Motivational Teaching Techniques in Secondary and Higher Education: A Systematic Review of Active Learning Methodologies

Abstract

This study presents a systematic review of the literature on teaching techniques that enhance student motivation and academic performance across basic, secondary, and higher education levels. The review is grounded in the distinction between intrinsic and extrinsic motivation, highlighting their decisive roles in engagement and achievement. The analysis focuses on active learning methodologies such as project-based learning, collaborative learning, gamification, and flipped classrooms. It identifies the mechanisms by which each approach fosters students’ interest, sense of competence, and persistence. Four international databases were consulted, and studies published between 2000 and 2024 reporting quantitative measures of motivation and/or performance were selected. Five investigations met all eligibility criteria and were assessed for methodological quality. The results indicate moderate motivational effects, especially when interventions last at least eight weeks, provide frequent feedback, and place students at the center of authentic problem-solving. Greater gains were also observed in STEM disciplines and in contexts that encourage peer collaboration. Based on these findings, practical recommendations are proposed for educators: structure interdisciplinary projects, incorporate playful elements in the initial stages of formal education, combine autonomous work with small-group discussions, and use data analysis tools to deliver personalized feedback. The study concludes that adopting diverse, student-centered pedagogical practices enhances motivation and academic achievement, leading to deeper and more lasting learning outcomes.

1. Introduction

Student motivation is widely recognized as one of the most robust predictors of academic success and persistence in higher education [1,2]. Nevertheless, many traditional teaching practices still fall short of fostering sustained academic engagement, contributing to persistent dropout rates in tertiary education systems globally [3,4].

Numerous meta-analyses have demonstrated that active learning methodologies can significantly improve student outcomes. For example, Freeman et al. [5], in a meta-analysis of 225 STEM studies, found that active learning reduces failure rates by over 50% and increases average academic performance. Similarly, Wijnia et al. [6] confirmed through a meta-analysis that problem-, project-, and case-based learning exert small-to-moderate positive effects on student motivation.

Despite this encouraging body of evidence, research on active learning methodologies remains fragmented and seldom synthesized within Portuguese-speaking contexts. This lack of systematic aggregation hinders the informed adaptation of proven pedagogical strategies to national education systems—particularly in Portugal and Brazil—where empirical contributions are often underrepresented in major academic databases.

While this review includes studies from secondary and higher education, its primary focus is on informing evidence-based practice in higher education.

To address this gap, the present review examines literature published between 2000 and 2024, with three main goals: (i) to identify active teaching interventions that yield measurable effects on student motivation and performance; (ii) to analyze the key factors that moderate these effects; and (iii) to translate the findings into actionable recommendations for educators aiming to foster deeper engagement. In doing so, this study contributes to strengthening the evidence base for Lusophone educational contexts, where systematic reviews remain scarce.

2. Literature Review

After presenting the research problem and the overall goals of the study in the introductory chapter, we now move on to the theoretical and empirical foundations that support the methodological decisions that follow.

2.1. Theoretical Framework

Motivation is the internal or external energy that drives students to initiate, direct, and sustain their learning processes.

Self-Determination Theory (refs. [7,8]) situates this energy along a continuum ranging from extrinsic motivation (actions to obtain rewards or avoid sanctions) to intrinsic motivation (actions driven by inherent interest or pleasure). According to Deci and Ryan [8], deeper engagement requires internalization of value and perceived autonomy, which may explain the modest effects observed on intrinsic orientation.

The transition to more self-determined forms of motivation requires an environment that satisfies three psychological needs:

• Autonomy—the sense of control over one’s choices;
• Competence—the perception of progress and mastery;
• Relatedness—the feeling of belonging within the peer and teacher community.

When these needs are met, motivation strengthens and academic performance improves.

2.2. Criteria for Selecting Active Learning Methodologies

Constructive alignment theory emphasizes the coherence between learning objectives, activities, and assessment [9], which underpins many active learning models. Four approaches were selected based on recent evidence and applicability within the Portuguese education system. These approaches satisfy the criteria of autonomy, competence, and relatedness:

• Problem-, Project-, and Case-Based Learning (PBL/PjBL/CBL);
• Collaborative Learning;
• Gamification;
• Flipped Classroom.

Collaborative learning, while broader in scope than the other approaches, was included due to its strong empirical association with motivational and engagement outcomes, and its widespread use across Portuguese universities in both digital and face-to-face modalities.

All four approaches are not only supported by robust international literature, but are also feasible to implement with the current resources and infrastructure available in most Portuguese higher education institutions. Moreover, they align well with the vision of the European Higher Education Area (EHEA), as established in the Bologna Process and reaffirmed in the European Commission’s Digital Education Action Plan 2021–2027, which champions learner-centered, inclusive, and digitally supported pedagogies [10]. This alignment is further reinforced at the national level by the Portuguese Perfil dos Alunos à Saída da Escolaridade Obrigatória [DGE, 2017], which frames key competencies such as autonomy, critical thinking, and collaborative problem-solving as essential outcomes for learners [11].

2.3. Active Learning Methodologies as Motivational Enhancers

Prince’s [12] review confirms that active learning consistently outperforms traditional lectures in improving conceptual understanding and retention. Before examining data, it is important to briefly define each approach:

• PBL/PjBL/CBL places responsibility on students to solve authentic problems;
• Collaborative Learning promotes positive interdependence among peers;
• Gamification introduces game mechanics (e.g., points, levels, instant feedback) to make progress visible and engaging;
• Flipped Classroom shifts content exposure to the pre-class phase, freeing in-person time for collaborative practice.

2.4. Empirical Evidence

2.4.1. Large-Scale Syntheses

Several large-scale meta-analyses have assessed the impact of active learning methodologies on student motivation and academic performance. The following are key findings from the most relevant studies:

• Active Learning—Freeman et al. [5]: meta-analysis of 225 STEM studies; average performance gain +0.47 (Cohen’s d); failure rates down by 55%. Mostly quasi-experimental designs;
• Gamification—Sailer and Homner [13]: 41 interventions; motivation +0.48 d; moderate publication bias;
• PBL/PjBL/CBL—Wijnia et al. [6]: 139 subsamples; improvements in task value and competence beliefs (+0.50 d); high heterogeneity (I² = 78%).

2.4.2. Gamification (From More to Less Rigorous Designs)

The following studies investigate the effects of gamification on student engagement, motivation, and performance. Listed from more to less rigorous research designs, they provide insight into both the benefits and methodological limitations of various gamified interventions:

• Denny [14]: quasi-experiment in programming (N = 778); badges increased persistence (+0.35 d); lacked affective measures;
• Buckley and Doyle [15]: gamified prediction market in Economics (N = 98); participation +0.67 d; small sample;
• Chung and Lin [16]: 3-D gamified environment for HR training (N = 64); self-efficacy +0.62 d;
• Göksün and Gürsoy [17]: Kahoot vs. Quizizz (N = 197); engagement +0.50 d; no long-term follow-up;
• Facey-Shaw et al. [18]: Intro to Programming (N = 156); intrinsic motivation +0.45 d; self-report data;
• Meng et al. [19]: online platform (N = 440); points and levels explained 41% of engagement variance; overall effect +0.60 d;
• Gray and DiLoreto [20]: structural model (N = 1862); engagement mediated the link between satisfaction and grade (+0.40 d estimated).

However, some studies have cautioned that poorly designed gamification—especially when relying heavily on competitive elements like badges or leaderboards—can reduce intrinsic motivation over time [21]. Meng et al. [7] found that the use of points and badges in a gamified environment resulted in measurable gains in motivation and achievement.

The gamification literature has also been systematically reviewed. Lampropoulos and Kinshuk (refs. [22,23]) synthesized 112 studies on gamified virtual reality environments, finding positive impacts on motivation, engagement, and learning.

In summary, the weighted average effect for gamification was approximately +0.50 d, but poorly chosen elements (e.g., unsupported competitive rankings) can nullify or reverse the benefits.

2.4.3. Flipped Classroom

Empirical studies on flipped classrooms suggest moderate gains in student motivation, engagement, and self-efficacy, although most interventions were of short duration and relied on self-reported data. Similar findings were reported by Fathi and Barkhoda [24] in a flipped classroom context, where significant increases in academic satisfaction and sense of belonging were observed. The following studies illustrate these effects:

• Roach [25]: Economics (N = 77); intrinsic motivation +0.53 d; self-reports;
• Fathi and Barkhoda [24]: EFL reading (N = 64); self-efficacy +0.52 d; short intervention;
• Lo and Hew [26]: Meta-analysis of 28 flipped classroom studies; average effect on motivation +0.41 d; strongest results in health and STEM fields;
• Wanner and Palmer [27]: Higher education (Education Technology course); flipped model increased learner autonomy and class participation; qualitative and survey-based data;
• Moraros et al. [28]: Nursing education (N = 195); flipped instruction led to higher engagement and improved attitudes toward learning compared to traditional lectures.

These findings collectively support the motivational value of flipped instruction across different educational contexts, particularly when paired with collaborative in-class activities and personalized feedback mechanisms. However, implementation challenges—such as preparation time, learner adaptation, and digital access—remain common limitations.

Several recent systematic reviews have focused exclusively on flipped classroom implementations. Baig and Yadegaridehkordi (ref. [29]) analyzed thirty higher education studies, emphasizing the role of digital tools and pedagogical activities. Similarly, Satparman and Apps [30] reviewed thirty-four K–12 studies and highlighted considerable variability in pre-class design, additional teacher workload, and technology-related challenges.

2.4.4. PBL/PjBL/CBL

The Wijnia et al. [6] meta-analysis confirmed a mean effect of d = +0.50, which corresponds to a medium effect size and typically reflects a practically meaningful improvement in educational settings, particularly in terms of student performance or motivation. However, the authors warned about the use of non-standardized scales and a concentration of studies in health sciences. Portuguese research in humanities remains scarce.

Bell argues that PBL fosters transferable 21st-century skills, including collaboration, problem-solving, and learner autonomy [31].

2.4.5. Collaborative Learning

Collaborative learning interventions consistently demonstrate moderate positive effects on student motivation, engagement, and academic satisfaction. A number of studies confirm that well-structured peer interactions can enhance perceived competence and relatedness—two core components of intrinsic motivation according to Self-Determination Theory:

• Gillies [32]: quasi-experimental study in secondary science classes (N = 234); collaborative learning groups significantly outperformed controls on engagement and task value (+0.48 d).
• Laal and Ghodsi [33]: survey-based study across engineering programs (N = 121); collaborative environments associated with higher academic motivation and satisfaction.
• Järvelä et al. [34]: observational study in higher education (N = 83); real-time collaborative learning sessions linked to increased emotional engagement and self-regulated learning behavior.
• Gokhale [35]: controlled study in undergraduate business (N = 96); group-based problem-solving resulted in greater persistence and peer feedback efficacy (+0.42 d).

These studies collectively suggest that collaborative learning environments foster a sense of social belonging and mutual accountability, contributing to more sustained motivation. However, variations in instructional scaffolding and group dynamics significantly influence outcomes, highlighting the importance of structured roles and reflective feedback mechanisms.

2.5. Critical Synthesis

The convergence of effects between +0.45 d and +0.60 d shows that active learning methodologies that satisfy autonomy, competence, and relatedness improve motivation and academic success.

However, three key gaps remain:

• The need for more randomized controlled trials (RCTs) with objective measures;
• Inclusion of underrepresented disciplines and low-resource institutions;
• Cost–benefit analyses to guide the adoption of expensive educational technologies.

Despite these limitations, the overall evidence supports the integration of these methodologies into Portuguese higher education.

3. Methodology

This chapter describes, following the transparency recommended by the PRISMA 2020 guidelines (ref. [36]), the method used to synthesize the empirical evidence discussed in Section 2.

3.1. Type of Study

This review was preregistered in the Open Science Framework (OSF) on 20 June 2025. Registration link: https://osf.io/8q4pk (accessed on 20 June 2025).

An integrative systematic review was conducted, including:

• Quantitative primary studies (randomized controlled trials—RCTs—and quasi-experiments);
• Qualitative or explanatory studies;
• Published meta-analyses and systematic reviews.

The quality of primary studies was assessed using the Mixed Methods Appraisal Tool (MMAT) (ref. [37]), and meta-analyses were evaluated using AMSTAR 2 (ref. [38]).

3.2. Research Question (PICO Model)

To structure the research question in a systematic and transparent manner, the PICO model (ref. [39]) was applied, identifying the population, interventions, comparison group, and outcomes of interest:

• P (Population): Students in lower secondary, upper secondary, and higher education;
• I (Intervention): Gamification, flipped classroom, project-/problem-/case-based learning (PBL/PjBL/CBL), and active/collaborative learning;
• C (Comparison): Traditional lecture-based instruction (for studies using comparative designs);
• O (Outcome): Motivation (self-efficacy, engagement, persistence) and academic performance (grades, pass rates). Outcomes such as satisfaction, enjoyment, or general attitudes were excluded unless directly linked to motivational constructs.

This leads to the following research question: What effects do these methodologies have—compared to traditional instruction (where applicable)—on student motivation and academic performance in formal educational settings?

3.3. Search Strategy and Databases

Databases searched: Scopus, Web of Science, ERIC, and Google Scholar (from January 2000 to March 2024). Inclusion criteria applied to all databases:

• Languages: English, Portuguese, or Spanish;
• Document types: Peer-reviewed journal articles, conference proceedings, or dissertations;
• Publication period: 1 January 2000–31 March 2024;
• Fields: Education, Educational Psychology, Science Education, Educational Technology,

The search strategy combined the following sets of terms:

(1)

Intervention Terms

“gamification” OR “flipped classroom” OR “project-based learning” OR “problem-based learning” OR “case-based learning” OR “active learning” OR “collaborative learning”

AND

(2)

Outcome Terms

“motivation” OR “self-efficacy” OR “persistence” OR “academic performance” OR “achievement”

AND

(3)

Population Terms

“student*” OR “pupil*” OR “middle school” OR “secondary school” OR “higher education”

Example of search query used in Web of Science:

TS = ((gamification OR “flipped classroom” OR “project-based learning” OR “problem-based learning” OR “case-based learning” OR “active learning” OR “collaborative learning”) AND (motivation OR “self-efficacy” OR persistence OR “academic performance” OR achievement) AND (student* OR pupil* OR “middle school” OR “secondary school” OR “higher education”))

The following supplementary procedures were adopted:

• Backward citation tracking (references cited in included studies);
• Forward citation tracking (articles citing included studies);
• Documentation of additional workload and resource/cost data, when available.

3.4. Eligibility Criteria

Studies were eligible for inclusion if they met the following criteria:

(a)

Explicit intervention using one of the four methodologies;

(b)

Empirical data enabling either (i) standardized effect size calculation (Cohen’s d) for comparative studies, or (ii) coded thematic analysis of motivational outcomes in qualitative designs;

(c)

Basic, secondary, or higher education setting;

(d)

Full text available in English, Portuguese, or Spanish;

(e)

Publication between 2000 and 2024;

(f)

Outcomes limited to motivation (self-efficacy, engagement, persistence) and/or academic performance (grades, test scores, pass rates); studies focusing solely on attitudes, preferences, or satisfaction were excluded.

Studies were excluded if they met any of the following criteria:

(a)

Opinion pieces,

(b)

Studies in non-formal or corporate settings,

(c)

Studies without full-text access or without sufficient data for effect size calculation.

Note: While most included studies used comparative designs (RCTs or quasi-experiments), two were qualitative and included to reflect context-specific motivational processes not captured in statistical analyses.

3.5. Study Selection and Reliability

The authors conducted all identification, screening, and full-text review phases with support from an AI assistant, which:

• Generated synonyms and alternative keywords;
• Helped prioritize records during title/abstract screening;
• Logged inclusion/exclusion decisions.

Importantly, all final eligibility decisions were made by the authors following full-text review. The AI was not used for any automated exclusion, effect size coding, or quality assessment.

The study selection process is illustrated in Figure 1, following the PRISMA 2020 flow diagram (ref. [36]).

3.6. Quality Assessment

To ensure methodological rigor, both primary studies and meta-analyses included in the review were evaluated using established appraisal tools:

• MMAT 2018 (ref. [37]): 9 primary studies were rated as high quality, and 5 as moderate;
• AMSTAR 2 (ref. [38]): 4 meta-analyses were rated as high quality, and 1 as moderate.

3.7. Data Extraction and Analysis

3.7.1. Extracted Information

For each study, the following information was recorded:

• Author, year, country, educational level, subject area, research design, active learning methodologies used, sample size, intervention duration, instruments, results, effect size (d), and additional resources (type, time, estimated cost).

Constructs were also coded for alignment with Self-Determination Theory (autonomy, competence, relatedness) when explicitly mentioned or measurable.

3.7.2. Global Effect Size

Effect sizes for individual studies were computed using Cohen’s d.

The overall effect size was calculated as a sample-size-weighted mean:

Global effect size: d = 0.50 (95% CI: 0.38–0.62).

3.7.3. Result Consistency

Moderate heterogeneity was observed among studies.

Excluding the three RCTs slightly reduced the global effect to d = 0.47, indicating robust findings.

3.7.4. Role of Meta-Analyses

The five meta-analyses were not pooled into the final calculation.

They served instead to validate whether the observed effects were consistent with previously reported ranges (+0.45 ≤ d ≤ +0.60).

3.7.5. Subgroup Results

To explore variability in outcomes, results were analyzed by study design and disciplinary domain. The following subgroup trends were observed:

• RCTs (N = 3): Mean d = 0.54;
• Quasi-experiments (N = 6): Mean d = 0.48;
• Qualitative studies (N = 2): Reported improvements in engagement and persistence;
• Disciplinary distribution: 6 studies in STEM, 3 in Social Sciences, 1 in Humanities, 1 interdisciplinary
  (Humanities were underrepresented).

All calculations regarding average weekly workload (M = 37.5 min; SD = 6.5) and the consolidation of resource data were performed with AI assistance (ref. [40]), using automatically generated spreadsheets from extraction notes.

Additionally, we reviewed each study for alignment with Self-Determination Theory (SDT) constructs—autonomy, competence, and relatedness—where explicitly reported or inferable from the outcome variables. This allowed us to assess not just the magnitude but the nature of motivational gains.

3.7.6. Costs and Additional Resources

Three studies (refs. [15,18,19]) reported additional faculty workload of 30–45 min per week. None of the included studies reported explicit monetary costs or economic indicators, which made it impossible to conduct any form of cost–benefit or cost-effectiveness analysis. This absence represents a significant gap in the literature, particularly given that the practical adoption and scalability of active learning strategies are often shaped by institutional resource constraints. To support evidence-informed decision-making, future research should incorporate cost-related data—such as instructional time, materials, and implementation resources—so that the economic viability of these pedagogical approaches can be properly evaluated.

A summary of the reported additional workload and resources required for each intervention is presented in

Table A1. Reported additional workload and infrastructure associated with the implementation of active learning methodologies in selected studies. No financial cost was explicitly quantified by the authors.

Study No.	Study, Country	Level/Subject Area	Active Learning Methodologies	Extra Faculty Workload *	Resources/ Infrastructure	Declared Monetary Cost
1	Ref. [15], Ireland	Higher Ed—Pharmacy	Gamification (online quiz + leaderboard)	≈30 min/week	Moodle Quiz + “Level Up!” plugin (open source)	No
2	Ref. [18], Jamaica	Undergraduate—Computer Science	Collaborative PBL	≈45 min/week	Trello® (free); Google Drive	No
3	Ref. [19], China	Engineering—Mechanical Design	3-D Project-Based Learning	≈40 min/week	3D printers (FDM); Fusion 360® (educational license)	No

* Estimated by the authors; values rounded to the nearest half-hour.

(see Appendix A).

3.8. Limitations

The analysis revealed several limitations that may influence interpretation of results:

• Limited number of randomized controlled trials (RCTs): Only 3 out of 11 primary studies;
• Heterogeneity in motivation measurement instruments;
• Short intervention duration: Typically, less than 16 weeks, possibly affecting sustainability of effects;
• Lack of economic data: No cost-effectiveness evaluation possible;
• Potential publication bias: Funnel plot showed slight asymmetry.

3.9. Summary

The consolidated results presented in this chapter provide a foundation for the pedagogical discussion in Section 4, which analyzes the individual findings and their implications for higher education in Portugal.

4. Results and Discussion

This section synthesizes the results presented in Section 2 and Section 3 (from 16 studies published between 2000 and 2024) and discusses them in light of European and international literature on academic motivation.

4.1. Sample Characteristics

The studies included in this review varied in design, scope, educational level, and methodological quality. The key characteristics of the sample are summarized below:

• Total number of studies: 16

  ○

  Primary studies: 11 (9 quantitative, 2 qualitative);

  ○

  Meta-analyses/systematic reviews: 5;

• Publication period: 2000–2024;
• Educational levels represented:

  ○

  Higher education (7);

  ○

  Secondary education (3);

  ○

  Lower/upper secondary (4);

  ○

  Multilevel (e.g., individual, group/classroom, institutional), as well as variations in design and scope (2);

• Active learning methodologies assessed (no. of primary studies):

  ○

  Gamification (5);

  ○

  PBL/PjBL/CBL (3);

  ○

  Flipped classroom (2);

  ○

  Collaborative learning (1);

• Total sample size (primary studies): ≈4950 students;
• Methodological quality (MMAT 2018):

  ○

  High: 9 studies;

  ○

  Moderate: 5 studies;

• Meta-analysis quality (AMSTAR 2):

  ○

  High: 4;

  ○

  Moderate: 1.

4.2. Quantitative and Qualitative Synthesis

Means, standard deviations, and average ratios from the included studies were systematically converted into standardized effect sizes using automated statistical procedures to ensure consistency across data sources:

• Overall mean effect on motivation and performance was d = 0.50 (95% CI = 0.38–0.62). However, motivational outcomes varied across studies. The most consistent gains were observed in self-efficacy and perceived task value—both closely linked to students’ sense of competence and relevance. However, only a subset of studies explicitly measured Self-Determination Theory (SDT) constructs such as autonomy, competence, and relatedness. In contrast, intrinsic motivation (i.e., enjoyment or curiosity-driven engagement) showed smaller and less consistent improvements. Persistence-related outcomes (e.g., task completion, reduced dropout) also improved modestly, particularly in gamified interventions and project-based contexts.
• Moderate heterogeneity was detected across studies (Q = 18.9; I² = 42%), indicating that the variability in reported effects cannot be attributed solely to sampling error. This suggests that study-level characteristics—such as the type of intervention, its duration, or the disciplinary context—likely contributed to differences in outcome magnitude. A key limitation of this review is the lack of formal analysis to account for this heterogeneity. While patterns in the data point to potential moderating factors (e.g., intervention length, academic domain, feedback strategies), the limited number of included studies prevented the use of subgroup analyses or meta-regressions. To address this, future research should aim to include broader and more diverse samples, enabling robust statistical examination of these moderators. The relevance of such moderating variables is further elaborated in Section 4.3.

The main characteristics and findings of the primary studies are summarized in

Table 1. Summary of the primary studies included in the review, detailing the type of active learning methodologies applied, educational context, sample size, outcome variables, and standardized effect sizes (Cohen’s d), and presence of Self-Determination Theory constructs when reported.

Study	Methodology	N	Main Variable	d	SDT Components Measured
[15]	Gamification	98	Participation	0.67	Relatedness
[17]	Gamification	197	Engagement	0.48	Relatedness
[19]	3-D Gamification	440	Motivation	0.60	Competence
[14]	Online badges	778	Persistence	0.35	Competence
[20]	Leaderboard model	1862	Performance	0.40	Competence
[25]	Flipped classroom	64	Reading self-efficacy	0.43	Competence
[16]	3-D Gamification	64	Self-efficacy	0.62	Competence
[18]	Collaborative PBL	156	Intrinsic motivation	0.45	Autonomy, Competence

, including the type of active learning methodologies implemented, sample size, target outcomes, and corresponding effect sizes (Cohen’s d).

Average standardized effect sizes (Cohen’s d) by active learning methodology, calculated based on the primary studies summarized in Table 1 and Figure 2. The number of studies per category varies, with gamification being the most represented. As such, these averages should be interpreted with caution. Differences between methodologies may also reflect variation in intervention duration, feedback frequency, and levels of learner autonomy.

Although only two qualitative studies were included, their findings offer meaningful complementary insights. Both reported increased student engagement and persistence, but more importantly, they highlighted affective and relational dimensions often missed by quantitative measures. For instance, participants described greater feelings of belonging, ownership over the learning process, and perceived relevance of activities to their personal goals. These emergent themes suggest that active learning methodologies may foster not only cognitive and behavioral gains, but also deeper motivational alignment and emotional investment.

Notably, none of the included studies were conducted in Portuguese-speaking countries, and only a small number addressed hybrid or post-pandemic instructional settings. This highlights a persistent underrepresentation of Lusophone research in global educational reviews, underscoring the need for both localized studies and broader regional syntheses. By addressing this gap, the present review contributes to the evidence base for Portuguese-speaking contexts, where systematic analyses remain limited.

4.3. Identified Moderators

Several moderating factors were identified that appear to influence the magnitude and consistency of the intervention effects. These moderators include:

• Duration ≥ 8 weeks → mean d increases to 0.60.
• Motivation type → strong effects on competence beliefs and task value; smaller changes in intrinsic orientation.
• Subject area → stronger effects in STEM and Health Sciences (d ≈ 0.55) than in Humanities (d ≈ 0.38).
• Gamification design → Frequent feedback (badges, visible leaderboards) enhanced outcomes; unsupported competitive rankings could nullify effects.
• Instructor autonomy → Teacher-customized interventions had slightly higher effects.

Comparing these findings with results from other regions reveals both convergence and contextual differences in the effectiveness of active learning methodologies. Recent meta-analyses conducted in Asian settings, for example, reported notably high average effects for gamification interventions (Hedges’ g ≈ 0.82), particularly when embedded in digital learning environments. Flipped classroom studies across Asia and Europe have also shown consistent medium-to-large effects (≈0.39 to 0.53), closely aligning with the overall mean effect size observed in the present review (d ≈ 0.50). In contrast, such interventions remain underexplored in Latin America, though isolated pilot studies—for instance, in Chilean physics education—suggest promising reductions in dropout rates and increased learner persistence. These international patterns corroborate the general efficacy of student-centered pedagogies but also underscore the importance of localized research adapted to specific sociocultural and institutional contexts.

Beyond statistical effects, qualitative findings contribute valuable nuance to our understanding of how active learning strategies influence students. In the two included qualitative studies, students described not only greater engagement, but also affective gains such as “feeling more connected to classmates” and “taking more pride in completing challenges.” These accounts suggest that active learning methodologies may foster an increased sense of belonging and ownership—dimensions not always captured by standardized motivation scales. For instance, one participant in a collaborative PBL study reported that “working in a team gave me more confidence to speak up, even outside of class”. Additionally, students also expressed a stronger identification with course content and felt less isolated in online settings.

4.4. Pedagogical Implications

The pedagogical recommendations derived from this review are especially intended for application in higher education settings, where structural flexibility and curricular autonomy allow for diverse active learning strategies. While some findings may generalize to upper secondary levels, institutional constraints differ considerably. Where outcomes such as satisfaction or enjoyment were reported, they were only retained if empirically linked to motivational constructs.

The following recommendations are grounded in empirical findings from the primary studies reviewed (see Table 1), and aim to guide educators in implementing motivationally supportive active learning practices.

Based on the findings of this review, several pedagogical strategies are recommended to enhance student engagement and motivation through active learning:

• Select authentic, context-rich problems for PBL/PjBL activities, following evidence from collaborative PBL implementations in [18];
• Introduce micro-gamification elements early on (e.g., weekly formative quizzes) to spark momentum, as supported by the findings of [15] and [17];
• In flipped models, reserve in-person time for structured discussion and group problem-solving;
• Establish clear interaction norms in virtual collaborative environments;
• Use learning analytics to provide personalized feedback, respecting data ethics and GDPR;
• Plan for additional teacher workload (~37 min/week, see Appendix A), and negotiate compensation or task redistribution.

This differentiation between motivational constructs is theoretically relevant. Gains in self-efficacy and task value suggest that active methods enhance students’ belief in their ability and the perceived importance of tasks—both predictors of sustained academic effort. However, more subtle dimensions like intrinsic interest or enjoyment were less consistently affected, echoing prior concerns that extrinsic tools (e.g., badges, points) may not fully internalize motivation unless carefully designed (see [14,20]), echoing findings from prior literature on gamification and motivation (refs. [8,41]).

4.5. Limitations of the Evidence

Despite the overall consistency of findings, several limitations should be acknowledged, as they may affect the interpretation and generalizability of the results:

(a)

Only 3 randomized controlled trials; quasi-experiments predominated;

(b)

Heterogeneous motivation instruments limit comparability;

(c)

No studies had ≥12-month follow-up; long-term effects are unknown. While short-term gains are promising, the limited duration of most interventions raises questions about the long-term sustainability of these motivational effects;

(d)

Economic data absent; no cost-effectiveness analysis possible;

(e)

Funnel plot revealed slight asymmetry, indicating possible residual publication bias.

In addition, although moderate heterogeneity was detected (I² = 42%), we were unable to perform subgroup or meta-regression analyses due to the limited number of eligible studies. As a result, it remains unclear whether the variability in effect sizes is attributable to factors such as discipline, intervention length, or measurement tools. These limitations underscore the need for more robust, long-term, and economically informed research on active learning methodologies in education.

The absence of economic data in the included studies prevented any assessment of cost-effectiveness, despite its practical importance for implementation.

4.6. Directions for Future Research

To address current gaps and strengthen the evidence base, future research should prioritize the following lines of inquiry:

• Multicenter randomized controlled trials with longitudinal follow-up;
• Individual participant data meta-analyses (IPD) to explore learner profiles;
• Development of motivation scales validated for Lusophone and hybrid contexts;
• Studies testing combined interventions (e.g., gamification + PBL, flipped + gamification);
• Integration of economic metrics (faculty time, licenses, hardware) into future assessments.

4.7. Conclusions

The active learning methodologies analyzed yield a moderate motivational gain (d ≈ 0.50) and reduce academic failure. Effects are stronger when:

• Interventions last at least 8 weeks;
• Students receive frequent and relevant feedback;
• Tasks carry personal or professional meaning;
• Pedagogical design supports autonomy, competence, and relatedness.

These results support the adoption of active learning methodologies in Portuguese higher education—provided they are accompanied by faculty training and realistic planning for the required extra time.

5. Conclusions and Recommendations

Following the detailed analysis in Section 2, Section 3 and Section 4, this final section consolidates the key findings into clear guidance for teaching practice and outlines strategic implications for future research.

5.1. Summary of Key Findings

Before presenting specific recommendations, a cross-sectional analysis of the evidence is provided to identify the interventions that consistently yield the greatest impact on student motivation and performance.

The following points summarize the most relevant findings:

• Student motivation is a robust predictor of meaningful learning and academic success.
• The overall effect of active interventions (11 studies, 2000–2024) was Cohen’s d = 0.50 (95% CI = 0.38–0.62).
• Three groups of strategies produced the most consistent effects:

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  Gamification (d ≈ 0.48): Points, badges, and leaderboards enhance engagement and performance (ref. [19]).

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  Problem-/Project-/Case-Based Learning (d ≈ 0.50): Solving authentic challenges boosts intrinsic motivation and competence (ref. [6]).

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  Active learning methodologies in STEM (d ≈ 0.47): Reduced failure rates by 55% (ref. [5]).

• Flipped classrooms (d ≈ 0.44) improve self-efficacy when in-class time is used for collaborative work.
• Interventions lasting ≥8 weeks, with frequent feedback and intrinsic motivation focus, generate more consistent outcomes.

Taken together, these findings align strongly with Self-Determination Theory (ref. [8]), which emphasizes the role of autonomy, competence, and relatedness in sustaining motivation. For instance, project- and problem-based approaches satisfy competence through mastery experiences, while collaborative and flipped models foster relatedness and shared accountability. Gamification, when carefully designed, supports autonomy by giving students visible progress markers and choice in learning pathways. This theoretical alignment helps explain why interventions that explicitly nurture these three needs achieved more consistent motivational outcomes.

Furthermore, the results can be situated within constructive alignment theory: interventions were most effective when learning objectives, instructional activities, and assessments were coherently structured to reinforce motivational drivers. Thus, this review not only synthesizes empirical findings but also demonstrates how these findings reinforce and extend theoretical models of motivation in education.

While these results confirm the motivational benefits of active methodologies, the small number of eligible studies (N = 5) limits generalizability. Effect sizes should therefore be interpreted with caution, as contextual factors (discipline, duration, delivery mode) introduce moderate heterogeneity.

5.2. Recommendations for Teaching Practice

Based on the synthesis above, the following are practical and actionable strategies educators can implement, adapted to their institutional context:

Recommended strategies for implementation include:

• Introduce micro-gamification elements (badges, leaderboards, timed quizzes) early in the course.
• Plan interdisciplinary projects lasting 6–8 weeks, with formative assessments and opportunities for both individual and group reflection.
• Organize student groups (3–4 members) with rotating roles and clear, shared objectives.
• Implement the flipped classroom model: deliver theoretical content via videos/readings before class; reserve class time for discussion and problem-solving.
• Use platforms that provide immediate feedback (e.g., moderated forums, real-time response tools, simulators).
• Foster a safe and inclusive classroom climate, where students feel comfortable expressing doubts and participating.
• Provide regular, specific, and constructive feedback.
• Anticipate additional workload and plan proactively for compensation or workload redistribution, especially in resource-constrained teaching environments.

These recommendations are consistent with motivational theory, as they encourage autonomy (choice in learning activities), competence (progressive mastery through feedback), and relatedness (peer collaboration), thereby creating the conditions for sustained intrinsic motivation.

5.3. Implications for Future Research

In light of the findings discussed above, the following areas are proposed as research priorities:

• Longitudinal studies (≥1 year) to evaluate the durability of effects.
• Multicenter randomized controlled trials to enhance external validity.
• Individual participant data meta-analyses (IPD) to identify which student profiles benefit most.
• Development of validated motivation scales for Lusophone and hybrid learning contexts.
• Integration of economic metrics (faculty time, licenses, equipment) into evaluations.
• Exploration of intervention combinations (e.g., gamification + PBL; flipped + gamification), supported by adaptive feedback via learning analytics.

Future studies should explicitly test the mechanisms predicted by motivational theories (e.g., SDT, expectancy-value theory), rather than focusing only on outcome measures. By doing so, researchers can clarify not only whether active learning works, but also why it works in terms of psychological needs and value perceptions.

5.4. Final Consideration

The convergence of evidence shows that combining digital technologies with active learning methodologies creates more stimulating learning environments, enhancing motivation, autonomy, and academic performance.

This reinforces theoretical claims that motivation is not merely an individual trait but the result of well-designed learning environments that fulfill students’ psychological needs. By integrating empirical outcomes with established frameworks, this review contributes to refining the theoretical understanding of how digital and active pedagogies interact with motivational processes.

To ensure these practices take root in Portuguese higher education, it is essential that institutions:

• Promote continuous professional development focused on designing motivational learning activities.
• Provide adequate technological infrastructure.
• Formally recognize the extra time required for planning and implementing such strategies.
• With these conditions in place, active learning methodologies can become part of the pedagogical culture, contributing to more relevant, inclusive, and effective education.

In an era of digital transformation and pedagogical reform, the adoption of these strategies is not merely an option, but a necessity for inclusive and effective higher education. In light of the moderate heterogeneity observed, we recommend that future systematic reviews employ meta-regression and subgroup analysis techniques to clarify how variables such as subject domain, intervention duration, or digital format influence effect sizes.