Motivation to Learn Online: An Analysis From Mcclelland’s Theory of Needs

Motivation has been identified as a main factor influencing engagement and achievement of learners in online learning. Studying the motivations of language learners is crucial to address the challenges of online learning. Thi s quantitative study is done to explore learners’ online motivation (Fowler, 2018) from the point of view of McClelland’s (1965) theory. McClelland’s theory suggested three basic needs that people derive from their life experiences: Need for Achievement, Need for Affiliation and Need for Power. This study used a 5-point Likert Scale instrument consisting of 4 sections. Section A has 3 items on demographic profile. Section B has 12 items on Power, section C has 14 items on Achievements and section D has 12 items on Affiliation. A purposive sample of 156 participants from a public university in Malaysia responded to the survey. The major findings reveal the positive impact of McClelland’s Needs Theory on online language learning. Most students believed in their online learning abilities in terms of self-efficacy and desired motivation, control belief factors also led them to achieve desired outcomes. Students were also influenced by intrinsic and extrinsic motivation to perform better in online language learning. Students felt engaged and can interact freely in the language classroom. A strong positive correlation was found between all the variables to motivate online language learning. It is recommended that the motivation to learn languages online be further explored from a variety of perspectives and theories. Future research could also examine the comparison of motivation theories on online learning.


Background of Study
Online learning has become globally popular as it can reduce the time and distance issues associated with traditional education (Panigrahi et al., 2018).The motivation of lecturers and students is one of the main factors determining the acceptance and use of online learning in higher education (Mahande & Akram, 2021).In terms of online language learning, White (2017) asserted that motivation is a necessary prerequisite for second language acquisition and has been identified as a key factor influencing engagement and achievement of learners.Ushida (2013) also agreed that the students' motivation plays an important role in the successful implementation of online learning and, if used effectively, the online learning environments can enhance students' motivation to learn a second language.Jiang & Xie (2022) further highlighted that successful online learning depends largely on learners' motivation and their ability to manage the learning process, therefore one of the main challenges of online language learning is to motivate students to learn in an isolated and independent learning environment.
To motivate an individual, it is important to understand their needs and inclinations as suggested by David McClelland.In his Theory of Needs, McClelland mentioned three basic needs that people derive from their life experiences: Need for Achievement, Need for Affiliation and Need for Power (Turabik & Baskan, 2015;Vero & Puka, 2017).The Need for Achievement includes the desire to do better, to solve problems and to master complex tasks.The Need for Affiliation is the desire for friendly and warm relationships with others.Passive individuals will try to avoid interpersonal conflict at any time, even when it may be necessary to complete a task.The Need for Power is the desire to control others and influence their behaviour (Vero & Puka, 2017).
Due to the pandemic of Covid-19 worldwide, studying the motivations of language learners is crucial to address the challenges of online learning.In Malaysia, the readiness of language students in transition from face-to-face learning to online learning, and the relationship between learning styles and motivations were examined in past studies (Ismail et al., 2022;Jamian et al., 2022).Therefore, this study further examines Malaysian public university students' motivation for online language learning through the lens of McClelland's Theory of Needs.

Statement of Problem
Since the outbreak of the Covid-19 pandemic, online learning has gained prominence in education.Online learning involves teaching and learning at a distance, using the internet and digitalizing technologies that incorporate synchronous and asynchronous learning.The characteristics of online learning that require students' engagement and autonomy are associated with motivation factors (Beluce & Oliveira, 2015).Many studies have applied motivation theories to investigate student satisfaction and engagement in the online learning environment.Rahmat et al (2021) examined online learning motivation and satisfaction among undergraduate and postgraduate students and found that undergraduate and postgraduate students were satisfied with the engagement of fellow students and lecturers in the online course.However, undergraduates were able to balance life commitments better than postgraduate students.She et al (2021) found that self-efficacy is the main motivation factor for promoting students' behavioral, cognitive, and emotional engagement.Active interaction showed a higher level of students' self-efficacy and engagement to achieve academic goals and increase learning satisfaction in an online learning environment.Gustiani et al (2022) applied Self-Determination Theory (SDT) to investigate students' intrinsic and extrinsic motivation factors in an online learning environment.The study found that students were motivated more by extrinsic factors than intrinsic factors.Students' motivation to acquire new knowledge and enjoy new learning experiences is influenced by regulation and environmental conditions.All intrinsic factor elements, such as satisfaction, interest, and enjoyment contributed highly to the online learning process, except encouragement contributed least.On the other hand, Hartnett (2016) revealed that online students are more intrinsically motivated than face-toface learning.Esra and Sevilen (2021) compared the intrinsic and extrinsic factors between online learning and face-to-face learning.The study concluded that students' extrinsic and intrinsic motivation was higher in face-to-face learning than in the online learning environment due to lack of social interaction, mismatch between expectations and content, and problems of organization in the learning environments.Walker et al (2006) investigated the motivational construct identification, which encompasses perceptions of belonging and valued in an academic context, along with intrinsic motivation, extrinsic motivation, and selfefficacy as predictors of cognitive engagement in191 college students.The study found that the intercorrelations between measures of identification with academics, self-efficacy, and intrinsic motivation were all positive, as were the correlations of these three variables with meaningful cognitive engagement.Nevertheless, extrinsic motivation predicted lower cognitive engagement.
Hence, this study is done to investigate students' online learning motivation from the perspective of McClelland's (1965) Theory of Needs.In doing so, student motivation was measured from the perspectives of power, achievement and affiliation.Specifically, this study is done to answer the following questions; • How does power influence motivation to learn online?

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How does achievements influence motivation to learn online?

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How does affiliation influence motivation to learn online?

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Is there a relationship between variables for motivation to learn online?

Literature Review
Demotivation for Learning Online Demotivation is defined as a decrease in the basic motivation of a behavioral intention due to specific internal and external forces (Kikuchi, 2015), and concerns the negative process that pulls learners down (Zhang, 2020).Demotivating factors can be divided into internal and external factors.Internal factors are related to negative student outcomes, including lack of self-confidence and attitude among group members.External sources of demotivation are factors from the outside, such as the teachers and book or references related problems (Çankaya, 2018;Kikuchi, 2015).Demotivation also occurs when student motivation drops from high to an average level (Kikuchi, 2015).Demotivated students can be motivated again, whereas amotivation is a lack of motivation (Zhang, 2020).Amotivation 'is a state of motivational apathy in which students harbor little or no reason (motive) to invest the energy and effort that is necessary to learn or to accomplish something (Cheon & Reeve, 2015).Furthermore, students are unwilling to continue an activity because they cannot see the merits of that activity (Kojima, 2021).Adara & Farid (2022) investigated the demotivating factors of online language learning students during the Covid-19 pandemic and found that the most important factors demotivating students were teachers' competence and teaching style, and inadequate school facilities.The least pronounced demotivating factor is the lack of intrinsic motivation.The study concluded that online learning is a less ideal means to teach English, even though intrinsic motivation to learn English is focused on personal or career gains in the future.

Motivation for Learning Online
Motivation is one of the most important factors affecting the efficiency and success of learning in an online environment.It is the process that makes students work towards a goal (Kikuchi, 2015), or the main factor that provides learners with a sustainable effort to learn (Çankaya, 2018).Motivation determines students' persistence in a course, their engagement, the quality of their work and the achievements they attain (Hartnett, 2016).There are two types of motivation, intrinsic and extrinsic.Deci & Ryan (2000) defined intrinsic motivation as motivation that comes from the individual and leads to enjoyment of the learning process and increases student competence in learning an academic task; extrinsic motivation is the achievement or avoidance of something outside the self.Students with extrinsic motivation work towards achieving a desired grade or external rewards such as awards or money.
Intrinsic and extrinsic motivation are used to predict achievement and achievement related behaviors (Walker et al., 2016), and are especially critical in online learning environments because students find online learning is difficult to adapt due to external, social, and individual factors (Hijazi & AlNatour, 2021;Pham et al., 2022).In an online learning environment, students interact with lecturers and fellow students at a time of their choosing without the need for a physical classroom (Singh & Thurman, 2019).These characteristics of online learning are the difficulties that students face when trying to focus and engage in an online class.Nguyen et al. (2022) found that students had little interaction with lecturers and fellow students.Therefore, students felt lonely and bored in the online class.Thus, high level and sustained motivation are crucial to maintain student concentration in the process of online learning.

Past Studies on Motivation for Learning Language
There have been many past studies on the motivation for learning language.Li & Liu (2023)'s study theorised the language learning experience from a positive psychology (PP) perspective with reference to the PERMA model, which includes Positive emotion (P), Engagement (E), Relationship (R), Meaning (M) and Accomplishment (A).The primary consideration for analysis was how aspects of experience contribute to the motivational development of language other than English (LOTE) learners.Using a convenience sampling method, data were collected from eight international LOTE Chinese learners through written reports and semi-structured interviews and examined through thematic analysis.The findings suggest that PERMA may be a holistic framework within which language learning experiences can be appropriately interpreted.As revealed, participants' LOTE learning experiences were emotionally charged and these emotions were interwoven with experiences of engagement, relationships, meaning and achievement.It was also found that aspects of the experience interacted in the learning process and shaped learners' LOTE motivation in both positive and negative ways.The implications discussed point to the importance of incorporating PP principles into the language classroom to enhance LOTE motivation.
The study by Alrishan et al (2023) aimed to determine the effect of teacher personality on students' motivation to learn English in public schools in Abha, Saudi Arabia.Two questionnaires were designed to collect data using a descriptive correlation method.They were distributed electronically, and the study targeted a sample of English teachers in public secondary schools in Abha and a sample of randomly selected students in Abha high schools in a simple manner.The results of the study revealed that English teachers had personal characteristics that qualified them to teach in public high schools, were considered to have a highly recognised degree on average (3.6918) and that students were motivated to learn English on average (3.7828), which meant that they were highly recognised.The results also show the influence of the teacher's personality on the students' motivation to learn English.
The results also show that motivation to learn English can be predicted by the personality of the teacher.
Jiang & Xie (2022) conducted a study on motivating online language learners.They concluded that motivation in online Chinese learning environments is an under-researched area in the existing literature.This study introduces SDT as an appropriate theoretical framework to address the challenges of learning and motivation in online Chinese language learning.This study also examines the design of online Chinese learning environments from an SDT perspective.It begins with a brief overview of motivational issues in online Chinese language learning and then discusses the application of the SDT theoretical framework to online Chinese language learning environments.In addition, it reviews findings from the online Chinese learning literature and suggests instructional design strategies that can promote and support students' motivation to learn.
In the context of this study, Power is gained through the learners' Expectancy and this is measured by (i) self-efficacy and (ii) control of learning beliefs.Next, Achievement is achieved through Value and is measured by (i) Intrinsic Goal Orientation, (ii) Extrinsic Goal Orientation and (iii) Task Value.Finally, Affiliation is achieved through Social Support and this is measured by (i) Social Engagement and also (ii) Instructor Support.

Reliability of Survey
Table 2 shows the reliability of the survey.The analysis shows a Cronbach alpha of .961;thus, revealing a good reliability of the instrument chosen/used.Further analysis using SPSS is done to present findings to answer the research questions for this study.

Findings
Findings for Demographic Profile Q1.Gender

Findings for Power
This section presents data to answer research question 1-How does power influence motivation to learn online?In the context of this study, power is measured by expectancy motivation such as (a) self-efficacy and (b) control of learning beliefs.

Expectancy(E) (i) SELF-EFFICACY (ESE)
Figure 4-Mean for Self-Efficacy Figure 4 shows the results of the frequency with which students believe in their ability to control their motivation for online learning using the self-efficacy dimension.Item 6: "I expect to do well" recorded the highest mean score (M=3.9).Item 3: "I'm confident I can learn the basic concepts that are being taught" and item 5: "I'm confident I can do an excellent job on assignments and tests."both have the same mean (M=3.8).Two items had the lowest mean (M=3.2),namely item 2: "I'm certain I can understand the most difficult material presented in the readings."and item 4: "I'm confident I can understand the most complex material presented by the instructor".The mean scores for the other items (item1, 7 and 8) ranged from 3.6 to 3.4.Overall, the results suggest that students in this study "sometimes" believed in their ability to exert motivation for online learning from the perspective of self-efficacy and expectancy motivation.ii) CONTROL OF LEARNING BELIEFS (ECB) Figure 5-Mean for Control of Learning Beliefs Figure 5 shows the results of the frequency with which students control their learning beliefs.
The mean score for item 1: "I study in appropriate ways, then I'll be able to learn the material."was 4, and the mean scores for item 2: "It's my own fault if I don't learn the material taught."and item 3: "If I try hard enough, then I'll understand the material presented."were 4.1.The lowest mean score was for item 4: "If I don't understand the material presented, it's because I didn't try hard enough."(M=3.9).The results show that most of the control beliefs factors often facilitate students to accomplish their desired outcomes.

Findings for Achievements
This section presents data to answer research question 2-How does achievements influence motivation to learn online?In the context of this study, achievements is measured by value motivation through (a) intrinsic goal orientation, (b) extrinsic goal orientation and (c) task value.ECBQ1If I study in appropriate ways, then I'll be able to learn the material.
ECBQ2It's my own fault if I don't learn the material taught.
ECBQ3If I try hard enough, then I'll understand the material presented.
ECBQ4If I don't understand the material presented, it's because I didn't try hard enough.

Value (V) (i) Intrinsic Goal Orientation (VI)
Figure 6-Mean for Intrinsic Goal Orientation Figure 6 shows the mean for intrinsic goal orientation.The highest mean score is 3.6 for item 3: "The most satisfying thing for me is trying to understand the content as thoroughly as possible".Next at a mean score of 3.4 are item 1："I prefer material that really challenges me, so I can learn new things" and item 2: "I prefer material that arouses my curiosity, even if it's difficult to learn".The lowest mean is 3.3 for item 4: "I choose assignments that I can learn from even if they don't guarantee a good grade".Generally, students "sometimes" have an intrinsic motivation with learning the subject.
(ii) Extrinsic Goal Orientation(VE) Figure 7-Mean for Extrinsic Goal Orientation VIQ2I prefer material that arouses my curiosity, even if it's difficult to learn.
VIQ3The most satisfying thing for me is trying to understand the content as thoroughly as possible.
VIQ4I choose assignments that I can learn from even if they don't guarantee a good grade.VEQ1Getting a good grade is the most satisfying thing for me.
VEQ2The most important thing for me is to improve my overall grade point average, so my concern is getting a good grade.
VEQ3I want to get better grades than most of the other students in my classes.
VEQ4I want to do well in my classes because it's important to show my ability to my family, friends, employer, or others.
Figure 7 presents the mean for extrinsic goal orientation.Item 1 which stated "Getting a good grade is the most satisfying thing for me" scored the highest mean value 4.6.Next mean score is 4.4 for item 2: "The most important thing for me is to improve my overall grade point average, so my concern is getting a good grade".Item 4: "I want to do well in my classes because it's important to show my ability to my family, friends, employer, or others" scored mean value 4.3 and item 3: "I want to get better grades than most of the other students in my classes" scored the lowest mean value 4.1.The results indicate that extrinsic motivation "often" influences students to perform better in their studies.
(iii) Task Value (VT) Figure 8-Mean for Task Value Figure 8 demonstrates the mean for task value.The highest mean is 4.2 for 4 items.The items are item 3: "I am very interested in the content area of this course", item 4: "I think the course material in this class is useful for me to learn", item 5: "I like the subject matter of this course" and item 6: "Understanding the subject matter of this course is very important to me".Next, item 2: "It is important for me to learn the course material in this class" scored mean value 4.1.The lowest mean score of 3.9 is for item 1: "I think I will be able to use what I learn in this course in other courses".The results show that students "often" understand and know the importance of the course material and they are "sometimes" able to apply what they have learned to other courses.

Findings for Affiliation
This section presents data to answer research question 3-How does affiliation influence motivation to learn online?In the context of this study, affiliation is measured by social support through (a) social engagement, and (b) instructor support.VTQ2It is important for me to learn the course material in this class.
VTQ3I am very interested in the content area of this course.
VTQ4I think the course material in this class is useful for me to learn.
VTQ5I like the subject matter of this course.
VTQ6Understanding the subject matter of this course is very important to me.
Social Support (S) (i) Social Engagement (SSE) Figure 9-Mean for Social Engagement Figure 9 shows the mean scores of student engagement in online learning.Item 3: "I enjoy class discussions."recorded the highest mean (M=4), indicating that students "often enjoy" class discussion.The lowest mean score was recorded for item 1: "I feel "disconnected" from my teacher and fellow students in classes."(M=2.5),indicating that students "seldom feel" disconnected from teachers and classmates.Mean scores for the other items ranged from 3.9 to 3.4, indicating that students are "sometimes" able to engage in class, communicate freely and have good relationships with their classmates.
(ii) Instructor Support (SIS) Figure 10-Mean for Instructor Support Figure 10 shows the results of instructor support for online learning.The highest mean scores were obtained on three items: item 2: "The instructor responds to questions, clearly, completely, and in a timely manner.",item 4: "The instructor provides the guidance I need to be successful in this class."and item 5: "The instructor presents the material in a way that makes it relevant to me." (M=4.3).In contrast, the lowest mean score was for item 1: "I feel like I can freely communicate with the instructor in this class."(M=3.9).The mean scores for the other items ranged from 4.2 to 4. Overall, students felt that the instructor "often" provides support during online learning, and that students are "sometimes" able to communicate freely with the instructor in class.

Findings for Relationship between Variables for Online Learning Motivation
This section presents data to answer research question 4-Is there a relationship between variables for motivation to learn online?
To determine if there is a significant association in the mean scores between power, achievements and affiliation, data is analysed using SPSS for correlations.Results are presented separately in table 3, 4 and 5 below.

Table 3 Correlation between power and achievements
Table 3 shows there is an association between power and achievements.Correlation analysis shows that there is a high significant association between power and achievements (r=.782**) and (p=.000).According to Jackson (2015), coefficient is significant at the .05level and 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 also a strong positive relationship between power and achievements.

Table 4 Correlation between power and affiliation
Table 4 shows there is an association between power and affiliation.Correlation analysis shows that there is a high significant association between power and affiliation (r=.690**) and (p=.000).According to Jackson (2015), coefficient is significant at the .05level and 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 also a strong positive relationship between power and affiliation.

Table 5 Correlation between achievements and affiliation
Table 5 shows there is an association between power and affiliation.Correlation analysis shows that there is a high significant association between power and affiliation (r=.781**) and (p=.000).According to Jackson (2015), coefficient is significant at the .05level and 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 also a strong positive relationship between achievements and affiliation.

Summary of Findings and Discussion
The findings reveal the positive impact of McClelland's Needs Theory on online language learning.The investigation of the Need for Power showed that most students believed in their online learning abilities in terms of self-efficacy and desired motivation, and that control belief factors also led them to achieve desired outcomes, which is consistent with She et al (2021) who emphasised that self-efficacy and engagement enabled students to achieve academic goal in their research outcomes.
Next, investigation on the Need for Achievements indicates students were influenced by intrinsic and extrinsic motivation to perform better in online language learning.This result is in line with the arguments of Hijazi & AlNatour (2021); Pham et al (2022) that motivation is vital in an online learning environment.It also supports Harnett's (2016) view that online students are more intrinsically motivated than face-to-face learners.
The result of the Need for Affiliation demonstrated that students felt engaged and can interact freely with their classmates and instructors in the language classroom.However, this finding is different from the study done by Nguyen et al (2022) who found that students had little interaction with lecturers and fellow students.As a result, students felt abandoned and disinterested in the online classroom.In order to overcome this problem, teachers undoubtedly play an important role because as suggested by Alrishan et al (2023), a teacher's personality affects students' motivation.In addition, as recommended by Jiang & Xie (2022), there are a few instructional design strategies that will help to increase students' motivation to learn.
Finally, correlation analyses identified associations between power and achievements, power and affiliation, achievements and affiliation.A strong positive correlation was found between all these variables to motivate online language learning.In other words, Power through Expectancy, Achievements through Value and Affiliation through Social Support were closely related and interacted in students' learning.This interaction will enable learners to work towards the goals described by (Kikuchi, 2015).

Pedagogical Implications and Suggestions for Future Research
This study has practical implications for teaching and learning languages online.An immediate impact of this study is that academics and instructors have a clearer plan to increase students' motivation to learn languages online.They can use a variety of more advanced online teaching methods to cater for the changes in education in the 21 st century.Online language learning can be tailored to meet the needs of students, particularly the technological requirements, so that student motivation can be maximised.Education reform is a dynamic process that requires constant examination and research.It is therefore recommended that the motivation to learn languages online be further explored from a variety of perspectives and theories.Future research could also examine the comparison of motivation theories on online learning.

Figure 1 -
Figure 1-Conceptual Framework of the Study-Motivation to Study Online
4.2 4.3 4.4 4.5 4.6 4.7 will be able to use what I learn in this course in other courses.

Table 1
Distribution of Items in the Survey