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Modeling learning-oriented motivation in health students: a system dynamics approach

BMC Psychology volume 12, Article number: 512 (2024) Cite this article

Abstract

Background

Evidence shows that motivational practices focused on utility, importance, and autonomy shape university students’ motivational orientation toward learning. On the other hand, the relationship between these variables and motivational orientation toward learning is not linear and requires models that describe their behavior over time.

Method

In this study, mathematical modeling based on system dynamics methodology is used to simulate in health students the temporal dynamics of the motivational orientation toward learning based on the behavior of these variables in different scenarios.

Results

The results indicate that a) Mastery is sensitive to changes in frequency when importance and autonomy practices are performed; b) the development of Mastery is critical in the first three semesters of academic life, but its loss is hardly recoverable even when practices are incorporated in subsequent semesters; c) Utility-focused motivational practices have no significant effect on the development of learning-oriented motivation.

Conclusion

These findings have significant practical implications for higher education. Understanding the critical role of Mastery in the early stages of academic life and the limited potential for recovery if lost can help raise awareness of the importance of early implementation of motivational practices focused on relevance and autonomy.

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Introduction

Traditionally, studies on the motivational effect on learning have been conducted from cross-sectional approaches. Evidence shows that university students’ motivational orientation toward learning is shaped by utility, importance, and autonomy (Cf. [1]). In this sense, it is necessary to advance in models that describe the dynamics of these variables over time.

To respond to this need, it is essential to explore in depth how motivation to learn behaves in university students in the health area when their professors intend certain motivational practices. For this purpose, a methodology based on mathematical modeling is used to describe different sets of parameters that show the temporal dynamics of the motivational variables that explain mastery in these students.

Motivational orientation to learning

The notion of motivational orientation toward learning or mastery (mastery) is framed by Goal Theory [2,3,4,5,6]. Under this perspective, students tend to pursue two main types of goals in their academic pursuits: learning goals that focus on mastering content and performance goals that focus on performance, e.g., getting good results [6].

The motivational orientation adopted by the learner influences various aspects, such as self-efficacy [7], critical thinking [8], self-control [9], cognitive and self-regulation strategies [10, 11], and epistemic beliefs [12]. Added to the above is the effect on persistence [13], depth levels of information processing [14], and academic help-seeking [15, 16].

Within this framework, it is essential to mobilize the cognitive and volitional resources to want to learn and, thus, to deeply appropriate the knowledge and professional competencies required in university education. Although this is common to all professions, it is especially relevant in careers with a high social impact, such as health careers.

Variables linked to learning motivation

The main theoretical approaches that shed light on academic motivation are self-determination, social-cognitive, and expectancy-value theories.

Self-determination theory recognizes that motivation is influenced by satisfying three basic psychological needs: autonomy, competence, and relatedness [17]. The satisfaction of these three psychological needs favors general psychological well-being [18, 19] and, ultimately, to better mental health [20], as well as a more intrinsic motivation toward learning [21].

Key to this theoretical model is the recognition that intrinsic motivation arises from a process of internalizing motives and self-determination, mediated primarily by experiences of autonomy [22]. Consequently, delving into the dynamics of rewards and punishments and the facilitation of self-determination (autonomy) is essential to understanding why students develop an internal or external motivational orientation toward learning (Cf. [23]).

A complement to the contributions coming from the Self-determination theory is the Sociocognitive theory, which highlights the importance of the self-efficacy belief (or feeling of competence) to perform a task or achieve specific goals successfully [24]. For its part, Expectancy/Value Theory (EVT) includes a variable analogous to self-efficacy in its expectancy component and complements it with the notion of task value as an explanatory factor. This task value dimension encompasses the perceived utility, importance, cost, and interest [25,26,27].

Effective motivational practices for orienting motivation towards learning

In a recent study [1] it was found that among the motivational practices of university teachers, those that significantly explain the motivational orientation towards learning (Mastery) in health students are those that focus on utility (β = 0.110 [CI = 0.013, 0.207], SD = 0.049), autonomy (β = 0.113 [CI = 0.046, 0.179], SD = 0.033), and importance (β = 0.203 [CI = 0.103, 0.303], SD = 0.050). Together, these variables explain 23.5% of the variance (BF10 = 7.23e + 16) (see Fig. 1),

Fig. 1
figure 1

Inclusion probabilities in health students

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Autonomy, utility and importance: theoretical support

Behind these three types of teacher motivational practices identified as relevant predictors of learning orientation (autonomy, utility, and importance), underlies the theoretical support of self-determination and expectancy value theories.

Autonomy and self-determination

Within the framework of Self-Determination Theory [22, 28, 29], the need for autonomy is defined as a person’s ability to choose the actions they want to perform according to their values and desires. It implies internal acceptance and commitment toward motivated behavior [30, 31].

Evidence shows that in the educational context, teachers’ support for students’ autonomy generates intrinsic motivation, particularly when teachers respect the opinions, ideas, and suggestions delivered by their students and give them the freedom to act according to their interests [32, 33].

Current research in the context of higher education shows that the need for autonomy satisfaction is related to the type of autonomous motivation, academic adjustment and integration, emotional regulation, subjective well-being, and learning orientation [34,35,36].

Utility and importance in the framework of expectancy & value theory

The dimensions of importance and utility are supported by the Expectancy Value Theory. In this theory’s framework, task value and expectancy have important predictive value in learning [25].

Importance and utility correspond to different subdimensions of task value [26, 37, 38]. While importance expresses the value concerning more identity-related aspects of the person, the utility does so in terms of more instrumental, external, and mediated aspects, for example, future plans [39].

System dynamics, a contribution to the understanding of motivational phenomena

The variables identified as predictors of motivational orientation towards learning have typically been studied through regression models, which assume a linear relationship between the variables. Although these strategies have allowed progress in understanding this type of phenomenon, they fail to capture the variation of these variables over time. A methodological alternative to this type of analysis is system dynamics.

System dynamics is a methodology that studies complex systems over time, analyzing how their components interact and evolve in response to various influences [40, 41]. This methodology facilitates the identification of strategic intervention points to improve the system’s performance in question. Its ability to model and simulate the temporal behavior of these systems provides a valuable tool for informed decision-making and effective management of contemporary challenges [42,43,44].

The present work aims to model, from mathematical simulations, the effect of importance, autonomy, and utility on learning-oriented motivation over time. In this way, we wait to contribute to understanding this phenomenon.

Method

Mathematical modeling in cognitive processes

This study models the temporal dynamics of the motivational orientation towards learning based on the behavior of the autonomy, utility, and importance variables, simulating this phenomenon in different scenarios over time. Methodologically, this work is based on the hypothetic-deductive nature of mathematical modeling [45].

So, to generalize the regression model identifying different factors that affect the dynamic evolution of the motivational orientation toward learning, a system dynamics model was built. This model is described in Fig. 2.

Fig. 2
figure 2

Full model

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Motivational orientation towards learning (Mastery) was considered as the dependent variable. The predictor variables were the motivational effect of teaching practices focused on utility, autonomy, and importance considering their frequency and value perceived by the student; and as a parameter, with a high level of uncertainty (stochastic), the time in which utility is identified, autonomy is acquired, and importance is recognized; respectively. The model (see Fig. 2) also considered the loss rate of the effect on mastery.

Data analysis

The data analysis techniques of this study are based on the system dynamics theory applied to a Kolmogorov system [46]. This system of differential equations fulfills the stability conditions to determine that a model has a solution and guarantees the asymptotic stability of the model. In addition, a sensitivity analysis to the stochastic parameter of the model (time) was carried out using the Monte Carlo method [47], thus offering solutions to complex problems involving uncertainty and variability.

In all cases, the number of iterations in the Monte Carlo method was 2000 per parameter. The approximation of the dynamic system was performed using the Fourth Order Runge Kutta method, and all simulations were performed in Vensim PLE. Thus, with these techniques, a confidence curve was constructed that evidenced different scenarios of the motivational orientation toward learning or “Mastery”.

Results

To describe the behavior of the motivational orientation towards learning over time, considering the influence of the study’s predictor variables (utility, autonomy, and importance), the contribution of each variable was analyzed in two scenarios: low and high frequency of these practices. Then, this effect is analyzed based on the scenarios according to the time in which importance is consolidated and autonomy is acquired, respectively. Secondly, a Monte Carlo simulation is carried out to evaluate mastery sensitivity in high- and low-frequency conditions of importance and autonomy-focused practices, as well as the loss of motivational orientation to learning.

In Fig. 3, each line represents the effect of the predictor variables on learning-oriented motivation in scenarios of high and low frequency. For example, the red line plots how mastery behaves over time if there is a high frequency of utility practices, and the blue line represents the same effect in a low-frequency scenario of autonomy-focused practices.

Fig. 3
figure 3

Effect on learning-oriented motivation in high and low frequency scenarios of predictor variables

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Role of utility in the model

A first analysis shows that utility-focused practices only weakly affect learning-oriented motivation. On the other hand, practices of autonomy and importance affect mastery much more. It is also found that regardless of the scenario of high or low-frequency of utility-focused practices, mastery remains almost unchanged (see Fig. 4 and 5).

Fig. 4
figure 4

Mastery in high and low-frequency utility practice conditions

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Fig. 5
figure 5

Mastery in conditions of high and low frequency of autonomy practices

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Effect of motivational practices focused on autonomy

The modeling of the effect of autonomy practices shows that around week 100 (approximately at the beginning of the 2nd year of training), a high frequency of these practices has a substantive effect on mastery, significantly distancing it from the effect of a low frequency of these practices.

In this sense, the sensitivity of the effect of autonomy practices shows that with a 75% probability (see Fig. 6), a high frequency of this type of practices has a significant gain effect on the motivational orientation towards learning, much greater than the loss due to a low frequency.

Fig. 6
figure 6

Sensitivity of mastery under conditions of high and low frequency of autonomy practices

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Effect of importance-focused practices.

Learning-oriented motivation is affected by importance-focused practices. It is observed that these practices are relevant from the beginning of the training. Unlike what happens with autonomy practices, which require some time to evidence their effect on mastery, in this case, importance-focused practices tend to have a palpable effect from the beginning of training (Fig. 7).

Fig. 7
figure 7

Mastery under conditions of high and low frequency of importance-focused practices

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Unlike what happens with motivational practices focused on autonomy, those that favor importance, the motivational orientation to learning is immediately sensitive to the frequency of this variable. Although the range of effects is wide from the first weeks, it becomes stronger from the second year of training (see Fig. 8), where, with a 75% probability, the gain in mastery due to the high-frequency increases and stabilizes around week 160 (approx. beginning of the 4th semester).

Fig. 8
figure 8

Mastery sensitivity in high and low frequency conditions of importance-focused practices

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The joint effect of practices focused on autonomy and importance

When there is a high frequency of both variables, the analysis shows that the joint effect on learning motivation reaches its peak and stability at the beginning of the third semester of studies (Fig. 9).

Fig. 9
figure 9

Mastery under conditions of high frequency of importance and autonomy focused practices

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Sensitivity to time in integrating the effects of these practices

Obviously, not all students take the same amount of time to integrate the motivational effect of these practices. Some ones are quicker, others slower. In the case of the effect of importance practices (Fig. 10), the speed in integrating the effect of these practices is less important than in the case of autonomy practices. In this second variable, students' rapid adoption of greater autonomy has important effects on greater mastery (Fig. 11). However, a delay in acquiring autonomy has a potential adverse effect if the acquisition of autonomy goes beyond week 160 (approx. beginning of the fourth semester).

Fig. 10
figure 10

Sensitivity of the mastery to time needed of identification of importance

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Fig. 11
figure 11

Sensitivity of the mastery to time needed to acquire autonomy

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Sensitivity of mastery to loss

Finally, different values of loss of motivational orientation to learning were simulated through the Monte Carlo method. The results show that regardless of the causes of these losses, the effect is especially important in the first three academic semesters. This implies that mastery tends to be lost if Importance and Autonomy-focused practices are not carried out frequently and jointly during this period. With a 75% probability (Fig. 12), it will not be recovered throughout the five years of academic life (undergraduate).

Fig. 12
figure 12

Sensitivity of mastery to loss

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Discussion and conclusion

This study aimed to simulate the temporal dynamics of the motivational orientation toward learning in students of health careers from the behavior of three variables: utility, importance, and autonomy. These simulations were carried out in different scenarios using mathematical modeling.

This paper goes beyond the finding of an average effect of the dependent variables on motivational orientation to learning. In particular, this paper suggests a more comprehensive view of the temporal development of motivational orientation to learning since it incorporates scenarios of greater or lesser frequency of motivational practices.

The analyses considered two types of dimensions: controllable, such as the frequency of practices, and non-controllable, such as the time it takes to incorporate the effects of these practices.

The results indicate that learning-oriented motivation is affected in a differentiated manner by these three practices. However, motivational practices focused on utility, that is, on making academic content relevant from a pragmatic logic, such as "this content will be useful for the next course", do not significantly affect the development of learning-oriented motivation. On the contrary, mastery does change significantly over time when the frequency of practices that favor autonomy and importance is high.

Mastery is also sensitive to the time it takes for the student to identify the importance and acquire autonomy. Although the effect on Mastery only manifests itself from the second year onwards, this does not mean that it is not important to favor this type of practice from the first day of school. The student learns to be autonomous, which takes time [48], so it may take time for its effect on the motivational orientation toward learning to manifest itself. This result aligns with previous studies showing that teacher support for perceived autonomy predicts autonomous motivation and is negatively related to controlled motivation. Autonomous motivation predicts engagement, effort, and learning [34].

Simulation scenarios show that mastery is highly sensitive to loss. This implies that if the mastery is lost in the early stages of training, it does not recover.

On a practical level, it is evident that it is better when practices that jointly favor autonomy and relevance are carried out because it allows the motivational orientation towards learning to be earlier and more robust. This effect will have consequences on the student's learning process, affecting, for example, self-efficacy [7], critical thinking [8], self-control [9], cognitive and self-regulation strategies [10, 11] depth levels of information processing [14].

Along with the above, this set of motivational practices focused on importance and autonomy would positively impact mental health and student well-being through learning orientation [49, 50]. This effect would be especially relevant for students born after 2000, whose mental health has been affected as a consequence of confinement during the pandemic [51].

Limitations and projections

Although it is an advance over the traditional view offered by analyses based on regression models, simulating different possibilities of the motivational orientation towards learning, there are some limitations and challenges that should be addressed in future studies.

This study is limited to modeling mastery behavior considering data from health care degrees. It would be important to replicate the baseline study and confirm that these motivational variables (utility, autonomy, and importance) are the best predictors of mastery in other training areas, too.

Finally, the formulation of the model does not incorporate the different factors that explain the loss of motivational orientation toward learning, among which are satisfaction or academic regulation.

In summary, this study reflects the need for teachers to apply practices that support autonomy and importance early on, without which the learning orientation and process may be compromised. Obviously, this requires, in addition, specific motivational training (Cf. [52]).

Availability of data and materials

The data underlying the regression indices that underlie this modeling are available at https://doi.org/10.5281/zenodo.10427851.

Abbreviations

ANID:

Agencia Nacional de Investigación y Desarrollo, (Chile) [National Agency for Research and Development]

FONDECYT:

Fondo Nacional de Desarrollo Científico y Tecnológico [The National Fund for Scientific and Technological Development]

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Funding

This research was funded by Agencia Nacional de Investigación y Desarrollo, ANID – Chile, through the Regular FONDECYT project 1210626 and Millennium Nucleus for the Science of Learning [NCS2022_026] support.

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Authors and Affiliations

  1. Departamento de Análisis de Datos, Facultad de Ingeniería, Universidad Autónoma de Chile, Talca, Chile

    Juan Gabriel Vergaño-Salazar

  2. Departamento de Ciencias Sociales, Facultad de Ciencias Sociales, Artes y Humanidades, Universidad de Antofagasta, Antofagasta, Chile

    Milenko Del Valle

  3. Facultad de Ciencias de la Salud, Universidad Católica del Maule, Talca, Chile

    Carla Muñoz

  4. Departamento de Educación e Innovación, Facultad de Educación, Universidad Católica de Temuco, Temuco, Chile

    Jorge Miranda

  5. Facultad de Ciencias de la Educación, Universidad Católica del Maule, Talca, Chile

    Andrea Precht & Jorge Valenzuela

  6. Millennium Nucleus for the Science of Learning, Talca, Chile

    Carla Muñoz & Jorge Valenzuela

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  1. Juan Gabriel Vergaño-Salazar
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Contributions

All authors [JGV, JV, MDV, JM, CM, AP] participated substantively in the conception, analysis, interpretation, and discussion of the results and in the writing of the manuscript; JGV performed the mathematical modeling. All have approved the submitted version.

Corresponding author

Correspondence to Jorge Valenzuela.

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Ethics approval and consent to participate

This study did not involve human participants. However, it was part of a larger project approved by the scientific ethics committee of Universidad Católica del Maule (Acta n° 62/2021).

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The authors declare no competing interests.

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Vergaño-Salazar, J.G., Del Valle, M., Muñoz, C. et al. Modeling learning-oriented motivation in health students: a system dynamics approach. BMC Psychol 12, 512 (2024). https://doi.org/10.1186/s40359-024-02014-y

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  • DOI: https://doi.org/10.1186/s40359-024-02014-y

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BMC Psychology

ISSN: 2050-7283

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