types of teaching methodologies are possible. The easiest of the lot is conducting webinar style presentations where the faculty uses meeting apps like Cisco Webex, Google Meet and Zoom. But these have its own disadvantages as the faculty are dependent on the vagaries of the Internet Connectivity and electric supply. Besides they are restricted by the technology being employed. Nkonge et al (2006) cited inadequacy of suitable hardware and software tools, internet connection speed, attitude learners towards online learning, instructors limited technical expertise, students lack of orientation, and difficulty in accepting to develop and design the material to online mode have created as barriers to acceptance of the online teaching –learning method.
Still with no other option people have shifted en masse to learning online especially through platforms like Coursera, Udemy, EdX, NPTEL and the like. The online education market growth has ballooned. The growth of the e-learning market is such that globally it is expected to reach 65.41 billion dollars by 2023 growing at a cumulative average growth rate of 7.07% as per a Research and Markets study (2018).
One of the ways out of this was the Learning Management Systems. Learning Management Systems (LMSs) are defined as online learning technologies for the creation, management and delivery of course material (Sabharwal et al. 2018; Turnbull et al 2019)There are free learning management systems like Moodle and Canvas which are very popular aside from the paid ones like Docebo, Talent LMS, SAP Litmos LMS and Adobe Captivate Prime. According to a Research and Markets study (2018) the global learning management system (LMS) is expected to increase from 5.05 billion USD in 2016 to 18.44 billion USD by 2025 growing at a rate of 15.52%.
3. Objective of the Study:
2. To find out factors involved in online learning satisfaction. 3. To find out factors involved in online learning recommendation.
4. Literature Review
There are a number of things that affect the effectiveness of online learning. One of the main factors is the instructor competency, his commitment and his connect with the learners. Wisneski et al (2015) finds that in comparison to face to face classes, during online teaching, teacher need to provide more words of motivation, support, affirmation and validation of students to keep them engaged in the classes.
According to Thistol et al (2016) clear assessment practices, including communication of deadlines and assessment requirements, have been found to positively influence student engagement and course completion. Easton (2003) emphasized instructor presence as among the most critical of factors related to student success online. He also found that effective instructor–student communication in online learning environments relies on timely and clear interactions through a variety of formats, including email, chat, live class questions, and assessment and feedback provision. In the absence of more immediate feedback methods available to on-campus instructors (e.g., face-to-face consultation), the assessment and feedback provided in online learning environments needs to be as clear and valuable as possible to promote student understanding as per Darabi et al (2006). Teacher support online involves effective monitoring of student progress, anticipation and resolution of key learning queries, and establishment and maintenance of rapport.
Cuellar (2002) observed that training and support for faculty members are very important in online teaching because they have very little experience in developing and designing the teaching materials. Allen et al (2008) in another study found teachers genuinely feels they required additional support from their institutions otherwise the quality of their delivery will be impacted. Wisneski et al (2015)further states that the high presence of teachers in online learning shows that teachers are ready to receive online learning.
User friendly course structure and flexibility are very important in online learning. There should be a flow and coherence. Richardson et al (2003) found that instead of having to be at a specific location at a specific time, online educational students have the freedom to communicate with instructors, address classmates, study materials, and complete assignments from any internet-accessible point. This type of flexibility grants students much-needed mobility and, in turn, helps make the educational process more enticing. According to Lundberg et al. (2008) the student may prefer to take an online course or a complete online-based degree program as online courses offer more flexible study hours. For example, a student who has a job could attend the virtual class watching instructional film and streaming videos of lectures after working hours.
Alcorn et al. (2014) evaluated satisfaction of online education from the number of class participants, the participation rate of homework, the completion rate and the improvement of grades. Asarbakhsh and Sars (2013) believed that the broken-down system, failed video connection or unusable use affected user satisfaction. From the perspective of users and designs, David et al (2010) pointed out visual content was quite important to improve participation and interaction of users.
According to Paul et al (2019) in online learning, the student is dependent upon access to an unimpeded Internet connection. If technical problems occur, online students may not be able to communicate, submit assignments, or access study material. This problem, in turn, may frustrate the student, hinder performance, and discourage learning.
Xie et al (2006) observed that in online learning when on line discussion were made as a necessity for the course the posts by students were increased otherwise the response by students were low. Dennen et al. (2005) opined that participation of students increased because of discussion and students response were positively impacted on topic discussions. According Fung et al (2004) to students stopped contributing if they feel the questions are not interested.
Hrastinski (2009) put forward a theory in his research: if we wanted to enhance online learning, we needed to enhance online learner participation. Miri et al (2020) showed in their research the need for rethinking the way conventional online ethics courses are developed and delivered; encouraging students to build confidence in learning from distance, engaging them in online active and interactive experiences
Anderson et al. [2001] argued that students preferred classmates to lead online discussions rather than asking lecturers to lead discussions, and they posted more messages each week when classmates facilitated online discussions than when instructors did. Seo [2007] highlighted that compared to instructor-led online discussions, online discussions led by peers led to more posts and more substantive posts.
Confident students take responsibility for creating meaningful leaning experiences by efficiently monitoring their academic work time, persisting on tasks when confronted with academic challenges, and accurately monitoring the quality of their work through frequent evaluations Pajares (2002). Improved learner self-efficacy is necessary for supporting the principle of time on task because students who are confident about their skills maintain the academic persistence necessary for high levels of academic achievement (Lent et al, 1984; Pintrich et al, 1990).
A proactive and appropriate pedagogy is the need of the hour for effective online learning. Hacker et al (2000) observed that the principle of active learning suggests that effective teaching engages students in authentic learning activities that require them to select, organize, and integrate their experiences with existing knowledge to create new cognitive schema. Authentic instructional activities that include simulations, case-based examples, and other problem-solving exercises not only increase interactive learning but also support the principle of high expectations. Clear performance expectations that accompany authentic instructional activities inform students of the criteria necessary for demonstrating acceptable and proficient levels of performance. When performance expectations for these complex tasks are clearly communicated, students not only have a better understanding of the criteria required for successful performance but also gain insights about expectations necessary for real-world problem-solving (Magnani et al, (1999); Vye et al. 1998).
According to Svinicki (1999) the principle of cooperation among students is aligned with the constructivist notion that social interaction enhances learning. Mills et al (1998) found that a deeper understanding of concepts occurs when students have opportunities to talk, listen, and reflect with their peers as they engage in collaborative problem-solving exercises that require them to apply newly acquired knowledge and skills.
The principle of prompt feedback encourages students to be responsible learners by promoting self-efficacy (Bandura, 1986) or confidence in their abilities to successfully accomplish learning tasks. Research has demonstrated that self-efficacy increases when students are supplied with immediate and frequent performance feedback (Schunk, 1983)
According to Yengin et al (2010) there is a disadvantage for online learning in the matter of immediate feedbacks. In those systems the instructor can give you immediate feedback only in instant messaging or video conferencing sessions. Other feedbacks such as posting a feedback to your response/answer or e-mailing etc .are almost asynchronous and therefore they are always coming after when the students perform actions. In immediate feedback students have a chance to see their mistake quickly because they can have an opportunity to discover their incorrect action just looking backward.
5. Research Methodology:
5.1 Descriptive statistics of the sample population
Table -1 Gender
Frequency Percent Valid Percent Cumulative Percent
Female 221 73.7 73.7 73.7 Male 79 26.3 26.3 100.0 Total 300 100.0 100.0 Type of College Govt 26 8.7 8.7 8.7 Aided 31 10.3 10.3 19.0 Self-Financing 232 77.3 77.3 96.3 Prefer not to say 11 3.7 3.7 100.0 Total 300 100.0 100.0 Type of Program Science 8 2.7 2.7 2.7
Arts 117 39.0 39.0 41.7 Engineering 104 34.7 34.7 76.3 Prefer not to say 71 23.7 23.7 100.0 Total 300 100.0 100.0
Post Graduate/ Under Graduate
Under Graduate 148 49.3 49.3 49.3 Post Graduate 152 50.7 50.7 100.0 Total 300 100.0 100.0 College Location Panchayath 168 56.0 56.0 56.0 Municipality 92 30.7 30.7 86.7 Corporation 40 13.3 13.3 100.0 Total 300 100.0 100.0 Residence Location Panchayath 205 68.3 68.3 68.3 Municipality 62 20.7 20.7 89.0 Corporation 33 11.0 11.0 100.0 Total 300 100.0 100.0
5.2 Data Collection & Analysis: The questionnaire with 32 variables was distributed through online mode
with the help of Google forms to college students in Ernakulum district, Kerala. Random sampling method was used and 475 responses were received out of which only 300 were of acceptable quality with full response. All complete response were coded and entered into SPSS to perform descriptive analysis, reliability, validation and model building.
5.3 Reliability Test: The reliability test is used to measure the questionnaire stability and consistency which
should be between 0 and 1. The reliability test results of the study is presented in Table 2. In this questionnaire, Cronbach’s α coefficients were all greater than 0.7, indicating that reliability of the questionnaire administered is satisfactory.
Reliability Statistics Table -2
Cronbach's Alpha No of Items
.967 32
5.4 Validity Test
KMO (Kaiser–Meyer–Olkin) test and Bartlett tests were conducted to check the validity of the questionnaire. The test perform content validity and structure validity. The test result shown in the table 3 below show that the sample taken for the study is adequate. The value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factor.
KMO and Bartlett's Test Table -3
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .952
Sphericity df 435
Sig. .000
5.5 Effective influencing factors – An assessment using Factor Analysis: Factor analysis was attempted to
identify underlying variables, or factors, that explains the pattern of correlations within a set of observed variables. Factor analysis is also used for data reduction/structure detection and identified a small number of factors that explain most of the variance that is observed in a much larger number of manifest variables. The purpose of data reduction is to remove redundant variables from the data file, perhaps replacing the entire data file with a smaller number of uncorrelated variables. Present study has been carried out with principal component analysis and Varimax with Kaiser Normalization. The loading of the original variable with factors were analysed. Principal component factor with Eigen value of 1 or greater were rotated by Varimax method (Table 4).
Rotated Component Matrixa Table -4
Component
1 2 3 4 5
Q1 The instructor communicated effectively. .859
Q2 The instructor was enthusiastic about online teaching. .868 Q4 The amount of contact with the instructor was satisfactory (e.g., e-mail, discussions, face-to-face meeting, etc.)
.859
Q5 The instructor was concerned about student learning .720 Q6 The instructor was generally respectful of student learning.
.868
Q8 The course was structured so that I could discuss assignments with other students
.859
Q9 I felt comfortable interacting with the instructor and other students.
.868
Q10 This course included activities and assignments that provided students with opportunities to interact with one another
.555
Q11 This course included interactive assignments and links to examples from the Web that directly involved me in the learning process.
.575
Q12 This course used realistic assignments and problem-solving activities that were interesting and motivated me to do my best work
.524
Q13 The course allowed me to take responsibility for my own learning.
.645
Q15 My questions about learning Management System were responded to promptly
.733
Q16 My questions about course assignments were responded to promptly
.733
Q17 I was provided with supportive feedback related to course assignments
.755
Q18 The course was structured to be user friendly .668
Q19 The course was designed to provide an efficient learning environment.
.643
Q20 The course allowed me to complete assignments across a variety of learning environments
.813
Q23 This course provided good examples and links to other examples published on the Web that helped to explain concepts and skills
.643
Q24 The assignments for this course were of appropriate difficulty level.
.813
Q27 The course was designed so that technology would minimally interfere with learning
.729
Q28 Flexibility was permitted when completing course assignments.
.695
Q30 I was given choices about the types of activities or assignments that I would complete to demonstrate learning of important course concepts.
.769
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations.
The items evolved in the dimension reduction (rotated component matrix) were named technically/ logically and found that Instructor Learner Connect, Course Coherence, Interactive Learning, Proactive & Appropriate Pedagogy, Prompt Feedback are the factors influencing the online effective learning.
Factor One: Instructor Learner Connect
The instructor was enthusiastic about online teaching. The instructor was concerned about student learning The instructor was generally respectful of student learning. I felt comfortable interacting with the instructor and other students.
5.5.1The first factor was identified and named to be Instructor Learner Connect because the extracted
statements indicates the connect between the instructor and students. Factor Two: Course Coherence
The course was structured to be user friendly
The course was designed to provide an efficient learning environment.
This course provided good examples and links to other examples published on the Web that helped to explain concepts and skills
The course was designed so that technology would minimally interfere with learning Flexibility was permitted when completing course assignments.
I was given choices about the types of activities or assignments that I would complete to demonstrate learning of important course concepts.
5.5.2The Second factor was identified and named to be Course Coherence because the extracted statements
indicates about the course structure and its delivery.
Factor Three: Interactive Learning
This course included activities and assignments that provided students with opportunities to interact with one another
This course included interactive assignments and links to examples from the Web that directly involved me in the learning process.
This course used realistic assignments and problem-solving activities that were interesting and motivated me to do my best work
The course allowed me to complete assignments across a variety of learning environments The instructor facilitated the course effectively
The assignments for this course were of appropriate difficulty level.
5.5.3The Third factor was identified and named to be Interactive Learning because the extracted statements
indicates the mode of teaching and learning.
Factor Four: Proactive & Appropriate Pedagogy
The course allowed me to take responsibility for my own learning.
My questions about learning Management System were responded to promptly My questions about course assignments were responded to promptly
I was provided with supportive feedback related to course assignments
5.5.4The Forth factor was identified and named to be Proactive & Appropriate Pedagogy because the
extracted statements indicates proactive and appropriate pedagogical approach. Factor Five: Prompt Feedback
The instructor communicated effectively.
The amount of contact with the instructor was satisfactory (e.g., e-mail, discussions, face-to-face meeting, etc.)
The course was structured so that I could discuss assignments with other students
5.5.5The Fifth factor was identified and named to be Prompt Feedback because the extracted statements
indicates feedback methods adopted by the instructors support the effectiveness of the learning environment.
5.6 Regression Analysis: Regression analysis was used to identify the relationship between independent
variables and dependent variable. A model of the relationship was hypothesized, and estimated parameter values were used to develop an estimated regression equation. The estimated regression equation confirmed to predict the value of the dependent variable from the given values of the independent variables.
Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -.299 .311 -.961 .337
Instructor Learner Connect -.054 .098 -.039 -.554 .580
Course Coherence .570 .107 .380 5.311 .000
Interactive Learning .305 .088 .297 3.444 .001
Proactive & Appropriate Pedagogy
-.265 .114 -.160 -2.320 .021
Prompt Feedback .235 .093 .170 2.527 .012
a. Dependent Variable: Overall satisfaction level on online learning
5.7 Relationship model The R value is 0.615, which represents that there is a fairly good correlation between
independent variable Instructor Learner Connect, Course Coherence, Interactive Learning, Proactive & Appropriate Pedagogy, Prompt Feedback and dependent variables Overall satisfaction level on online learning. Similarly, nearly 37 percent of the variance is explained (R2 = 0.379) in the regression model
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .615a .379 .367 .86234
a. Predictors: (Constant), Instructor Learner Connect, Course Coherence, Interactive Learning, Proactive & Appropriate Pedagogy, Prompt Feedback
6 Limitation of The Study:
The study was conducted online and the Google form having the questionnaire was sent through email and social media. Though the researcher tried to reach out to maximum students, due to poor internet connectivity and low model mobile phones the responses were limited and not complete. The perception of students could be different due to difference in approach by faculty members and institutions.
7. Conclusion
India’s apex regulatory body of higher education, UGC, has taken the present educational scenario very seriously and put some efforts proactively to resolve the deadlock of completing courses and examinations in on-going semesters as well as issued circulars regarding the academic calendar after the recommendations of one of the committees constituted by UGC itself. It has also become mandatory for all the universities in India to complete the 25% syllabus through online teaching mode and 75% face-to-face interaction UGC (2020). The educational scenario of the post-COVID-19 outbreak would such that it would not be easy to manage teaching learning situations without using online teaching platforms rigorously. Having seen the fearsome monster of coronavirus, it can be anticipated that in the upcoming years students would face multiple challenges including quality education, hands-on experience, laboratory work, library visit, peer tutoring, remedial teaching, research and innovation. The tentative solution to the hardships of post-COVID-19 educational problems is to maintain an equilibrium of online and offline learning classes, that is a hybrid mode.
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