The impact of instructional behaviors on learning motivation via subjective task value in high school students in Cambodia

Instructional behavior plays a key role in learning motivation. In many developing countries, students’ learning motivation needs to be restored, primarily through effective teaching. This

research investigated the impact of instructional behaviors on learning motivation among high school students in Cambodia, emphasizing the mediating role of subjective task value. This study

was conducted in three provinces of Cambodia, and a sample was obtained by convenience sampling. A total of 515 participants (42.72% male and 56.70% female) were lower secondary and high

school students. Structural equation modeling (SEM) was applied to test the proposed relationship between the two first-order constructs of instructional behaviors and learning motivation.

The results revealed direct positive associations between all of the constructs in the model. Autonomy support was a significant predictor of both subjective task value and intrinsic

motivation. Cooperative learning support was significantly associated with only subjective task value and had no direct effect on intrinsic motivation. Conversely, video lecture support

positively predicted extrinsic motivation. Additionally, subjective task value played an important role as a mediator of the relationship between autonomy support and cooperative learning

support. These results emphasize the importance of developing instructional behaviors to support students’ learning motivation.

Flexible education to cope with difficult and restrictive situations is the most important thing to implement instantly1,2. Flexible education is able to rapidly help face challenging times

and tasks to restore the educational system, which increases the human capital linked to social needs and global trends3,4. The need- and trend-related education of society plays a crucial

role in achieving the economic goals of each country5. Developing countries are hindered by various obstacles more than developed countries are during difficult times6,7. Hence, flexible

education should be supervised simultaneously but this is difficult in developing countries8. The gap between developed and developing countries might be much narrower if all stakeholders

jointly concentrate and show resolve, especially concerning teacher instructional methods. Specifically, students’ educational outcomes are greatly affected by teaching behavior through

efficient instructional methods9.

During and after the COVID-19 pandemic, many countries faced a lack of ample support for academic study10. Students experienced a decline in academic motivation, potentially due to the rapid

changes in learning contexts and formats brought by the pandemic11. Students with low learning motivation and those from low-income families experienced the most hardships during the

COVID-19 pandemic12. Students may lack the motivation to succeed in their studies if they do not perceive the value of learning or if teachers fail to create an engaging and supportive

learning environment13. Students will be pessimistic after learning without motivation. Motivation plays a crucial role in effective learning, as it not only enhances academic performance

but also promotes positive behavior and a fulfilling student experience. Understanding how to inspire children and young people to learn is essential for providing them with the best

foundation for success in life13. Conesa et al.14 conducted an analysis of the basic psychological needs of students in classrooms at the elementary and secondary levels. The findings

highlight the crucial role of teachers in addressing and supporting students’ psychological needs to foster an effective learning environment. Many studies suggest that to promote learning

motivation in various situations, including difficult times, flexible instructional behaviors may be appropriate1,11. Thus, teachers’ teaching behavior is an important predictor of students’

academic motivation and positive learning outcomes. Therefore, teachers should adopt effective teaching strategies, such as autonomy support15,16, video lecture support17, and cooperative

learning18 to increase learners’ motivation, particularly in challenging educational contexts such as the recent COVID-19 pandemic and in online courses19,20.

Enhancing learning motivation among high school students requires effective teaching strategies that promote spontaneous engagement and intrinsic interest. According to existing studies, one

of the most effective teaching strategies is based on self-determination theory (SDT)21. To promote students’ intrinsic motivation, SDT highlights the importance of meeting their core

psychological demands for autonomy, competence, and relatedness. Students’ willingness to study can be significantly increased by teachers who offer education that encourages autonomy,

cultivates a sense of cooperative learning, provides technologies for learning, and provides an environment that encourages students to use such technologies22. These strategies work

especially well for assisting high school students in managing their learning aspirations and encouraging sustained academic achievement. Learners who become accustomed to support through

that instructional behavior have higher intrinsic and extrinsic motivations.

Additionally, some studies have explored the role of instructional behaviors and digital technologies in enhancing students’ learning motivation and academic outcomes23. These studies focus

primarily on the direct impacts of instructional strategies and technological tools on motivation. However, few studies have examined a comprehensive framework incorporating multiple

instructional dimensions or investigating how instructional behaviors influence subjective task value and intrinsic and extrinsic motivations. This is particularly the case with studies on

subjective task value as a mediating variable. To address these gaps, this study conceptualizes learning motivation by examining its relationship with instructional behaviors, specifically

autonomy support, video lecture support, and cooperative learning. The aim is to understand how these instructional behaviors contribute to shaping subjective task value and intrinsic and

extrinsic motivations, which are key components of learning motivation.

Restoring students’ learning motivation after the COVID-19 pandemic should be considered a key focus in designing effective instructional strategies to prepare for future crises. Previous

studies have identified autonomy support as one of the strongest predictors of motivation15,16, although its influence may vary across different contexts, as shown in prior research.

Additionally, video lecture support24 and cooperative learning methods25,26 have been linked to enhanced learning motivation. In light of these findings, this research seeks to address the

following practical and effective research objectives.

This study investigates the relationship between efficient instructional behavior and learning motivation among school students in Cambodia. Specifically, it examines whether subjective task

value mediates the effects of different dimensions of instructional behavior on learning motivation.

Motivation theories break down motivation into intrinsic and extrinsic motivations and subjective task value. According to SDT27, intrinsic motivation refers to whatever inspires people to

learn or work internally, such as curiosity, self-competence, self-interest, and mastery learning. In contrast, extrinsic motivation refers to whatever inspires people to learn or work

externally, such as punishment, pressure, grades, self-expression, and other forms of persuasion27,28. When learners perceive intrinsic motivation, they consistently endeavor to perform

tasks and engage in learning procedures29. Intrinsic motivation encourages learners to engage in learning with enthusiasm, positive emotions, and self-growth, primarily when supported by a

growth mindset. It fosters more profound learning and self-regulation21,28. However, extrinsic motivation, such as rewards or recognition, is essential to initiate engagement at the early

stages- particularly for those without prior academic success. These external motivators can eventually be internalized, leading to more autonomous learning. Therefore, effective education

should balance both intrinsic and extrinsic motivation to support learners throughout their development21.

Grounded in expectancy-value theory, subjective task value refers to a type of internal motivation driven by the degree of utility, attainment, and intrinsic value30. When learners perceive

academic education, including task value, they gain pleasure and usefulness and set an explicit goal for their future30. Chan et al.31 argued that learning motivation grounded in SDT is

associated with subjective task value grounding expectancy-value theory. Theorists also argue that when intrinsic value forms, intrinsic motivation is grown, whereas when attainment and

utility value form, extrinsic motivation is grown32. Several previous studies revealed that intrinsic and extrinsic motivations are associated with subjective task value33,34. EVT suggests

that motivation depends on students’ expectations of success and the value they place on tasks, including attainment, utility, intrinsic value, and cost30. In contrast, SDT emphasizes

fulfilling psychological needs—autonomy, competence, and relatedness—as the basis of motivation21. SDT is a precursor to EVT, as supportive environments that satisfy these needs help

students build self-determined motivation, enhancing their task value and success expectations. For instance, when teachers support autonomy and competence, students are more likely to

believe in their abilities and value their learning, linking both theories in a developmental sequence of motivational growth30,32.

Instructional behavior improves learners’ proficiency through effective teaching methods23. Centered on previous studies and original theory, efficient instructional behaviors are divided

into three categories: autonomy support, learning structure, and involvement35.

In this study, autonomy support refers to the extent to which teachers encourage students to take ownership of their learning by providing choices, fostering independence, and supporting

self-directed decision-making36. This conceptualization is rooted in SDT21, which defines autonomy as a core psychological need essential for intrinsic motivation. However, within the

framework of instructional behaviors, autonomy support specifically refers to teacher-driven strategies that promote student agency in learning environments, such as allowing students to

select assignments, choose group members, or express opinions about classroom activities37,38. Instructional autonomy support focuses on the external role of teachers in facilitating student

autonomy within structured educational settings39.

Learning structure refers to how well teachers convey learning information through explicit instruction by encouraging them to use follow-up learning strategies40. For example, learners

might be motivated when offered explicit support through mechanical systems in the teaching methodology41. Video lecture support is an influential learning structure that promotes student

learning and engagement, and might be important teaching material for increasing classroom progress and for having fascinating teaching techniques42. Videos linking clear textual and visual

support tools encourage students to be motivated to learn and to be engaged through entire online and offline class activities43,44. Despite growing interest in using videos to support

instruction in learning, many schools in developing countries and vocational education sectors still have little understanding of how different online video types or styles can facilitate

student learning45.

Learning involvement refers to the degree of self-connection between learners, as well as between learners and teachers collaboratively in the learning process46. Previous studies have

considered cooperative learning as a type of learning involvement47. Teachers’ consistent involvement improve students’ social skills48. Previous studies have revealed that cooperative

learning is the most important predictor of intrinsic motivation and subjective task value47.

On the basis of the theoretical foundation and previous research discussed above, this study formulated the conceptual framework shown in Fig. 1. This research seeks to fill gaps in existing

research by investigating the effects of first-order constructs of instructional behaviors (autonomy support, video lecture support, and cooperative learning) on first-order constructs of

learning motivations (subjective task value, intrinsic motivation, and extrinsic motivation). Additionally, this study examines the moderating role of subjective task value in these

relationships.

In the hypothetical model, this study initially assumed that effective instructional behaviors directly influence motivation based on established theories and empirical evidence in

educational psychology. Research in STD28,49 and EVT32 suggests that instructional behaviors directly impact students’ motivation. Studies have consistently demonstrated that teacher

behaviors that promote a positive learning environment, support student autonomy, and reinforce task value lead to increased motivation and engagement50.

Conceptual framework of the relationship between the first-order constructs instructional behaviors and learning motivations.

This study employed a quantitative cross-sectional research design. Convenience random sampling was used to select lower secondary and high school students from three provinces in Cambodia.

For this analysis, the participants consisted of a total of 515 students; 42.72% were male, and 56.70% were female. They had been studying in grades 8 (n = 166, 32.23%), 9 (n = 138, 26.80%),

and 10 (n = 211, 40.97%). The average age of the participants was 15.08 years (SD = 1.08), ranging from 13 to 18 years. The sample size was determined by the rule of thumb to select an

appropriate sample size. According to previous research51, a suitable sample should consist of five to ten times the number of items in the research model.

In support of this study, data were gathered through a survey of high school students in Cambodia by using a self-report questionnaire and were collected between 1 February and 28 February

2023. Ethical approval for this study was obtained from the research ethics committee of the Royal University of Phnom Penh, Cambodia (136/2023 RUPPKS). A permission letter was sent to the

principals of the high schools to request collaboration. Students were free to withdraw their participation in the questionnaire at any time. The data collected from the volunteer students

were kept confidential, and only the research team had access to the data. The privacy and anonymity of the participants were maintained throughout the study period.

To assess the students’ perceptions of learning motivation, the learning motivation questionnaire of Pintrich et al.52 was adapted for this study. Pintrich’s Motivated Strategies for

Learning Questionnaire (MSLQ) is designed to assess different aspects of student motivation and aligns closely with SDT, especially in how it distinguishes between types of motivation. While

SDT highlights the role of intrinsic motivation, which stems from fulfilling basic psychological needs, the MSLQ captures both intrinsic and extrinsic motivation. This makes it a valuable

tool for understanding students’ motivational experiences, often shaped by teaching practices and the learning environment.

This instrument consists of 16 items with three subscales (Table 1), namely, (a) subjective task value (six items, e.g., “I think the course material is useful for me to learn”), (b)

intrinsic motivation (five items, e.g., “I prefer course material that arouses my curiosity, even if it is difficult to learn”), and (c) extrinsic motivation (five items, e.g., “The most

important thing for me is improving my overall grade or grade point average”). The item scores were averaged to create a score for each aspect for each respondent. Higher scores indicated

higher learning motivation. The Cronbach’s alpha coefficients for subjective task value (0.73), intrinsic learning motivation (0.63), and extrinsic learning motivation (0.81) indicated good

internal consistency (Table 2).

The instructional behaviors scale was adapted from prior studies31,47,53. This measurement consists of 15 items that measure 3 components (Table 1), including (a) teachers’ autonomy support

(four items, e.g., “My teacher accepted my suggestions on how to do homework or exercises that I sought”), (b) teachers’ video lecture support (six items, e.g., “The lesson was well

explained in the video lecture”), and (c) cooperative learning support (five items, e.g., “When I did group work, I discussed my ideas with other students in my group”). The Cronbach’s alpha

values for autonomy support, video lecture support, and cooperative learning support were 0.72, 0.79, and 0.76, respectively, suggesting acceptable internal consistency (Table 2).

Both the learning motivation and instructional behavior questionnaires were self-reported measures, without reverse items included. The original questionnaires were in English and were

translated into Khmer using the back-translation technique by two bilingual Cambodian lecturers. After the scales were translated back into English, we compared the Khmer and English

versions of the scales to determine whether each item matched the initial meaning. The Khmer version of the scale was subsequently administered to 50 high school students to evaluate each

item’s appropriateness and face validity before the data were collected. For all the scales, the students were asked to rate each item on a 5-point Likert scale ranging from 1 (strongly

disagree) to 5 (strongly agree). The constructs, dimensions, and items used in this study are shown in Table 1.

Prior to initiating data analysis, the missing data for each variable included in this study were addressed as follows: (1) responses above 10% missing data were excluded from the study, and

(2) variables with missing data below 10%, the missing values were imputed using the observed mean of the corresponding variable. Preliminary analyses and descriptive statistics were used

to describe and summarize the data. Skewness and kurtosis values were checked for the normality of the data. Pearson correlation analysis was employed to assess the hypothesized

relationships between the variables. Before testing the hypothesized causal relationships, we assessed the construct validity of the measurement model using confirmatory factor analysis

(CFA). CFA was performed to validate a measurement model of each aspect of instructional behavior and learning motivation. The convergent validity of the measurement model was verified

through average variance extracted (AVE) values. Scale reliability was assessed through composite reliability (CR), Cronbach’s alpha (α), and McDonald’s omega coefficient. The discriminant

validity of the constructs was assessed via the heterotrait‒monotrait (HTMT) ratio of correlations, the square root of the AVE, the maximum shared variance (MSV), and the average shared

variance (ASV). The accepted level of discriminant validity is an HTMT value less than 0.9054, the square root of the AVE for each construct is higher than the correlation coefficient values

of the other constructs are51, and both the MSV and the ASV are lower than the AVE. The final analysis used structural equation modeling (SEM) with an MLR estimator to test the direct and

indirect relationships among the six constructs.

The goodness of fit of the measurement model and the structural equation model was assessed by the following indices and cutoff criteria: chi-square per degrees of freedom (χ2/df ≤ 3),

comparative fit index (CFI ≥ 0.90), Tucker‒Lewis index (TLI ≥ 0.90), root mean square error of approximation (RMSEA ≤ 0.08), and standardized root mean square residual (SRMR