1. Introduction
The European Higher Education Area and Royal Decree 1393/2007 has brought about a change in university education, focusing on the development of competencies, for which it is necessary to change the learning methods that have traditionally been used in university education (González-Gálvez et al., 2018). In the case of competencies, the aim is to achieve functional knowledge that efficiently responds to a task or problem of daily and professional life that requires a teaching and learning process (ANECA, 2013). It therefore highlights the need to provide students with the ability to use knowledge and not just acquire the knowledge itself, so that theory only makes sense with its practical application (González-Gálvez et al., 2018). In order to carry out the methodological change that this implies, it has been found that active learning favors the retention of knowledge and a deeper understanding of the subject matter learned (Littlewood et al., 2013; Subramanian et al., 2012), as well as a greater applicability of the content covered, also promoting other transversal but no less important characteristics such as autonomy or teamwork and group conflict resolution (García et al., 2010; Kimonen & Nevalainen, 2005; Summers & Svinicki, 2007).
In previous research, students have shown that, on occasions, subject practices based on traditional teaching methodologies are far removed from real professional practice and the scenarios where they take place (Esteban-Guitart, 2011), which is not in line with what has been established in the different legislations. In this sense, it has been established that for an activity, especially when they are practical, to generate significant learning, it has to meet five criteria or requirements: (1) it is an activity based on physical action; (2) it is an emotionally charged activity; (3) there is adequate instrumental mediation, (4) there is optimal social mediation; and (5) the activity encourages and needs directive psycho-psychological strategies, being linked to the planning of actions or projects, the inhibition of behaviors, the organization of information or the execution of action plans (del Río & Álvarez, 2002).
Among the methodologies that meet these characteristics is Problem-Based Learning (PBL). The basic concept of PBL consists of basing student learning on individual work and group work (cooperative) through the analysis of situations or problems close to real and professional life (Esteban-Guitart, 2011). In this way, PBL aims for students to approach the resolution of the situations posed by applying the scientific method (Gómez-Restrepo, 2005). This methodology has proven to be effective in the acquisition and application of practical knowledge in the university population, as it helps students to activate previous knowledge and use it to solve problems (Gómez-Restrepo, 2005), increase knowledge retention, increase interest in the specific area being worked on and increase autonomy (Norman & Schmidt, 1992), and allow for the development of other skills such as critical thinking, social interaction, metacognition, and emotional intelligence (Luy-Montejo, 2019).
In addition, research has recently been conducted in university populations on a variant of PBL, called Team-Based Learning (TBL) (Burgess et al., 2017, 2020). TBL is defined as a small-group, active learning pedagogical strategy that offers students the opportunity to apply conceptual knowledge through a sequence of activities that includes individual work, teamwork, and immediate feedback (Burgess et al., 2020). Its application is characterized by the need to form small heterogeneous groups, design the problems or case studies to be solved by the team, provide feedback at specific times from the teacher, and include self- and peer-assessment to encourage discussion of the cases to be solved (Burgess et al., 2017; Haidet et al., 2012). This methodology has been shown to have numerous benefits when implemented with university students, such as the possibility for students to find more than one explanation for complex cases (Alizadeh et al., 2024), the promotion of two-way learning for both teachers and students, exposure to novel teaching–learning methods (Yeung et al., 2023), participation in more active debates and discussions, and improved classroom climate (Alberti et al., 2021; Crow & Smith, 2003; Liebel et al., 2017).
Delving into the benefits of using non-directive methodologies, when analyzing the motivation generated by these types of innovative methodologies in students, one aspect to consider is the importance of novelty and the capacity for choice in the levels of intrinsic motivation and the academic achievements attained (Calderón et al., 2020). Not surprisingly, from the theory of self-determination it has been argued that among the basic psychological needs that the subject needs to satisfy in order to increase their motivation is novelty, together with classic autonomy, competence, and relatedness (González-Cutre et al., 2007; González-Cutre & Sicilia, 2019). As a consequence, bringing novelty to the teaching–learning process is an aspect that teachers should consider when proposing methodologies for different subjects (González-Cutre & Sicilia, 2019). However, there are no known studies that have analyzed the weight of novelty on the improvements found when carrying out the TBL methodology in university students of the Bachelor’s Degree in Physical Activity and Sport Sciences.
Similarly, scientific evidence regarding the impact of TBL on other psychological variables, such as motivation, the satisfaction of basic psychological needs, or university students’ goal orientation, remains scarce. This gap is of vital importance to address, given its influence on learning perception and engagement with the teaching–learning process (Chen & Zhang, 2022; Lei et al., 2024). In this regard, only one previous study conducted with physiotherapy students has reported a positive impact of TBL on intrinsic motivation, competence, and autonomy, which in turn influenced students’ engagement and perceived learning (Jeno et al., 2017). Nevertheless, the available scientific evidence is insufficient to draw definitive conclusions regarding the psychological impact of TBL. This is noteworthy, as this methodology enables the innovative design of classes and facilitates the modification of factors such as the motivational climate, which has been shown to play a key role in shaping students’ task- or ego-oriented approaches (Todorovich & Curtner-Smith, 2003). Therefore, further research is needed to determine the impact of TBL on university students, specifically by examining its role in shaping students’ psychological variables.
On the other hand, in recent years there has been a great increase in the use of information and communication technology (ICT) within society in general and the educational field in particular, which offers new didactic resources that favor the teaching–learning process. Experts point out that there will come a time when ICTs will become an integral part of teaching in education (European Commission, 2006). Although ICTs by themselves may not be a factor that increases learning (Carrera & Coiduras, 2012), their use in education through reflection and educational research offers possibilities for teachers to use these tools as instruments to improve the teaching–learning process (Akram et al., 2022). Moreover, given the increasing use of technology in the professional world, acquiring skills in the use of ICT becomes essential for the training of students (Kruskopf et al., 2024).
Given the need for scientific research that examines the impact of TBL on university students’ psychological variables in order to determine whether it is an appropriate methodology for implementation, the aim of this article was to analyze the influence of an innovative education program, based on the TBL technique and digitally supported tasks, on the task and ego motivational climates, the satisfaction of basic psychological needs in education, the intrinsic motivations, and the academic performances of university students, and analyze the role of sex, academic performance, and transversal skills in the results obtained.
2. Materials and Methods
2.1. Design
The present research followed a quasi-experimental research study, with an experimental group and two measurement points (pre-test and post-test), separated in time by four months, which corresponded to the implementation of the educational innovation program.
The independent variable of the present study was the application of the educational innovation program, which was based on the TBL model and the digitally supported tasks. The dependent variables of the study were the knowledge on the subject, motivational climate (MC), both ego- and task-oriented, basic psychological needs in education (autonomy, relation, and competence), intrinsic motivation (IM), including interest in knowledge, achievement, and stimulating experiences. These variables were assessed before and after the implementation of the innovative program. Academic performance and the perception of prior transversal competences were considered as control variables.
The study was conducted in accordance with the guidelines of the Transparent Reporting of Evaluations of Non-randomized Studies (TREND). Prior to the start of the study, approval was obtained from the Institutional Ethics Committee CE022414. In addition, all participants signed an informed consent form prior to data collection, explaining the purpose of the study, the treatment that would be given to the information collected, and the guarantees of confidentiality.
2.2. Sample
The sample size was calculated with the software RStudio (3.15.0 version, Rstudio Inc., Boston, MA, USA). An a priori significance level of α = 0.05 was set. The standard deviation (SD) was set based on the motivational climate dimension of the Perception of School Goal Emphasis Scale questionnaire from previous studies (SD = 13.57) (Vaquero-Cristóbal et al., 2021). With an error (d) of 3.25, the calculated sample needed was 67 participants.
In the present research, 68 students (mean age = 21.45 ± 1.57 years) were included. The participants of the study were 4th year students of the subject Sport Injury Prevention and Readaptation of the Physical Activity and Health itinerary of the Sport Sciences Degree, with the aim that the free choice of the subject by the students will lead to an increase in motivation and involvement of the students in the suggested dynamics (Calderón et al., 2020). The subject chosen to implement the innovative program had 4.5 ECTS (45 classroom hours), with a distribution of 50% of theoretical lessons and 50% of practical lessons to be developed in 12 consecutive weeks.
The sample recruitment was non-probabilistic and by convenience, accepting all the students who volunteered to participate and were enrolled in the subject. The recruitment of the sample was performed between the students enrolled in the subject, by announcing the research through an email sent via Virtual Campus. Also, an announcement was posted in the Sports Faculty noticeboard. The inclusion criteria were (a) to be enrolled in the subject “Sport Injury Prevention and Readaptation”, in Spanish or English; (b) to attend at least 80% of the practical classes; and (c) to complete both the pre-test and post-test. The exclusion criteria were (a) to miss more than 20% of the practical classes; (b) not complete the pre- or post-tests; and (c) not finish the subject project.
2.3. Procedures
Regarding the intervention of the project, at the beginning of the subject, students took a multiple choice test with the Google survey tool to check the students’ prior knowledge related to the contents of the subject and their competence in relation to the use of databases of scientific articles and the development of infographics and presentations, together with the questionnaires to evaluate the MC, basic psychological needs in education, IM, and academic self-concept. The questionnaires were completed in the students’ regular classroom, ensuring a calm and noise-free environment. The researchers were present during this process but did not influence participants’ responses in any way, limiting their role to addressing questions related solely to the content of the items.
After analyzing the results obtained, the subject professors designed the different case studies that made up the tasks to be solved by the students in the framework of the TBL methodology. An example of a case to be solved by students would be “Young athlete playing handball. While performing a feint in the last match an opponent steps on his ankle, causing a grade 2 sprain of the anterior talofibular ligament. He has to be readapted after the injury”. Students were free to choose the case study they wanted to work on, without the possibility of assigning the same case study to different groups. Once the working groups and the subject matter to be worked on were defined, in the practical sessions the theoretical–practical content presented by the professors was interspersed with the autonomous work of the students to integrate the different contents in the resolution of the case they were working on. To guarantee the students’ deepening of the contents and their practical application through the learning problems posed, they worked throughout the course on the same case, progressively including the concepts that were developed during theoretical classes, within the framework of the case study and “design thinking” techniques.
The final product of the project was a rehabilitation plan for the initially chosen injury, which had to include strength, flexibility, and sensorimotor training tailored to the specific nature of the injury. These content blocks were introduced progressively throughout the course and assembled to ensure consistency. To advance in case resolution, a four-phase process was established each time new theoretical and practical content had to be included in problem solving, as indicated in the TBL methodology (Burgess et al., 2020). First, the students prepared individually by studying the recommended content in the theoretical classes that was relevant to the resolution of the case. Next, there was an evaluation phase in which each student presented to their group the approach they considered most appropriate for resolving the case, arguing their position and reaching a consensus on the content to be included in their proposal. In the next phase, students tested the proposed exercises in a real situation before presenting them to their classmates and after consulting possible doubts with the professors. Periodically, every two weeks during the development of the subject, each group had to present its progress to the rest of the groups, with the aim of encouraging debate and feedback among classmates to collaborate in the resolution of the problems posed, which was the fourth step in incorporating the new theoretical content into the resolution of the case. The development of these practical sessions was related to the final project of the subject, in which students had to present the work completed during the course of the sessions using the material and audiovisual resources they consider necessary to develop their approach to the case in question.
At the end of the four-month intervention, participants completed the same questionnaires administered at the beginning of the study under the same conditions and again using the Google survey tool.
2.4. Instruments and Evaluation
To determine the students’ starting level in terms of subject knowledge and transversal competences, an ad hoc questionnaire of short questions on predominantly practical and applied content and dichotomous selection questions will be administered, based on previous studies with similar objectives (Vaquero-Cristóbal et al., 2021). Students’ self-perception about their competency to carry out scientific searches, develop audiovisual material, and disseminate academic content will also be evaluated.
In order to find out whether the implementation of the project has significant effects on the students’ academic performance, the scores obtained by the students before and after the intervention will be analyzed, together with the mark in the subject project.
To measure the perceived task-oriented motivational climate, an adaptation of the Perceptions of School Goal Emphases Scales (Kaplan & Maehr, 1999), consisting of 15 items, is used, of which 7 items assess task motivational climate and 8 items assess ego motivational climate. Responses are evaluated according to a Likert-type scale from 1 (strongly disagree) to 5 (strongly agree). Higher punctuations in the scales are related to a higher tendency to be ego- or task-oriented, respectively. This questionnaire has shown good internal consistency and reliability in previous studies (Cronbach’s α = 0.75–0.81) (Kaplan & Maehr, 1999).
To find out students’ perception of their basic psychological needs in the field of university education, students will self-complete the Satisfaction of Psychological Needs in Education Scale (ESNPE) (León et al., 2011). It consists of 15 items to measure autonomy, competence, and relatedness, with five items related to each category, with Likert-type responses ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores in the dimensions of autonomy, relatedness, and competence are related to greater satisfaction of basic psychological needs in education. This questionnaire has shown good internal consistency and reliability in previous studies (Cronbach’s α = 0.71–0.81) (León et al., 2011).
To measure intrinsic motivation (IM), the 12 items belonging to this factor of the Educational Motivation Scale will be used (Núñez et al., 2005), with four items for each of the MI types: MI to knowledge, MI to achievement, and MI to stimulating experiences, with seven-point Likert-type scale scores from 1 (not at all matched) to 7 (fully matched). The results are expressed as the mean of the items composing each of the three scales. Higher values in MI to knowledge, MI to achievement, and MI to stimulating experiences dimensions mean more intrinsically motivated students. This questionnaire has shown good internal consistency and reliability in previous studies, with Cronbach’s α = 0.76 for MI knowledge; Cronbach’s α = 0.73 for MI stimulating experiences; and Cronbach’s α = 0.78 for MI achievement (Núñez et al., 2005).
2.5. Statistical Analysis
The Kolmogorov–Smirnov test was used to determine the normal distribution of the variables, and sphericity, kurtosis, and homogeneity were also checked. Descriptive analysis of the different variables was carried out. A t-test for related samples was used to analyze the differences between the pre-test and post-test, including the result in the content questions, the transversal competences, task and ego orientation, basic psychological needs in education and IM knowledge, achievement, and stimulating experiences. The effect of sex, the final project mark, and the perceived competence in the transversal competences on the differences between measurement moments were checked with an ANCOVA test for repeated measurements. The effect size was calculated using Cohen’s d or partial eta squared (η2p), depending on the test carried out. The significance level was set a priori at p = 0.05. The statistical analyses were performed using SPSS v24 software (IBM, Endicott, NY, USA).
3. Results
Descriptive statistics (mean ± SD) of the variables related to motivational climate, basic psychological needs in education, IM, competence, and knowledge on the matter are shown in Table 1. The analysis of the differences between the pre-test and post-test showed that there was a statistically significant increase in all the variables after the intervention (t = −1.96 to −4.54; p < 0.001 to 0.049), except for the competence in audiovisual material and the three dimensions of IM.
Regarding the effect of the covariables in the differences found, the results are shown in Table 2 and Table 3. The project mark did not show influence in the differences between the pre- and the post-test results in the variables analyzed (p = 0.392–0.922). Neither the transversal competence level in scientific search (p = 0.088–0.843), the creation of audiovisual material (p = 0.056–0.956) or the experience in academic content diffusion (p = 0.296–0.960) showed effect on the difference between measurement moments. Sex was the only covariable that showed influence in the differences, specifically in the task orientation motivational climate (p = 0.042), autonomy (p = 0.011), and relatedness (p = 0.016) dimensions of the basic psychological needs in education and the three dimensions of the IM (F = 4.16–7.86; p = 0.009–0.049).
4. Discussion
The aim of this study was to analyze the impact of an innovative educational program, based on TBL combined with digitally supported tasks, on the motivational climate, satisfaction of basic psychological needs, intrinsic motivation, academic self-concept, and performance of university students. The results show that, after implementing the program, there were statistically significant improvements in most variables assessed, specifically in task- and ego-oriented motivational climate, autonomy, competence, relatedness, content knowledge, and perceived competence in scientific search and academic content dissemination. However, there were no significant improvements in students’ intrinsic motivation dimensions or perceived competence in audiovisual material creation.
These findings align with previous studies highlighting the benefits of active methodologies such as PBL and TBL in higher education, particularly in terms of promoting deeper learning, autonomy, teamwork, and practical application of knowledge (Gómez-Restrepo, 2005; Luy-Montejo, 2019; Norman & Schmidt, 1992). The observed increase in task-oriented climate supports earlier research suggesting that TBL favors active engagement and shared responsibility among students (Burgess et al., 2017, 2020). The significant improvement in the satisfaction of basic psychological needs (autonomy, competence, and relatedness) also corroborates the relevance of designing learning environments that fulfill these needs, as postulated by self-determination theory (González-Cutre & Sicilia, 2019). In fact, a recent systematic review with meta-analysis indicated that methodologies that require the active involvement of students favor a better learning climate, compared to more traditional strategies (Uttl et al., 2017). This is because the use of these types of innovative methodologies allows students to become directly involved in the teaching–learning process and set their own pace, relegating teachers to a more secondary role in which their main activity will consist of helping and guiding students so that, through their own experience, they are able to complete the set activities, which has a positive impact on their predisposition (Navarro Mateos et al., 2021; Razali & Mohamad Nasri, 2023).
Interestingly, although overall motivational climate and satisfaction of psychological needs improved, intrinsic motivation did not significantly increase. This contrasts with previous research suggesting that novelty and choice in learning tasks can boost intrinsic motivation (Calderón et al., 2020; González-Cutre et al., 2007). One possible explanation may be that, despite the innovative approach, the integration of digital tools and case-based work over a semester might not be time enough for the students to assimilate the methodology in the context of traditional teaching. Furthermore, intrinsic motivation is known to be a complex concept influenced by various personal and contextual factors that go beyond instructional design alone. Previous research has shown that not all students feel comfortable with the implementation of methodologies that require active participation on their part (López-Alegría & Fraile, 2023), generating a negative motivational response when the traditional teaching methodology they are accustomed to is not used. In addition, it should be noted that previous scientific literature has shown that students’ sense of learning may be lower when active methodologies are implemented, compared to traditional passive methodologies, even when actual learning is better with active methodologies (Uttl et al., 2017). In this line, Deslauriers et al. (2019) suggested that when students experience the increased cognitive effort associated with active learning, they initially take that effort to signify poorer learning, and that factor may have a detrimental effect on students’ motivation.
Regarding the effect of covariables, only sex showed an influence on the observed changes, particularly in task orientation, autonomy, relatedness, and intrinsic motivation dimensions. This is partially consistent with previous research suggesting that male and female students may perceive and respond differently to active methodologies (Vaquero-Cristóbal et al., 2021). The results regarding motivation differ from those found in previous research in which females showed higher intrinsic motivation and commitment to university education compared to males (Kuśnierz et al., 2020; Luitel, 2024). Regarding ego and task orientation, previous research has not been able to draw a clear conclusion since, in the university setting, it has been found that there are no differences between males and females in task orientation (Belli, 2015), while in ego orientation the results are very heterogeneous. And, with respect to basic psychological needs, previous research has shown no major differences in satisfaction with autonomy and competence between males and females at university, while males tended to score higher on relatedness than females (Sumin et al., 2023). One possible explanation for these results could be that the context in which the intervention takes place is a determining factor, as differences have been shown in different university grades depending on teaching methodology, age or ethnicity (Lewis, 2003; Stolk et al., 2021). The motivational climate of the students, as well as the academic context generated between the teacher and the students, may also influence these variables (Moreno-Murcia et al., 2008).
Furthermore, it is important to mention that the type of innovative methodology chosen has completely different effects on students (Debs et al., 2019). This is because each methodology has specific characteristics that make its impact on the variables of motivation, participation, and involvement completely different (Suyo-Vega et al., 2024). In this case, TBL proposes joint and direct collaboration with classmates to resolve the learning situation, which could have a greater impact on female students. However, it does not include a gamified or competitive component that other methodologies do have and that could be more effective on other psychological constructs of the male students (Carrasco-Gomez et al., 2023; Mauri-Medrano et al., 2024). Therefore, choosing a single innovative methodology that is beneficial and provides benefits in all psychological constructs is difficult to achieve since students have very diverse characteristics. Future research should continue to analyze how the classroom methodology used in the Bachelor’s Degree in Physical Activity and Sport Sciences affects gender. In contrast, final project marks and initial perceived competence in transversal skills had no significant effect on the improvements. This suggests that the positive impact of the educational innovation was broadly shared among participants, regardless of their initial skill levels or project performance.
The significant improvements in perceived competence for scientific search and academic content dissemination also highlight the added value of integrating digital resources and reflective tasks within the TBL framework. This resonates with the growing consensus that, while ICTs alone do not guarantee better learning outcomes (Carrera & Coiduras, 2012), their strategic integration into active learning designs can enhance students’ transversal competences relevant to professional practice (Akram et al., 2022; Kruskopf et al., 2024). Moreover, this is in line with previous research, which has shown that learning potential is higher with methodologies that allow the direct implication of the student rather than those in which the student is a passive subject of the teaching–learning process (Deslauriers et al., 2019). Moreover, in the university context, providing practices that are approximated to the work reality becomes especially relevant, and in this sense, methodologies such as TBL are an advantage, since it has been observed that students’ learning is contextual (Laurillard, 1979), which could result in a greater transfer to the required professional competences.
It is noteworthy that the present study presents certain novelties compared to previous research. The TBL methodology has seen a substantial increase in research in recent years, especially in those professional fields where professional performance requires specific practical skills in the field of knowledge, such as nursing and medicine (Alizadeh et al., 2024). In this regard, the main findings suggest that active methodologies are more effective in helping students acquire practical knowledge about the subjects, become more involved in class, and be more satisfied with the education they receive (Sterpu et al., 2024). However, in many cases, traditional lectures continue to be the methodology of choice in faculties, with the concern that this entails regarding the teaching and learning process for students when their role is passive (Imran et al., 2022). This is also the case in Sports Science higher education where, despite sharing certain similarities with healthcare professions, such as the need to develop specific professional skills in the field of knowledge, active methodologies are not always the most common. In higher education Sports Science, active methodologies such as the Flipped Classroom (Procopio et al., 2024) or Sport Education (Fernandez-Rio & Casey, 2021) account for the majority of interventions in this regard. Despite the positive effects that TBL has shown in other populations, only one study has been found in the field of Sport Management that uses this methodology, which has reported positive effects on the skills most sought after by employers (Dane-Staples, 2023). That is why this study can make an important contribution to the use of TBL methodologies in Sports Science higher education, as it has been shown to improve students’ knowledge, motivation, and satisfaction, and can serve as a basis for future research in this field.
This study has several limitations that should be acknowledged. Firstly, the sample was limited to students from a single optional subject within the Physical Activity and Sport Sciences degree, and was reduced to only 68 students, which may limit the generalizability of the findings to other disciplines or contexts as well as the performance of secondary analyses, such as dividing the sample into subgroups based on sex. Secondly, the study design was quasi-experimental without a control group, making it difficult to attribute the observed effects solely to the intervention, as other external factors could have influenced the results. Thirdly, the sample size, although adequate according to the power calculation, was relatively small, which may reduce statistical power to detect subtle effects. Fourthly, the intervention’s duration (one semester) may also have been insufficient to produce significant changes in more stable constructs, such as intrinsic motivation. And fifthly, although the program integrated digital tools, the choice and type of tools were predetermined by the research team; allowing greater student autonomy in selecting digital resources might have increased perceived novelty and engagement.
Despite these limitations, the study provides relevant evidence on the benefits and challenges of implementing TBL combined with ICT in university education, offering practical insights for educators and researchers aiming to foster deeper learning and motivational engagement in students.
Future research should consider longitudinal designs, with one academic year duration at least, to explore whether these effects persist over time and whether repeated exposure to such methodologies leads to greater changes in the different variables. Additionally, quantitative and qualitative studies with a control group and bigger samples in different university contexts that allow the division by sex could help shine light onto students’ experiences and the specific elements they find most engaging or challenging within TBL contexts supported by ICT.
5. Conclusions
This study provides evidence of the positive effects of integrating TBL with digitally supported tasks in a university context. The intervention led to significant improvements in students’ task- and ego-oriented motivational climate, satisfaction of basic psychological needs (autonomy, competence, and relatedness), content knowledge, and perceived competence in scientific search and dissemination of academic content. However, the program did not produce significant changes in intrinsic motivation dimensions or perceived competence in creating audiovisual materials, suggesting that further refinement of the design is needed to better engage students’ internal motivational drivers. The influence of sex as a covariate highlights the need to consider individual differences when designing and implementing active methodologies. Overall, the findings support the potential of combining TBL and digital tools to enhance students’ learning experience and key psychological variables. Future research should explore long-term effects, involve larger and more diverse samples, and integrate qualitative approaches to gain deeper insight into students’ perceptions and motivations.
Conceptualization, M.A.-S., F.E.-R. and R.V.-C.; methodology, M.A.-S., A.M.-O. and R.V.-C.; formal analysis, M.A.-S. and A.M.-O.; investigation, M.A.-S., A.M.-O. and F.E.-R.; data curation, M.A.-S. and A.M.-O.; writing—original draft preparation, M.A.-S. and A.M.-O.; writing—review and editing, M.A.-S. and A.M.-O.; supervision, R.V.-C.; project administration, M.A.-S.; funding acquisition, M.A.-S., F.E.-R., A.M.-O., and R.V.-C. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Universidad Católica San Antonio de Murcia (protocol code CE022414 and date of approval 23 February 2024).
Informed consent was obtained from all subjects involved in the study.
The database is available at the following link:
This paper is part of the project PID-10-23, funded by the Catholic University San Antonio, under the program “Ayudas a la realización de proyectos de innovación docente 2023”.
The authors declare no conflicts of interest.
Footnotes
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Analysis of differences between pre- and post-test on content questions, ego and task orientation, basic psychological needs, and intrinsic motivation.
| Variable | Pre-Test | Post-Test | Mean Diff. | t | p | 95% CI | d |
|---|---|---|---|---|---|---|---|
| Scoring content questions | 3.03 ± 0.96 | 3.76 ± 1.26 | −0.73 | −3.07 | 0.004 | −1.21; −0.25 | 0.50 |
| Competence Sci Search | 3.41 ± 0.94 | 3.69 ± 0.82 | −0.28 | −1.96 | 0.049 | −0.57; −0.01 | 0.81 |
| Competence AV material | 3.63 ± 0.97 | 3.81 ± 0.86 | −0.18 | −1.79 | 0.083 | −0.40; 0.02 | 0.59 |
| Competence AC diffusion | 3.22 ± 0.94 | 3.56 ± 0.95 | −0.34 | −2.47 | 0.019 | −0.62; −0.06 | 0.78 |
| Ego orientation | 1.07 ± 0.79 | 1.57 ± 1.09 | −0.50 | −2.66 | 0.010 | −0.88; −0.13 | 0.32 |
| Task orientation | 2.05 ± 1.62 | 3.11 ± 1.80 | −1.06 | −3.33 | 0.001 | −1.69; −0.42 | 0.40 |
| Autonomy | 1.71 ± 1.48 | 2.89 ± 1.71 | −1.18 | −3.99 | <0.001 | −1.77; −0.59 | 0.48 |
| Relatedness | 1.79 ± 1.74 | 3.17 ± 1.85 | −1.39 | −3.98 | <0.001 | −2.08; −0.69 | 0.48 |
| Competence | 1.73 ± 1.76 | 3.16 ± 1.80 | −1.43 | −4.54 | <0.001 | −2.06; −0.80 | 0.55 |
| IM knowledge | 3.79 ± 2.72 | 4.32 ± 2.57 | −0.53 | −1.05 | 0.298 | −1.54; 0.48 | 0.13 |
| IM achievement | 3.56 ± 2.59 | 4.25 ± 2.55 | −0.69 | −1.38 | 0.171 | −1.68; 0.30 | 0.17 |
| IM stimulating experiences | 3.75 ± 2.74 | 4.31 ± 2.58 | −0.56 | −1.13 | 0.262 | −1.55; 0.43 | 0.14 |
Sci Search: scientific search; AV material: audiovisual material; AC diffusion: academic content diffusion; IM: intrinsic motivation.
Analysis of the effect of sex and project mark on the differences between pre- and post-test on content questions, ego and task orientation, basic psychological needs, and intrinsic motivation.
| Variable | Pre–Post × Sex | Pre–Post × Project Mark | ||||
|---|---|---|---|---|---|---|
| F | p | η 2 p | F | p | η 2 p | |
| Scoring content questions | 0.30 | 0.588 | 0.01 | 0.75 | 0.392 | 0.02 |
| Ego orientation | 1.05 | 0.313 | 0.03 | 0.52 | 0.474 | 0.02 |
| Task orientation | 4.49 | 0.042 | 0.12 | 0.28 | 0.600 | 0.01 |
| Autonomy | 7.34 | 0.011 | 0.18 | 0.04 | 0.853 | 0.00 |
| Relatedness | 6.48 | 0.016 | 0.16 | 0.38 | 0.542 | 0.01 |
| Competence | 2.83 | 0.102 | 0.08 | 0.15 | 0.706 | 0.00 |
| IM knowledge | 7.65 | 0.009 | 0.19 | 0.08 | 0.785 | 0.00 |
| IM achievement | 5.97 | 0.020 | 0.15 | 0.03 | 0.855 | 0.00 |
| IM stimulating experiences | 4.16 | 0.049 | 0.11 | 0.01 | 0.922 | 0.00 |
IM: intrinsic motivation.
Analysis of the effect of transversal competences on the differences between pre- and post-test on content questions, ego and task orientation, basic psychological needs, and intrinsic motivation.
| Variable | Pre–Post × Sci Search | Pre–Post × AV Material | Pre–Post × AC Diffusion | ||||||
|---|---|---|---|---|---|---|---|---|---|
| F | p | η 2 p | F | p | η 2 p | F | p | η 2 p | |
| Scoring content questions | 0.20 | 0.662 | 0.01 | 1.21 | 0.281 | 0.04 | 1.05 | 0.313 | 0.03 |
| Ego orientation | 0.04 | 0.843 | 0.00 | 5.67 | 0.084 | 0.16 | 0.03 | 0.866 | 0.00 |
| Task orientation | 3.10 | 0.088 | 0.09 | 2.49 | 0.125 | 0.08 | 0.10 | 0.760 | 0.00 |
| Autonomy | 1.03 | 0.318 | 0.03 | 0.21 | 0.652 | 0.01 | 0.02 | 0.879 | 0.00 |
| Relatedness | 2.76 | 0.107 | 0.08 | 5.45 | 0.056 | 0.15 | 1.13 | 0.296 | 0.04 |
| Competence | 4.93 | 0.034 | 0.14 | 0.00 | 0.956 | 0.00 | 0.00 | 0.960 | 0.00 |
| IM knowledge | 1.54 | 0.224 | 0.05 | 3.17 | 0.085 | 0.10 | 0.02 | 0.888 | 0.00 |
| IM achievement | 0.56 | 0.459 | 0.02 | 3.55 | 0.069 | 0.11 | 0.15 | 0.699 | 0.01 |
| IM stimulating experiences | 0.07 | 0.790 | 0.00 | 2.35 | 0.136 | 0.07 | 0.40 | 0.531 | 0.01 |
Sci Search: scientific search; AV material: audiovisual material; AC diffusion: academic content diffusion; IM: intrinsic motivation.
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Abstract
Traditional teaching methods are often far from aligning with professional practice demands. Team-Based Learning (TBL), a variant of Problem-Based Learning, may foster motivation, autonomy, and deeper knowledge acquisition, especially in those educative contexts linked to practical knowledge. The objective of the present research was to explore the impact of a TBL program with digital support on Sport Sciences students’ psychological and learning outcomes. A quasi-experimental design with pre- and post-tests was applied to 68 fourth-year students (mean age = 21.45 ± 1.57 years). The intervention spanned 12 weeks, where the students had to solve specific case studies linked to the theoretical content of the subject and its applicability. Variables measured included motivational climate, satisfaction of basic psychological needs, intrinsic motivation, transversal competences, and academic performance. Significant improvements were observed in task- and ego-oriented climate, autonomy, competence, relatedness, knowledge scores, and competence in scientific searches and academic dissemination (p < 0.05). No significant changes were found in intrinsic motivation or audiovisual material competence. Sex influenced several outcomes, while project marks and prior transversal skills did not. TBL combined with digital tools enhanced learning outcomes and key psychological needs, though intrinsic motivation remained unchanged. Findings highlight the value of active methodologies in higher education, while underscoring the need for long-term, broader studies.
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Details
; Mateo-Orcajada Adrián 2
; Esparza-Ros, Francisco 3
; Vaquero-Cristóbal Raquel 2
1 Kinanthropometry International Chair, Universidad Católica San Antonio de Murcia, Guadalupe, 30107 Murcia, Spain; [email protected] (M.A.-S.); [email protected] (F.E.-R.), Faculty of Sport, Universidad Católica San Antonio de Murcia, Guadalupe, 30107 Murcia, Spain
2 Research Group Movement Sciences and Sport (MS&SPORT), Department of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, San Javier, 30720 Murcia, Spain; [email protected]
3 Kinanthropometry International Chair, Universidad Católica San Antonio de Murcia, Guadalupe, 30107 Murcia, Spain; [email protected] (M.A.-S.); [email protected] (F.E.-R.)