Basic Psychological Needs and Student Engagement in Work Placement Learning Tasks: A Moderated Mediation Model

Abstract

This study investigates how autonomy, competence, and relatedness needs impact student engagement during work placement learning tasks (student engagement henceforth), mediated by intrinsic motivation-to-know, and moderated by perceived instructor autonomy support. Conducted with 311 undergraduate students across 116 Nigerian firms/organizations, our analysis utilized a cross-lagged panel approach. We found positive associations between autonomy, competence, relatedness, and student engagement. Additionally, intrinsic motivation-to-know mediated the relationships between these needs and student engagement. Perceived autonomy support moderated both direct and indirect effects of competence and relatedness on engagement via intrinsic motivation-to-know, across varying levels. These findings have significant implications for both theory and practice.

Plain language summary

In this study, we rely upon the assumptions of the basic psychological needs theory to examine how autonomy, competence and relatedness might enhance student engagement in work placement learning tasks.

We collected data at three time points from students undertaking work placement learning to learn these effects which were mediated by intrinsic motivation to know and moderated by perceived autonomy support. We found support for our proposed hypotheses that intrinsic motivation to know was an important underlying psychological factor via which such relationships may exist.

Introduction

Work placement learning is defined as “a higher education institution’s planned skills development and employability enhancement program that enables students who are in the penultimate years of their undergraduate program to spend some time in organizations/firms or industries under the supervision of faculty and industry-based supervisors or instructors to gain relevant skills for future paid or self-employment” (Okolie, 2022, p. 2). Previous studies have recognized the positive effects of work placement learning on student skills development (Chukwuedo & Ementa, 2022; Okolie et al., 2023; O’Donovan, 2018), decision-making about future careers (Crawford & Wang, 2015; Okolie et al., 2023), school-to-work transition, employability, and job creation (Nwosu et al., 2022; Okolie, 2022; O’Donovan, 2018), intellectual development, and integrating theoretical concepts into practice (Smith & Hodge, 2019). As Poulter and Smith (2006, p. 43) have explained, “Placement offers students the opportunity to learn about the attitudes of the organization for which they are working and to relate these attitudes to knowledge and skills learned as part of his/her degree.” In some countries, work placement learning is known by various terminologies such as internships, work-based learning, work-integrated learning, and student industrial work experience and can last between 6 months to 1 year (Anjum, 2020; Okolie, 2022; Okolie et al., 2022). For example, in Nigeria, it is known as the Student Industrial Work Experience Scheme. It is a compulsory 6-month program for all undergraduate students in sciences, health, technology, engineering, science/technical education, and business/management (Chukwuedo & Ementa, 2022; Industrial Training Fund, 2013; Okolie et al., 2022). However, a key priority in these debates is the need to learn how student engagement in work placement learning (student engagement henceforth) can improve.

The literature highlights that students undertake work placement learning in firms/organizations to gain skills, knowledge, and attitudes for entering the labor market (Anjum, 2020; Chukwuedo & Ementa, 2022; Okolie et al., 2022). Student engagement, defined as a psychological investment in learning tasks (Newmann et al., 1992), is crucial for achieving these goals. Drawing from Newmann et al. (1992), we conceptualize student engagement in work placement learning as the psychological investment of energy, time, and effort toward acquiring relevant work-related skills for academic completion and post-graduation employment. Previous studies (Fedesco et al., 2019; Olafsen et al., 2021; Orji & Ogbuanya, 2020) have highlighted that conducive learning environments, where students perceive their basic psychological needs as satisfied, can enhance student engagement across various dimensions: behavioral, emotional, cognitive, and agentic. For clarity, we draw upon the previous literature to conceptualize conducive learning environments (Elom et al., 2023; Fedesco et al., 2019; Olafsen et al., 2021; Orji & Ogbuanya, 2020) as characterized by supportive and interactive classroom or workplace learning settings where instructors foster a sense of belonging, encourage open communication, and provide resources tailored to individual learning styles and needs. It prioritizes student well-being, stimulates curiosity, and promotes active engagement through collaborative activities, constructive feedback, and opportunities for hands-on learning experiences. Handelsman et al. (2005) further emphasized that increased engagement leads to better learning outcomes, categorizing it into skills, emotions, interaction, and performance, and providing measurement scales for future research. Despite the recognized importance of student engagement for academic achievement (Froiland & Oros, 2014; Zhang et al., 2022), empirical evidence regarding factors influencing student engagement in work placement learning environments and the psychological mechanisms underlying such influence is lacking. Previous research (Okolie et al., 2023; Orji & Ogbuanya, 2020) has called for further investigation into these factors. Therefore, this study responds to this gap by contributing to the understanding of factors influencing student engagement in skills-learning tasks across various learning environments.

The Basic Psychological Needs Theory (BPNT; Deci & Vansteenkiste, 2004) posits that individuals thrive when their autonomy, competence, and relatedness needs are met. These needs have been linked to positive academic outcomes, including achievement and performance (Fedesco et al., 2019; Niemiec & Ryan, 2009; Olafsen et al., 2021; Parker et al., 2021). Building upon R. M. Ryan and Deci’s (2000) framework, autonomy involves individuals feeling in control of their behaviors and goals, competence pertains to mastering tasks and overcoming challenges, and relatedness encompasses feeling a sense of belonging and care. The satisfaction of these needs is crucial for motivating individuals toward learning tasks (Vansteenkiste et al., 2020). Previous research (e.g., Benlahcene et al., 2021; Dunn & Kennedy, 2019; Reeve et al., 2019) has shown that meeting autonomy, competence, and relatedness needs can enhance various forms of engagement, including behavioral, emotional, cognitive, and agentic engagement. Consequently, in this study, we examine how autonomy, competence, and relatedness influence student engagement in the work placement learning context. Deci and Ryan (2000) assert that individuals need to perceive their goal-directed activities as rewarding and have autonomy over how they pursue their learning goals to succeed. Intrinsic motivation plays a crucial role in influencing student engagement in learning tasks, as students are more likely to engage when tasks align with their interests and satisfy their autonomy, competence, and relatedness needs (e.g., Reeve et al., 2019). Vallerand et al. (1992) categorized intrinsic motivation into three types: intrinsic motivation-to-know (curiosity, exploration, learning goals), intrinsic motivation to accomplish tasks (pleasure from achieving goals), and intrinsic motivation to experience stimulation (sensory pleasure, esthetic experiences, fun, excitement).

Given the focus of the current study on student engagement in the workplace context, which involves exploration of assigned tasks, curiosity in solving complex tasks, and working toward achieving learning goals during work placement learning (e.g., Chukwuedo & Ementa, 2022; Okolie, 2022), we consider only intrinsic motivation-to-know as an important psychological factor via which basic psychological needs satisfaction may influence student engagement. We rely upon the BPNT assumptions to argue that students undertaking work placement learning can better acquire new skills when they explore the learning tasks, develop higher curiosity in solving given tasks, work toward achieving placement learning goals (academic achievement and gaining work-related skills), and emotionally respond to learning contents of their placement learning tasks. Against this backdrop, we consider intrinsic motivation-to-know as a potential mediator variable in the autonomy, competence, relatedness, and student engagement nexuses.

Recent studies (Parker et al., 2021; Reeve & Cheon, 2021) have extended BPNT by highlighting the role of perceived teacher autonomy support in enhancing student engagement in academic activities. Reeve and Cheon (2021, p. 54) defined the teacher autonomy-supportive approach as adopting “a student-focused attitude and an understanding interpersonal tone that enables the skilful enactment of seven autonomy-satisfying instructional behaviours to serve two purposes-support intrinsic motivation and support internalization.”Parker et al. (2021) found that perceived teacher autonomy support significantly enhanced student engagement through self-determination. However, as noted by Okolie et al. (2023), during work placement learning, both faculty and industry-based supervisors play instructional roles, providing guidance, emotional and informational support, and helpful feedback to students. Industry-based instructors also assign placement learning tasks and supervise students for successful completion. Drawing upon previous research (Parker et al., 2021; Reeve & Cheon, 2021; William & Deci, 1996), we conceptualize perceived work placement learning instructor autonomy support (perceived autonomy support henceforth) as the extent to which industry-based instructors make students feel valued and cared for, providing a conducive learning environment to enhance engagement in all tasks. Considering autonomy support as a potential moderator, we posit that it can strengthen the direct and indirect effects of autonomy, competence, and relatedness on student engagement via intrinsic motivation-to-know.

This study has identified several gaps in the literature, including the lack of empirical findings regarding (a) the positive links between autonomy, competence, and relatedness needs and student engagement in the work placement learning context, (b) the mediating effect of intrinsic motivation-to-know in these relationships, and (c) the conditional direct and indirect effects of perceived autonomy support on the autonomy, competence, and relatedness, and student engagement nexus via intrinsic motivation-to-know. Therefore, we rely on the assumptions of BPNT (Vansteenkiste et al., 2020) as an underpinning theory to systematically address these theoretical and empirical gaps and contribute to knowledge. The significant contributions of this study are driven by the need to (a) learn how students can engage more in work placement learning tasks, (b) explore the role of intrinsic motivation-to-know as a psychological factor influencing student engagement in the workplace learning context, (c) examine the potential impact of perceived autonomy support on strengthening the influence of autonomy, competence, and relatedness on student engagement across varying levels, and (d) promote research aimed at enhancing autonomy, competence, and relatedness needs of students in work placement learning settings to inform skills development policies and practices, given the limited empirical findings in this area.

Theoretical and Hypotheses Development

The Basic Psychological Needs Theory (BPNT), a sub-theory of the self-determination theory (Deci & Ryan, 2000), underpins this study. It posits that individuals inherently possess three basic needs: autonomy, competence, and relatedness, which are innate rather than learned traits and operate universally across gender, culture, and time. R. M. Ryan and Deci (2000, p. 68) further explain that when these needs are satisfied, individuals experience positive effects on their natural propensities for growth, integration, constructive social development, and overall well-being. Additionally, the BPNT asserts that fulfilling these basic psychological needs is crucial for fostering enthusiasm, intrinsic motivation, and engagement in adaptive functioning (R. Ryan & Deci, 2017). Previous studies (Benlahcene et al., 2021; Deci & Ryan, 2000; Ejaz et al., 2023) have demonstrated that satisfying learners’ basic psychological needs in any learning environment can enhance trust, motivation, respect, and cooperation between students and instructors. Thus, in a conducive learning environment where students perceive satisfaction with their autonomy, competence, and relatedness needs, they are likely to invest themselves both physically and psychologically in their learning endeavors.

At the core of the self-determination theory, which gave rise to the Basic Psychological Needs Theory (BPNT), motivation has been decomposed into intrinsic and extrinsic forms (Deci et al., 2017; Deci & Ryan, 2000; Dunn & Kennedy, 2019). It is conceptualized as a psychological force that explains individuals’ level of effort and persistence in tackling challenging tasks. In this study, our focus lies specifically on intrinsic motivation-to-know (Vallerand et al., 1992), which elucidates individuals’ investment of time, energy, and effort in pursuing learning goals, exploring tasks, and displaying curiosity and intrinsic intellectuality. It is crucial to recognize that students may encounter various difficult learning tasks where their intrinsic motivation-to-know acts as a driving force toward positive outcomes. Previous research (Deci & Vansteenkiste, 2004) has highlighted that fulfilling individuals’ basic psychological needs can guide their direction and persistence in performance behaviors, thereby enhancing their intrinsic motivation-to-know in learning environments. In light of this discussion and drawing upon the BPNT, we aim to investigate how basic psychological needs (autonomy, competence, and relatedness) may influence student engagement in the work placement learning context, considering the mediating role of intrinsic motivation-to-know and the moderating role of perceived autonomy support.

Linking Autonomy, Competence and Relatedness to Student Engagement

In the work placement learning setting, the need for autonomy reflects students’ desire to experience a sense of free will and self-endorsement for their skills and learning behaviors (e.g., R. Ryan & Deci, 2017). In essence, students are likely to feel autonomous when they can freely express their ideas and opinions on how placement learning activities or tasks can best address their skill needs, exert control over their learning, have choices in how they approach tasks, make decisions based on their values and beliefs, and take responsibility for these decisions and learning actions. When the work placement learning environment is conducive and satisfies students’ autonomy needs, they are more likely to self-regulate their learning and internalize the value of tasks, leading to increased engagement over time. Similarly, the need for competence reflects students’ desire to master learning tasks, acquire various skills, understand how to perform well in work placement learning activities and have confidence in their abilities (e.g., Niemiec & Ryan, 2009). In the work placement learning context, satisfying the need for competence may involve exposing students to challenging skills and learning tasks that allow them to demonstrate their ability to accomplish difficult tasks, fostering a sense of competence, and reinforcing the belief that their learning goals are achievable over time.

The need for relatedness reflects students’ desire for belonging, closeness, and connection with their work placement learning instructors (supervisors). When this need is fulfilled, students may feel more connected to their industry-based instructors and other workplace employees, whom they perceive as significant (Okolie, 2022). Additionally, meeting relatedness needs may help students establish a supportive social network and feel cared for by their instructors or supervisors. Fedesco et al. (2019, p. 2) observed that “the more students perceive the satisfaction of basic psychological needs, the more likely they are to experience effective performance such as greater depth of processing, persistence, and academic achievement.” Drawing upon the assumptions of BPNT (Vansteenkiste et al., 2020), we anticipate that meeting these inherent basic psychological needs in a work placement learning setting may influence students’ development of higher intrinsic motivation-to-know, subsequently increasing their engagement in learning tasks over time. Based on the above discussion and BPNT (Vansteenkiste et al., 2020), we hypothesize that:

• Hypothesis 1: (a) autonomy at T1, (b) competence at T1 and (c) relatedness at T1 would positively associate with student engagement at T3.

The Mediating Role of Intrinsic Motivation-to-Know

As previously elucidated in this study, intrinsic motivation entails students perceiving their learning activities as interesting and having autonomy in determining their approaches to succeed in their tasks. It encompasses students experiencing joy and a sense of accomplishment in their learning process and personal investment toward achieving their goals (Reeve & Cheon, 2021). Vallerand et al. (1992) defined intrinsic motivation-to-know as the “engagement in an activity for the pleasure and satisfaction experienced while learning, exploring, or attempting to understand something new” (p. 1005). This suggests that students in work placement learning may be intrinsically motivated-to-know when undertaking tasks such as developing reports on previous management meetings, participating in the production and testing of new products, designing business plans, reporting customer feedback to their superiors, and deriving pleasure and satisfaction from these activities. To effectively assess intrinsic motivation-to-know in various learning contexts, Vallerand et al. (1992) developed and validated a 4-item scale for measuring intrinsic motivation among students in learning environments. In a study involving higher education agricultural students, Karimi and Sotoodeh (2020) noted that intrinsic motivation could potentially mediate the relationship between basic psychological needs satisfaction and academic engagement. Although their study did not specify the type of intrinsic motivation mediating the relationship (e.g., Vallerand et al., 1992), the present study specifically focuses on intrinsic motivation to know as a mediator—a crucial gap addressed herein. Thus, drawing on the BPNT and previous literature reviewed, we hypothesize that:

• Hypothesis 2: Intrinsic motivation-to-know would mediate the effects of (a) autonomy at T1, (b) competence at T1 and (c) relatedness at T1 and student engagement at T3.

The Moderating Role of Perceived Instructor Autonomy Support

Student engagement refers to students’ active participation and involvement in learning (Parker et al., 2021). It is crucial to explore factors that can enhance both the direct effects of autonomy, competence, and relatedness on student engagement and the indirect effects via intrinsic motivation-to-know. The BPNT emphasizes the importance of providing students with autonomy-supportive teaching and learning environments (William & Deci, 1996). The self-determination theory posits that one’s learning environment can influence individuals’ internal drive and motivation to engage in learning tasks (Ahn et al., 2018; Reeve, 2013; R. M. Ryan & Deci, 2000). Black and Deci (2000) defined autonomy support as individuals in positions of authority, such as work placement learning instructors or supervisors, acknowledging students’ perspectives, feelings, and providing them with relevant information and opportunities for choice while minimizing pressure and demands. Reeve and Cheon (2021) observed that an autonomy-supportive teaching environment can create a conducive learning atmosphere, facilitating intrinsic motivation formation and internalization through a student-centered approach.

Instructors administering learning tasks to students in work placement settings can enhance the direct effects of autonomy, competence, and relatedness on students’ intrinsic motivation-to-know, leading to increased engagement. Additionally, perceived autonomy support plays a crucial role in work placement learning. Drawing upon the BPNT (Vansteenkiste et al., 2020) and previous studies (Parker et al., 2021; Reeve & Cheon, 2021), we argue that instructors fostering autonomy support may empower students to feel autonomous and self-directed, thereby fostering greater engagement. Practices such as offering meaningful choices, emphasizing content relevance, providing rationales, listening to students, and encouraging initiative contribute to perceived autonomy support (Ahn et al., 2018). Consequently, students experiencing higher perceived autonomy support at the outset of work placement learning are likely to exhibit increased intrinsic motivation-to-know, leading to enhanced student engagement throughout the placement period. In light of this discussion, we propose that:

• Hypothesis 3. The interaction between autonomy support at T1 and (a) autonomy at T1, (b) competence at T1 and (c) Relatedness at T1 on intrinsic motivation-to-know at T2 would be positive such that the effects would be stronger for students who perceived high autonomy support.

• Hypothesis 4: Autonomy support at T1 would positively moderate the indirect effects of (a) autonomy at T1, (b) competence at T1 and (c) relatedness at T1 on student engagement at T3 via intrinsic motivation-to-know at T2, and the indirect effects would be conditional upon the level of autonomy support at T1.

The addition of hypotheses 3 and 4 suggests testing a moderated mediation model (Figure 1; Hayes, 2017).

Figure 1.

Conceptual model.

Methods

Procedures and Sample

All university students included in this study were in the penultimate years of their respective degree programs and were currently engaged in work placement learning across various organizations/firms in Nigeria. To recruit participants, we initially identified the course representatives from 14 purposefully selected departments across three Nigerian public universities. These course representatives, totaling 42 across the selected departments and universities, were tasked with overseeing various aspects of their classes and disseminating information among students (e.g., Okolie, 2022). We conducted two separate online meetings with the recruited course representatives to explain the aim of the study and encourage their full participation. Upon agreement, we created a dedicated WhatsApp forum containing an official invitation letter for participation, which the course representatives then shared in their departmental WhatsApp groups for their colleagues to freely join the study.

In the Nigerian higher education system, WhatsApp instant messaging has proven effective for sharing lecture materials and information among students, with many functional forums managed by course representatives (Okolie, 2022). With their assistance, we recruited 311 students within 3 weeks. Of these, 173 (55.63%) were male and 138 (44.37%) were female, aged between 20 and 26 years old, who willingly joined the study via the provided WhatsApp link. Confidentiality was assured, and approvals were obtained from their respective Heads of Department. These students were placed in 116 firms/industries across various Nigerian cities for their work placement learning programs. The firms/industries represented various sectors, including production and repairs (22; 7.07%), sales and marketing (34; 10.93%), media and advertising (29; 9.32%), hospitality (49; 15.76%), real estate (48; 15.43%), financial and banking (37; 11.89%), transportation (16; 5.14%), agriculture and agri-business (35; 11.25%), oil and gas (22; 7.08%), and power/electricity (19; 6.12%). Participants were informed that data would be collected at three-time points (T1, T2, and T3) over 2-month intervals during their work placement learning periods, and they were free to withdraw at any time. This involved responding to the same questionnaire on autonomy, competence, relatedness, intrinsic motivation-to-know, perceived autonomy support, and student engagement across all three measurement points.

We developed a structured questionnaire using Google Form, a free online survey tool, containing all measurement scale items for this study. The questionnaire link was shared in a special WhatsApp forum dedicated to the study. To ensure data completeness, we made all items mandatory in the form. Additionally, to mitigate order effects, we randomized the item statements across all measurement scales. At T1 (the second month into the placement program), all 311 participants completed the questionnaire. The same participants completed the survey at T2 (fourth month) and T3 (sixth month, end of the program). We maintained communication with participants twice monthly to provide updates on data collection progress. Participants received NGN500 worth of phone call credit as incentives at each measurement point (Okolie, 2022). Despite repeated reminders about withdrawal options, there was no attrition, and no missing data occurred due to the mandatory form settings. Participants were distributed across departments as follows: Biochemistry (33; 10.61%), Industrial Chemistry (16; 5.14%), Food Science and Technology (47; 15.11%), Agricultural Economics (39; 12.54%), Crop Science (27; 8.68%), Business Education (14; 4.50%), Vocational and Technical Education (14; 4.50%), Fisheries and Aquaculture (9; 2.89%), Applied Biology and Zoology (22; 7.07%), Computer Science (19; 6.11%), Architecture (9; 2.89%), Civil Engineering (25; 8.04%), Chemical Engineering (21; 6.75%), and Geology (16; 5.14%).

Confirmatory Factor Analysis

We conducted confirmatory factor analysis (CFA) to assess the validity and reliability of the measurement scales, using established thresholds for acceptable data fit: Comparative Fit Index (CFI), Incremental Fit Index (IFI), and Tucker-Lewis Index (TLI) >0.90; Normed Chi-square (χ²/df) < 0.3; Root Mean Square Error of Approximation (RMSEA) < 0.06; Standardized Root Mean Square Residual (SRMR) < 0.08; and PClose > 0.05 (Hu & Bentler, 1999). Additionally, composite reliability (CR) > 0.60 and Cronbach’s Alpha > .70 were used for reliability assessment, while discriminant validity (DV) was evaluated by comparing the square root of average variance extracted (AVE) with the correlations of latent variables in the CFA. Consistent with prior research methods (Nwosu et al., 2022; Okolie, 2022), we reported model fit measures for T1 data and CR, DV, AVE, and Cronbach’s alpha (α) values for T1, T2, and T3 data.

Measures

Autonomy

This was measured by using the 4-item “Autonomy sub-scale” of the Basic Psychological Needs scale (Fedesco et al., 2019). The responses ranged from (1 = Strongly disagree to 7 = Strongly agree). A sample item included: “I feel like I can pretty much be myself in this programme.” The original scale reported Cronbach’s α = .72. In the current study, the one-factor CFA model at T1 showed a good data fit: χ² = 2.86; df = 2; χ²/df = 1.43; CFI = 0.98; TLI = 0.95; IFI = 0.98; SRMR = 0.03; RMSEA = 0.04 and PClose = 0.57. The validity and reliability values at T1 were: CR = 0.92; AVE = 0.81, DV = 0.89, and Cronbach’s α = .93; at T2: CR = 0.93; AVE = 0.79; DV = 0.89 and Cronbach’s α = .94 and at T3: CR = 0.94; AVE = 0.80; DV = 0.90 and Cronbach’s α = .95 respectively.

Competence

The 4-item “competence satisfaction sub-scale” of the Basic Psychological Needs scale (Olafsen et al., 2021) was used. The responses ranged from (1 = Strongly disagree to 5 = Strongly agree). A sample item included: “I feel that I can successfully complete difficult tasks” and Cronbach’s α for the original scale was 0.87. In the current study, the one-factor CFA model at T1 showed a good data fit: χ² = 3.38; df = 2; χ²/df = 1.69; CFI = 0.99; TLI = 0.97; IFI = 0.99; SRMR = 0.03; RMSEA = 0.05 and PClose = 0.35. The validity and reliability values were, at T1: CR = 0.91; AVE = 0.74, DV = 0.86 and Cronbach’s α = .92; at T2: CR = 0.89; AVE = 0.72; DV = 0.85 and Cronbach’s α = .90 and at T3: CR = 0.90; AVE = 0.78; DV = 0.88 and Cronbach’s α = .92 respectively.

Relatedness

This was measured by using the 5-item “Relatedness-Instructor Sub-scale” of the Basic Psychological Needs scale (Fedesco et al., 2019). The responses ranged from (1 = Strongly disagree to 6 = Strongly agree). A sample item included: “The instructors in this programme care about me,” and the original scale reported a Cronbach’s α = .86. In the present study, the one-factor CFA model at T1 yielded a good data fit: χ² = 5.75; df = 5; χ²/df = 1.15; CFI = 0.99; TLI = 0.97; IFI = 0.99; SRMR = 0.02; RMSEA = 0.05 and PClose = 0.61. The validity and reliability values were: at T1; CR = 0.93; AVE = 0.79, DV = 0.89 and Cronbach’s α = .95; at T2: CR = 0.90; AVE = 0.72; DV = 0.85 and Cronbach’s α = .91; and at T3: CR = 0.88; AVE = 0.71; DV = 0.84 and Cronbach’s α = .89 respectively.

Perceived Instructor Autonomy Support

The 6-item short version of the learning climate questionnaire (William & Deci, 1996) which has been widely used in learning environment-based studies (e.g., Black & Deci, 2000; Jang et al., 2009) was used to measure this variable. The scale was designed to measure students’ perceptions of their instructors’ autonomy support in their learning environments (e.g., Parker et al., 2021). Items were rated on a 7-point scale of (1 = Strongly disagree to 7 = Strongly agree). A sample item included: “My instructors try to understand how I see things before suggesting a new way to do things.”Reeve (2013) reported that the original scale showed a strong Cronbach’s α reliability coefficient across multiple waves of data collection (0.87–0.92) respectively. In the present study, the one-factor CFA model showed a good data fit at T1: χ² = 14.22; df = 9; χ²/df = 1.58; CFI = 0.98; TLI = 0.96; IFI = 0.98; SRMR = 0.03; RMSEA = 0.06 and PClose = 0.21. The validity and reliability values were: at T1; CR = 0.91; AVE = 0.75, DV = 0.87 and Cronbach’s α = .92; at T2; CR = 0.94; AVE = 0.78; DV = 0.88 and Cronbach’s α = .95, and at T3; CR = 0.90; AVE = 0.73; DV = 0.85 and Cronbach’s α = .91 respectively.

Intrinsic Motivation-to-Know

We used the 4-item “Intrinsic Motivation-to-Know” sub-scale of the 28-item “Academic Motivation Scale” (Vallerand et al., 1992) to assess the variable. The 4-item sub-scale intrinsic motivation knowledge originally had a Cronbach’s α = .84. Responses ranged from (1 = Not at all to 5 = Exactly). A sample item included: “My studies allow me to continue to learn about many things that interest me.” In the current research, the one-factor CFA model yielded a good data fit at T1: χ² = 4.08; df = 2; χ²/df = 2.04; CFI = 0.98; TLI = 0.95; IFI = 0.98; SRMR = 0.02; RMSEA = 0.05 and PClose = 0.22. The validity and reliability values were; at T1: CR = 0.92; AVE = 0.76, DV = 0.87 and Cronbach’s α = .92; at T2: CR = 0.91; AVE = 0.74; DV = 0.86 and Cronbach’s α = .91, and at T3: CR = 0.90; AVE = 0.72; DV = 0.85 and Cronbach’s α = .89 respectively.

Student Engagement in Work Placement Learning Tasks

This was measured by using the 9-item skills engagement questionnaire (Handelsman et al., 2005). Responses ranged from (1 = Not at all engaged, 6 = Extremely engaged). A sample item included: “I do all the homework problems.” The original scale had Cronbach’s α = .82. In the present study, the one-factor CFA model at T1 had a good data fit: χ² = 48.06; df = 27; χ²/df = 1.78; CFI = 0.97; TLI = 0.94; IFI = 0.97; SRMR = 0.05; RMSEA = 0.07. The validity and reliability values were: at T1; CR = 0.93; AVE = 0.77, DV = 0.88 and Cronbach’s α = .93; at T2; CR = 0.90; AVE = 0.72; DV = 0.85 and Cronbach’s α = .91, and at T3; CR = 0.92; AVE = 0.74; DV = 0.86 and Cronbach’s α = .94 respectively.

Common Method Bias

In addition to efforts made during data collection to reduce common method bias such as using different response ratings scales such as 5, 6, and 7 points, and order effects, we conducted Haman’s one-factor test (Harman, 1967) with all the scales at T1, T2, and T3. The results revealed that common method bias was not an issue in the data; 30%, 28%, and 31% were explained by the first factors at T1, T2, and T3 respectively.

Results

Descriptive Statistics Analysis

Table 1 shows the results of the descriptive statistics and correlations among variables in this study across the three time-point.

Table 1.

Mean, Standard Deviation and Bivariate Correlations of the Study Variables.

		Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1	Autonomy T1	12.03	5.04	1
2	Autonomy T2	13.2	5.77	.40**	1
3	Autonomy T3	14.47	5.41	.45**	−.05	1
4	Competence T1	14.03	3.91	.34**	.16**	.22**	1
5	Competence T2	14.99	4.62	−.01	.02	−.05	.04	1
6	Competence T3	14.86	4.86	.11	.33**	.03	.52**	.06	1
7	Relatedness T1	12.42	3.97	.36**	.15*	.15**	.46**	.08	.23**	1
8	Relatedness T2	14.94	5.7	.12*	.40**	−.06	.23**	.03	.42**	.47**	1
9	Relatedness T3	15.86	4.82	.11	.06	.04	.18**	.11*	.07	.38**	.24**	1
10	Support T1	13.11	5.89	.12	−.02	.09	−.08	−.04	−.03	.02	−.08	.05	1
11	Support T2	12.63	5.58	.04	.03	.12*	.01	.05	.08	.05	−.02	.04	.57**	1
12	Support T3	13.77	5.32	−.09	−.13*	.09	−.05	.04	−.11	.03	−.05	.01	.41**	.35**	1
13	IM-Know T1	14.38	4.76	.14*	−.08	.19**	.16**	−.06	−.05	.20**	−.07	−.01	.17**	.13*	.18**	1
14	IM-Know T2	13.2	3.01	.32**	.18**	.22**	.46**	−.03	.27**	.41**	.28**	.16**	.07	.03	.04	.41**	1
15	IM-Know T3	12.88	5.32	.08	−.05	.11	.18**	−.16**	.01	.14*	−.05	−.09	−.03	−.06	−.12	.37**	.37**	1
16	Engagement T1	14.11	4.92	.13*	.10	.06	.20**	−.03	.04	.15*	.14*	.02	.02	.04	−.09	.14	.15**	.02	1
17	Engagement T2	15.08	4.19	.08	.10	.02	−.01	.44**	−.04	.01	.07	.09	−.06	.05	−.07	−.11	−.05	−.22	−.04	1
18	Engagement T3	14.51	3.64	.40**	.13*	.26**	.44**	−.04	.25**	.46**	.25**	.13*	.01	.01	−.16	.20**	.49**	.19**	.35**	−.05

Note. T = time; Support = perceived instructor autonomy support; IM-Know = intrinsic motivation to know; Engagement = student engagement in work placement learning skills acquisition tasks.

**

p < .01. *p < .05.

Hypotheses Testing

We conducted a cross-lagged panel model analysis using path analysis in AMOS version 24 to test the proposed hypotheses. For clarity, the cross-lagged panel approach is a powerful analytical technique used to explore causal relationships between variables measured at multiple time points. This method allows researchers to assess the direction and strength of relationships between variables over time, providing insights into potential causal pathways (e.g., Okolie, 2022; Sánchez-Álvarez et al., 2019).

We calculated the estimates using the maximum likelihood with bootstrapping (5,000 resampling at 95% bias-corrected confidence intervals (95% CI). The cross-lagged panel model included autonomy at T1, competence at T1 and relatedness at T1 (independent variables), autonomy support at T1 (moderator variable), intrinsic motivation-to-know at T2 (mediator variable), autonomy support at T1_x_autonomy at T1, autonomy support at T1_x_competence at T1, and autonomy support at T1_x_relatedness at T1 (interactions) and student engagement at T3 (dependent variable). Also, other variables not hypothesized but permitted to correlate within the three waves in the model include autonomy at T2 and T3, competence at T2 and T3, relatedness at T2 and T3, autonomy support at T2 and T3, intrinsic motivation-to-know at T1 and T3, and student engagement at T1 and T2. This single full panel design tested the direct effects, indirect effects and moderation effects simultaneously and the model showed an acceptable structural model fit: χ² = 35.44; df = 20, χ²/df = 1.77, CFI = 0.97, IFI = 0.96, TLI = 0.93, SRMR = 0.03, RMSEA = 0.05 and PClose = 0.44 (Hu & Bentler, 1999).

As shown in Table 2, the results of hypotheses 1a, 1b, and 1c revealed that autonomy at T1 (β = .17, p < .001), competence at T1 (β = .18, p < .001) and relatedness at T1 (β = .22, p < .001) associated positively with student engagement at T3. This indicates that the hypotheses were accepted. Although not hypothesized in this study, the analysis showed positive links between autonomy at T1 (β = .12, p < .05), competence at T1 (β = .29, p < .001) and relatedness at T1 (β = .33, p < .001) and intrinsic motivation-to-know at T2. Intrinsic motivation-to-know at T2 had a positive effect on student engagement at T3 (β = .24, p < .001). While these direct effects were not hypothesized, testing to confirm them in this study based on previous studies (Parker et al., 2021; R. Ryan & Deci, 2017) is relevant, aligns with the BPNT postulations, and serves as a foundation for testing the hypothesized path model. The mediation analysis at a three-time study was calculated using the user-defined estimands function of the AMOS software at 95% CI and the estimates were considered statistically significant at 0.001 or 0.05 levels if the 95% CI for the mediation effects do not include Zero (Shrout & Bolger, 2002). As presented in Table 2, the results of hypotheses 2a, 2b, and 2c revealed that intrinsic motivation-to-know at T2 mediated the effects of autonomy at T1 (β_indirect = .02, p < .05, 95% CI [0.01, 0.05]), competence at T1, (β_indirect = .06, p < .001, 95% CI [0.02, 0.13]), and relatedness at T1 (β_indirect = .07, p < .001, 95% CI [0.003, 0.12]) on student engagement at T3. Thus, hypotheses 2a, 2b, and 2c were accepted. However, the total effects were significant and positive (β_autonomy = .14; p < .001, 95% CI [0.08, 0.22]; β_competence = .23; p < .001, 95% CI [0.14, 0.40] and β_relatedness = .28; p < .001, 95% CI [0.15, 0.45]). The cross-lagged panel analysis at 95% CI totally and significantly explained 34.60% variance in intrinsic motivation-to-know at T2 (95% CI [0.21, 0.48]; p < .001) and 39.10% in student engagement (95% CI [0.24, 0.51]; p < .001).

Table 2.

Unmoderated Direct and Indirect Effects.

Direct effects	Estimates	Standard errors	95% Confidence intervals
			[Lower limit, Upper limit]
Autonomy at T1 Student Engagement at T3	0.17***	0.05	[0.06, 0.28]
Competence at T1 Student Engagement at T3	0.18***	0.07	[0.08, 0.34]
Relatedness at T1 Student Engagement at T3	0.22***	0.08	[0.07, 0.39]
Autonomy at T1 Motivation at T2	0.12*	0.06	[0.01, 0.24]
Competence at T1 Motivation at T2	0.29***	0.05	[0.16,0.41]
Relatedness at T1 Motivation at T2	0.33***	0.05	[0.19, 0.44]
Mediator
Motivation at T2 Student Engagement at T3	0.24***	0.07	[0.10, 0.41]
Indirect effects
Autonomy at T1 Motivation at T2 Engagement at T3	0.02*	0.02	[0.01, 0.05]
Competence at T1 Motivation at T2 Engagement at T3	0.06***	0.02	[0.02, 0.13]
Relatedness at T1 Motivation at T2 Engagement at T3	0.07***	0.02	[0.03, 0.12]

Note. T = time; Motivation = intrinsic motivation to know; Autonomy Support = perceived instructor autonomy support; Student Engagement = student engagement in work placement learning skills acquisition tasks. All standardized regression weights are reported.

***

p < .001. *p < .05.

The analysis of hypothesis 3, which proposed the interaction effects of autonomy support at T1 and the independent variables on intrinsic motivation-to-know at T2 (Table 3) was also conducted in the same full panel model using the user-defined estimands function in AMOS following the procedures (Hayes, 2017). We mean-centered the autonomy support at T1 and independent variables at T1, multiplied the terms to create the interaction terms (Shieh, 2011) and computed the conditional direct and indirect effects at −1SD (low), Mean (average) and +1SD (high) along the autonomy support at T1 (Hayes, 2017). The analysis (Table 3) showed that the interactions of autonomy support at T1_x_autonomy at T1 was not statistically significant (β = .02, p = .73); but autonomy support at T1_x_competence at T1 (β = −.14, p < .05) and autonomy support at T1_x_relatedness (β = .13, p < .05) on intrinsic motivation-to-know at T2 were statistically significant. The conditional effects at the three levels (simple slope) along the moderator autonomy support at T1 on intrinsic motivation-to-know at T2 (Table 3) shows that at a low level (β = .42, p < .001) and at an average level (β = .29, p < .001) the interaction (i.e., autonomy support at T1_x_competence at T1 were positive and significant but not significant at a high level (β = .17, p = .10). Also, the conditional effect of the interaction of autonomy support at T1_x_relatedness at T1 were statistically significant and positive at low level (β = .24, p < .001) at average (β = .33, p < .001) and high (β = .43, p < .001) levels. Thus, hypotheses 3a, 3b, and 3c were accepted.

Table 3.

Conditional Direct and Indirect Effects.

	Estimates	Standard Errors	95% Confidence intervals
			[Lower limit, Upper limit]
Interaction effects
Autonomy support at T1_x_Autonomy at T1 Motivation at T2	0.02	0.08	[−0.08, 0.11]
Autonomy support at T1_x_Competence at T1 Motivation at T2	−0.14*	0.07	[−0.24, −0.03]
Autonomy support at T1_x_Relatedness at T1 Motivation at T2	0.13*	0.07	[0.03, 0.24]
Conditional Effects of the Moderator (Autonomy Support at T1_x_ Competence at T1) on Intrinsic Motivation to Know at T2
Low (−1SD)	0.42***	0.06	[0.22, 0.65]
Average (Mean)	0.29***	0.05	[0.16, 0.42]
+1SD (High)	0.17	0.06	[−0.04, 0.34]
Conditional Effects of the Moderator (Autonomy Support at T1_x_Relatedness at T1) on Intrinsic Motivation to Know at T2
Low (−1SD)	0.24***	0.07	[−0.45, 0.21]
Average (Mean)	0.33***	0.08	[0.02, 0.49]
+1SD (High)	0.43***	0.09	[0.36, 0.75]
Conditional Indirect Effects
Competence at T1 Motivation at T2 Student Engagement at T3
Low (−1SD)	0.09***	0.02	[0.03, 0.23]
Average (Mean)	0.06***	0.02	[0.02, 0.13]
High (+ 1 SD)	0.04*	0.03	[0.01, 0.08]
Relatedness at T1 Motivation at T2 Student Engagement at T3
Low (−1SD)	0.05***	0.03	[0.02, 0.11]
Average (Mean)	0.07***	0.02	[0.03, 0.15]
High (+1 SD)	0.09***	0.02	[0.03, 0.25]

Note. T = time; Motivation = intrinsic motivation to know; Autonomy Support = perceived instructor autonomy support; Student Engagement = student engagement in work placement learning skills acquisition tasks. All standardized regression weights are reported.

***

p < .001. *p < .05.

Table 3 also shows the results of the conditional indirect effects of autonomy support at T1_x_competence at T1 and autonomy support at T1_x_relatedness at T1 on student engagement at T3 via intrinsic motivation-to-know at T2 (hypothesis 4) at three levels (−1SD, the Mean and +1SD) along the moderator (autonomy support at T1). For the conditional indirect effects of autonomy support at T1_x_competence at T1 on student engagement at T3 via intrinsic motivation-to-know at T2, the analysis showed positive and significant indirect effects at −1SD (β_low = .09, p < .001, 95% CI [0.03, 0.23]) and the Mean (β_average = .06, p < .001, 95% CI [0.02, 0.13]), whereas it was not significant at +1SD (β_high = .04, p < .05, 95% CI [0.01, 0.08]). For the conditional indirect effects of autonomy support at T1_x_relatedness at T1 on student engagement at T3 via intrinsic motivation-to-know at T2, the results showed positive and significant effects at −1SD (β_low = .05, p < .001, 95% CI [0.02, 0.11]), the Mean (β_average = .07, p < .001, 95% CI [0.03, 0.15]) and +1SD (β_high = .09, p < .001, 95% CI [0.03, 0.25]). The index of moderated mediation model (Hayes, 2017) for the Omnibus tests of conditional indirect effects at 95% CI revealed that autonomy at T1 (β_Index = .01, p = .69, 95% CI [−0.08, 0.13]) was not statistically significant, whereas, competence at T1 (β_Index = −.13, p < .001, 95% CI [−0.33, −0.03]) and relatedness at T1 (β_Index = .10, p < .001, 95% CI [0.02, 0.25]) were statistically significant, indicating that the indirect effects of competence at T1 and relatedness at T1 were conditional upon the levels of perceived autonomy support at T1. Hence, hypotheses 4b and 4c were accepted whereas hypothesis 4a was rejected.

Discussion

The current study employed the BSNT assumptions (R. M. Ryan & Deci, 2000; Vansteenkiste et al., 2020) to illustrate that when students’ basic psychological needs (autonomy, competence, and relatedness) are adequately fulfilled in the work placement learning environment, they are more likely to demonstrate higher levels of student engagement through intrinsic motivation-to-know. Moreover, perceived autonomy support is expected to enhance these positive effects across all levels (i.e., low, average, and high). A key strength of this study lies in its utilization of a time-lagged data collection approach, which elucidates how independent variables influence both the dependent and mediator variables over time. Additionally, employing a robust cross-lagged panel analysis enabled the exploration of relationships between variables across the three waves, ensuring reliable findings (Sánchez-Álvarez et al., 2019; Shrout & Bolger, 2002). To our knowledge, this research represents the first attempt to apply BPNT assumptions to examine a moderated mediation model involving basic psychological needs and student engagement in skill learning tasks, with measurements taken at three time points spaced 2 months apart, within the context of work placement learning. Initially, we posited that students perceiving satisfaction of their autonomy, competence, and relatedness needs in the work placement learning setting would demonstrate increasing engagement in their learning tasks over time (Hypotheses 1a, 1b, and 1c), findings that were consistent with our hypotheses.

These findings were anticipated based on the BPNT assumptions (Deci & Vansteenkiste, 2004) and previous research establishing positive relationships between the three basic psychological needs and student engagement in various academic settings (e.g., Froiland & Oros, 2014; Karimi & Sotoodeh, 2020; Niemiec & Ryan, 2009). However, confirming them in the present study is crucial, as they provide foundational hypotheses for testing other relationships. The results imply that students who felt empowered to control their learning goals, experienced a sense of mastery over assigned tasks, and perceived support and encouragement from workplace supervisors or instructors, exhibited higher engagement in their skill-learning tasks over time. These findings underscore the importance of promoting students’ satisfaction of basic psychological needs in all learning environments to enhance their engagement in learning tasks (Niemiec & Ryan, 2009; Reeve, 2012).

Not hypothesized in this study were the findings that demonstrated a positive association between autonomy, competence, and relatedness needs with intrinsic motivation-to-know, which in turn was positively associated with student engagement over time. These significant and positive findings align with the BPNT assumptions (Deci & Vansteenkiste, 2004) and previous research indicating a positive relationship between basic psychological needs and intrinsic motivation, which can subsequently influence student engagement positively (e.g., Fedesco et al., 2019; Karimi & Sotoodeh, 2020; Reeve, 2013). Although not initially hypothesized, these positive links are noteworthy and provide a foundation for the moderated mediation model proposed in this study. The confirmation of these direct relationships also supports existing literature (Vallerand et al., 1992) on the potential positive role of intrinsic motivation-to-know as both a predictor and mediator variable in the nexus between basic psychological needs satisfaction and student engagement.

Our findings indicate that the basic psychological needs of autonomy, competence, and relatedness had positive indirect effects on student engagement in skills learning tasks over time via intrinsic motivation-to-know (Hypotheses 2a, 2b, and 2c). These significant results suggest that satisfying these basic psychological needs not only contributes to higher student engagement in their skills learning tasks over time but also fosters higher intrinsic motivation-to-know, which further enhances their engagement in these tasks. This underscores the significance of intrinsic motivation-to-know as a crucial underlying psychological factor through which basic psychological needs influence student engagement in skills learning tasks in the workplace setting. Our findings align with previous research (Karimi & Sotoodeh, 2020; Vallerand et al., 1992; Zhang et al., 2022) and BPNT assumptions (R. M. Ryan & Deci, 2000; Vansteenkiste et al., 2020), indicating that students who feel satisfied with their basic needs for autonomy, competence, and relatedness are more likely to engage in learning activities, particularly when they have high intrinsic motivation. These findings highlight the importance of encouraging students to find enjoyment in their skills-learning activities, as it can stimulate exploration, curiosity in problem-solving, goal-setting, and emotional investment in learning materials, ultimately facilitating the successful completion of learning tasks over time. Thus, our findings complement previous studies (Benlahcene et al., 2021; Karimi & Sotoodeh, 2020) that have examined the significant impact of intrinsic motivation on student engagement.

We found that autonomy support at T1 interacted with competence at T1 and relatedness at T1, significantly enhancing the positive effects of basic psychological needs on students’ intrinsic motivation to know, leading to higher student engagement over time (Hypotheses 3b and 3c). These findings suggest that while fulfilling students’ competence and relatedness needs may foster higher intrinsic motivation-to-know, the presence of higher perceived autonomy support can further amplify these effects, resulting in increased student engagement over time. Additionally, our results indicated that the effects of the interaction between autonomy support and competence, and relatedness on intrinsic motivation-to-know leading to higher student engagement, were strengthened across low, average, and high levels as students progressed in their skills learning tasks (i.e., simple slopes, see Table 3). This suggests that, alongside satisfaction of basic psychological needs, students require increased autonomy support from their supervisors or instructors to cultivate higher intrinsic motivation-to-know and subsequently enhance their engagement in skills learning tasks during work placement (e.g., Parker et al., 2021; Vallerand et al., 1992; Vansteenkiste et al., 2020). Furthermore, our analysis revealed no statistically significant interaction between perceived autonomy support and autonomy on intrinsic motivation-to-know over time in this population (Hypothesis 3a). This non-significant finding suggests that students who already feel satisfied with their autonomy needs may engage more in their work placement learning activities regardless of the level of support and encouragement they perceive from their instructors toward completing their skills learning tasks.

Our findings indicated that the indirect effect analysis (hypotheses 4b and 4c) revealed significant positive moderation by perceived autonomy support on the indirect effects of competence and relatedness on student engagement via intrinsic motivation-to-know, across low, average, and high levels of perceived autonomy support. These results suggest that perceived autonomy support in the work placement learning setting can amplify the impact of competence and relatedness on student engagement through intrinsic motivation-to-know, leading to increased engagement in learning tasks over time. This aligns with prior literature (Reeve & Cheon, 2021; R. Ryan & Deci, 2017), indicating that students thrive in environments where instructors provide opportunities for motivational satisfaction. Our findings offer valuable insights for future research in the work placement learning context, drawing on the foundational principles of BPNT.

Theoretical Implications

The findings of this study significantly enrich the theoretical landscape by confirming and extending the assumptions of BPNT within the context of work placement learning. Specifically, the positive associations between autonomy, competence, and relatedness with student engagement validate the central premise of BPNT that fulfilling basic psychological needs is essential for promoting intrinsic motivation and, consequently, sustained engagement in learning activities. Moreover, the identification of intrinsic motivation-to-know as a mediating factor between these needs and student engagement underscores the importance of integrating motivational constructs into the BPNT framework. This not only reinforces the theory’s applicability across diverse educational settings but also highlights the nuanced pathways through which psychological needs influence student behavior and engagement over time (e.g., Olafsen et al., 2021; R. M. Ryan & Deci, 2000).

Additionally, the study’s findings on the moderating role of perceived instructor autonomy support contribute to a more refined understanding of the conditions under which BPNT can be optimally applied. By demonstrating that perceived autonomy support enhances the effects of competence and relatedness on intrinsic motivation and engagement, this research suggests a multi-faceted approach to understanding how external factors interact with individual psychological needs. This insight broadens the theoretical scope of BPNT by illustrating how contextual variables, such as autonomy support, can amplify or attenuate the impact of basic needs on student outcomes (R. Ryan & Deci, 2017; Vansteenkiste et al., 2012). Consequently, these findings open new avenues for theoretical exploration, encouraging researchers to examine the complex interplay between individual needs and environmental factors in shaping learning experiences and outcomes.

The results highlight the critical importance of fostering supportive workplace environments for students engaged in work placement learning. By nurturing students’ basic psychological needs for autonomy, competence, and relatedness, organizations can effectively stimulate the development of intrinsic motivation, thereby enhancing student engagement in skill-learning tasks. This places a responsibility on organizations hosting placement programs to prioritize the psychological well-being of students to ensure successful outcomes (Zhang et al., 2022). These insights not only contribute to theoretical advancements but also provide valuable guidance for industry-based instructors, higher education administrators, and students participating in placement programs. Ultimately, the findings emphasize the importance of attending to students’ psychological needs and creating autonomy-supportive environments to optimize engagement and learning outcomes in workplace settings.

Practical Implications

Based on the findings of this study, several practical implications emerge for enhancing student engagement in work placement learning tasks through the lens of basic psychological needs theory. Firstly, educators and supervisors should prioritize creating environments that support students’ autonomy, competence, and relatedness within the workplace setting. This can be achieved by involving students in goal-setting processes, providing opportunities for skill mastery, and fostering supportive relationships with colleagues and mentors (Abula et al., 2020). By ensuring these needs are met, educators can expect to observe heightened levels of intrinsic motivation among students, which serves as a key driver of sustained engagement over time. Institutions could benefit from implementing training programs for supervisors that emphasize the importance of autonomy-supportive practices, as these have been shown to significantly enhance students’ intrinsic motivation and subsequent engagement in learning tasks.

Secondly, recognizing the moderating role of perceived autonomy support is crucial. Institutions should encourage supervisors and instructors to actively provide autonomy support in work placement settings, adjusting their mentoring styles to cater to varying student needs. This involves offering meaningful choices, constructive feedback, and acknowledging students’ perspectives in decision-making processes. By cultivating a supportive atmosphere that values students’ autonomy, institutions can maximize the positive effects of competence and relatedness on student engagement (Fedesco et al., 2019). Moreover, this study underscores the importance of sustained efforts to maintain autonomy-supportive environments throughout students’ educational journeys, as these contribute not only to immediate engagement but also to long-term motivation and skill development. This approach can enhance students’ development of intrinsic motivation-to-know, thereby boosting their engagement in learning environments where their basic psychological needs are met (Reeve, 2013). Both higher education institutions and industries involved in work placement learning can leverage our findings to ensure that students are placed only in organizations and firms that provide autonomy-supportive learning environments (e.g., Elom et al., 2023). This ensures that students’ basic psychological needs are satisfied, fostering the development of intrinsic motivation-to-know and ultimately enhancing their engagement in skills-learning tasks. Integrating these insights into educational practices can foster a more conducive learning environment that enhances student engagement and promotes holistic development in work placement settings.

Limitations and Future Research Directions

While our study employed robust analysis techniques, several limitations merit consideration. Firstly, we focused solely on skills engagement in the workplace learning context, neglecting other dimensions of student engagement such as behavioral, emotional, cognitive, and agentic engagement. Future research could employ qualitative methods to explore these aspects further, providing richer insights. Additionally, our sample comprised university students undertaking work placement learning, which could have influenced participant responses. Future studies might explore differences by including students who have completed placement programs. Qualitative approaches could also elucidate the roles of perceived autonomy support and intrinsic motivation-to-know in enhancing student engagement. Furthermore, our study was conducted in a single country, potentially limiting the generalizability of findings. Replication in diverse cultural contexts could offer valuable insights into the subject matter, particularly in developing world contexts.

Conclusion

Based on our study findings, we provide support for the assumptions of BPNT (R. M. Ryan & Deci, 2000; Vansteenkiste et al., 2020) regarding the importance of an autonomy-supportive learning environment facilitated by instructors who demonstrate care toward students for enhancing engagement in skill learning tasks. This study contributes to BPNT by addressing the theoretical and empirical gap concerning the role of perceived autonomy support and intrinsic motivation to know within a moderated mediation model, thus enriching the body of BPNT research within the context of work placement learning. Our results highlight that students engaged in work placement learning, whose basic psychological needs are met, demonstrate increased engagement in learning new skills through heightened intrinsic motivation-to-know. Furthermore, we find that perceived instructor autonomy support enhances both the direct and indirect effects of competence and relatedness at various levels (i.e., low, average, and high). Hence, our study makes a significant contribution to this field of knowledge. Nevertheless, further research is warranted to ascertain the specific types of perceived autonomy support that are most impactful for students undertaking work placement learning in organizations or firms.