INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Exploring the Underlying Dimensions of Science Learning

Motivation in Secondary School Students Using a Mixed Methods

Approach

Aduo Frank., Emmanuel Adjei., Mahama Salifu., Kintampo Lydia Awuni., Rockson Ofori Amanfo,

Department of Integrated Science Education, University of Education, Winneba

DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100293

Received: 29 November 2025; Accepted: 06 December 2025; Published: 08 December 2025

ABSTRACT

This mixed methods study investigates the multidimensional nature of science learning motivation among

secondary school students and its impact on engagement and academic achievement. Quantitative data

collected via the Science Motivation Questionnaire II from a stratified sample of students revealed that

intrinsic motivation, self-efficacy, task value, and mastery goal orientation significantly correlate with

engagement and academic performance in science. Self-efficacy exhibited the strongest relationships,

underscoring the importance of students’ confidence in their science learning capabilities. Complementary

qualitative interviews enriched these findings by capturing students’ lived experiences, highlighting how

personal interest, perceived relevance, confidence, and clear goal orientation interact to drive sustained

motivation. The qualitative themes illustrated the emotional and cognitive processes shaping students’

motivation, confirming the dynamic and socially situated nature of motivation in science education.

Delimitations due to geographical scope, sample size, self-report biases, and complexities in integrating mixed

methods findings are acknowledged. Despite these constraints, the study contributes valuable insights for

educators and policymakers seeking to enhance science motivation through autonomy-supportive teaching,

confidence-building interventions, and goal-focused curriculum design. The findings also emphasize the

necessity of personalized motivational strategies tailored to diverse learner profiles and sociocultural contexts.

This research advances theoretical understanding and offers practical recommendations for fostering

motivated, engaged, and successful science learners, contributors to educational improvement efforts in

contemporary science education.

Keywords: Science motivation; Secondary education; Student engagement; Self-efficacy; Mixed method

INTRODUCTION

Motivation has long been identified as a foundational driver of students’ engagement and achievement within

science education. Early theoretical perspectives emphasize that motivation fuels and sustains goal-directed

learning behaviors by initiating effort, maintaining persistence, and directing learners’ attention toward

academic objectives (Brophy, 2004; Slavin, 2018). Over time, research has expanded to show that several

psychological elements—such as curiosity, interest, intrinsic orientation, and task value—interact with

motivational processes to enhance students’ learning experiences (Sprinthall & Oja, 2014). These elements

not only support cognitive involvement but also stimulate emotional engagement, thereby reducing

disengagement, boredom, and feelings of irrelevance during science learning (Palmer, 2005; Driscoll, 2000).

In recent decades, motivation has increasingly been regarded as a multidimensional construct essential for

students’ participation, persistence, and ultimate success in science, especially during the secondary school

years when attitudes toward science are still being shaped. Recent research shows that students’ motivation in

science is shaped by multiple interconnected factors, including their intrinsic curiosity, beliefs about their ability

to succeed, judgments about the relevance of science to their lives, and the emotions they experience during

learning. Together, these elements strongly influence how deeply students participate in science activities and

how persistently they engage with challenging ideas (Tuan et al., 2005; Eccles & Wigfield, 2020). When students

are highly motivated, they are more likely to apply effective learning strategies, show consistent involvement in

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

classroom tasks, and ultimately perform better in science-related assessments (Glynn & Koballa, 2023; Schunk

& DiBenedetto, 2020). However, despite considerable progress, the motivational dynamics underlying

secondary school students’ science learning remain insufficiently understood. Much of the existing research

has examined either quantitative patterns or qualitative experiences independently. This divide in the existing

literature creates a significant limitation in fully understanding how students’ thoughts, emotions, and learning

environments interact to influence their motivation in science. A mixed-methods design is therefore essential, as

it enables researchers to explore these interconnected influences in greater depth—capturing both the measurable

patterns and the nuanced experiences that shape students’ motivation to learn science (Mahzum et al., 2020;

Osborne & Dillon, 2008; Areepattamannil, 2014). Such an integrated investigation would generate richer

insights into how different motivational components interact to support students’ engagement, attitudes, and

academic performance in science. Ultimately, these findings can inform the design of targeted instructional

practices and policy interventions aimed at fostering long-term motivation and improving science learning

outcomes among secondary school students.

Justification

Despite numerous studies on science learning motivation, there is a critical need to integrate qualitative and

quantitative perspectives through mixed methods to fully capture the complex and multifaceted nature of

motivation in secondary school students. This integration will enrich understanding and guide more effective

instructional strategies.

Purpose of the Study

This study aims to explore and quantify the underlying dimensions of science learning motivation among

secondary school students using a mixed methods approach to provide a holistic understanding of motivational

factors and their influence on engagement and academic performance.

Significance of the Study

Findings will inform educators, curriculum designers, and policymakers by offering evidence-based insights into

motivational dynamics, thereby aiding the development of targeted interventions to enhance science learning

motivation and improve student outcomes.

Research Objectives

1. To identify and describe the underlying dimensions of science learning motivation in secondary school

students.

2. To measure the relationships between these motivational dimensions and students’ engagement and

academic achievement in science.

Research Questions

1. What are the key dimensions of science learning motivation as perceived by secondary school students?

2. How do the identified motivational dimensions relate to students’ engagement and academic performance

in science?

LITERATURE

Theories of Motivation in Education

In educational settings, motivation theories play a crucial role in understanding and enhancing student

engagement and learning outcomes. One of the most influential frameworks is Self-Determination Theory

(SDT), which distinguishes types of motivation based on the degree to which they are internalized. According

to Ryan and Deci (2000), intrinsic motivation arises from genuine interest and enjoyment in the learning task

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itself, promoting autonomy, competence, and relatedness. These psychological needs are essential for fostering

deep engagement and sustained motivation in science learning. Conversely, extrinsic motivation involves

performing tasks for external rewards or pressures, which can vary in their degree of internalization and impact

on learning outcomes.

Another prominent theory is Expectancy-Value Theory, articulated by Eccles and Wigfield (2002). This

perspective emphasizes that motivation depends on a student’s belief in their likelihood of success (expectancy)

and the value they assign to the task. When students anticipate success and perceive science learning as

meaningful or useful, their motivation and effort increase accordingly.

Achievement Goal Theory, elaborated by Ames (1992), examines the purposes driving students' academic

behaviors. It differentiates between mastery goals, focused on understanding and self-improvement, and

performance goals, which center on demonstrating ability relative to others. Mastery-oriented students tend to

exhibit more persistent and adaptive motivation, which is critical in challenging subjects like science.

Bandura’s Social Cognitive Theory adds a vital component by focusing on self-efficacy—the belief in one’s

ability to succeed in specific tasks. High self-efficacy encourages students to take on challenges, persist through

difficulties, and regulate their learning, thereby enhancing motivation and academic achievement in science

(Bandura, 1986).

Attribution Theory, first introduced by Weiner (1985), offers a vital framework for understanding how students

interpret their academic achievements and setbacks. The theory suggests that learners assign causes to outcomes

along dimensions of locus (internal vs. external), stability (stable vs. unstable), and controllability (controllable

vs. uncontrollable). These attributions strongly influence students’ future motivation, learning behaviors, and

persistence. Specifically, when learners credit their successes to controllable internal factors—such as effort,

strategy use, or consistent practice—they are more likely to exhibit sustained perseverance, set ambitious goals,

and adopt adaptive learning strategies. In contrast, when failures are attributed to uncontrollable factors, such as

perceived inherent ability or external circumstances, motivation can decline, and students may disengage from

challenging tasks (Dweck, 2006; Graham & Taylor, 2016; Schunk & DiBenedetto, 2020).

Complementing this theoretical lens, Keller’s ARCS Model of Motivational Design (1987) provides a structured

approach for fostering and maintaining student motivation in educational settings. The model identifies four

critical elements: Attention, which involves capturing and sustaining learners’ curiosity and interest; Relevance,

which connects instructional content to students’ personal goals, prior experiences, and real-world applications;

Confidence, which helps learners build self-efficacy and anticipate success; and Satisfaction, which enhances

motivation through meaningful feedback, recognition, and a sense of achievement. By integrating these

components, educators can design instructional experiences that not only stimulate immediate engagement but

also encourage long-term commitment, resilience, and positive attitudes toward learning (Keller, 2010; Marzano

& Pickering, 2011; Reeve, 2016; Ryan & Deci, 2020).

Together, attributional insights and motivational design principles emphasize the importance of addressing both

students’ cognitive interpretations and the instructional environment, creating a holistic strategy for supporting

sustained motivation and effective learning outcomes in science education. By integrating insights from

Attribution Theory and the ARCS Model, educators and researchers gain a richer understanding of both the

psychological processes and instructional conditions that influence students’ motivation. These frameworks

collectively highlight that students’ beliefs about their learning and the design of learning environments are both

central to promoting sustained academic engagement and improved performance

Science Learning Motivation

Science learning motivation is a critical factor influencing students’ engagement, persistence, and achievement

in science education. Extensive research has explored how motivation drives students’ learning behaviors,

directing their focus, effort, and resilience in science classrooms. Motivation embodies complex, multifaceted

constructs influenced by cognitive, emotional, and contextual factors.

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Previous studies have demonstrated that motivation plays a vital role in science learning, influencing conceptual

understanding, critical thinking, learning approaches, and overall academic achievement (Broph, 2004).

Motivation plays a crucial role in helping students actively build their own understanding of scientific concepts

(Cavas,2011).

Recent literature underscores the importance of integrating these theoretical perspectives to capture the dynamic

and multidimensional nature of science learning motivation. Research indicates that student motivation is

strongly linked to engagement indicators such as enthusiasm, persistence, and academic achievement, mediated

by teacher support, instructional quality, and positive learning environments. Employing mixed methods

research designs enables a holistic exploration of how motivational constructs operate and interact within the

complexity of real classroom settings, yielding actionable insights for enhancing science education outcomes.

Measurement of Motivation

In science education research, motivation is primarily assessed using reliable and validated self-report

questionnaires designed to capture multiple dimensions of motivation. The Motivation for and Engagement with

Science Questionnaire (MSEQ) is a well-established instrument that measures both intrinsic and extrinsic

motivation as well as engagement with science content in K-12 students. Measurement of students’ motivation

in science learning has advanced through the development of instruments that capture multiple motivational

constructs. One of the most widely used tools is the Science Motivation Questionnaire II (SMQ-II), developed

by Glynn and colleagues. The SMQ-II evaluates several dimensions of science motivation, including self-

efficacy, use of active learning strategies, intrinsic motivation, grade-oriented motivation, and career-oriented

motivation. Its design has undergone extensive psychometric testing, including Rasch analysis and other

reliability and validity assessments, ensuring its applicability across diverse student populations. The

instrument’s frequent adoption in both secondary and higher education research underscores its effectiveness in

capturing learners’ motivational orientations toward science and its practical relevance for educational

assessment and research (Glynn et al., 2011; Sorge et al., 2016; Ragusa, USC STEM Education).

Beyond science-specific tools, broader instruments such as the Academic Motivation Scale (AMS) are commonly

used to examine motivational processes across different educational domains. The AMS is grounded in self-

determination theory and measures motivation along a continuum from intrinsic motivation to extrinsic

regulation and amotivation. This framework enables researchers to explore how students’ autonomy, goal

orientation, and self-perceptions influence engagement and learning behaviors. The AMS has been widely

applied across cultures and educational contexts, demonstrating strong psychometric properties and offering a

comprehensive approach for understanding individual differences in motivation among learners (Vallerand et

al., 1992; Ryan & Deci, 2020; Liu et al., 2022). Collectively, instruments like the SMQ-II and AMS provide

robust frameworks for evaluating and comparing the motivational profiles of students in both science-specific

and general educational settings, supporting evidence-based strategies for enhancing engagement and

achievement.

Impact of Motivation on Science Engagement and Achievement

Numerous studies have demonstrated that motivation significantly influences students’ engagement in science

learning activities and their subsequent academic achievement. Motivated students exhibit higher levels of

cognitive and behavioral engagement, including sustained attention, effort, and persistence when facing

challenges (Fredricks, Blumenfeld, & Paris, 2004). Intrinsic motivation, in particular, is positively associated

with deep learning strategies and greater academic success in science (Ryan & Deci, 2000). Conversely, low

motivation or amotivation correlates with disengagement and poor performance. Teacher support, task

relevance, and student self-efficacy play mediating roles by enhancing motivation and engagement, thus

promoting higher academic outcomes (Bandura, 1986; Eccles & Wigfield, 2002). Motivation in science

education is crucial, as it inspires students to actively engage with the subject, reducing boredom and allowing

them to appreciate its relevance and beauty (Palmer, 2005). Students who exhibit higher levels of interest and

motivation tend to achieve better academic results in science, with their performance significantly enhanced

when they are strongly motivated to learn (Cavas, 2011). Altun found that students with lower levels of

motivation are more likely to fail in the subject, while those with higher motivation tend to achieve better results

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in science (Altun, 2009). Likewise, Shih and Gamon highlighted that the level of student motivation has a

significant impact on their academic performance (Shih & Gamon, 2001).

Contextual and Cultural Influences

Motivation in science learning does not occur in a vacuum; it is shaped by a variety of contextual and cultural

factors. Socioeconomic status, cultural values regarding education, teacher expectations, and classroom climate

all influence students' motivational orientations (Tuan et al., 2005). For example, learners’ motivational

tendencies often vary across cultural contexts. Students raised in environments that prioritize collective success

and group harmony may develop motivation patterns that differ significantly from those in more individualistic

cultures, where personal achievement and autonomy are emphasized. Students’ cultural backgrounds play a

significant role in shaping their beliefs about learning, their attitudes toward achievement, and their persistence

in the face of academic challenges. Cultural norms and values influence how learners define success, prioritize

goals, and regulate effort, thereby affecting their motivation and engagement in educational tasks. For instance,

individuals from collectivist cultures may place a higher value on collaborative achievement and social

expectations, whereas those from individualistic cultures may emphasize personal accomplishment and self-

reliance. Such cultural dimensions also interact with classroom practices and teacher expectations, highlighting

the importance of considering sociocultural context when examining motivation and academic behavior (Markus

& Kitayama, 1991; Hofstede, 2001; Chiu et al., 2012; OECD, 2016).

In addition, gender-related societal expectations and long-standing stereotypes about who is naturally suited for

science can strongly influence students’ motivational beliefs. Stereotypes about science ability can undermine

interest and confidence, particularly among groups historically underrepresented in STEM, such as girls, who

may internalize these biases despite having equivalent skills. These societal and cultural influences, together

with classroom experiences, shape students’ engagement, self-efficacy, and future aspirations in science

disciplines (Nosek et al., 2009; Eccles & Wigfield, 2020; Archer et al., 2012; Wang & Degol, 2017). Recognizing

these influences is essential when interpreting motivational data and designing interventions tailored to diverse

learner populations.

Theoretical framework

The framework can be anchored by Self-Determination Theory (SDT), which highlights the crucial role of

intrinsic motivation driven by autonomy, competence, and relatedness needs (Ryan & Deci, 2000). SDT provides

a foundational understanding of the internal psychological resources that propel students toward deep, sustained

engagement in science.

To enrich this foundation, Expectancy-Value Theory (Eccles & Wigfield, 2002) can be incorporated to explain

how students’ beliefs about their likelihood of success and the value they assign to science tasks influence their

motivation and persistence. This theory contextualizes motivation in terms of personal relevance and outcome

expectations.

Achievement Goal Theory (Ames, 1992) adds a dimension regarding students’ goals—whether mastery-oriented

or performance-oriented—that shape their engagement strategies and resilience in science learning. Bandura’s

Social Cognitive Theory (1986) contributes the concept of self-efficacy, emphasizing how confidence in one's

capabilities impacts effort and perseverance.

Attribution Theory, originally proposed by Weiner (1985), offers insight into the ways learners make sense of

their academic outcomes. According to this perspective, students draw conclusions about the causes of their

successes or failures—whether these causes are internal or external, stable or unstable, and controllable or

uncontrollable—and these interpretations significantly influence their future motivation, persistence, and

engagement. When learners attribute achievement to internal and controllable factors such as effort or strategy

use, they tend to sustain motivation and adopt adaptive learning behaviors. In contrast, attributing failure to fixed

or uncontrollable factors can undermine confidence and reduce willingness to continue working on academic

tasks (Graham & Taylor, 2016; Dweck, 2006).

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Building on this foundation, contemporary motivational research recognizes that students’ learning experiences

are shaped by more than personal cognitive interpretations. A comprehensive theoretical lens integrates

individual motivational processes with emotional dynamics and the broader contextual influences that surround

learners. These include classroom climate, instructional practices, peer interactions, and cultural norms that

shape students’ beliefs, values, and attitudes toward learning. This integrated perspective highlights that

students’ motivation in science is shaped not only by personal beliefs and attitudes but also by the social and

cultural contexts in which learning takes place, influencing their engagement and persistence (Membiela et al.,

2023; Tuan et al., 2005; Wentzel & Brophy, 2014; Ryan & Deci, 2020; Eccles & Wigfield, 2020).

This holistic viewpoint enables a deeper understanding of how cognitive, emotional, and environmental factors

intersect to influence students’ willingness to participate, persist, and succeed in science education.

By linking these theories, the framework provides a comprehensive lens to examine the dynamic interplay of

motivational constructs affecting science learning motivation, engagement, and achievement.

Conceptual framework

A conceptual framework suitable for this study integrates key motivational theories and empirical findings that

explain how various motivational dimensions influence science learning engagement and achievement in

secondary school students.

At the core, Self-Determination Theory (SDT) provides a foundational lens by emphasizing intrinsic motivation

driven by satisfaction of autonomy, competence, and relatedness needs (Ryan & Deci, 2000). This framework

helps explain why students engage more deeply when they perceive science learning as personally relevant and

when supported by a nurturing environment.

Complementing SDT, Expectancy-Value Theory (Eccles & Wigfield, 2002) adds dimensions of students’

expectations for success and the value they assign to science, which directly affect their willingness to invest

effort. Coupled with Achievement Goal Theory (Ames, 1992), which distinguishes mastery-oriented goals from

performance goals, the framework captures the purposes underlying students’ motivation in science learning

contexts.

Social Cognitive Theory’s concept of self-efficacy (Bandura, 1986) strengthens the framework by highlighting

the role of students’ beliefs about their capabilities, influencing persistence and academic performance.

Attribution Theory, as articulated by Weiner (1985), offers valuable insight into how learners interpret the causes

of their academic outcomes and how these interpretations shape subsequent motivation. Students’ beliefs about

why they succeed or fail influence the effectiveness of instructional feedback, guiding educators in designing

support strategies that reinforce adaptive attributions and sustained effort. When teachers understand how

students explain their performance, they can tailor feedback to promote resilience, persistence, and constructive

learning behaviors (Graham & Taylor, 2016). This comprehensive conceptual framework guides the mixed

methods study by linking identified motivational constructs to observable engagement behaviors and academic

outcomes in science learning. It supports examination of both the qualitative depth of students’ motivational

experiences and quantitative measurement of their interrelationships for actionable educational insights.

Research Paradigm

This study is grounded in the pragmatic paradigm, a philosophy that supports the use of multiple methods to

address complex research questions. Pragmatism allows the blending of quantitative and qualitative approaches

to generate practical, actionable insights. It recognizes the value in diverse data types and prioritizes research

outcomes beneficial for real-world educational challenges.

Research Design Integration

The study employs an explanatory sequential mixed methods design. Initially, quantitative data are collected and

analyzed to identify broad motivational patterns. Subsequently, qualitative data collection and analysis provide

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context and depth, explaining and expanding upon the quantitative results. Integration occurs at the design level

by sequencing methods; at the method level by using complementary data collection tools; and at the

interpretation level by merging findings to form a holistic understanding.

Population

The target population consists of secondary school students engaged in science learning. This group is selected

to ensure the study captures motivation dynamics relevant to adolescent learners in formal educational settings.

Sample and Sampling Techniques

For the quantitative phase, stratified random sampling ensures representative coverage across different grades

and schools. For the qualitative phase, purposive sampling selects participants based on motivation profiles

revealed from the survey, focusing on diversity to uncover varied motivational experiences.

Instrumentation

The quantitative instrument is the Science Motivation Questionnaire II (SMQ II), widely validated for measuring

multiple motivation dimensions. Qualitative data collection uses semi-structured interview protocols crafted to

explore students’ perceptions and contextual factors influencing their motivation.

Data Collection Procedures

Quantitative surveys will be administered in classroom settings with standardized instructions. Following initial

analysis, selected students will participate in interviews conducted face-to-face or virtually, adhering to ethical

guidelines including informed consent and confidentiality. Scheduling coordinates with school administration to

minimize disruption.

Data Analysis and Integration

Quantitative data will be analyzed using descriptive and inferential statistics to identify motivation patterns and

associations. Qualitative data will undergo thematic analysis to extract rich, contextualized insights. Integrated

interpretation will triangulate data to corroborate findings and provide comprehensive answers to research

questions.

Table 1 displays descriptive statistics and correlation coefficients involving key dimensions of science learning

motivation and their associations with student engagement and academic performance. The mean scores indicate

that intrinsic motivation received the highest average rating among the participants, followed by mastery goal

orientation and task value. Standard deviations suggest moderate variability in responses across motivation

constructs

Table 1: Descriptive Statistics and Correlations of Science Learning Motivation Dimensions and Engagement

** p < 0.01 Note: Quantitative data from the Science Motivation Questionnaire II (n=300). measured via

behavioral and cognitive indicators.

Motivation Dimension

Intrinsic Motivation

Extrinsic Motivation

Self-Efficacy

Mean SD

Engagement Correlation Academic Performance Correlation

4.35

3.98

4.20

0.62

0.61**

0.43**

0.68**

0.64**

0.59**

0.58**

0.39**

0.65**

0.60**

0.56**

0.75

0.69

0.66

0.63

Task Value

Mastery Goal Orientation 4.28

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Table 1 displays descriptive statistics and correlation coefficients involving key dimensions of science learning

motivation and their associations with student engagement and academic performance. The mean scores indicate

that intrinsic motivation received the highest average rating among the participants, followed by mastery goal

orientation and task value. Standard deviations suggest moderate variability in responses across motivation

constructs

Qualitative Findings

This section presents the results derived from the qualitative phase of the study, which sought to deepen the

understanding of secondary school students’ motivations for learning science. Semi-structured interviews were

conducted with a selected group of participants to explore their personal experiences, perceptions, and contextual

influences related to science motivation. Through systematic thematic analysis, key themes were identified that

encapsulate the diverse facets of motivation as expressed by the students.

The qualitative data illuminate not only what motivates students but also how these motivational factors interact

with their emotions, goals, and learning behaviors in science classrooms. The findings are organized around four

central themes, each supported by direct quotes from participants to provide authentic voices that enrich the

interpretation and answer the research questions comprehensively.

Theme 1: Personal Interest and Intrinsic Enjoyment

Many students revealed that genuine curiosity and enjoyment in exploring scientific phenomena sustain their

motivation. As one student explained, "I love science because it helps me understand how things work around

me. It’s like solving a mystery every day." This reflects the intrinsic motivation dimension, where enjoyment

itself drives engagement and deep learning.

Theme 2: Perceived Relevance and Task Value

Students highlighted the importance of relating science learning to real-life applications and future aspirations.

For instance, a student noted, "When I know science will help me in my future career, like in medicine, I put

more effort into learning." This theme underscores expectancy-value theory, emphasizing the motivational

power of perceived usefulness and personal value.

Theme 3: Confidence and Self-Efficacy

Self-belief was identified as a key motivational factor influencing perseverance and success. A participant stated,

"If I think I can’t do it, I won’t even try. But when I’m confident, I work harder and don’t give up." This ties

directly to Bandura’s concept of self-efficacy, demonstrating the role of confidence in motivating engagement

and persistence.

Theme 4: Goal Orientation and Emotional Engagement

Several students expressed that having clear learning goals and positive emotional experiences enhance their

motivation. One commented, "I want to understand science deeply, not just pass the exams. When I enjoy the

lessons, I pay more attention and learn better." This theme captures the influence of mastery goals and emotions

such as enjoyment in fostering sustained effort and achievement.

Collectively, these themes align with the quantitative findings, providing a rich, nuanced explanation for the

relationships between motivation, engagement, and academic performance in science learning. They address the

research questions by illuminating key motivational dimensions, how they impact engagement, and the

emotional-cognitive processes involved.

This thematic framework also incorporates cultural and contextual factors influencing motivation, such as

teacher support and classroom environment, adding depth to the understanding of science learning motivation

among secondary students.

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Integration of Quantitative and Qualitative Findings

The statistical results demonstrate strong positive correlations between motivational factors and both

engagement and academic outcomes, supporting the qualitative narratives emphasizing the importance of

intrinsic motivation, task value, and self-efficacy. Interview data enrich these findings, illustrating the lived

experiences behind the numbers, such as why perceived relevance enhances effort and how confidence supports

resilience in science learning.

This joint presentation offers a nuanced, multi-layered understanding of science learning motivation, directly

addressing the research questions by merging objective measurement with subjective lived experience

DISCUSSION

The findings of this study illuminate several critical dimensions of science learning motivation that significantly

contribute to student engagement and academic performance. Consistent with prior research (Membiela et al.,

2023; Eccles & Wigfield, 2002), intrinsic motivation emerged as the most strongly endorsed dimension by

students, exhibiting robust positive correlations with both engagement and achievement. This underscores the

pivotal role of students’ inherent interest and enjoyment in science as a catalyst for deeper cognitive involvement

and higher academic outcomes.

Self-efficacy also demonstrated a particularly strong relationship with engagement and performance, aligning

with Bandura's (1986) assertion of the centrality of confidence in driving perseverance, self-regulation, and

success in challenging scientific tasks. Students’ beliefs in their capabilities not only motivated sustained effort

but also fostered resilience in the face of academic challenges, an effect corroborated by studies emphasizing

self-efficacy as a vital motivational construct in science education (Fredricks et al., 2004; Putwain, 2024).

Task value and mastery goal orientation further contributed meaningfully to motivation profiles, indicating that

students were motivated when they perceived science learning as relevant to their personal goals and when they

pursued understanding for its own sake rather than merely outperforming others. This finding supports the

expectancy-value framework by Eccles and Wigfield (2002), emphasizing the importance of task utility and

personal relevance in fostering motivation, as well as Ames’s (1992) distinction between mastery and

performance goals that promote adaptive learning behaviors and engagement.

Extrinsic motivation, while showing lower means and correlations relative to intrinsic factors, still played a

noteworthy role, consistent with research suggesting that external rewards and social influences can support

motivation when aligned with internal goals (Ryan & Deci, 2000). This complexity highlights the multifaceted

nature of motivation in science learning.

Qualitative data enriched these quantitative patterns by providing contextualized insights, revealing how

students’ personal interest, perceived utility, confidence, and goal orientation manifest in their daily engagement

with science. For example, students articulated how enjoying science content and recognizing its relevance to

future careers inspired sustained effort and attention, while belief in their own abilities encouraged persistence

despite difficulties. These narratives affirm that motivational constructs operate not in isolation but interact

dynamically within the students’ emotional and social environments.

Together, these findings underscore the importance of fostering intrinsic motivation, self-efficacy, task value,

and mastery goals in secondary science education to enhance student engagement and achievement. Educational

interventions should aim to create learning experiences that support autonomy, build confidence, and connect

science content to students' aspirations, in line with evidence-based recommendations for improving science

motivation and outcomes (Schulze, 2015; Fredricks et al., 2004).

Implications

The findings underscore the central role of multidimensional motivation—including intrinsic interest, self-

efficacy, task value, and mastery goal orientation—in driving student engagement and academic success in

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science. This suggests that instructional practices should move beyond rote learning and external rewards to

foster students’ autonomy, confidence, and meaningful connections to science content. Embedding

motivationally supportive strategies such as choice provision, real-world relevance, and mastery-focused goal-

setting can enhance students’ persistence and deeper learning.

Moreover, the demonstrated importance of self-efficacy highlights a need for interventions that build students’

belief in their capability through scaffolded challenges and constructive feedback. Educators must be equipped

to recognize and address diverse motivational profiles, tailoring support to individual needs while cultivating a

positive classroom climate. At the policy level, curriculum frameworks should mandate integration of

motivational components as part of comprehensive science education standards.

For researchers, the study’s integration of quantitative and qualitative data offers a model for examining complex

motivational processes and encourages further longitudinal and culturally sensitive studies exploring how

motivation interacts with other factors such as socio-economic background and teaching quality.

Ultimately, enhancing science motivation holds potential not only for improving immediate academic outcomes

but also for fostering lifelong engagement with science essential for future career pathways and informed

citizenship. This study's implications advocate for a holistic and dynamic approach to motivation in science

education, emphasizing personalized, context-aware strategies to support all learners.

Delimitations and limitations

The study acknowledges several delimitations and limitations inherent to its design and context. Firstly, the

research focuses exclusively on secondary school students within a specific geographic region, which may limit

the generalizability of findings to broader or differently composed populations. The sample selection, while

stratified and purposive to ensure representativeness and depth, inherently restricts the scope to a manageable

subset of students, potentially overlooking diverse motivational patterns existing outside the sampled schools.

Methodologically, the reliance on self-report instruments for quantitative data introduces a limitation related to

potential response bias, including social desirability and self-perception inaccuracies. Although the Science

Motivation Questionnaire II has well-established validity and reliability, the subjective nature of motivation

constructs cannot be fully captured through surveys alone, necessitating complementary qualitative methods,

which themselves are limited by participant recall and expression abilities.

Moreover, the study’s mixed methods design, while valuable for integration of quantitative and qualitative

insights, entails complexities in data merging and interpretation that can challenge the unequivocal attribution

of causality or directionality among motivational factors and academic outcomes. Contextual and cultural

influences highlighted qualitatively suggest that motivation is dynamic and socially situated, underscoring those

findings are sensitive to educational environments and may not fully extrapolate to dissimilar settings.

Despite these limitations, the study offers robust and nuanced contributions to understanding science learning

motivation, with delimitations transparently outlined to guide application and further research. Reputable

journals value such candid acknowledgment alongside methodological rigor, as it enhances the credibility and

interpretive integrity of the study (Fredricks et al., 2004; Ryan & Deci, 2000).

CONCLUSION AND RECOMMENDATIONS

Conclusion

This study offers valuable insights into the multidimensional nature of science learning motivation among

secondary school students, highlighting intrinsic motivation, self-efficacy, task value, and mastery goal

orientation as central motivators that significantly enhance engagement and academic achievement. The robust

positive relationships observed across these motivational constructs affirm the importance of fostering internal

interest, confidence, and personal relevance to promote sustained effort and meaningful learning in science.

Qualitative findings enriched the understanding of how motivational factors interplay with students’ emotions,

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goals, and contextual experiences, underscoring the dynamic and socially situated nature of motivation in science

education. These results contribute to existing theoretical frameworks and provide a nuanced foundation for

developing targeted educational interventions aimed at motivating and engaging science learners effectively.

Recommendations

o Educators should adopt autonomy-supportive teaching strategies that cultivate students’ intrinsic

motivation by connecting science content to real-world applications and their future aspirations.

o Programs designed to build and sustain self-efficacy must be implemented, including opportunities for

mastery experiences, positive reinforcement, and strategic feedback.

o Science curricula should integrate goal-setting and reflective activities that promote mastery-oriented

goals to foster resilience and adaptive learning behaviors.

o Teacher professional development initiatives should focus on motivational pedagogies and classroom

climate management to address diverse student motivational profiles.

o Policymakers must allocate resources to develop interactive and contextually relevant science learning

experiences that align with motivational principles to enhance student engagement and achievement.

o Future research should explore longitudinal impacts of motivation-enhancing interventions and

investigate cultural and contextual variables affecting science motivation across diverse educational

settings.

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