Darwin Castillo1,2,3*
Javier Carrión1
Cristian Chamba4
Yuliana Jiménez-Gaona1,2,3
María José Rodríguez-Álvarez2
Vasudevan Lakshminarayanan3,5- 1Departamento de Química, Facultad de Ciencias Naturales y Exactas, Universidad Técnica Particular de Loja, Loja, Ecuador
- 2Instituto de Instrumentación para Imagen Molecular (i3M) Universitat Politècnica de València, Consejo Superior de Investigaciones Científicas (CSIC), Valencia, Spain
- 3Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON, Canada
- 4Maestría en Educación, Mención Enseñanza de la Matemática, Universidad Técnica Particular de Loja, Loja, Ecuador
- 5Departments of Physics, Electrical and Computer Engineering, and Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
The conceptual understanding and motivation for learning and teaching math constitute a challenge for didactic research at all levels of education. However, it is essential in higher education levels like the university, where achieving advanced reasoning and connecting the nature of math with professional applications is important. This systematic literature review analyzes peer-reviewed research published between 2000 and 2023, focusing on didactic and instructional strategies applied that enhance comprehension and engagement in undergraduate and higher mathematics education. Following PRISMA guidelines, we get a final analysis of 30 studies where the semiotic representations and gamification strategies are considered key strategies to achieve conceptual and motivated understanding of math in the context of higher education. Semiotic methods from Duval’s theoretical framework emphasize the coordination of symbolic, graphical, and algebraic registers to promote deep conceptual learning. As an active learning method, gamification is highly effective for enhancing student engagement and motivation, helping students overcome their apprehension toward mathematics. While most studies explored these strategies independently, this review identifies gaps in integrative approaches. It highlights the need for further research on their combined impact, especially when representational depth is aligned with motivational design. The 2000–2023 window captures the consolidation of semiotic frameworks and the expansion of ICT and gamification in higher mathematics education.
1 Introduction
Mathematics, as constructed over millennia of history beginning with the earliest ancient civilizations, has been fundamental to human development. Undoubtedly, modern civilized society would be impossible without mathematics (Boyer and Merzbach, 2019).
In our daily lives, we use math: when we go to the store, when we buy groceries, when we go traveling, when we count the points in our games, etc. It means that math can be considered an essential element in our lives. However, when we are learning math, along the entire academic path, for many students, it is generally perceived as a complex subject with a lot of complexity to understand, and often disconnected from real-life problems and applications.
As Dan Meyer pointed out (Klaassen, 2023), this negative perception is often reinforced by traditional and passive instructional methods emphasizing procedural knowledge over connecting mathematical content with students’ realities and cognitive development. The challenges and difficulties with math are more visible in higher education because students are expected to perform advanced mathematical reasoning while often lacking foundational conceptual tools.
In this context, over the last few years, there has been continuous research focused on different strategies and innovative pedagogical methodologies to achieve a better conceptual understanding of mathematics while maintaining a sustained engagement with the students. Multiple studies indicate that middle and university level students struggle to apply mathematical knowledge to real-world contexts due to insufficient integration of innovative pedagogical strategies in classroom instruction (Strømgren et al., 2014; Brozo and Crain, 2018).
Therefore, as AlAli et al. (2023) and Wardat et al. (2023) suggest, promoting the development of the students’ mathematical thinking skills through contextually meaningful and cognitively engaging strategies is fundamental. In that way, learners could develop the ability and competence to visualize and solve problems with accurate reasoning and efficiency, instead of following merely rules or procedures.
In higher education mathematics, students often experience difficulty connecting abstract concepts to real-world cases, which undermines persistence and confidence. Over the last two decades, research and the need to cultivate a deep understanding of math with engagement and motivation, has inspired a diverse set of strategies and innovations, including: semiotic approaches, integration of information and communication technologies (ICTs) (Hoyles and Noss, 2003), immersive learning environments (Amable Vivanco-Galvan et al., 2018), STEAM-based learning (Perignat and Katz-Buonincontro, 2019; Lakshminarayanan and McBride, 2015), collaborative projects, gamification (Tashtoush et al., 2023; Milovanovic et al., 2021; Jiménez-Gaona et al., 2019), virtual and augmented reality tools (Rodríguez, 2022; Villacís Macías et al., 2022), and recently the use of Artificial Intelligence (AI) based applications (Wardat et al., 2023). These approaches inspire exploration, reasoning, and social interaction over the traditional frontal and procedural instruction that still predominates in many university classrooms.
In this sense, this systematic review focuses on exploring and identifying how these didactic strategies are deployed in university mathematics education, aiming to promote two core elements: conceptual understanding and student motivation. The methodology of this study was a systematic review of the literature; therefore, the following databases were used as sources of information: Scopus, PubMed, Web of Science, Science Direct, IEEE Xplore, and Google Scholar.
By examining studies from 2000 onwards under PRISMA guidelines, this review answers the following guiding question:
• What are the most frequently studied didactic strategies for improving conceptual understanding and student motivation in higher education mathematics?
The results and findings of this review are concentrated on two strategies: semiotic representations, which facilitate the conceptual processing of math through symbolic, graphical, and algebraic coordination (Duval, 2006); and gamification, which enhances engagement and reduces math anxiety through instructional designs based on games.
The structure of the paper is as follows: Section 2 presents the theoretical framework concerning teaching theory and learning theory, semiotic representations, and motivation and educational technology centered on gamification. Section 3 presents the review methodology and selection criteria. Section 4 discusses the review’s findings and results regarding semiotic and gamification strategies. Section 5 provides conclusions, limitations, and implications for practice and future research.
2 Theoretical framework
2.1 Teaching and learning theory
Learning theory (Bada and Olusegun, 2015) describes how students receive, process, and retain knowledge during learning. Learning strategies encompass the thoughts and behaviors that help students acquire new information and integrate it with their existing knowledge (Yip, 2012). Cognitive, emotional, and environmental influences, as well as prior experience, all play a role in how understanding, or worldview, is acquired or changed and knowledge and skills are retained (te Braak et al., 2022).
A key teacher competency in this regard is the ability to collect information on students’ learning progress, make diagnostic inferences, and respond through an ongoing, interactive process (Chapman, 2013). In particular, teachers must be able to recognize and understand students’ difficulties, infer a broad range of strengths and weaknesses, provide targeted feedback, and design appropriate tasks to foster students’ mathematical thinking (Dorier and Mass, 2020).
2.2 Semiotic representations in mathematics learning
Duval (1995, 1999, 2006, 2017) proposed the theory of semiotic representation, which enounces that the understanding and comprehension of mathematical concepts depend on the ability to coordinate and express multiple and different semiotic registers, which means the capacity to express math in various forms, such as verbal, graphical, symbolic, and tabular forms.
This theory is particularly significant in calculus, geometry, and algebra, where translation between registers allows for the acquisition of a deep understanding, e.g., from a function graph to its symbolic expression. Misalignment between semiotic representations is frequently linked to conceptual misunderstanding and fragmented knowledge, reinforcing the need for pedagogical strategies that explicitly design activities to develop this coordination.
Recent literature (Pedersen et al., 2021; Caligaris et al., 2019) expands Duval’s theory by analyzing how tasks and digital environments mediate semiotic transformations. Moreover, researchers such as Burgos et al. (2021) and Salazar (2018) explore the ontosemiotic complexity and cognitive demands inherent in university-level math problem-solving.
2.3 Motivation theories in mathematics education
Motivation is central to sustaining mathematical engagement and reducing anxiety, especially in abstract or cognitively demanding domains.
Keller (1987) defines four pillars: Attention, Relevance, Confidence, and Satisfaction (ARCS Model of Motivation), often reflected in gamified strategies, game-based learning, problem-based learning, and real-world math applications. Many reviewed studies link improved learning outcomes to instructional designs aligned with these dimensions (Pehlivan and Arabacioglu, 2023; Chapman and Rich, 2018).
The development of autonomy, competence, and relatedness as intrinsic motivators is called Self-Determination Theory (SDT) (Deci and Ryan, 1985). Adaptive gamification environments, flipped classrooms, and collaborative models tend to fulfill these psychological needs, improving students’ perseverance and academic self-concept (Rivera and Garden, 2021; Hassan et al., 2021; Bennani and Maalel, 2022).
These ideas help us see how particular teaching decisions, whether using game-like feedback, showing students their progress, or allowing them to choose how they represent their ideas, can boost motivation in university (higher education) classrooms.
2.4 Gamification and educational technology
Gamification (Sobrino-Duque et al., 2022), defined as the use of game mechanics and game elements in a non-game context, has attracted considerable attention and has been applied across a wide range of fields to motivate and engage individuals in the performance of specific tasks, activities, and the resolution of various problems (Kapp, 2012).
The integration of digital tools in university mathematics teaching has been accelerated by increased access to educational technologies and growing recognition of their motivational affordances (Bouchrika et al., 2021). In this sense, in e-learning education, gamification enriches the math learning experience, as seen in the studies from (Bouchrika et al., 2021; Lubis et al., 2014). Their analysis shows how gamification in math could be translated into smartphone apps to increase the effectiveness of the engagement of math students.
As an educational tool, gamification facilitates learning, encourages motivation, improves student engagement and lesson interactivity, and encourages students to expand their knowledge (Bennani and Maalel, 2022; Behl et al., 2022; Sabri et al., 2022). However, effective use requires alignment with meaningful mathematical reasoning, not merely entertainment (Jiménez-Hernández et al., 2020; Faghihi et al., 2014).
3 Materials and methods
This systematic review identifies didactic strategies in university mathematics education that specifically foster conceptual understanding and engagement motivation. The search strategy was guided by Preferred Reporting Items for Systematic Reviews (PRISMA 2020) criteria (Page et al., 2021) and based on the methodologies proposed by Torres-Carrion et al. (2018) and Kitchenham (2004).
3.1 Search term framework
The conceptual framework used in this work allows us to focus and restrict the subject to didactic strategies for teaching mathematics. The method proposed by (Vicente Torres-Carrion et al., 2018), called “conceptual mindfact” (mentefacto conceptual), helped to organize the scientific thesaurus keywords for the research topic (Figure 1).
Figure 1. Conceptual mindfact (mentefacto conceptual) identifies keywords for a systematic literature search in scientific databases.
3.2 Conducting the review
Once the keywords concerning the research theme are identified, the next step is to organize a semantic search structure that allows us to get the documents for the review analysis. Table 1 presents the semantic search structure (Torres-Carrion et al., 2018), such as entering specific search literature (documents) in scientific databases.
Table 1. Keywords used in the search for global semantic structures.
The first level represents the teaching search; the second corresponds to the keyword Mathematics. The third level is relevant for applying the strategy for analyzing scientific documents. The fourth level is the search for global semantic structure.
3.3 PRISMA
3.3.1 Identification and screening
The global semantic structure (see Table 1) used allows us to identify 525 documents through a worldwide search, especially on Scopus and Google Scholar (427 documents) and Web of Science (WoS) (98 documents).
3.3.2 Eligibility and inclusion
Inclusion criteria:
1. Studies focused on university/high-level mathematics education, or with extrapolable implications for the higher-education context.
2. Studies that implement didactic strategies with measured outcomes in conceptual understanding/comprehension, and/or motivation/engagement.
3. Studies concerning didactic strategies (e.g., gamification, semiotic representations, ICT-enhancing teaching, and cooperation).
Exclusion criteria:
1. Studies that are not peer-reviewed
2. Theoretical studies without any didactic application.
3. Studies without a connection to mathematical reasoning, comprehension, or motivation for learning.
4. Studies centered on non-university educational levels, unless they offer explicit, extrapolatable implications for higher-education contexts.
5. Studies related to engineering or psychological strategies
3.3.3 Study selection and reason for exclusion
From 525 records, 160 duplicates were removed; 365 were screened by title/abstracts, and 255 were excluded for document type or out of scope. A total of 110 full texts were assessed, and 80 were excluded for the following reasons: (R1) no higher education context or non-extrapolable (n = 43), (R2) no target outcomes in conceptual/motivation (n = 16), (R3) purely theoretical/no didactic application (n = 12), (R4) non-math-education focus (n = 9). A total of 30 studies were included for synthesis, focused on strategies based on conceptual understanding of math and enhancing the motivation to learn it in university/higher education mathematics contexts (Figure 2).
Figure 2. Preferred reporting items for systematic reviews (PRISMA) flow diagram (Moher et al., 2009; Hutton et al., 2016). Reasons for full-text exclusion: R1 = no higher Education context or not extrapolable (n = 43); R2 = no target outcomes in conceptual/motivation (n = 16); R3 = theoretical/no didactic implementation (n = 12); R4 = non-math-education focus (n = 9).
3.4 Maps in VOSviewer
VOS viewer (Van Eck and Waltman, 2010) is a software tool at the Leiden University Center for Science and Technology Studies designed for building and visualizing bibliometric networks. These networks can be built based on citations, bibliographic linkage, co-citation, or co-authorship relationships, including journals, researchers, or individual publications.
Several studies show the application of the VOSviewer in different fields, such as economy (Perianes-Rodriguez et al., 2016; Iliescu, 2021), engineering and computer science (Castillo et al., 2021; Wang et al., 2022), and, of course, also in math education research (Ersozlu and Karakus, 2019; Verma et al., 2021; Hanif Batubara et al., 2022; Veith et al., 2023).
We used VOSviewer software version 1.6.15 for analysis to construct and display bibliometric maps. The data for this objective were obtained from Scopus due to its coverage of a broader range of journals.
3.5 Clustering
For structure visualization, we one-hot encoded four categorical features per study: didactic strategies, education level (School/Secondary/University), outcome categories (Motivation; Conceptual; Both; Other), and instrument category (e.g., questionnaire/test/task-analysis). PCA was applied to reduce dimensionality to 2D, and K-means (k = 3) was run to identify profiles.
4 Results and discussion
4.1 Publication evolution
The global semantic structure search (Table 1) found 525 documents of different types (articles, presentations, and reviews) from 2001 to December 1, 2023. Figure 3 shows the number of publications and their evolution in this period.
Figure 3. Evolution of publications during the last two decades. We can see that there has been an increase in the trend of research in math education since 2015.
4.2 Keywords and citations
Regarding the keywords and related publications, Figures 4, 5 present the map of the network of publications about the citations and keywords, respectively. Both of them were designated in VOSviewer with the database from Scopus. In the map, the density of the yellow color in each keyword indicates the number of repetitions in the total number of scientific documents. The most used keywords are mathematics education, students, and teaching.
Figure 4. Network of publications related to keywords in global semantic search. In this figure, it can be seen That the words “teaching,” “student,” and “mathematics education” are the most used.
Figure 5. Citation map between documents. This figure shows that the most cited author is Duval regarding semiotic representations. The color scale (violet to yellow) indicates the number of citations per document, and the points’ diameter shows the normalization of citations, according to Van Eck and Waltman (2011, 2014). The purple dots are the authors with fewer than 10 citations, and the yellow dots are those with more than 60 citations.
4.3 Didactic strategies
Table 2 describes the scientific documents related to our research topic in this project. Figure 6 shows the distribution of the documents analyzed in this review, (a) by level of education and (b) by country.
Table 2. Documents of the systematic review related to the research topic and their findings.
Figure 6. The distribution of the documents analyzed in this review is (a) by level of education, and (b) by country.
According to Table 2 and through the analysis of the documents, we can see in Figure 7 that gamification (56.7%), semiotic representations (36.7%), and learning theories (6.7%) are the strategies most applied to improve or research math learning at the university level. These findings reflect that the research trends favoring motivational and interactive approaches in higher education math have evolved over the last two decades.
Figure 7. Percentage distribution of didactic strategies identified in this project of university-level mathematics education studies. Gamification is the most prevalent strategy (56.7%), followed by semiotic representations (36.7%) and learning theories (6.7%).
4.4 Matrix correlation and clustering
A cross-analysis of the didactic strategies and their associated learning outcomes is presented in Figure 8 and Table 3. It is seen that Gamification is associated with motivation principally; semiotic representations with conceptual understanding, and learning theories are related to other outcomes due to their theoretical focus.
Figure 8. Matrix correlation of didactic strategies and learning outcomes identified in the review studies. It is seen that gamification is strongly associated with motivational outcomes; semiotic representations are linked to conceptual understanding. A limited number of studies simultaneously address (3) both cognitive and affective domains, highlighting a gap in integrative pedagogical approaches.
Table 3. Studies by strategy and outcomes concerning the matrix correlation of didactic strategies and learning outcomes identified in the review studies.
4.5 Discussion
This review sets out to identify the didactic strategies that promote conceptual understanding and student motivation in higher/university mathematics teaching. In this sense, the analysis of the 30 studies guides the answer to the research question, showing that there are two families of strategies dominating in their intended outcomes: (i) gamification for motivational engagement, and (ii) semiotic representations for conceptual learning (see Figures 6a, 7, 8).
4.5.1 Quantitative evidence
Across the analysis of the studies included, gamification is the most prevalent strategy (56.7%), followed by semiotic representations (36.7%), and learning theories (6.7%) (See Figures 6a, 7).
Concerning the learning outcomes and the didactic strategies, it is seen in the strategy-outcomes matrix (see Figure 8; Table 3) that the gamification studies report the motivation/engagement outcomes, while the semiotic representations studies are associated with the conceptual understanding. Only 10% of the studies (3) report outcomes in both domains, underscoring in that way, a narrow intersection between cognitive depth and affective engagement in current practice.
In Figures 9, a clustering analysis using Principal Component Analysis (PCA) + K-means (k = 3) is presented, where it is possible to appreciate the formation of three stable clusters through the analysis of four variables: didactic strategy, education level, outcome category, and instruments.
Figure 9. Cluster profiles (k = 3) of didactic strategies in university mathematics education, categorized as semiotic–conceptual (C0), gamification–motivational (C1), and gamification–general (C2). Principal Component Analysis (PCA) was applied to variables related to strategies, instruments, and educational levels, resulting in three distinct clusters. Ellipses indicate the approximate dispersion and grouping of studies within each cluster.
The three resulting clusters: C0 (yellow squares), C1(orange circles and pink triangles), and C2(pink circles) are delineated and labeled at their centroids. The clusters could be interpreted as three distinct profiles of didactic strategies in university mathematics education.
a. C0: group studies centered on semiotic conceptual approaches, where the conceptual understanding is prioritized through symbolic, visual, and diagram registers. PCA space indicates low overlap with gamification approaches.
b. C1: studies with gamification motivational orientation, where the strategy is predominantly oriented to student motivation and engagement using game mechanics. This cluster also has some integrations with theoretical frameworks, but with less emphasis on semiotic representation.
c. C2: gamification general/hybrid orientation, presents gamification strategies with contextualized learning experiences and general applications. The PCA location at the upper left indicates shared variance with C1 in motivational elements but diverges in specific application focus.
The spatial PCA shows a separation between clusters, suggesting that cognitive and motivational approaches are often pursued independently, and it also reflects differences in methodological design and pedagogical intention. In this context, this differentiation highlights the potential to explore hybrid strategies that combine motivation strengths from gamification with the cognitive depth of semiotic representations.
4.5.2 Semiotic representations, depth in conceptual understanding
Studies focusing on semiotic representations highlight the essential role of coordinating multiple representational registers, such as symbolic, graphical, verbal, and algebraic, to facilitate and promote a deeper mathematical reasoning.
Several studies (Hitt, 1998; Ledesma, 2011; Burgos et al., 2021) mention the importance of semiotic representations as a key to understanding and addressing the challenges of acquiring the mathematical concepts of calculus and precalculus. Similarly, other authors say representations are crucial for students’ and expert mathematicians’ mathematical activity (Morgan, 2006; Iori, 2018). The different representations foster deep understanding and conceptual learning by reinforcing students’ ideas and skills (Ainsworth et al., 1997; Even, 1998; Winsløw, 2003). Studies such as those proposed by Brock et al. (2020) raise the bar on rigor and encourage students to solve problems creatively while gaining valuable data on their growth as thinkers and mathematicians (Hancock and Karakok, 2021).
In that way, it emphasizes the importance of research in the semiotic representations, which, according to Duval (2006, 2017), produce a deep comprehension of learning abstract mathematical objects. For instance, when students use semiotic representations to explore and solve real-world problems collaboratively, they engage more deeply with the material, applying theoretical knowledge in practical contexts (Moyer-Packenham et al., 2022). Integrating semiotic representations with other instructional strategies, such as problem-based learning or collaborative projects, can enhance their effectiveness.
4.5.3 Gamification enhances engagement and motivation
Gamification strategies, including Game-Based Learning (GBL), have shown strong potential to increase learner engagement, reduce mathematics anxiety, and create meaningful learning experiences when tasks are situated in authentic contexts (Lubis et al., 2014).
Moreover, nowadays, gamification can be enhanced through artificial intelligence tools. In the literature, Alneyadi and Wardat (2023) show how ChatGPT could provide students with a positive influence in the learning of magnetism concepts, so in that way, the use of AI models could improve educational outcomes (Lubis et al., 2014). Their study shows how gamification in math could be translated into smartphone and AI apps to increase the effectiveness of the engagement of math students. According to the explored literature, in recent years, there has been a substantial increase in research focused on gamification, which could be influenced by the educational disruptions of the COVID-19 pandemic (Bouchrika et al., 2021).
Gamification as an active learning strategy is combined with other active methodologies such as Problem-Based Learning (PBL), Flipped Classroom, Project-based learning, and real context situations; e.g., several studies demonstrated better performance and understanding of the students about the math concepts (Hassan et al., 2021; Pehlivan and Arabacioglu, 2023) and also emphasized the importance of gamification, combining with flipped classrooms, due to its allowing students to engage in more active and motivating learning activities (Husain et al., 2023). The Flipped classroom provides the possibility that the students could develop their ideas and acquire skills that directly have implications for the progress of significant learning (Lo and Hew, 2020; Lo et al., 2021), and is also recognized as a powerful strategy for teaching and learning math in university courses.
In math problems, contextualization means that the students can identify the variables in a specific real problem and, after, could propose different ways of solving the situation using the mathematical concepts and relations between them. Some successful examples reported in the university context and secondary education with extrapolable results are mentioned in math contextualized studies in different fields like health, engineering, and biology (Jiménez and Castillo, 2017; Chapman and Rich, 2018; Jiménez, 2018; Jiménez-Gaona et al., 2019; Rivera and Garden, 2021; Sobrino-Duque et al., 2022; Amable Vivanco-Galvan et al., 2018).
In sum, gamification can increase intrinsic motivation and engagement to learn math (Buckley and Doyle, 2014; Chapman and Rich, 2018; Bouchrika et al., 2021), contribute to improving performance and consolidating concepts, e.g., in subjects such as algebra and calculus (Faghihi et al., 2014; Faghihi et al., 2017; Hafzah et al., 2019; Jiménez-Hernández et al., 2020); and represents a powerful tool for teachers at all levels of the education system (Rivera and Garden, 2021).
4.5.4 Challenges, future proposals, and limitations
Concerning the challenges, studies, such as Meij et al. (2022), mention the gap between the theories of teaching, the reality in education, and the formation of educators. This point suggests the potential existence of a discrepancy between theoretical educational paradigms and their practical implementation in teacher education (Uzun and Arslan, 2009; Pedersen et al., 2021).
Implementing semiotic representations in teaching practices could be challenging, especially for teachers without a pedagogical formation and training in semiotic strategies. As Iori (2018) highlights, the success of semiotic representations in teaching mathematics depends on the ability of the teachers to select the appropriate representations that align with the learning objectives and levels of understanding of the students.
Similarly, embedding real-world contextualized mathematical tasks within gamified environments could be challenging due to the demand for careful design when the aim is to achieve deep conceptual learning while maintaining the students’ motivation.
In this sense, one of the most significant gaps identified is the lack of studies integrating semiotic representations and gamification within a unified pedagogical design (see Figures 8, 9). Under this consideration, our result suggests exploring hybrid approaches that combine cognitive depth of semiotics with the motivational dynamics of gamification, creating richer learning experiences that support comprehension and sustained engagement. This aligns with the observed evolution in the literature from representation-focused research to motivation-focused approaches and now toward hybrid frameworks.
Finally, building on these insights, rather than claiming established synergistic effects, we frame integration as a research agenda: embed semiotic rigor (semiotic register conversions) inside motivationally sound gamified progressions; pair conceptual and motivational outcomes with validated instruments; and use active controls to isolate mechanisms. This combination could encourage students’ teaching and preparation for solving complex problems in authentic, real-world scenarios in higher education and at all levels.
4.6 Limitations
One of the principal limitations of this project is that the studies were explored only for a unique level of education; thus, future research should focus on diverse student populations and studies that combine experimental and mixed methods across diverse educational contexts and learning outcomes could enrich the generalizability of findings. Also, a limitation of this study is that the evidence is limited to a set of countries, and it does not consider the classroom climates and teacher roles, constraining generalizability across levels and regions.
5 Conclusion
The literature review of this work has identified two primary didactic strategies that have garnered significant research attention and demonstrated effectiveness in teaching mathematics: the use of semiotic representations and gamification.
The theoretical basis for using semiotic representations in university-level mathematics education is grounded in Duval’s theory, which aims to foster a deep cognitive understanding of mathematical concepts by focusing on symbolizing and interpreting mathematical ideas.
Conversely, gamification is an active teaching-learning strategy that presents mathematics as an engaging and approachable subject. By incorporating game-like elements into the learning process, gamification helps to reduce students’ anxiety toward mathematics and promotes a more positive learning experience. However, it is essential to mention that if we only apply gamification, we risk losing the rigor of the math.
In this sense, a proposal that combines semiotic representations with gamification can create a synergistic effect that optimizes engagement and comprehension in mathematics education. Semiotic representations facilitate deep cognitive processing, while gamification makes learning more enjoyable and accessible. This integrated approach can enhance students’ ability to visualize and conceptualize mathematical ideas, strengthening their analytical skills and understanding of complex concepts.
Additionally, it is crucial to create environments that encourage discussion and collaborative knowledge construction, recognizing the teacher’s role as a facilitator and guide in the learning process. By strategically implementing semiotic representations and gamification across all educational levels, from higher education to lower levels, educators can offer a compelling approach to enhance both the learning and teaching experiences in mathematics. This comprehensive strategy makes learning more enjoyable and significantly improves students’ analytical capabilities and conceptual understanding.
Author contributions
DC: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing. JC: Formal analysis, Visualization, Writing – original draft, Writing – review & editing. CC: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – original draft. YJ: Conceptualization, Methodology, Visualization, Writing – review & editing. MR-Á: Conceptualization, Formal analysis, Supervision, Validation, Visualization, Writing – review & editing. VL: Formal analysis, Supervision, Validation, Visualization, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Acknowledgments
The authors acknowledge Universidad Técnica Particular de Loja for supporting this project. DC also acknowledges the support from Universitat Politècnica de València through the Assistance Call Doctoral Student Mobility.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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References
Ainsworth, S., Bibby, P., and Wood, D. J. (1997). Information technology and multiple representations: new opportunities–new problems. J. Inf. Technol. Teach. Educ. 6, 93–105. doi: 10.1080/14759399700200006
AlAli, R., Wardat, Y., and Al-Qahtani, M. (2023). SWOM strategy and influence of its using on developing mathematical thinking skills and on metacognitive thinking among gifted tenth-grade students. Eurasia J. Math. Sci. Technol. Educ. 19:em2238. doi: 10.29333/ejmste/12994
Alneyadi, S., and Wardat, Y. (2023). ChatGPT: revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in emirates schools. Contemp. Educ. Technol. 15:ep448. doi: 10.30935/CEDTECH/13417
Amable Vivanco-Galvan, O., Castillo-Malla, D., and Jimenez-Gaona, Y. (2018). Multidisciplinary hackathon: Strengthening project-based learning. Revista Electronica Calidad en la Educacion Superior, 9, 119–135.
Bada, S., and Olusegun, S. (2015). Constructivism learning theory: a paradigm for teaching and learning. J. Res. Method Educ. 5, 66–70. doi: 10.9790/7388-05616670
Behl, A., Jayawardena, N., Pereira, V., Islam, N., Del Giudice, M., and Choudrie, J. (2022). Gamification and e-learning for young learners: a systematic literature review, bibliometric analysis, and future research agenda. Technol. Forecast. Soc. Change 176:121445. doi: 10.1016/j.techfore.2021.121445
Bennani, S., and Maalel, A. (2022). Adaptive gamification in e-learning: a literature review and future challenges. Comput. Appl. Eng. Educ. 30, 628–642. doi: 10.1002/cae.22477
Bouchrika, I., Harrati, N., Wanick, V., and Wills, G. (2021). Exploring the impact of gamification on student engagement and involvement with e-learning systems. Interact. Learn. Environ. 29, 1244–1257. doi: 10.1080/10494820.2019.1623267
Brock, C., Zygouris-Coe, V., Welsh, K., Kniss, K., and Hayden, A. (2020). Engaging in disciplinary literacy instruction: a review of read, write, inquire: disciplinary literacy in grades 6–12. J. Adolesc. Adult. Lit. 64, 114–118. doi: 10.1002/JAAL.1075
Brozo, W. G., and Crain, S. (2018). Writing in math: A disciplinary literacy approach. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 91, 7–13.
Buckley, P., and Doyle, E. (2014). Gamification and student motivation. Interact. Learn. Environ. 24, 1162–1175. doi: 10.1080/10494820.2014.964263
Burgos, M., Bueno, S., Pérez, O., and Godino, J. D. (2021). Onto-semiotic complexity of the definite integral: implications for teaching and learning calculus. J. Res. Math. Educ. 10, 4–40. doi: 10.17583/REDIMAT.2021.6778
Caligaris, M. G., Schivo, M. E., and Romiti, M. R. (2019). Communication competence in mathematics: analysis of the evolution of calculus student skills throughout their freshmen year. Int. J. Learn. Teach. Educ. Res. 18, 49–62. doi: 10.26803/ijlter.18.4.3
Castillo, D., Lakshminarayanan, V., and Rodríguez-Álvarez, M. J. (2021). MR images, brain lesions, and deep learning. Appl. Sci. 11, 1–41. doi: 10.3390/app11041675
Chapman, J. R., and Rich, P. J. (2018). Does educational gamification improve students’ motivation? If so, which game elements work best? J. Educ. Bus. 93, 315–322. doi: 10.1080/08832323.2018.1490687
Chapman, O. (2013). Mathematics teachers’ learning through inquiry. Stud. Sci. Math. Hung. 1, 122–150. doi: 10.25749/SIS.3709
Chinna, S., and Sunkesula, M. A. (2023). Jigsaw method: Addressing the learning loss in mathematics among secondary school students due to COVID-19 pandemic. Nonlinear Stud. 30.
Deci, E. L., and Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.
Dorier, J., and Mass, K. (2020). “Inquiry-based mathematics education” in Encyclopedia of mathematics education. ed. S. Lerman (Berlin: Springer Netherlands).
Duval, R. (1995). Sémiosis et pensée humaine: registres sémiotiques et apprentissages intellectuels Vol. (4). Berne: Peter Lang.
Duval, R. (1999). Representation, Vision and Visualization: Cognitive Functions in Mathematical Thinking. Basic Issues for Learning.
Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics. Educ. Stud. Math. 61, 103–131.
Duval, R. (2017). Mathematical activity and the transformations of semiotic representations. In Understanding the mathematical way of thinking–The registers of semiotic representations (pp. 21–43). Cham: Springer International Publishing.
Ersozlu, Z., and Karakus, M. (2019). Mathematics anxiety: mapping the literature by bibliometric analysis. Eur. J. Math. Sci. Technol. Educ. 15:1673. doi: 10.29333/ejmste/102441
Even, R. (1998). Factors involved in linking representations of functions. J. Math. Behav. 17, 105–121. doi: 10.1016/S0732-3123(99)80063-7
Faghihi, U., Aguilar, D., Chatman, D., Gautier, N., Gholson, J., Gholson, J., et al. (2017). How to apply gamification techniques to design a gaming environment for algebra concepts. E-Learning, E-Education, and Online Training: Third International Conference, eLEOT 2016, Dublin, Ireland, August 31–September 2, 2016, Revised Selected Papers -Springer 180, pp. 62–70.
Faghihi, U., Brautigam, A., Jorgenson, K., Martin, D., Brown, A., Measures, E., et al. (2014). How gamification applies for educational purpose specially with college algebra. Proc. Comput. Sci. 41, 182–187. doi: 10.1016/j.procs.2014.11.102
Finck Brandt, C., Pereira Baccon, A. L., Brandt, C. F., and Baccon, A. L. P. (2015). The teaching and learning of equations: problems and possibilities during the transition from high school to higher education. Creat. Educ. 6, 961–975. doi: 10.4236/ce.2015.610098
Hafzah, R., Rahim, A., Tanalol, S. H., Ismail, R., Baharum, A., Rahim, E. A., et al. (2019). Development of gamification linear algebra application using storytelling. 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, pp. 133–137.
Hancock, E., and Karakok, G. (2021). Supporting the development of process-focused metacognition during problem-solving. Primus 31, 837–854. doi: 10.1080/10511970.2020.1772914
Hanif Batubara, I., Saragih, S., Syahputra, E., Armanto, D., Sari, I. P., Lubis, B. S., et al. (2022). Mapping research developments on mathematics communication: bibliometric study by vosviewer. Al-Ishlah 14, 2637–2648. doi: 10.35445/alishlah.v14i1.925
Hassan, M. A., Habiba, U., Majeed, F., and Shoaib, M. (2021). Adaptive gamification in e-learning based on students’ learning styles. Interact. Learn. Environ. 29, 545–565. doi: 10.1080/10494820.2019.1588745
Hitt, F. (1998). The role of the semiotic representations in the learning of mathematics. Proc. Br. Soc. Res. Learn. Math. 18, 23–28.
Hoyles, C., and Noss, R. (2003). “What can digital technologies take from and bring to research in mathematics education?” in Second international handbook of mathematics education. ed. A. Bishop (Berlin: Springer Netherlands), 323–349.
Husain, A., Abdelhafez Qasem, A.-S., and Khazalah, F. S. (2023). Students’ achievement in a flipped database management course: the impact of flow theory gamification elements. J. Inf. Technol. Educ. Res. 22, 409–428. doi: 10.28945/5206
Hutton, B., Catalá-López, F., and Moher, D. (2016). The PRISMA statement extension for systematic reviews incorporating network meta-analysis: PRISMA-NMA. Med. Clín. 147, 262–266. doi: 10.1016/j.medcle.2016.10.003
Iliescu, A. N. (2021). Conceptual atlas of the Knowmad literature: visual mapping with VOSviewer. Manag. Dyn. Knowl. Econ. 9, 379–392. doi: 10.2478/mdke-2021-0025
Iori, M. (2018). Teachers’ awareness of the semio-cognitive dimension of learning mathematics. Educ. Stud. Math. 98, 95–113. doi: 10.1007/s10649-018-9808-5
Jiménez-Gaona, Y., Delgado-Ramón, J., and Castillo-Malla, D. P. (2019). Aprendizaje de la matemática basado en el contexto de las ciencias: mathematics learning based on the science context. Rev. Electron. Calid. Educ. Super. 10, 53–73. doi: 10.22458/caes.v10i2.2603
Jiménez-Hernández, E. M., Oktaba, H., Díaz-Barriga, F., and Piattini, M. (2020). Using web-based gamified software to learn boolean algebra simplification in a blended learning setting. Comput. Appl. Eng. Educ. 28, 1591–1611. doi: 10.1002/cae.22335
Jiménez, Y., and Castillo, D. (2017). ‘Educación de calidad mediante la estrategia Design Thinking’, in Conference Proceedings EDUNOVATIC 2017, pp. 472–481.
Jiménez, Y. (2018). “Estrategias lúdicas para la enseñanza-aprendizaje de la matemática a nivel superior” in Transforming education for a changing world. eds. C. López-García and J. Manso (Eindhoven, NL: Adaya Press), 170–179.
Kapp, K. M. (2012). The gamification of learning and instruction: Game-based methods and strategies for training and education. San Francisco: Pfeiffer.
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. J. Instr. Dev. 10, 2–10. doi: 10.1007/BF02905780
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33, 1–26.
Klaassen, L. (2023). Dan Meyer:“Maths has an obvious perception problem among students”. The UNESCO Courier, 2023, 17–18.
Lakshminarayanan, V., and McBride, A. C. (2015). The use of high technology in STEM education. Education and training in optics and photonics: ETOP 2015. Proceedings of SPIE, 9793. Available online at: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9793/97930D/The-use-of-high-technology-in-STEM-education/10.1117/12.2223055.full (Accessed August 15, 2024).
Ledesma, E. (2011). Representation registers in the solution of calculus problems. Creat. Educ. 2:270. doi: 10.4236/ce.2011.23036
Lee, J. Y., Pyon, C. U., and Woo, J. (2023). Digital twin for math education: a study on the utilization of games and gamification for university mathematics education. Electronics 12:3207. doi: 10.3390/electronics12153207
Lo, C., Hsieh, M., Lin, H., and Hung, H. (2021). Influences of flipped teaching in electronics courses on students’ learning effectiveness and strategies. Int. J. Environ. Res. Public Health 18:9748. doi: 10.3390/ijerph18189748
Lo, C. K., and Hew, K. F. (2020). A comparison of flipped learning with gamification, traditional learning, and online independent study: the effects on students’ mathematics achievement and cognitive engagement. Interact. Learn. Environ. 28, 464–481. doi: 10.1080/10494820.2018.1541910
Lubis, F. F., Rosmansyah, Y., and Supangkat, S. H. (2014). Math workout series: enhancing learning application with gamification. In: 2014 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, pp. 290–294.
Maarif, S., Wahyudin, W., Noto, M. S., Hidayat, W., and Mulyono, H. (2018). Geometry exploration activities assisted with dynamic geometry software (DGS) in a teacher education classroom. Infinity J. 7, 133–146. doi: 10.22460/infinity.v7i2.p133-146
Meij, E., Smits, A., and Meeter, M. (2022). How and why learning theories are taught in current Dutch teacher education programs: identifying a gap between paradigm and reality in teacher education. Teach. Teach. Educ. 109:103537. doi: 10.1016/j.tate.2021.103537
Mildenhall, P., and Sherriff, B. (2018). Using multiple metaphors and multimodalities as a semiotic resource when teaching year 2 students computational strategies. Math. Educ. Res. J. 30, 383–406. doi: 10.1007/s13394-017-0212-8
Milovanovic, J., Shealy, T., and Katz, A. (2021). Higher perceived design thinking traits and active learning in design courses motivate engineering students to tackle energy sustainability in their careers. Sustainability 13:12570. doi: 10.3390/su132212570
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Antes, G., Atkins, D., et al. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6:97. doi: 10.1371/journal.pmed.1000097
Morgan, C. (2006). What does social semiotics have to offer mathematics education research? Educ. Stud. Math. 61, 219–245. doi: 10.1007/s10649-006-5477-x
Moyer-Packenham, P. S., Roxburgh, A. L., Litster, K., and Kozlowski, J. S. (2022). Relationships between semiotic representational transformations and performance outcomes in digital math games. Technol. Knowl. Learn. 27, 223–253. doi: 10.1007/s10758-021-09506-5
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. doi: 10.1136/bmj.n71
Pedersen, M. K., Bach, C. C., Gregersen, R. M., Højsted, I. H., and Jankvist, U. T. (2021). Mathematical representation competency in relation to use of digital technology and task design—a literature review. Mathematics 9:444. doi: 10.3390/math9040444
Pehlivan, F., and Arabacioglu, T. (2023). The effect of gamification on math achievement, motivation, and learning strategies in flipped classrooms. Int. J. Educ. Lit. Stud. 11, 309–317. doi: 10.7575/aiac.ijels.v.11n.4p.309
Perianes-Rodriguez, A., Waltman, L., and Van Eck, N. J. (2016). Constructing bibliometric networks: a comparison between full and fractional counting. J. Informetr. 10, 1178–1195. doi: 10.1016/j.joi.2016.10.006
Perignat, E., and Katz-Buonincontro, J. (2019). STEAM in practice and research: An integrative literature review. Thinking Skill and cre. 31, 31–43.
Rincon-Flores, E. G., Santos-Guevara, B. N., Martinez-Cardiel, L., Rodriguez-Rodriguez, N. K., Quintana-Cruz, H. A., and Matsuura-Sonoda, A. (2023). Gamit! Icing on the cake for mathematics gamification. Sustain, 15, 2334.
Rivera, E., and Garden, C. (2021). Gamification for student engagement: a framework. J. Further High. Educ. 45, 999–1012. doi: 10.1080/0309877x.2021.1875201
Rodríguez, J. L. (2022). “Exploring dynamic geometry through immersive virtual reality and distance teaching” in Mathematics Education in the Age of Artificial Intelligence. eds. P. R. Richard, M. P. Vélez, and S. Van Vaerenbergh (Berlin: Springer), 343–363.
Sabri, Z., Fakhri, Y., and Moumen, A. (2022). The effects of gamification on E-learning education: systematic literature review and conceptual model. Stat. Optim. Inf. Comput. 10, 75–92. doi: 10.19139/soic-2310-5070-1115
Salazar, J. V. F. (2018). “Semiotic representations: a study of dynamic figural register” in Signs of signification: Semiotics in mathematics education research. eds. L. Radford, G. Schubring, and F. Seeger (Rotterdam: Sense), 217–233.
Saleem, A. N., Noori, N. M., and Ozdamli, F. (2022). Gamification applications in e-learning: a literature review. Technol. Knowl. Learn. 27, 139–159. doi: 10.1007/s10758-020-09487-x
Sobrino-Duque, R., Martínez-Rojo, N., Carrillo-de-Gea, J. M., López-Jiménez, J. J., Nicolás, J., and Fernández-Alemán, J. L. (2022). Evaluating a gamification proposal for learning usability heuristics: Heureka. Int. J. Hum.-Comput. Stud. 161:2774. doi: 10.1016/j.ijhcs.2022.102774
Strømgren, B., Berg-Mortensen, C., and Tangen, L. (2014). The use of precision teaching to teach basic math facts. Eur. J. Behav. Anal. 15, 225–240.
Tashtoush, M. A., Wardat, Y. A., and Elsayed, A. M. (2023). Mathematics distance learning and learning loss during COVID-19 pandemic: teachers’ perspectives. J. High. Educ. Theory Pract. 23, 162–174. doi: 10.33423/jhetp.v23i5.5933
te Braak, P., Van Droogenbroeck, F., Minnen, J., van Tienoven, T. P., and Glorieux, I. (2022). Teachers’ working time from time-use data: consequences of the invalidity of survey questions for teachers, researchers, and policy. Teach. Teach. Educ. 109:536. doi: 10.1016/j.tate.2021.103536
Temple, C., and Doerr, H. M. (2012). Developing fluency in the mathematical register through conversation in a tenth-grade classroom. Educ. Stud. Math. 81, 287–306. doi: 10.1007/s10649-012-9398-6
Torres-Carrion, P., Rocio Rodriguez, G., Vicente, P., and Soledad, C. (2018). Methodology for systematic literature review applied to engineering and education. in: Morales 2018 IEEE Global engineering education conference (EDUCON), 2018-April, pp. 1364–1373.
Uzun, S. Ç., and Arslan, S. (2009). Semiotic representations skills of prospective elementary teachers related to mathematical concepts. PRO 1, 741–745. doi: 10.1016/J.SBSPRO.2009.01.130
Van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. doi: 10.1007/s11192-009-0146-3
Van Eck, N. J., and Waltman, L. (2011). Text mining and visualization using VOSviewer. Available online at: http://arxiv.org/abs/1109.2058 (Accessed September 20, 2023).
Van Eck, N. J., and Waltman, L. (2014). “Visualizing bibliometric networks” in Measuring Scholarly Impact. eds. Y. Ding, R. Rousseau, and D. Wolfram (Berlin: Springer), 285–320.
Veith, J. M., Beste, M. L., Kindervater, M., Krause, M., Straulino, M., Greinert, F., et al. (2023). Mathematics education research on algebra over the last two decades: quo vadis? Front. Educ. 8:1211920. doi: 10.3389/feduc.2023.1211920
Verma, R., Lobos-Ossandón, V., Mérigo, J. M., Cancino, C., and Sienz, J. (2021). Forty years of applied mathematical modelling: a bibliometric study. Appl. Math. Model. 89, 1177–1197. doi: 10.1016/j.apm.2020.07.004
Villacís Macías, C., Zea Silva, C., Campuzano Rodríguez, S., and Chifla Villón, M. (2022). Aprendizaje basado en proyectos y la gamificación para generar el aprendizaje activo en los estudiantes. Ciencia Unemi 15, 35–43. doi: 10.29076/issn.2528-7737vol15iss39.2022pp35-43p
Wang, L., Wang, H., Huang, Y., Yan, B., and Chen, Z. (2022). Trends in the application of deep learning networks in medical image analysis: evolution between 2012 and 2020. Eur. J. Radiol. 146:110069. doi: 10.1016/j.ejrad.2021.110069
Wardat, Y., Tashtoush, M. A., AlAli, R., and Jarrah, A. M. (2023). ChatGPT: a revolutionary tool for teaching and learning mathematics. Eur. J. Math. Sci. Technol. Educ. 19:em2286. doi: 10.29333/ejmste/13272
Winsløw, C. (2003). Semiotic and discursive variables in cas-based didactical engineering. Educ. Stud. Math. 52, 271–288. doi: 10.1023/a:1024201714126
Yip, M. C. W. (2012). Learning strategies and self-efficacy as predictors of academic performance: a preliminary study. Qual. High. Educ. 18, 23–34. doi: 10.1080/13538322.2012.667263
Zabala-Vargas, S. A., García-Mora, L., Arciniegas-Hernández, E., Reina-Medrano, J., De Benito-Crosetti, B., and Darder-Mésquida, A. (2022). Didactic strategy mediated by games in the teaching of mathematics in first-year engineering students. Eur. J Math Sci Tech Ed. 18:em2082. doi: 10.29333/ejmste/11707
Keywords: teaching math, strategies, didactic, mathematics, education, VOSviewer, gamification, semiotic representations
Citation: Castillo D, Carrión J, Chamba C, Jiménez-Gaona Y, Rodríguez-Álvarez MJ and Lakshminarayanan V (2025) Didactic strategies for conceptual understanding and motivation in university mathematics: a systematic review. Front. Educ. 10:1536470. doi: 10.3389/feduc.2025.1536470
Edited by:
Alfonso Garcia De La Vega, Autonomous University of Madrid, SpainReviewed by:
Zhuoying Wang, The University of Texas at Austin, United StatesSagar Mani Neupane, Kathmandu University, Nepal
Natalia Ruiz López, Autonomous University of Madrid, Spain
Copyright © 2025 Castillo, Carrión, Chamba, Jiménez-Gaona, Rodríguez-Álvarez and Lakshminarayanan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Darwin Castillo, ZHBjYXN0aWxsb0B1dHBsLmVkdS5lYw==