Assignment 3 12 OCTal mechanism linking motivation to engagement thus extending foundational motivation theories .docx

This study addresses a central challenge in digital education by investigating the psychological process through which learning motivation translates into online engagement. The study proposes a quantitative model to explore the structural relationships between Learning Motivation (IV), Online Engagement (DV), and Learning Satisfaction (Mediator). The central inquiry is to examine the extent to which IV directly influences DV and whether it is mediated by Mediator.
The research holds significant theoretical and practical importance. Theoretically, it contributes to the field by empirically testing a process model that positions satisfaction as an important psychological mechanism linking motivation to engagement thus extending foundational motivation theories, such as Self-Determination Theory (Ryan & Deci, 2000), into the digital learning context.

1. 1. Introduction
This study addresses a central challenge in digital education by investigating the psychological
process through which learning motivation translates into online engagement. The study
proposes a quantitative model to explore the structural relationships between Learning
Motivation (IV), Online Engagement (DV), and Learning Satisfaction (Mediator). The central
inquiry is to examine the extent to which IV directly influences DV and whether it is mediated
by Mediator.
The research holds significant theoretical and practical importance. Theoretically, it
contributes to the field by empirically testing a process model that positions satisfaction as an
important psychological mechanism linking motivation to engagement thus extending
foundational motivation theories, such as Self-Determination Theory (Ryan & Deci, 2000),
into the digital learning context.
Practically, the findings will provide an evidence based framework for instructional designers
and institutions. It will clarify whether initiatives should prioritize directly boosting learner
motivation or focus on enhancing the qualities of the learning experiences that drive
satisfaction thereby improving active engagement and its associated outcomes. This model
offers a diagnostic framework to help institutions proactively support learners and enhance the
return on investment in digital education initiatives.
2. Quantitative Methodology Proposal
Research Method
Research is a survey based quantitative design that examines the relationship among three
variables: learning motivation, online engagement, and learning satisfaction. Gender and
education are used as moderators while age, experience, qualifications, and marital status are
control variables.
Research Design
Data collection will be a cross sectional design, which is frequently applied in technology-
enhanced learning studies and deemed suitable for examining causal relationships (Hair et al.,
2021).
Sampling Method
The target participants are online learners chosen by random sampling techniques. Simple
random sampling guarantees equal selection chances, reducing bias and increasing
representativeness (Westfall, 2018). There will be 361 responses, exceeding the number
advised for PLS-SEM analysis (Hair et al., 2021).
Data Collection Instruments
There will be four sections to the structured online questionnaire: (1) Learning Motivation (5
items), (2) Online Engagement (5 items), (3) Learning Satisfaction (5 items) and (4)

2. Demographics (age, gender, marital status, education, and experience). A 5-point Likert scale,
a reliable tool for gauging attitudes and perceptions in social science studies, will be used to
rate each item (1 being strongly disagree and 5 being strongly agree) (Joshi et al., 2015).
Data Analysis Techniques
PLS-SEM SmartPLS 4.0 will be used to analyze data in two stages: (1) Measurement Model
Assessment, examining validity (Convergent and Discriminant) and reliability (Cronbach's
Alpha, Composite Reliability), and (2) Structural Model Assessment, will look at path
coefficients, R2, and hypothesis significance. For exploratory research involving complex
models, this approach is suitable (Hair et al., 2021).
3. Critical Analysis of Method and Ethics
Strengths of the Quantitative Method
The survey based quantitative design was appropriate as it allowed clear measurement of
learning motivation, satisfaction, and online engagement. It also supported testing a theoretical
model connecting motivation and satisfaction with engagement in digital learning. Using an
online questionnaire enabled wide, low-cost participation, while the 5-point Likert scale
ensured consistent and easy-to-analyze responses. A sample of over 300 participants increased
statistical reliability and checks for reliability and validity confirmed that the constructs were
accurately measured.
Limitations of the Method
The cross-sectional design limits the ability to draw cause-and-effect conclusions. Although
random sampling helps reduce bias, achieving full randomness online is difficult and may
affect generalizability. The study also relies on self-reported data, which can be influenced by
social desirability. Additionally, it does not track participants over time so changes in
motivation and engagement cannot be observed.
Ethical Considerations
Ethical care is a key part of the study. Participants gave informed consent, participated
voluntarily and were told they could withdraw anytime. No personal identifiers were collected
and data were kept confidential. Only essential demographics, such as age, gender, education,
experience, and marital status, were gathered to support the analysis. The survey was brief and
neutral, encouraging honest responses with integrity and reliable results.

3. 4. Research Objectives:
-To examine the influence of learning motivation on online engagement among students.
-To investigate the mediating role of learning satisfaction in the relationship between learning
motivation and online engagement.
-To assess the moderating effects of gender and education level on the relationship between
learning motivation, learning satisfaction, and online engagement.
Research Questions:
-How does learning motivation influence online learning?
-Does Learning satisfaction mediate the relationship between learning motivation and
online engagement?
-Do gender and education level moderate the relationship between learning motivation,
learning satisfaction, and online engagement?
Hypothesis:
●H1: Learning motivation has a positive effect on online engagement (Ryan&
Deci,2000).
●H2: Learning satisfaction mediates the relationship between learning motivation and
online engagement (Eom & Ashill, 2016).
H3: Gender moderates the relationship between learning motivation and online
engagement, such that the relationship is stronger for female students than male
students (Venkatesh & Morris, 2000).
●H4: Education level moderates the relationship between learning motivation and
online engagement, such that the relationship is stronger for students with higher levels
of education (Artino, 2008).
5 .Conceptual Framework Development
The conceptual framework examines the connection between learning motivation, learning
satisfaction, and online engagement, while also investigating the roles of moderators or control
variables, highlighting age and education as primary variables. The framework is based on the
Self-Determination Theory, which explains how motivation is the key variable in the study.
When motivation is high, students are likely to be more satisfied with their learning journey.
The satisfaction gained is a significant factor in determining the effort and energy invested in
learning.

4. Core Constructs
The core constructs and relationships are represented in:
● IV: Learning Motivation is the main driving point in DV
● Mediator: Learning Satisfaction mediates between IV and DV.
● DV: Online Engagement is the students’ active involvement in an online learning
environment.
Dependent Variable
Engagement is the expected outcome, evident by participation, task completion, and staying
connected with the course. It shows how IV and Mediator affect it.
Visual Path
The visual framework diagram shows Motivation as the starting point, Satisfaction as the
mediator, and Engagement as the outcome while, age, gender, education, marital status,
experience and qualification are added as moderators or control variables.
6.PLS-SEM Analysis.
In the study, learning motivation (IV) and online engagement(DV) were studied. Learning
satisfaction (Mediator). Control variables (age, marriage status, experience, and
qualifications). No missing values were detected.
Indicator Reliability:

5. Item-specific accuracy refers to a variable or construct that is designed to measure the latent
construct, is called an indicator's reliability (Hair et al., 2021).
In Figures 1 and 2, factor loadings for the items in DV are > 0.7, indicating that the items in
the variable are represented accurately. The same applies to items related to Mediator and DV.
Moreover, the moderators and control variables’ factor loading is 1.00. Further indicating that
the items and their constructs have a strong relationship and are significant. Overall, outer
loadings range from 0.70 to 1.00, suggesting reliable and valid measures of indicators for their
constructs.
Internal Consistency:
Construct Reliability
The statistical measure that tests the internal consistency and underlying concepts of items
within a construct is called Cronbach's Alpha (α) (Nunnally, 1978). In Figures 1 and 3, the α
value for IV, Mediator, and DV is 0.855, 0.896, and 0.858, respectively. All are above 0.70,
suggesting that the construct measures are significant and reliable. Moreover, the Average
variance extracted (AVE) is known as a measure for determining the amount of variance
captured by a construct, which is linked to its measurement (Hair et al., 2021). In Figure 3, the
AVE values for IV, Mediator, and DV are 0.636, 0.706 and 0.640, respectively. This is more
than 0.5, which suggests that at least 50% of the variance in the indicators of the above
variables is related.
Discriminant Validity:
It is defined as the testing of two variables to determine whether they are distinct and not
strongly correlated (Dirgiatmo, 2023). Among the multiple types of discriminant validity tests.
In this study, two tests were conducted. They are the Fornell-Lacker criterion and the
Heterotrait-Monotrait Ratio.
In Figure 4, all corresponding variables are greater than their corresponding variables.
According to Fornell & Larcker (1981)when the square root of AVE in construction is greater
F
Figure SEQ Figure \* ARABIC 5 Fornell-Lacker criterion

6. than all other constructs. This indicates that all variables in the data are unique and different
from other concepts. In Figure 5, all constructs except for learning motivation (0.889) the
values below 0.85. According to Dirgiatmo (2023), HTMT values below 0.90 indicate that the
two constructs are distinct and valid. Supporting the Fornell and Larcker Criterion conclusion
Multi-regression Analysis
Figure 6 Graphical Output of Bootstrapping of Conceptual Framework
Figure SEQ Figure \* ARABIC 7 Direct Path Coefficient Effect
Figure SEQ Figure \* ARABIC 8 Indirect Path Coefficient

7. Figures 7 and 8 show the relationships in the conceptual framework. Path coefficients show
significance and the strength between variables. There are four significant direct effects and no
indirect effects.
They are:
Direct
Age → DV: β= -0.141, p=0.00
Education → Mediator: β=-0.064, p=0.030
IV → Mediator: β=0.824, p=0.00
IV → DV: β=0.628, p=0.00
As shown above, Age as a control variable has a weak (β=-0.141) negative coefficient with
(DV), but is significant as the p-value is <0.05. This means that as the age of the students
increases, their DV tends to decrease. Moreover, (IV)has 54.8 % variance in DV (R
2
= 0.548).
With (f
2
=0.628), indicating a large impact on DV. The positive coefficient (β=0.824) suggests
a very strong relationship. If IV increases, DV also increases. Thus, HI is accepted. This also
supports (Deci & Ryan, 2000). Furthermore, IV influences Mediator.
IV explains 61.7% of the variance in (R
2
= 0.617).With a strong (β=0.628) relationship. This
shows that DV increases satisfaction.
Mediator has weak, non-significant relationship with DV. Suggesting that the Mediator does
not mediate between IV and DV. Thus, H2 is rejected. Additionally, education and gender do
not moderate IV and DV. Meaning H3 and H4 are rejected.
6.CONCLUSION
This study confirms that learning motivation has a strong direct effect on online engagement.
However, learning satisfaction does not mediate this relationship despite being strongly
predicted by motivation. Demographically, age negatively affects engagement, education
slightly impacts satisfaction while gender shows no significant effects. Results indicate that
enhancing motivation directly, rather than through satisfaction, is the most effective strategy
for improving online engagement.

8. References
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