Exploring Motivation to Learn Online Through Mclelland’s Theory

Students' motivation for online learning was negatively impacted as a result of the abrupt shift from the traditional face-to-face learning style to remotely digital learning as motivation is one of the elements influencing student’s success and performanc e. Thus, this study aims to explore motivation to learn online through Mclelland’s Theory. Methodologically, this study employs a quantitative approach and 110 students from one of the public universities as the respondents. Based on the study findings, it was revealed that via Mclelland’s Theory of Motivation has proven the motivation student’s online learning. Besides, the results also show that there is an association between the need of power, the need of achievement and the need of affiliation and the motivation of learning online


Background of Study
The world has been confronted with the pandemic scenario and Malaysia also included. The health crisis was clarified as a Public Health Emergency of International Concern on 30 January 2020 (World Health Organization, 2020). The phenomenon of this disease outbreak is a serious issue as it threatens the whole world. COVID-19 has alarmed the public globally of an emerging novel coronavirus that is causing major public health and governance challenges (Zheng, 2020). Moreover, the cruel impact of a pandemic has put the world in uproar. In Malaysia, Prime Minister Tan Sri Muhyiddin Yassin has announced that the Movement Control Order (MCO) will be implemented on March 18, 2020, to combat the COVID-19. The execution of Malaysia Movement Control Order (MCO) is completely essential given the rise of COVID-19 cases throughout the country (Abdullah, 2020). Education is one of the main drivers in a country's growth and success. Nevertheless, the education industry has been impacted by the COVID-19 pandemic. There is wide disruption amongst the teachers and schoolchildren, and of course the universities worldwide due to coronavirus (Ali, 2020). Thousands of universities and schools throughout the world were forced to close as a result of COVID-19, affecting millions of students. This impacts 4.9 million Malaysian students in schools and 1.2 million Malaysian students in higher education institutions such as public and private universities, polytechnics, and community colleges, as well as 130,000 international students (Rahman, 2020). Due to the Covid health crisis, which has left both students and educators feeling anxious, online courses were quickly put into place in this situation.
Since the unpredictable nature of online learning, many issues about its efficacy and effects on teachers and students have inevitably arisen. The motivation of the students in a virtual platform, which was novel to all parties involved, served as the researchers' overarching research issue in this study. Resilience and psychological health were additional factors that helped them succeed in online learning. Students have worked hard to learn even online because of their excitement and desire to comprehend and add to their knowledge. Several psychological difficulties and severe mental health consequences, including stress, anxiety, depression, anger, and confusion, arose progressively throughout the COVID-19 pandemic, according to (Duan and Zhu, 2020). However, Thakur and Jain (2020) indicates that there are differ psychology effect to widespread feelings of hopelessness and it associated with adverse effects, including suicidal actions. The key reasons behind this COVID-19 suicide pandemic, however, are the fundamental psychology and inability of the individual and mass society to cope with the situation.

Statement of Problem
Researchers have conducted studies on the motivation to learn online, and they believe that this factor is the main barrier for students and instructors to achieve success when involved in online learning. Online learning has been widely used throughout the world because it is said to be able to reduce time and distance problems associated with traditional learning (Mahande & Akram, 2021), particularly when the world is faced with the threat of disease, such as during the Covid19 pandemic, or natural disasters that have limited their daily movement. According to Harnett (2016), online learning is a complex phenomenon that is heavily influenced by individual characteristics in a specific context. The issue of motivation for online learning is very important to investigate because students are not actively engaged in online classes and frequently drop out as a result of the motivation problem (Harnett, 2016). Dornyei (2015) discovered that motivation is a critical factor that has a profound effect on student performance even when the surroundings are equipped with the most sophisticated facilities. According to research, motivated students learn more easily than unmotivated students. The lack of a motivating factor always makes learning and teaching difficult and frustrating (Filgona et al., 2020). A common challenge encountered during online learning is the teaching instructor's lack of experience. This is because, in most cases, students are exposed to more physical learning than virtual learning (Chiu et al., 2021). As a result, determining the extent of student motivation to learn online is an important factor to investigate.

Research Questions •
How does the need for power influence motivation to learn online? • How does the need for achievement influence motivation to learn online? • How does the need for affiliation influence motivation to learn online? • Is there a relationship between all variables?

Literature Review
Demotivators for Online Learning Shea (2007) did study of factors that enable and constrain faculty participation in online teaching and learning environments. Through analysis of data from 386 faculty teaching online in 36 colleges in a large state university system, the top demotivator is inadequate compensation for perceived greater work than for traditionally delivered courses, especially for online course development, revision and teaching.
On the other side, Nabila et al (2021) conducted research that aimed at investigating the level of demotivation and the demotivating factors experienced by Indonesian EFL learners during home online English learning as response to social distancing order amid the COVID-19 pandemic. This research employed quantitative and qualitative data. Questionnaire with 27 items was the main instrument used to obtain data about learners' level of demotivation and demotivators as well as to identify the frequency of each demotivator. An open-ended question was attached at the end of the questionnaire to dig out other additional demotivators. An interview was also conducted to gain supporting data for in depth analysis. The results showed that the level of the 198 students' demotivation was 2.9, categorized as lowly demotivated. The most-frequently rated demotivators in home online English learning were lack of interaction with 78.9% responses followed by 66.2% for increasing assignments and 62.1% for slow Internet connection. In terms of content and material, expectation to use grammatically correct English was the most demotivating factor with 59.6% responses. The study also revealed additional demotivators namely unsupportive parents, doing house chores, and Wi-fi absence.
In one report from the survey from the Student Experience in the Research University (SERU) Consortium in the United States of America, notably 76% of all undergraduates identified the lack of motivation for online learning as the biggest obstacle and 56% of graduate and professional students felt the same way. Other leading obstacles students reported included lack of interaction with other students, inability to effectively learn in an online format, and distracting home environments or lack of access to suitable study spaces. The survey of 22,519 undergraduate students and 7,690 graduate and professional students from five large public universities began in May 2020 and includes responses data through June 11, focusing on students' experiences in the spring semester when studies at institutions across the country transitioned online due to pandemic (Daugherty, 2022) COVID-19 pandemic has impacted educational institutions worldwide due to a sudden transition of the learning system, which has affected students' academic performances in several aspects, and this also impacted Malaysia. This qualitative study by Ying et. al (2021) examines Malaysian undergraduates' challenges and experiences in coping with low academic motivation during the COVID-19 pandemic with this qualitative study. One to one semi-structured interviews were conducted among a total of 24 undergraduates in Malaysia between the age of 19 to 24 through an online platform. Four analytic themes are presented: (1) intrinsic challenges; (2) extrinsic challenges; (3) intrinsic motivation, and (4) extrinsic motivation. As far as the demotivators or challenges is, the intrinsic challenges refer to the challenges faced by an individual on a personal level, and it involves low self-discipline and future uncertainties. Meanwhile extrinsic challenges are known as the external or environmental challenges faced by an individual. The demotivators faced by the participants during this pandemic are lack of physical academic interaction as well as studying in a nonconducive learning environment.

Motivators for Online Learning
Online learning on its test when pandemic COVID19 has changed norms of various activities precisely in the field of education. The Ministry of Education had been forced to change the normal practices of teaching. Not only academics and teachers need to adapt to the situation but also the students. Various studies done in identifying the motivation in online learning. Motivation means rather than an end. Motivation being defined as "a theoretical construct to explain the initiation, direction, intensity, persistence, and quality of behaviour, especially goal-directed behaviour" .
Motivation is one of the important forces in a successful learning process. From the perspective of instructors or teachers, motivation in online learning can be enhanced through the attention of the students throughout the class session. Secondly, how the learning activities represented to the learners as reflecting their needs, interests, and motives as compared to the content wise. Thirdly, confidence, focusing on the learners' experience and expectation through its performance. Finally, satisfaction by the efforts that give the learners positive reinforcement (Chantorn Chaiprasurt and Vatcharaporn Esichaikul, 2022).
Sri Gustiani (2020) identified types of motivation: intrinsic and extrinsic as the driving force to the learning activities among students. As intrinsic as motivation from the inside brings joy and interest to discover new things driven by interest, ambition, aspiration, awareness, competency, physical and psychological condition. Meanwhile, extrinsic is external factors such as reward, punishment and benefits. Miltiadou and Savenye (2003) from the perspective of social cognitive theory, there are 3 family of motivational constructs: Individuals' perceptions about their ability to accomplish a task, individuals' reasons or purposes for engaging in the task and thirdly individuals 'techniques and strategies for accomplishing a task. From the above mentioned motivation, the researcher finds motivation is more to individuals' thought rather than intuition or instinct.

Past Studies on Online Learning Motivation
The study by Rahim, et.al (2021) was done to examine the online learning engagement. It investigated how online learning can affect learners' participation in their learning process: engagement, learner to instructor engagement and learner to content engagement. 75 learners participated in this quantitative research. The survey has 14 items excluding the demographic profiles. The items are categorised into: Learner-to-Learner Engagement (6 items); Learner-to-Instructor Engagement (7 items); and (3) Learner-to-content Engagement (7 items). Data is analysed using SPSS version 26 to reveal the frequency of responses. Findings revealed that learners are highly active and authentic learners during the class.
Next, the quantitative study by Lokman et. al (2021) investigated motivation to learn. A survey was used to collect data from 70 respondents. The questionnaire consisted of eight sections on demographic profile, motivation scale; values, expectancy and affective component were posed. The study found that students are highly motivated by extrinsic reward compared to intrinsic reward in their learning. Good grades are still the main attraction in the motivation to learn. However, students are found to be daunted by the thought and experience of going through learning sessions and sitting for assessments. This implies the need to embed more effective components into the curricula. Soh et. al (2022) conducted a quantitative study to investigate how learner's motivation online learning presence is influenced by learners' motives to study. 89 respondents participated in the study. The survey used has 24 items using 5-Likert scales. Findings revealed that the most satisfying thing among students in this program is trying to understand the content of the courses. Learners aim to get good grades in the classes and that is among the most satisfying things for them. Expectancy components revealed students' perception of self-efficacy in which they believe they will receive excellent results and the belief they had control beliefs for learning. As for affective components, the study revealed that students feel their heart beating fast when they take an exam.

Conceptual Framework
Sometimes, learning online requires more pushing factors to keep the learners going. According to Rahmat (2022), motivation is often crucial in online learning because learners must be able to relate new learning to pre-existing knowledge. Online learners may still have the same needs of face-to-face learners; however, the depth of the needs may differ. This study is rooted (refer to figure 1) from McClelland's (1965) theory of needs. According to McClelland (1965), there are three motivating drivers. They are (a) power, (b) achievement, (c) affiliation. Power is obtained when the person feels he/she has control over the situation or over the task assigned. In the context of online learning , power can be situated to the feeling of (i) expectancy and this can be achieved when the person feels he/she has control of his/her own learning beliefs. This control is further enhanced through self-efficacy. Next, McClelland (1965) found that another motivating driver is (b) achievement. In the context of online learning, achievement can be felt when learners give (ii) value to the learning. This value is embedded through internal and external goal orientations and also task value. The last motivating driver is (c) affiliation. This refers to the need to belong , especially for social acceptance. In the context of this study, affiliation is gained when learners (iii) gain social support. This can be done through social engagement and also instructor support.

Methodology
This quantitative study is done to investigate learners' motivation towards learning. 110 respondents participated in this study. The 5-likert scale instrument used a survey adapted from (Aldefer, 1969;Pintrich & De Groot, 1990). Table 1 shows the distribution of items in the survey. Section A is the demographic profile. Section B has 12 items on Power, section C has 14 items on Achievement and section D has 12 items on Affiliation.  Table 2 shows the reliability statistics for the survey. SPSS analysis revealed a Cronbach alpha of .955 thus revealing a high reliability of the instrument used. Data is then analysed to reveal mean scores to answer all the research questions for this study.

Findings
Findings for Demographic Profile Q1. Gender Questionnaires have been distributed to 7 programmes whose respondents were Degree, Professional and Master's students from the Faculty of Administrative Science and Policy Studies. Based on the figure 4, shows that the highest respondents comes from AM228 with 56% responses, followed by AM226 (BEV) with 16% responses AM225 (BCA) 11% response, AM770 (EMAS) 10%, AM700 (MOCA) 6% and AM550 (ICSA ) 1%.

Findings for Power
This section presents data to answer research question no1: How does the need for power influence motivation to learn online? In the context of this study, power is measured by expectancy through (i) self-efficacy and (ii) control of learning beliefs.
(i) Self-Efficacy (ESE) Figure 5-Mean for Self-Efficacy Figure 5 shows the findings for self-efficacy. Eight questions were asked in order to measure the mean for self-efficacy and it is found that the respondents are confident that they can do an excellent job in assignments and tests, as well as they expect that they can do well and they are confident that they can learn the basic concepts that they are taught. All these three showed the same score of mean at 3.9. This is followed by the belief from the respondents that they receive excellent grades in the class with the mean score of 3.7. The same mean score also involves the difficulty of the classes, the teachers and the respondents skills, they think that they can do well. As for the question whether they can master the skills being taught, the mean score is 3.6 and the lowest mean score is 3.5. This is related to the confidence level that the respondent can understand the most complex material presented by the instructor and the respondent certain that they can understand the most difficult materials presented in readings.
(ii) Control of Learning Beliefs (ECB) Figure 6-Mean for Control of Learning Beliefs Figure 6 presents the mean for Control of Learning Beliefs. The item "If I study in appropriate ways, then I'll be able to learn the material" has the highest mean of 4. Followed by the item "It's my own fault if I don't learn the material taught" and item "If I try hard enough, then I'll understand the material presented" had a mean of 3.9. The item "If I don't understand the material presented, it's because I didn't try hard enough" had a lowest mean of 3.6.

Findings for Achievement
This section presents data to answer research question no 2 : How does the need for achievement influence motivation to learn online? In the context of this study, achievement is measured by value through (i) intrinsic goal orientation, (ii) extrinsic goal orientation and (iii) task value.  Figure 7 shows the result of intrinsic goal orientation. Based on the findings, the most satisfying thing for the respondent is trying to understand the content as thoroughly as possible with a mean score of 3.8. The other three of the questions show a mean score of 3.5. It includes the questions on which the respondent prefers material that really challenges them so they can learn new things, the respondent prefers material that arouses their curiosity even if it is difficult to learn and the respondent chooses assignments that they can learn from even if they do not guarantee a good grade.
(ii) Extrinsic Goal Orientation (VE) Figure 8-Mean for Extrinsic Goal Orientation Figure 8 shows the result of extrinsic goal orientation. Based on the findings, the respondents believe that receiving a good grade is the most satisfying thing for them with a mean score of 4.5. Moreover, the respondents agree that improving their overall grade point average was the most important factor for them, which increases their concern about getting a good grade in a class with a mean score of 4.4. Besides that, respondents were shown to value their ability to perform well in the class as this will demonstrate to their family, friends, and employers that they are capable of doing well in their studies with a mean score of 4.2. Lastly, with a mean score of 4.1, the respondents prefer to perform better academically by getting good grades than the majority of their classmates.
(iii) Task Value (VT) Figure 9-Mean for Task Value Based on figure 9, for the task value, respondents score 4.2 for both the importance of understanding the subject and they believe the course material is important for them to learn. Respondents also believe the course material is important and it will be able to be used to help them to score in another subject, as they score 4.1 for both Q1 and Q2. The respondents were also interested in the contents of the course and they liked the subject matter of the course by scoring 4 for both Q3 and Q5.

Findings for Affiliation
This section presents data to answer research question no 3 : How does the need for affiliation influence motivation to learn online? In the context of this study, affiliation refers to (i) social engagement and (ii) instructor support.  Figure 10 shows the results for social engagement. From the findings it shows that the highest mean score was the respondent enjoyed class discussions with the score of 3.8. The other four of the questions being asked show the same mean score of 3.6. It includes the respondent paying attention in classes, the respondent feeling like they can freely communicate with other students in classes, and the respondents also having strong relationships with fellow students in this course. The lowest mean score with 3.1 is about the respondent feeling "disconnected" from their teacher and fellow friends.
(ii) Instructor Support (SIS) Figure 11-Mean for Instructor Support Figure 11 presents the mean for Instructor Support. The item "The instructor provides the guidance I need to be successful in this class", item "The instructor presents the material in a way that makes it relevant to me" and item "In this course, I have the freedom to guide my own learning. have the highest mean of 4.1. Followed by the item "The instructor responds to questions, clearly, completely, and in a timely manner" had a mean of 4. Item "The instructor's expectations for me in this class are clear" and item "The instructor provides regular feedback that helps me gauge my performance in this class" had a mean of 3.9. The item "I feel like I can freely communicate with the instructor in this class" had a lowest mean of 3.8.

Findings for Relationship across Variables
This section presents analysed data to answer research question 4: Is there a relationship between all variables? To determine if there is a significant association in the mean scores between power, achievement and affiliation, data is analysed using SPSS for correlations. Table 3, 4 and 5 show there is an association between power, achievement and affiliation. Analysis is divided into looking at the correlation between (a) power and achievement, (b) power and affiliation, and (c) affiliation and achievement.
(a) Power and Achievement Table 3 Correlation between Power and Achievement Table 3 shows the correlation between power and achievement. Correlations analysis shows that there is a high positive significant association between power and achievement (r= .724**) and (p=.000). The coefficient is significant at the .05 level so this association is therefore significant. According to Jackson (2015), positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means there is a strong positive significant relationship between power and achievement. Table 4 Correlation between Power and Affiliation Table 4 presents the correlation between power and affiliation. Correlations analysis shows that there is a high positive significant association between power and affiliation (r= .755**) and (p=.000). The coefficient is significant at the .05 level and this association is therefore significant. According to Jackson (2015), positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is a strong positive significant relationship between power and affiliation.

(b) Power and Affiliation
(c) Affiliation and Achievement Table 5 Correlation between Affiliation and Achievement Correlations analysis shows that there is a high positive significant association between affiliation and achievement (r= .691**) and (p=.000). The coefficient is significant at the .05 level and this association is therefore significant. According to Jackson (2015), positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is a strong positive significant relationship between affiliation and achievement.

Summary of Findings and Discussion
This study found that there is a strong positive relationship between power, achievement and affiliation. This has proved by McLelland theory of needs related to motivation to learn online that individuals motivated typically have a strong desire to set difficult goals and achieve them. They prefer to work in a results-oriented environment and welcome any feedback on their work. Individuals who value achievement take calculated risks to achieve their goals and may avoid both high-risk and low-risk situations. As mentioned in the literature, online learning is a type of distance education enabled by technological devices used by isolated learners in their own settings away from the main education source. Hence, McLelland agreed that people with a high need for achievement perform better than those with a moderate or low need for achievement.

Pedagogical Implications and Suggestion for Future Research
Online learning is now regarded as one of the best opportunities for professional grooming and learning, and its popularity is growing in the whole world. The findings of the study indicate that motivation is a critical factor, particularly in virtual learning. This is due to the fact that each student faces unique learning challenges. As a result, initiatives that ensure students' continued interest in virtual learning must be prioritised. Online learning may present some difficulties due to the nature of its delivery. It can result in learner disengagement, minimal participation, or even withdrawal. As a result, in order to have a positive learning experience, it is critical to identify and discuss the factors influencing learner engagement and motivation in an online course. Both the facilitator and the learner must be technologically savvy in order to master the online learning environment. To develop collaborative learning skills for online education, learners should have the following abilities: social learning skills, discursive skills, reflective skills, and evaluation skills.