F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.165428.1 Research Article Articles Implementation of didactic strategies for assurance of learning with students at risk of academic failure in general chemistry

[version 1; peer review: 2 approved with reservations]

 Monsalve-Silva Santiago Conceptualization Data Curation Formal Analysis Investigation Methodology Resources Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-4242-1773 1 Bermudez-Aponte José Javier Funding Acquisition Project Administration Resources Validation Writing – Original Draft Preparation Writing – Review & Editing 2 Vera-Monroy Sulma Paola Conceptualization Formal Analysis Investigation Methodology Project Administration Supervision Validation Writing – Original Draft Preparation https://orcid.org/0000-0002-7573-4151 a 1 Engineering School, CAPSAB Research Group, Universidad de La Sabana, Campus del Puente del Común, km 7 Autopista Norte de Bogotá, Chia, Cundinamarca, 250001, Colombia Education School, Educación y Educadores Research Group, Universidad de La Sabana, Campus del Puente del Común, km 7 Autopista Norte de Bogotá, Chia, Cundinamarca, 250001, Colombia sulmavemo@unisabana.edu.co

No competing interests were disclosed.

 3 7 2025 2025 14 644 25 6 2025 Copyright: © 2025 Monsalve-Silva S et al. 2025 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

Assurance of Learning is a process focused on ensuring the quality and effectiveness of education. It involves implementing practices, policies, and procedures intended to ensure the development of skills, competencies, and knowledge expected in a specific educational program or course. In the subjects Chemistry and General Chemistry I at the Universidad de La Sabana, persistent issues with low academic performance have resulted in rising course repetition rates, lack of motivation, and student dropout.

 Methods

The present study examines targeted didactic strategies for students at risk of academic failure in these subjects, representing an innovation in the field of education and contributing to the domain of learning assurance in Chemistry. It was conducted under a mixed-methods approach with a quasi-experimental and cross-sectional design. A predictive trend analysis was carried out, and strategies were identified, selected, and implemented with consideration for students’ perceptions and assessments. Two samples were analyzed: a characterization sample of 411 students who took the subjects between the second semester of 2020 and the first semester of 2023, and an experimental sample comprising students from the second semester of 2023 and the first semester of 2024.

 Results

The results revealed that participation in the implemented strategies led to improved academic performance, particularly for students with lower Saber 11 scores. Strategy 1 (leveling course) showed the greatest positive impact, with statistically significant differences in performance. Moreover, students reported high satisfaction with the strategies, especially regarding their usefulness, engagement, and relevance.

 Conclusions

These findings suggest that personalized and contextualized didactic strategies are effective in supporting students at risk of academic failure in general chemistry courses.

 Chemistry teaching Higher Education Learning strategy Chemistry Universidad de La Sabana ING-303-2022 This project was financed by Dirección General Investigación de la Universidad de La Sabana, project code ING-303-2022, “Artificial intelligence: a management tool for learning assurance in basic science subjects in engineering programs”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 1. Introduction

At university level, the teaching of chemistry remains highly relevant as this fundamental science provides students with the knowledge necessary to understand the living and physical environment. It fosters connections between science, technology, and society, with the ultimate goal of promoting scientific competencies. 1 Basic science courses underpin professional training disciplines by providing a foundational knowledge base. Riza et al., 2 Uriel et al., 3 and Xeidakis 4 have emphasized that understanding basic science is essential for integrating technology, solving problems, and addressing the challenges of 21 st-century society. In the context of engineering education, the ability to apply foundational knowledge is one of the most important learning outcomes and, indeed, is recognized as the primary learning outcome by the Accreditation Board for Engineering and Technology (ABET).

Nevertheless, these subjects are often perceived by students as complex and difficult to understand. In the case of chemistry, Cardellini 5 highlights that this subject involves working with invisible and intangible phenomena, making comprehension highly abstract. Such challenges can impact academic performance and are often associated with high failure rates, which in turn lead to demotivation and, in some cases, student dropout. According to Améstica-Rivas et al., 6 this situation represents a significant resource burden for all stakeholders in the learning process, including families, students, and educational institutions. At the national level in Colombia, the Ministry of National Education is tasked with increasing access to education and reducing student dropout rates. To achieve this, it must collaborate closely with higher education institutions (HEIs) to foster student retention and ensure that students complete their degree programs successfully.

Firstly, it is important to recognize that university dropout is a common problem in many countries that has garnered significant attention in academic research. 7, 8, 9 Studies have identified various influencing factors, including sociocultural, economic, and political conditions; study environments; academic and social integration; personal effort and motivation; students’ sociodemographic backgrounds; and external circumstances. 10, 11, 12 As Rueda et al. 13 argue, dropout negatively impacts both students’ futures and the development and growth of society as a whole. A closer examination of academic determinants reveals that course failures, course repetition, low academic performance, poor comprehension, ineffective teaching methodologies, and insufficient prior preparation strongly affect academic success. These factors may hinder students from developing the necessary skills to achieve their educational goals. 14

Performance gaps between students also present a pressing issue in higher education, particularly in STEM (Science, Technology, Engineering, and Mathematics) fields. 15 Many students face significant challenges across multiple courses, which researchers attribute to both curricular and disciplinary factors. In the case of chemistry, its inherently abstract nature—along with potential issues with classroom practices, curriculum design, teacher training, and students’ adaptation to the university environment—makes it a particularly demanding subject. 16 For this reason, educational institutions (EIs) must implement processes to ensure students’ academic success by emphasizing practical relevance and the development of knowledge and skills that help students become competent professionals. 17 Such approaches aim to guarantee educational quality and positively impact society.

Currently, EIs implement various strategies to facilitate learning, knowledge acquisition, and skills development. However, their strategies are typically designed for the general student population without prioritizing those at risk of academic failure. Within this context, the present study, conducted as part of the project “Artificial Intelligence: A Management Tool for Assurance of Learning in Basic Science Courses in Engineering Programs” at the Universidad de La Sabana, examined learning strategies designed to foster learning and academic success in the courses Chemistry and General Chemistry I offered by the Department of Chemical and Biotechnological Processes.

Chemistry is the science that studies substances and their interactions, focusing on matter, energy, and their transformations. 18, 19 It promotes the understanding of various phenomena in the surrounding environment, establishing itself as the conceptual foundation for fields such as engineering, health sciences, agricultural sciences, veterinary science, physics, and education. 20, 21 Chemistry has significantly contributed to societal development, leading to advancements in areas such as food production, health products, and plastics and other everyday materials.

According to Carrillo et al., 22 a major challenge in teaching and learning chemistry is variation in students’ preparedness on entering higher education. While some students arrive with a strong foundation in fundamental chemistry concepts and the tools needed for academic success, others possess minimal or no prior knowledge. Furió-Mas et al. 23 and Díaz et al. 24 note that a lack of basic knowledge and tools represents a significant challenge, as these are essential for mastering more complex concepts. Coupled with the high level of abstraction required to understand chemistry, these issues contribute to low performance in foundational courses, leading to demotivation and even dropout. Reyes et al. 25 report that many students repeat such courses two or more times, delaying the completion of their studies and diminishing their academic enthusiasm.

Additionally, Espitia et al., 26 Molina et al., 27 and Vera-Monroy and Monsalve-Silva 28 indicate that Colombian students often perceive chemistry as abstract, difficult, and lacking practical relevance. This perception leads to negative attitudes and apathy toward the subject, despite the importance of chemistry for understanding phenomena in their environments and professional fields.

At the Universidad de La Sabana, undergraduate programs in the Faculty of Engineering, including Industrial, Civil, Mechanical, Chemical, and Bioproduction Engineering, incorporate chemistry courses as part of their basic curriculum. In the case of the subject General Chemistry I, between the 2020-2 and 2023-1 academic periods, low academic performance on partial exams was linked to high failure rates and, consequently, increased dropout rates in this course, with (in the first, second, and third assessment periods, respectively) pass rates of 54.9%, 43.2%, and 38.0%; failure rates of 43.6%, 53.1%, and 53.6%; and dropout rates of 1.5%, 3.7%, and 8.4%. For the other subject, Chemistry, between the 2022-1 and 2023-1 periods, pass rates (for the same assessment periods) were 53.5%, 36.4%, and 49.7%; failure rates were 45.5%, 58.8%, and 43.9%; and dropout rates were 1.1%, 4.8%, and 6.4%.

Given the challenges of low academic performance in Chemistry and General Chemistry I courses at the Universidad de La Sabana, as manifested in increased repetition rates, demotivation, and dropout, the present study sought to develop and examine learning strategies targeted at students at risk of academic failure. Specifically, the study sought to address the following question: How can the learning of students classified by the Saber 11 tests as at risk of academic failure in the courses Chemistry and General Chemistry I within the engineering programs at the Universidad de La Sabana be ensured through the implementation of didactic strategies?

1.1 Theoretical framework

1.1.1 Assurance of learning

Assurance of Learning (AoL) is not merely an isolated initiative within educational institutions; rather, it represents a fundamental system for achieving educational objectives, positioned as a central activity in higher education and enhanced by national and international accreditation processes, which involve continuous monitoring of students’ progress throughout their educational programs. 29 Its impact is evident in both academic success and the overall quality of education. 30 In the AoL process, decisions are made, and improvements are implemented, as noted by Kokku (2021). Therefore, to carry out AoL effectively, educational programs must clearly define their learning outcomes and design targeted strategies to achieve them. In most cases, the primary responsibility for implementing AoL falls on educators, who execute various strategies intended to promote learning and, thereby, academic success.

In the current educational landscape, technology has had a marked impact on the development and implementation of AoL processes. Poveda-Pineda and Cifuentes-Medina 31 emphasize how the adoption of innovative technologies has revolutionized the establishment and monitoring of processes within educational program projects (PEPs). Furthermore, AoL demands a shift in perspective from an internal to an external view of learning efficacy and accountability. Institutions are increasingly focused on demonstrating accountability to the public, ensuring educational quality aligned with graduate profiles, meeting accreditation and legislative requirements, and facilitating continuous improvement and institutional decision-making. 32

1.1.2 Academic performance

There are multiple interpretations of academic performance, though generally, according to Torres et al., 33 these often depend on outcomes from the educational process, manifested in achievements attained as evaluated on the basis of regularly established criteria. Thus, evaluation processes are fundamental to understanding academic performance, as they provide information on the relative achievement of learning objectives. 34 Nonetheless, it is essential to recognize that academic performance is influenced by various factors, such as economic, social, and family contexts; motivation; the university environment; and students’ cognitive abilities. 35

Academic performance plays a crucial role in decision-making within teaching and learning processes. Several studies have shown that dropout rates are higher in the first year of university than in subsequent years (e.g., van Rooij et al. 36 ). Therefore, monitoring student performance during this period, particularly, can help identify opportunities for improvement and support the achievement of established goals within the academic process—namely, learning outcomes.

Finally, evaluating a training process’s effectiveness can be approached from two distinct perspectives. The first, an objective perspective, is that of academic performance, which encapsulates the synthesis of attributes related to the quality of educational experience resulting from the interaction of cognitive processes. 37 The other, a subjective perspective, is based on individual student perceptions. It is worth noting that both these perspectives are influenced by contextual factors, such as family, self-esteem, gender, marital status, and study habits. 38, 39

1.1.3 General performance in basic sciences in universities and engineering faculties

Basic sciences serve as a structural foundation of knowledge that enables a deep and critical understanding of the world around us. Their value lies in providing essential conceptual and methodological frameworks for interpreting natural, social, and technological phenomena—indispensable assets in an increasingly complex global landscape. Through these disciplines, one can better understand the interactions between human and natural systems and anticipate the impacts of emerging issues such as climate change, energy transitions, and public health challenges. Moreover, basic sciences equip future generations with the intellectual and scientific tools needed to address not only current problems but also as yet unrecognized ones. 40

Student retention in STEM disciplines is essential for societal development; however, it also presents a significant challenge for universities. Alarmingly, a considerable number of students who initially enroll in STEM programs choose to drop out or switch to other fields of study by their second year in university, as noted by Gupta and Hartwell. 16 This trend raises critical questions about how to sustain student interest in and engagement with science, technology, engineering, and mathematics, highlighting the need to implement effective strategies that foster greater retention and academic success in these areas.

In Colombia, the Colombian Association of Engineering Faculties (ACOFI, by its acronym in Spanish) has, since 2007, placed a focus on assessing engineering students’ competencies and learning outcomes. This effort relies on collecting reliable data on basic sciences through the Basic Sciences Enrollment Exam, which evaluates student performance in key areas such as mathematics, physics, chemistry and biology; provides valuable information on the quality of engineering education; and ensures adequate national standards for the training of future engineers. 41 To date, more than 22,500 Colombian students have taken the exam, with 20 HEIs participating in its most recent application (2022). Exam data provides valuable indicators for both instructors and those overseeing quality assurance processes. It not only helps identify strengths and weaknesses in teaching and learning processes but also serves to promote the review and analysis of microcurricula, highlighting areas that require greater attention and development.

According to the data presented in the EXIM inform, in the year 2021, the average score for biology decreased from 54.40 to 51.40, in comparison with that of 2020, though the general scores for the other areas increased: mathematics rose from 47.25 to 49.45, physics from 40.35 to 49.30, and chemistry from 40.44 to 52.00. However, by 2022, the average score for chemistry had decreased to 50.41. The exam assessed competencies such as abstraction, analysis, and synthesis; applying knowledge in practice; and identifying, formulating, and solving problems. The results reveal that while students demonstrated abilities to analyze information requiring basic chemical knowledge, they struggled to establish analyses and correlations with underlying scientific knowledge. Additionally, students showed a solid theoretical understanding of interpretative models for physicochemical changes but faced challenges in establishing mathematical relationships between the parameters describing those changes. Lastly, although students were able to identify the constituent elements of a problem, they encountered difficulties in effectively connecting these elements to physicochemical concepts, leading to weak mathematical interpretation in their academic performance. 41 Consequently, 52.1% of students were classified at a low level on the Chemistry evaluation scale.

1.1.4 Institutional actions to achieve academic success

Universities today play a crucial role in fostering knowledge acquisition aimed at solving contextualized problems. Institutions must adopt educational models that prioritize learning over traditional teaching, which has often been characterized by mechanistic actions and rote memorization. These models must contribute significantly to the development of professionals capable of leading sustainable transformations in their respective regions, emphasizing holistic education and human development. In a world marked by increasing complexity and dynamism, teaching centered solely on content is no longer sufficient.

Instead, universities should adopt competency-based education, which emphasizes a combination of knowledge, skills, and attitudes that help professionals to respond effectively to specific needs. 42 This shift requires a solid theoretical foundation, the formulation of new pedagogical objectives that embrace diversity, and the implementation of methods, approaches, and educational strategies that empower students to take ownership of their learning processes. Such an approach prepares students to adapt to different environments and trust their critical reasoning abilities. Ultimately, this paradigm fosters humanization in a world with no singular model of humanity, but rather a rich diversity of perspectives and experiences that deserve recognition and respect. 43, 44

Consequently, the traditional role of the professor as a mere transmitter of knowledge is being redefined. Professors are now expected to serve as guides and facilitators, helping students construct frameworks for understanding and interpretation that transform information into knowledge. This redefinition promotes autonomous learning, the capacity to learn how to learn, and lifelong learning, along with competency development. Pedagogical strategies have evolved to encourage integral learning, with Information and Communication Technologies (ICTs) serving as innovative tools for educational transformation and the humanization of learning processes. 44, 45

In this evolving context of the professor’s role and educational transformation, AoL has emerged as a central pillar of academic activity. The quality and relevance of education, particularly in management and business, have come under increasing scrutiny from various stakeholders, including students, employers, media, and educators. 29 AoL serves as a vital tool for assessing and enhancing the effectiveness of the educational process in this dynamic and demanding environment.

1.1.5 Strategies to foster assurance of learning

AoL, conceived as a process aimed at monitoring or demonstrating achievement in educational processes, requires careful planning, justification, and execution of curricular and pedagogical activities tailored to individual students’ needs while aligning with the required competencies and knowledge to be developed. 46 Such activities, however, are not always the same even within a specific course, nor does the same strategy suit all students, highlighting the need to adapt and personalize AoL processes to meet the diverse demands of learners.

Research has explored extracurricular strategies, which extend beyond the classroom, to strengthen AoL. Among these, peer learning has emerged as a fundamental approach, recognized for its effectiveness in helping students build confidence in their abilities and take active roles in their learning processes. Peer learning promotes mutual collaboration in teaching, yielding significant benefits for academic development. 47, 48 One peer-based strategy involves personalized student support through counseling, mentoring, or academic advising. 49 Online peer learning environments leveraging existing technologies can enhance academic performance and motivate students, and Razak & See 50 emphasize that such environments provide a conducive space for scaffolding the learning process through engaging activities that foster both academic success and motivation. In-person programs have also been shown to be effective, such as a mentoring program implemented by Yusta-Loyo et al. 51 at the University of Zaragoza that promoted collaboration between senior students and new entrants while maintaining continuous academic support from professors across different subjects. In addition to such peer-learning strategies, the value of learning communities focused on specific topics—alternatively known as focus groups—has also been recognized. 49 Learning communities facilitate collaborative knowledge construction and the development of high-level skills. 52

To enhance student participation and empowerment in their learning processes, various pedagogical and didactic strategies have been implemented, including the use of graphic organizers, virtual classes, and independent workshops. An illustrative example of these practices is presented by Bolton-King, 53 who designed workshop-based focus groups. In this approach, mentors received detailed lesson plans for each workshop, and through guided questions, they fostered reflection and debate among students. This created a collaborative environment and encouraged constructive peer feedback, thereby strengthening the learning process. Several studies have addressed strategies to ensure learning, including, 54– 57 who developed approaches in mentoring, coaching, and extracurricular academic advising. These strategies, based on diverse didactic principles, were implemented in both virtual and in-person learning environments. The findings reflect the diversity of approaches that can contribute to academic success and the formative growth of students. Finally, research on the design and implementation of academic leveling courses has demonstrated their relevance for strengthening foundational knowledge or specific areas critical to student development across various disciplines, including mathematics, programming, and English. 49

1.1.6 Standardized national exams

In Colombia, the responsibility for administering standardized national exams, intended to evaluate the quality of education at various levels, lies with the Colombian Institute for Educational Evaluation (ICFES), as established by Law 1324 of 2009. 58 This law also assigns the task of defining what content is to be assessed in these exams to the Ministry of National Education (MEN). 59 Currently, ICFES oversees various exams, including Saber 3°, Saber 5°, Saber 9°, and Saber 11°, which evaluate the quality of primary and secondary education. Additionally, the Saber Pro exam is used to assess the quality of higher education.

The Saber 11° exam is conducted at the end of eleventh grade to provide an official assessment of the quality of formal education amongst students completing secondary school. Moreover, under current regulations (ICFES, 2023), this exam can also be taken by individuals who have already obtained a high school diploma or have passed a high school validation test. The objectives of the Saber 11° exam, as stipulated in Decree 869 of 2010, are summarized in Figure 1.

Objectives of the Saber 11° test.

The Saber 11° exam comprises five components derived from the competencies outlined in the fundamental skill standards established by the MEN: 60 critical reading, mathematics, social sciences, natural sciences, and English. The natural sciences competencies contemplate students’ abilities to understand science as a universal and evolving discipline capable of explaining and predicting natural phenomena, as well as the conception of science as a dynamic human construct encompassing both theoretical and practical aspects, whose development is closely tied to evolving relationships between science, technology, and society. 59 In accordance with ICFES (2022) guidelines, the natural sciences component of the Saber 11° exam assesses students’ ability to understand and apply notions, concepts, and theories related to the natural sciences to solve problems. It also evaluates students’ capacities to explain how certain natural phenomena can occur based on observations, patterns, and scientific knowledge. The evaluation incorporates the inquiry process, which involves observation and pattern identification in data to deduce conclusions about natural phenomena.

Research has identified several statistically significant factors associated with strong academic performance in the Saber 11° exams, which include medium or high socioeconomic status, study schedules, access to ICTs (measuring students’ ability to use the internet, computers, and telephony from home), and parental education levels. 61, 62

2. Methodology 2.1 Methodology design

The present study used a mixed approach with an embedded concurrent dominant model design (DIAC), also known as an integrated design, 63 to collect both quantitative and qualitative data simultaneously or sequentially. In such a design, one type of data supports the other 64 ; in the present study, qualitative data were used to support explanations based on quantitative data. The research design was quasi-experimental in that participating students were selected based on academic criteria rather than randomly. 65 Finally, the study followed a cross-sectional correlational-causal approach, in which relationships between categories (in this case, the relationship between academic performance and the implementation of the strategies) were described at a specific point in time.

 2.2 Population and sample

The study was conducted using academic and characterization data on cognitive styles, perception channels, and metacognitive skills of students enrolled in the subjects Chemistry and General Chemistry I during the academic periods from 2020-2 to 2023-1, totaling 411 students who served as the control group. The study sample consisted of students enrolled in the engineering school programs during the 2023-2 and 2024-1 periods, comprising 60 students identified as being at risk of academic failure in the same two subjects, as selected through predictive trend analysis.

All participants received informed consent and were presented with the project, explaining their participation. For participants who were minors, a certificate was sent to their parents or guardians along with project information. All participants who agreed signed the written consent.

 2.3 Processes stages

To develop didactic strategies to support learning in the Chemistry and General Chemistry I courses, four implementation phases were proposed, as illustrated in Figure 2 and described in the subsequent subsections.

Design of predictive trend analysis:

Extraction of relevant data from exam performance in the Chemistry and General Chemistry I subjects, global and component Saber 11° exam scores, and the historical socio-demographic characterization of students.

Construction of distribution tables of Saber 11° exam performance for the population during the academic periods from 2020-2 to 2023-1.

Definition of trends within the performance indicators from the constructed distribution tables.

Proposal of strategies to be implemented in academic spaces:

Identification of relevant strategies (as identified by experts in the field) that have favored teaching-learning processes and achievement of academic success.

Selection of teaching strategies, framed on experiential learning processes, that promote intrinsic and extrinsic motivation, recognized as having a potential positive impact on learning in the subjects Chemistry and General Chemistry I.

Curricular alignment of selected strategies with assigned competencies and declared intended learning outcomes.

Design of 3 strategies that respond to the learning needs of the participating students and that promote personalized learning processes, framed within didactic sequences for each strategy. The following questions are to be addressed:

What is the formative and disciplinary purpose of the expected learning outcomes, and how do these relate to the students’ comprehensive development and life plans?

What knowledge, skills, abilities, and attitudes have the greatest priority for the achievement of academic standards?

What active methodologies, didactic approaches, and cognitive processes are most appropriate to promote deep, meaningful, and transferable learning?

How will students, considering their diversity of styles, backgrounds, and contexts, engage in personalized and engaging learning experiences?

What teaching resources, materials, technologies, and environments will be used to mediate learning?

What evidence, performance indicators, and formative assessment results allow verifying the level of achievement? (And how might these be improved?)

Implementation of the strategies in academic spaces:

Identification of students selected for being at risk of losing the performance indicators of the Chemistry and General Chemistry I subjects, using the constructed trends.

Implementation of the strategies with the identified groups of students, with all information generated during the development of the face-to-face sessions collected through a data collection instrument focused on student perceptions.

Evaluation of the impact of the implemented strategies on the academic success of the impacted students using formative evaluation processes.

Research phases for the development and implementation of teaching strategies to ensure the learning of basic chemistry. 2.4 Analysis categories

The analysis categories were divided into two groups: input variables and output variables. The first group (input variables) included results obtained from the Saber 11° exam, considering both individual scores and scores by area of knowledge; data collected through instruments that assessed students’ cognitive styles; information from perception channels used to understand how students interacted with information; and results obtained in the Metacognitive Skills Inventory. These variables were essential for classifying students and characterizing their individual profiles, which served as the basis for designing and implementing personalized didactic strategies. The second group (output variables) included students’ perceptions of the implemented didactic strategies, which helped assess their effectiveness and students’ satisfaction with their applicability. Additionally, the level of competency achievement was considered, as reflected in student performance in midterm exams. These output variables were essential for measuring the impact of the didactic strategies on student learning and evaluating their performance based on the established learning outcomes. Together, these analysis categories provided a comprehensive view of the effectiveness of the didactic strategies and students’ academic performance.

 2.5 Data analysis

Analysis of the collected quantitative data was conducted using IBM SPSS Statistics V25.0. Various statistical tests were applied, starting with the Kolmogorov-Smirnov test to assess the distribution of the data using p-values. Additionally, Spearman’s Rho test was used to examine correlations between variables, as it is a non-parametric statistical tool designed to evaluate the monotonic relationship between two variables.

Furthermore, for comparisons between the control and experimental groups, the Mann-Whitney U test was applied. This is a non-parametric test used to compare two independent samples and determine whether they came from populations with the same distribution. Both the correlation and comparative tests were performed depending on whether the data met the normality assumptions.

 2.6 Instruments

The instruments used were designed with the specific objectives of characterizing students and evaluating the designed strategies, especially with reference to students’ perceptions.

2.6.1 Perception channels

To characterize perception channels, the VAK questionnaire originally designed by Felder and Silverman 66 was used; this questionnaire has been adapted multiple times. 67– 69 The version adapted for the present study consisted of 10 items validated with a Cronbach’s Alpha coefficient of 0.77. 70 The questionnaire was implemented in the study of Gamboa et al. 71 to contextualize everyday situations, requiring respondents to recognize and select the sense through which they perceive them.

2.6.2 Cognitive styles

The characterization of students’ cognitive styles was assessed using the CHAEA questionnaire designed by Honey and Mumford, 72 later modified by Alonso. 73 It consisted of 80 randomly distributed questions to address the four fundamental cognitive styles, each represented by 20 items. Respondents had to indicate whether they “agreed” or “disagreed” with the statements presented, based on their own perceptions and preferences. 74 The validity and reliability of the questionnaire were determined by Alonso et al. 75 using Cronbach’s Alpha, with values of 0.627 for the active style, 0.725 for the reflective style, 0.658 for the theoretical style, and 0.588 for the pragmatic style. This instrument was also implemented in the study of Gamboa et al. 71

2.6.3 Metacognitive awareness inventory

The metacognitive skills of students who participated in the research were defined with the Metacognitive Awareness Inventory (MAI) developed by Schraw and Dennison in 1994. This is divided into two categories: cognition knowledge, with 17 items, and cognition regulation, with 35 items, each rated on a 5-point Likert scale. 76, 77 The cognition knowledge category includes subcategories such as declarative, procedural, and conditional knowledge, while the cognition regulation category encompasses elements such as planning, organization, monitoring, debugging, and evaluation, as detailed by Harrison and Vallin. 76 Planning involves organizing time, learning goals, and resources; organization refers to the systematization of learning activities; monitoring consists of overseeing the learning process during task execution; debugging is related to identifying weaknesses in learning to adjust strategies and improve performance; and evaluation focuses on analyzing the effectiveness of the strategies used in the learning process. Huertas et al. 78 validated this instrument in Colombia, explaining that declarative knowledge refers to an individual’s understanding of their own learning process, abilities, and application; procedural knowledge relates to understanding of their how to use learning strategies; and conditional knowledge focuses on when and why to apply different learning strategies.

2.6.4 Perception survey on the effectiveness of didactic strategies

After the implementation of each learning strategy, participating students completed a perception survey consisting of two sections. The first section was intended to assess the effectiveness of active participation methodologies (CEMPA) designed and validated by Carrasco et al., 79 but included only 9 of the original 25 questions, as the excluded questions were not related to the aims of the present study. This initial survey section was validated for the student population at the Universidad de La Sabana, obtaining a Cronbach’s Alpha of 0.959. 80 The second section of the survey explored students’ perceptions of the assurance of learning through each of the strategies, as well as their feedback and suggestions for improvement. All closed-ended questions were designed using a 5-point Likert scale.

 3. Results and discussion 3.1 Saber 11° results analysis

Firstly, specific scores from the Saber 11° exam were considered, including the global score, critical reading, natural sciences, and mathematics. Frequency tables were constructed accordingly ( Tables 1– 4). These tables were then related to student performance in the topics that have historically had the highest failure rates in midterm assessments.

Frequency table according to global scores obtained in the Saber 11° exam.
i Interval m i f i r i Measurement Stoichiometry
1 229 251 240 3 0.73% 1.75 * 0.67 *
2 251 273 262 17 4.14% 1.88 * 1.65 *
3 273 295 284 39 9.49% 2.15 * 1.68 *
4 295 317 306 69 16.79% 2.35 * 2.15 *
5 317 339 328 88 21.41% 2.81 * 2.59 *
6 339 361 350 88 21.41% 3.11 2.93 *
7 361 383 372 68 16.55% 3.56 3.55
8 383 405 394 25 6.08% 4.07 3.88
9 405 427 416 9 2.19% 4.18 4.44
10 427 449 438 5 1.22% 4.60 4.10

m i = class mark;

f i = absolute frequency;

r i = relative frequency.

Performance grades that indicate failure.

 Frequency table according to Saber 11° exam critical reading scores.
i Interval m i f i r i Measurement Stoichiometry
1 40 46 43 5 1.22% 2.03 * 1.25 *
2 46 52 49 10 2.43% 2.40 * 2.45 *
3 52 58 55 45 10.95% 2.28 * 1.79 *
4 58 64 61 91 22.14% 2.65 * 2.46 *
5 64 70 67 127 30.90% 2.95 * 2.63 *
6 70 76 73 100 24.33% 3.24 3.28
7 76 82 79 26 6.33% 3.90 3.80
8 82 88 85 1 0.24% 4.50 5.00
9 88 94 91 1 0.24% 4.50 5.00
10 94 100 97 5 1.22% 4.50 4.30

m i = class mark;

f i = absolute frequency;

r i = relative frequency.

Performance grades that indicate failure.

 Frequency table according to Saber 11° exam natural sciences scores.
i Interval m i f i r i Measurement Stoichiometry
1 39 45 42 4 0.97% 2.90 * 2.00 *
2 45 51 48 20 4.87% 3.03 2.03 *
3 51 57 54 31 7.54% 3.15 2.07 *
4 57 63 60 77 18.73% 3.43 2.42 *
5 63 69 66 116 28.22% 3.63 2.93 *
6 69 75 72 101 24.57% 3.82 3.21
7 75 81 78 53 12.90% 4.33 3.92
8 81 87 84 2 0.49% 3.75 3.63
9 87 93 90 0 0.00% N/A N/A
10 93 100 97 7 1.70% 3.95 4.00

m i = class mark;

f i = absolute frequency;

r i = relative frequency.

Performance grades that indicate failure.

 Frequency table according to Saber 11° exam mathematics scores.
i Interval m i f i r i Measurement Stoichiometry
1 35 42 38 2 0.49% 2.80 * 2.00 *
2 42 49 45 2 0.49% 2.55 * 1.69 *
3 49 56 52 23 5.60% 3.17 2.11 *
4 56 63 59 64 15.57% 3.31 2.23 *
5 63 70 66 106 25.79% 3.59 2.66 *
6 70 77 73 142 34.55% 3.72 3.21
7 77 84 80 51 12.41% 4.21 3.78
8 84 91 87 2 0.49% 4.45 3.50
9 91 98 94 3 0.73% 3.50 3.50
10 98 105 102 16 3.89% 4.41 3.96

m i = class mark;

f i = absolute frequency;

r i = relative frequency.

Performance grades that indicate failure.

As evidenced by the data presented in Tables 1– 4, competencies in critical reading and mathematics play a fundamental role in academic performance within the measurement and stoichiometry modules. These skills not only influence understanding and application of the concepts addressed but also constitute an essential prerequisite for solving problems in which knowledge, skills, and attitudes are applied—which in turn contributes to the achievement of a passing grade. In this regard, the development of such competencies at the high-school level is critical for supporting academic success at the university level and progression within the formative process of chemistry. This highlights the importance of strengthening these skills during earlier stages of education. However, given the evident lack of development in the pre-tertiary levels, it becomes necessary for HEIs to implement strategies that promote learning and foster the development of such competencies. This need aligns with national guidelines that emphasize the responsibility of HEIs for supporting students’ academic leveling and ensuring effective learning in disciplines such as chemistry.

Moreover, a clear correlation is observed between the scores obtained and the level of academic performance. However, to statistically verify this relationship, the Kolmogorov-Smirnov normality test was conducted, with the results presented in Table 5. These results show that the data distribution does not follow a normal distribution, indicating that the variables analyzed are non-parametric.

p-values from the Kolmogorov-Smirnov normality test.
Global score Critical reading score Mathematics score Social and citizenship score Natural science score Measurement Stoichiometry
p-value 0.178 0.001 0.000 0.000 0.004 0.000 0.000

Since this data does not follow a normal distribution, Spearman’s rank-order correlation (Spearman’s rho) test was used to evaluate the relationship between the variables analyzed. The results of this test, presented in Table 6, indicate the degree of association among the studied variables.

Correlations between Saber 11° exam scores and academic performance.
Global score Critical reading score Mathematics score Social and citizenship score Natural science score
Measurement p-value 0.000 0.000 0.000 0.000 0.000
Stoichiometry p-value 0.000 0.000 0.000 0.000 0.000

According to Table 6, these results indicate statistically significant correlations between students’ academic performance and their Saber 11° scores across all evaluated areas. This suggests that academic achievement in chemistry topics may be linked to broader competencies assessed by the national standardized test.

 3.2 Strategies design

For the design of the strategies, a characterization process was carried out to develop resources appropriate to addressing students’ needs. Accordingly, correlations between learning styles, perceptual channels, and metacognitive skills were examined in relation to academic performance.

As shown in Table 7, no significant correlations were found between academic performance and students’ individual characteristics. This supports findings from neuroscientific and educational psychology research regarding perceptual channels and learning styles; namely, that it is a myth that individuals learn better when instruction is tailored to their preferred style or channel. 81, 82 Learning is a far more complex process that depends on multiple interacting factors. 28

Correlations between learning styles, perceptual channels, metacognitive skills, and academic performance.
Learning styles Perceptual channels
Active Reflective Theoretical Pragmatic Visual Auditory Kinesthetic
Measurement Cor. Cf. 0.061 0.035 0.033 0.065 0.013 -0.034 0.036
p-value 0.217 0.478 0.501 0.189 0.799 0.496 0.467
Stoichiometry Cor. Cf. 0.006 0.085 0.063 0.091 0.068 -0.065 -0.011
p-value 0.903 0.088 0.205 0.068 0.173 0.190 0.829
Metacognitive skills
Planning Organization Monitoring Debugging Evaluation Declarative Procedural Conditional
Measurement Cor. Cf. -0.042 -0.086 -0.064 -0.023 -0.002 -0.074 0.020 -0.036
p-value 0.399 0.082 0.194 0.647 0.969 0.136 0.691 0.466
Stoichiometry Cor. Cf. -0.034 -0.030 -0.015 0.015 -0.028 -0.047 0.014 0.008
p-value 0.496 0.548 0.763 0.757 0.573 0.349 0.783 0.870

However, studies have demonstrated that learning is more effective when diverse approaches are integrated, rather than relying on a single dominant style. 81, 83 The teaching of measurement and stoichiometry involves analytical skills, mathematical reasoning, and conceptual understanding, none of which rely solely on a specific perceptual channel. For example, a student who identifies as a “visual learner” may not successfully master stoichiometric relationships merely by viewing chemical diagrams. Solving such problems requires mathematical practice, numerical analysis, and logical reasoning. Perceptual channels (visual, auditory, kinesthetic) are related to the way that information is initially received but do not determine cognitive processing capacity or academic performance. In chemistry, concepts such as moles, chemical equations, and units of measurement demand logical and mathematical reasoning rather than sensory perception alone. Thus, regardless of a student’s preferred perceptual channel, they must engage in calculations, interpret graphs, and solve problems that require deeper cognitive abilities.

Metacognitive skills, such as planning, monitoring, and self-regulation, are important for overall academic success. 84 However, while metacognition can help students recognize errors in their thinking, it cannot compensate for a lack of mastery in foundational principles. Being aware of one’s learning is insufficient on its own; students must also actively engage in problem-solving to improve performance. In this sense, academic success in chemistry depends on students’ comprehension of fundamental physical quantities, unit conversions, and stoichiometric relationships. The process of problem-solving strengthens reasoning and facilitates knowledge application. Therefore, identifying effective learning strategies and techniques that promote the development of logical-mathematical thinking is essential.

 3.3 Strategy design

3.3.1 Leveling/reinforcement course

First, a leveling (or reinforcement) course was implemented, based on the guidelines established in Decree 869 of 2010, which emphasize that the classification of students supports their prioritized participation in academic leveling programs and the implementation of dropout prevention measures. This course was divided into seven sections: diagnosis, measurement, dimensional analysis, practical case study, general notions of chemistry, final case study, and course closure. The course resources included explanatory videos, theoretical presentations, interactive and practical exercises, formative assessments, and case studies. The course’s curricular alignment is illustrated in Figure 3; the course’s online interfaces are shown in Figure 4.

Curricular alignment of the leveling course. Interactive interfaces of the leveling course.

In implementing the leveling course, students were first classified according to the identified models, focusing on those with global Saber 11° scores below 339 and language scores below 70. The resources and work schedule were then shared with them digitally, and their participation was monitored continuously. As part of this monitoring process, student engagement in evaluations and activities was reviewed. Structured case studies were conducted to develop critical reading skills aimed at problem identification, formulation, and resolution. The development of critical and analytical thinking in chemistry is essential to train professionals capable of analyzing problems, evaluating scientific information, and making informed decisions. Critical thinking in chemistry involves the ability to interpret data, formulate hypotheses, design procedures, and rigorously analyze results; it is a disciplinary and intellectual process that helps students make logical and reflective judgments about how to proceed in problem-solving contexts, and chemistry students can significantly enhance these skills through project-based learning and the integration of metacognitive strategies. 85

3.3.2 Gamified kit

The second strategy designed was titled the “Conversion Conquerors Kit,” created to help students master the use of conversion factors and their application in chemistry. Through a gamified approach, three sets of activities were presented, including educational materials such as cards, boards, and practical exercises. The objective was for students to develop unit conversion skills through exploration, dimensional analysis, and problem-solving in context. The material was divided into three modules:

Symbol conversion: introduction to symbols and basic units through 25 cards, in which students explored these elements as a foundation for developing conversions.

Unit matching: use of a didactic board with markers to identify unit equivalences and create pairs, strengthening the understanding of relationships between magnitudes.

“Conquering” factors: use of cards to carry out conversions through dimensional analysis.

Students had to apply the conversion factors to practical problems and exercises. Each module included interactive cards, practical exercises, and a guide to facilitate progressive development of conversion skills. QR codes were also used to access additional missions and challenges. The implementation methodology included the following phases:

Exploration: students were presented with cards showing symbols and units they had to recognize.

Matching game: activities for matching equivalent units.

Dimensional analysis: exercises requiring correct application of conversion factors.

Contextual application: solving chemistry problems that involve the conversions learned.

The kit’s curricular alignment is shown in Figure 5, and examples of the kit’s virtual card designs are shown in Figure 6.

Curricular alignment of the “Conversion Conquerors Kit”. Sample cards from the “Conversion Conquerors Kit”.

The development of the “Conversion Conquerors Kit” strategy was grounded in various pedagogical and theoretical approaches related to learning scientific and mathematical concepts, dimensional analysis, and unit conversion in the context of chemistry, with an emphasis on meaningful learning and knowledge construction.

According to Ausubel, 86 meaningful learning occurs most effectively when new information is linked to existing cognitive structures. In unit conversion, students must integrate conversion factors with their prior knowledge of physical and mathematical magnitudes. Novak 87 argues that meaningful learning, in contrast with rote memorization, promotes long-term retention. In chemistry, understanding units and their relationships supports the correct application of conversion factors in complex problems, which is why the “Conversion Conquerors Kit” strategy began with basic symbol identification before advancing to higher-order skills.

From a constructivist perspective, knowledge is actively built through interaction with the environment. 88 Vygotsky 89 emphasized the importance of social mediation in learning processes. In the “Conversion Conquerors Kit”, students interacted with cards and learning tools to explore conversion concepts, promoting experiential learning. The use of gamified materials strengthens the conceptualization of unit equivalence and dimensional analysis, in alignment with Bruner’s 90 view that learning is enhanced when students interact tangibly with concepts. Game-based learning has been shown to be effective in increasing student motivation and engagement in scientific disciplines. 91 Gamification in chemistry education has been employed to develop analytical skills and promote active learning. 92 Moreover, the use of cards, boards, and interactive challenges in the kit fosters autonomous learning and skill development in unit conversion. According to Vera-Monroy et al., 93 integrating games into educational processes enhances conceptual understanding and helps students apply knowledge in varied contexts.

Finally, instruction should be connected to real-world problem-solving situations to ensure learning transfer. 94 Chi and Vanlehn 95 argue that contextualized learning improves students’ abilities to apply knowledge across disciplines. In chemistry and engineering, unit conversion is a fundamental skill, enabling accurate interpretation of experimental data and equation solving. Therefore, the “Conversion Conquerors Kit” strategy incorporated applied problems to promote knowledge transfer to practical scenarios.

 3.4 Assessment of the strategies

To assess the effectiveness of the two strategies used in the present study—the leveling course and the gamified kit—students’ performance on competency-based tests and their perceptions were analyzed. Initially, results were gathered from students who participated in only the first strategy, only the second strategy, and those who engaged in both, regardless of their classification in the Saber 11° exam. The results are presented in Table 8.

Average grade according to student participation.
Leveling course Gamified kit Both
Participated 3.53 3.47 3.54
Did not participate 3.07 3.14 3.09

As shown in Table 8, in all cases, the participants’ performance was higher than that of non-participants. This suggests that participation in the strategies had a positive effect on academic outcomes. However, to determine whether these differences were statistically significant, the Mann-Whitney U test was applied, as shown in Table 9.

Mann-Whitney U test p-values.
Leveling course Gamified kit Both
p-value 0.096 0.246 0.090

As shown in Table 9, there were no statistically significant differences in performance. Although participation in the strategies seems to have led to higher average grades, the improvements were not significant at conventional levels. However, the combined implementation of both strategies exhibited a synergistic effect, resulting in a p-value closer to the threshold for statistical significance. This suggests a possible interaction between the strategies, which could have enhanced their impact on academic performance.

Next, the effect of prioritizing strategies was analyzed by examining the performance of students with global scores below 339 and specific scores below 70 in both critical reading and mathematics. Results for participants and non-participants are shown in Table 10.

Average grade according to student participation by Saber 11° classification.
Leveling course Gamified kit Both
Participated 3.31 3.24 3.29
Did not participate 2.48 2.60 2.58

As shown in Table 10, participants in either or both strategies exceeded the minimum passing grade of 3.0. Conversely, students who did not participate in any strategy failed to meet the passing grade in all cases, with average scores between 2.48 and 2.60. However, it is important to note that grades should not be viewed as the final goal of learning, but rather as reflections of achieving learning outcomes and developing competencies. From this perspective, the presence of structured educational strategies plays a fundamental role in fostering meaningful learning processes and reducing the risk of academic failure. Notably, the leveling course strategy had the greatest positive impact, with a difference of 0.83 points, while the gamified kit had a smaller, yet still positive, impact of 0.64 points. The statistical significance of these differences was analyzed, and the results are shown in Table 11.

Mann-Whitney U test p-values for prioritized students.
Leveling course Gamified kit Both
p-value 0.007 0.035 0.027

The leveling course strategy yielded the lowest p-value (0.007), indicating that the performance difference between participants and non-participants was highly significant. This supports the conclusion that this strategy had a real impact on improving learning outcomes. The low probability that the observed difference occurred by chance reinforces the importance of implementing this strategy, as it offers the possibility of substantial performance improvements. Nevertheless, although it is essential to emphasize that grades are merely a consequence of an educational process, the average grade of 3.31 among participants in the leveling course ( Table 10) emphasizes that its value lies not only in helping students meet passing criteria but in fostering learning needed for long-term academic success. This, moreover, aligns with the principles outlined in Decree 869 of 2010, which emphasize the need for leveling courses to promote academic success.

As shown in Table 11, the gamified kit strategy also resulted in a statistically significant difference (p = 0.035), confirming its effectiveness in enhancing academic performance. Although the average score improvement of 3.24 was smaller than that associated with the reinforcement course (3.31), this outcome still reflects the development of relevant skills and competencies amongst the participants. Statistical significance further validates the gamified kit strategy as a valuable tool to support academic success.

The p-value for the gamified kit strategy (0.035) also indicates a statistically significant difference between those who participated and those who did not, confirming the positive impact of the intervention. Although the score difference and significance level are lower than those observed for the reinforcement course, the results still demonstrate that the gamified kit supports student achievement. Students using only the gamified kit strategy achieved an average grade of 3.24, surpassing the minimum passing grade (3.0), evidencing the development of key academic skills and knowledge appropriation.

As also shown in Table 11, the p-value for the combination of both strategies (0.027) indicates that their combined impact is statistically significant, although not as strong as that of the reinforcement course alone. However, the average grade of participants in the reinforcement course alone (3.31) is not substantially greater than that of participants in both strategies (3.29). This suggests that, while combining both strategies may be useful, it does not generate a stronger synergistic effect than that of the leveling course alone.

Finally, students’ perceptions of the implemented strategies were assessed, as presented in Figure 7. The results obtained from show a highly positive perception of the implemented teaching strategies. All statements evaluated obtained averages above 4.3 (on a scale of 1 to 5), indicating high overall satisfaction.

Student perceptions of the strategies.

The highest-rated item was “teaches how to follow instructions”, with a perfect score of 5.00, reflecting the notion that the strategies effectively support the development of procedural skills and the ability to follow directions, which are essential aspects for educational contexts aimed at strengthening guided student autonomy. This was followed by items rated at 4.92: “allows demonstration of what has been learned”, “is to my liking/I like it”, and “encourages participatory attitudes”. These scores suggest that students not only find the strategies useful for expressing their learning but also perceive them as engaging and conducive to active participation.

Other highly rated items (≥4.7) include “innovative way to promote problem-solving skills” (4.83), “facilitates learning of the topic” (4.83), and “engages students in their own learning” (4.80). These results indicate that the strategies significantly contributed to the development of higher-order competencies, such as problem-solving, deep understanding of content, and active engagement in the learning process.

The item “similar strategies should be designed for other subjects” received a mean score of 4.75, reflecting students’ recognition of the strategies’ values beyond their immediate context and their potential for transferability and scalability across other curricular areas.

Although the item “is essential for the course” received the lowest score (4.33), this still falls within a positive range. It suggests that, while the strategy is perceived as valuable, it may be seen more as complementary rather than as a central component of the courses. This perception highlights an opportunity for improved curricular integration of active learning strategies within instructional design.

Student acceptance and positive evaluation are critical for the success of any educational strategy. When students perceive extracurricular activities as relevant and beneficial, they are more likely to engage actively, thereby amplifying the associated learning benefits. Furthermore, favorable perceptions can increase motivation and commitment, which are both essential elements for effective learning. 96 In this way, extracurricular strategies play a fundamental role in higher education by complementing traditional academic instruction and contributing to students’ holistic development. They are essential in higher education as they enhance and promote the development of key skills for professional success.

Positive student perceptions are crucial to the effectiveness of these activities, as perception directly influences participation and engagement. Academic literature supports these findings, emphasizing the need to integrate and value extracurricular activities in the university setting. The present study’s findings corroborate those of Díaz-Iso and García-Olalla, 97 who likewise argue that participation in extracurricular activities can have a positive impact on university students’ comprehensive development, enhancing both their academic performance and personal growth.

 4. Conclusions

The design and implementation of personalized and contextualized didactic strategies for students at risk of academic failure proved to be effective in improving academic performance in the courses of Chemistry and General Chemistry I. The leveling course strategy showed the most substantial positive impact, with statistically significant differences in academic outcomes between participants and non-participants, especially among students with low Saber 11° exam scores. The gamified kit strategy (“Conversion Conquerors Kit”) also had a positive impact; beyond its direct contributions, its design also fostered engagement, motivation, and active participation. When integrated with structured reinforcement strategies, the gamified kit seems to have had a complementary effect that enhanced not only academic outcomes but also the development of scientific thinking and problem-solving skills. This demonstrates the value of incorporating innovative and playful strategies in higher education to support competency-based learning and long-term academic success.

Students’ perceptions of the implemented strategies were highly positive, indicating strong levels of satisfaction regarding their usefulness, engagement, and applicability. These findings support the strategies’ scalability and relevance for other academic subjects.

No significant correlations were found between academic performance and students’ learning styles, perceptual channels, or metacognitive skills, reinforcing the notion that learning is a complex process that requires diverse competency-based strategies rather than reliance solely on individual preferences.

Overall, these findings highlight the importance of strengthening AoL processes in basic science courses, showing that early, targeted interventions can help reduce course repetition and dropout rates in higher education.

Finally, the results of this study emphasize the value of active learning, critical thinking, and student motivation as key components in the development of effective teaching strategies to ensure educational quality and student success in engineering programs.

 Ethics and consent Ethics approval and consent to participate

The study was approved by the Research Ethics Committee (REC) of the Universidad de La Sabana during its regular session No. 136 on November 24, 2024. Likewise, the General Directorate of Research authorized the start of the project under code ING-303-2022.

All participants included in the research signed the written informed consent approved by the ethics committee, attached to the extended data.

 Authors contributions

Monsalve-Silva S: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review and Editing; Bermúdez-Aponte, JJ: Funding Acquisition, Project Managment, Resources, Validation, Writing – Review and Editing; Vera-Monroy SP: Conceptualization, Formal Analysis, Research, Methodology, Supervision, Validation, Writing – Original Draft Preparation.

 Data availability Extended data

Extended data - Implementation of didactic strategies for assurance of learning with students at risk of academic failure in general chemistry. Zenodo. 2025. doi: https://doi.org/10.5281/zenodo.15540413. 98

The project contains the following extended data:

Questionnaires

Example consent forms

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Underlying data

“Dataset - Implementation of didactic strategies for assurance of learning with students at risk of academic failure in general chemistry”. Zenodo, June 25, 2025. doi: Zenodo. https://doi.org/10.5281/zenodo.15677974. 99

Dataset associated with this article supporting the results and analyses.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Acknowledgments

We thank the Universidad de La Sabana and the Dirección General de Investigación for the support received in the development of this project.

 References Ministerio de Educación Nacional: Estándares Básicos de Competencias en Ciencias Naturales y Ciencias Sociales Formar en ciencias: ¡el desafía! Lo que necesitamos saber y saber hacer. Ministerio de Educación Nacional;2004. Rahmat RA b OK Yusof KM Hamid R : Relating basic Courses to Engineering Problems. Procedia Soc. Behav. Sci. Oct. 2012;60:52–55. 10.1016/j.sbspro.2012.09.345 Uriel C Sergio S Carolina G : Improving the understanding of Basic Sciences concepts by using Virtual and Augmented Reality. Procedia Comput. Sci. 2020;172:389–392. 10.1016/j.procs.2020.05.165 Xeidakis G : Engineering Education Today: The Need for Basics or Specialization. Eur. J. Eng. Educ. Jan. 1994;19(4):485–501. 10.1080/03043799408923316 Cardellini L : Chemistry: Why the Subject is Difficult? Educacion Quimica. May 2012;23:305–310. 10.1016/S0187-893X(17)30158-1 Améstica-Rivas L King-Domínguez A Gutiérrez DAS : Efectos económicos de la deserción en la gestión universitaria: el caso de una universidad pública chilena. Hallazgos. 2021;18(35):209. 10.15332/2422409X.5772 Lizarte EJ Gijón J : Prediction of early dropout in higher education using the SCPQ. Cogent Psychol. Dec. 2022;9(1). 10.1080/23311908.2022.2123588 Arias A Linares-Vásquez M Héndez-Puerto NR : Undergraduate Dropout in Colombia: A Systematic Literature Review of Causes and Solutions. J. Lat. Educ. Feb. 2023;23:612–627. 10.1080/15348431.2023.2171042 Foster C Francis P : A systematic review on the deployment and effectiveness of data analytics in higher education to improve student outcomes. Assess. Eval. High. Educ. Aug. 2020;45(6):822–841. 10.1080/02602938.2019.1696945 Gutiérrez D Vélez JF López J : Indicadores de deserción universitaria y factores asociados. EducaT: Educación virtual, Innovación y Tecnologías. 2021;2(1):15–26. 10.22490/27452115.4738 Rodríguez M : La investigación sobre deserción universitaria en Colombia 2006-2016. Tendencias y resultados. Pedagogía y Saberes. Jun. 2019; (51):49–66. 10.17227/pys.num51-8664 Kehm BM Larsen MR Sommersel HB : Student dropout from universities in Europe: A review of empirical literature. Hung. Educ. Res. J. Sep. 2019;9(2):147–164. 10.1556/063.9.2019.1.18 Rueda SM Urrego D Páez E : Perfiles de riesgo de deserción en estudiantes de las sedes de una universidad colombiana. Revista de Psicología. 2020;38(1):275–297. 10.18800/psico.202001.011 Acevedo F : Factores explicativos del abandono de los estudios en la educación superior en contextos socio-académicos desfavorables. Revista Española de Pedagogía. May 2020;78(276). 10.22550/REP78-2-2020-02 Harris RB Mack MR Bryant J : Reducing achievement gaps in undergraduate general chemistry could lift underrepresented students into a ‘hyperpersistent zone. Sci. Adv. Jun. 2020;6(24):eaaz5687. 32577510 10.1126/sciadv.aaz5687 PMC7286681 Gupta T Hartwell SK : Enhancing Student Retention in General and Organic Chemistry: An Introduction. 2019;1–12. 10.1021/bk-2019-1341.ch001 Thummathong R Thathong K : Chemical literacy levels of engineering students in Northeastern Thailand. Kasetsart J. Soc. Sci. Sep. 2018;39(3):478–487. 10.1016/j.kjss.2018.06.009 Chamizo JA Castillo D Pacheco I : La naturaleza de la química. Educación química. 2012;23(2):298–304. 10.1016/S0187-893X(17)30157-X Sosa P : El largo y sinuoso camino de la Química. Educación Química. Oct. 2015;26(4):263–266. 10.1016/j.eq.2015.09.006 Jimenez MA Figueredo Y Zamora I : Química y las ciencias básicas biomédicas. Alternativa tecnológica. MediSur. 2020;18(4). López M d M Blanco Á Serrano J : Valoración de la utilidad de la Química por estudiantes de Ingeniería Mecánica: Efecto de una propuesta didáctica. Educación Química. Jan. 2017;28(1):14–21. 10.1016/j.eq.2016.09.004 Carrillo I Hernández M Albéniz J : Memoria final del proyecto: nuevas metodologías para la mejora del proceso de enseñanza y aprendizaje de la Química. ETSI Industriales, ETSI Navales, EUIT Industrial. 2007. Furió-Mas C Azcona R Guisasola J : Enseñanza de los conceptos de cantidad de sustancia y de mol basada en un modelo de aprendizaje como investigación orientada. Investigaciones Didácticas. 2006;24(1):43–58. 10.5565/rev/ensciencias.3813 Díaz R Valdés C Hernández S : Valoración del nivel de conocimiento y habilidades con que ingresan los estudiantes a la carrera de medicina. Educación Médica Superior. 2001;15(2). Reyes MS Porro S Pirovani ME : Actitudes hacia la química en estudiantes universitarios conforme avanzan en la carrera. Revista de la Facultad de Ciencias Químicas. 2015;11:70–76. Espitia YL Monsalve-Silva S Vera-Monroy SP : Estrategias para la enseñanza-aprendizaje de las prácticas de laboratorio de química e ingeniería química durante la pandemia por Covid-19. Revolución Educativa en la Nueva Era. Serna E , editor. 1st ed. 2022; vol.II: pp.843–857. Molina MF Carriazo JG Farías DM : Actitudes hacia la química de estudiantes de diferentes carreras universitarias en Colombia. Quim Nova. Sep. 2011;34(9):1672–1677. 10.1590/S0100-40422011000900032 Vera-Monroy SP Monsalve-Silva S : Implementación de un modelo de educación basado en resultados para evaluar el cambio en las habilidades metacognitivas, el desempeño académico y la percepción de los estudiantes. Revolución Educativa en la Nueva Era. Serna E , editor. 1st ed. 2022; vol.II: pp.668–680. Mantai L Calma A : Beyond assuring learning: Greater challenges ahead for management educators. Int. J. Manag. Educ. Nov. 2022;20(3):100723. 10.1016/j.ijme.2022.100723 Wilkerson JR : Rubrics meeting quality assurance and improvement needs in the accreditation context. Qual. Assur. Educ. Dec. 2019;28(1):19–32. 10.1108/QAE-04-2019-0045 Poveda-Pineda DF Cifuentes-Medina JE : Incorporación de las tecnologías de información y comunicación (TIC) durante el proceso de aprendizaje en la educación superior. Formación universitaria. Dec. 2020;13(6):95–104. 10.4067/S0718-50062020000600095 Sampson SD Betters-Reed BL : Assurance of Learning and Outcomes Assessment: a Case Study of Assessment of a Marketing Curriculum. Mark. Educ. Rev. Oct. 2008;18(3):25–36. 10.1080/10528008.2008.11489045 Torres JM Romero LE Mawency V : Factores que intervienen en el aprendizaje de la Química desde las representaciones sociales de la juventud. Revista Boletín REDIPE. 2021;10(11):156–164. 10.36260/rbr.v10i11.1524 Gallardo KE : Evaluación del aprendizaje: retos y mejores prácticas. 2013. Vera-Monroy SP Monsalve-Silva S : What Happened to Academic Achievement in Chemical Engineering?: A Study in the Days of COVID-19. Ingenieria y Universidad. Dec. 2022;26. 10.11144/Javeriana.iued26.whaa Rooij E van Brouwer J Fokkens-Bruinsma M : A systematic review of factors related to first-year students’ success in Dutch and Flemish higher education. Pedagogische Studiën. 2018;94(5):360–404. Inostroza-Inostroza FA : Estudiantes con déficit atencional enfrentando tareas de comprensión lectora y producción de textos: Un estudio comparativo en alumnado de 4° año de educación primaria en Chile. Revista Electrónica Educare. Aug. 2017;21(3):1. 10.15359/ree.21-3.7 Chilca ML : Autoestima, hábitos de estudio y rendimiento académico en estudiantes universitarios. Propósitos y Representaciones. Apr. 2017;5(1):71. 10.20511/pyr2017.v5n1.145 Coschiza CC Martín Fernández J Gapel Redcozub G : Características socioeconómicas y rendimiento académico. El caso de una universidad argentina. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación. 2016;14.3(2016). 10.15366/reice2016.14.3.003 Spiro M : Las ciencias básicas son la condición sine qua non para el desarrollo sostenible. Año Internacional de las Ciencias Básicas para el Desarrollo Sostenible. Proyecto IYBSSD 2022. UNESCO;2022. Accessed: May 29, 2022. International Year of Basic Sciences for Development – Scientists for the Planet. Reference Source Asociación Colombiana de Facultades de Ingeniería: Resultados generales Examen de Ciencias Básicas EXIM 2022. 2022. Reference Source Salman M Ganie SA Saleem I : The concept of competence: a thematic review and discussion. Eur. J. Train. Dev. May 2020;44(6/7):717–742. 10.1108/EJTD-10-2019-0171 López M : Ser persona y hacerse persona en el cambio de época: desafíos para la formación integral en la escuela y la universidad. Revista Latinoamericana de Estudios Educativos. 2014, May 29; vol.64(3): pp.61–81. 2022. Reference Source Estrada-Perea BM Pinto-Blanco AM : Análisis comparativo de modelos educativos para la educación superior virtual y sostenible. Revista Universidad y Sociedad [seriada en línea]. 2021;17:168–184. 10.18041/1900-3803/entramado.1.6131 Alarcón R : Conferencia Inaugural Universidad 2014, 9no Congreso Internacional de Educación Superior. La Habana: MES;2014. Bolaño M Duarte N González KJ : Aseguramiento del aprendizaje en la educación superior en medio de la nueva realidad. Industriastria 4.0 y sociedad del conocimiento. Corporación Centro Internacional de Marketing Territorial para la Educación y el Desarrollo and Corporación CIMTED;2022. Polkowski Z Jadeja R Dutta N : Peer Learning in Technical Education and it’s Worthiness: some facts based on implementation. Procedia Comput. Sci. Jan. 2020;172:247–252. 10.1016/J.PROCS.2020.05.039 Raymond A Jacob E Jacob D : Peer learning a pedagogical approach to enhance online learning: A qualitative exploration. Nurse Educ. Today. Sep. 2016;44:165–169. 27429347 10.1016/J.NEDT.2016.05.016 Espinosa-Castro J-F Hernández-Lalinde J Mariño LM : Estrategias de permanencia universitaria. Archivos Venezolanos de Farmacología y Terapéutica. 2020;39(1). 10.5281/zenodo.4065045 Razak RA See YC : Improving academic achievement and motivation through online peer learning. Procedia Soc. Behav. Sci. Jan. 2010;9:358–362. 10.1016/J.SBSPRO.2010.12.164 Yusta-Loyo JM Cepero-Ascaso MD Prieto-Martín J : Peer Mentoring at the University Level: The Importance of Organization. Procedia Soc. Behav. Sci. Jul. 2015;196:233–236. 10.1016/J.SBSPRO.2015.07.035 Wibeck V Dahlgren EM Öberg AG : Learning in focus groups: an analytical dimension for enhancing focus group research. Qual. Res. 2016;7(2):249–267. 10.1177/1468794107076023 Bolton-King RS : Student mentoring to enhance graduates’ employability potential. Sci. Justice. May 2022;62:785–794. 36400500 10.1016/J.SCIJUS.2022.04.010 Baranik LE Wright NA Reburn KL : Mentoring relationships in online classes. Internet High Educ. 2017;34:65–71. 10.1016/J.IHEDUC.2017.05.001 Benta D Bologa G Dzitac S : University Level Learning and Teaching via E-Learning Platforms. Procedia Comput. Sci. 2015;55:1366–1373. 10.1016/J.PROCS.2015.07.123 Fong S Mansor WFAW Zakaria MH : The Roles of Mentors in a Collaborative Virtual Learning Environment (CVLE) Project. Procedia Soc. Behav. Sci. 2012;66:302–311. 10.1016/J.SBSPRO.2012.11.272 Ligadu C Anthony P : E-Mentoring ‘MentorTokou’: Support For Mentors and Mentees During The Practicum. Procedia Soc. Behav. Sci. 2015;186:410–415. 10.1016/J.SBSPRO.2015.04.144 Congreso de Colombia: Ley 1324 de 2009. 2009;1297. Instituto Colombiano para la Evaluación de la Educación: “Guía de orientación”. 2022. Ministerio de Educación Nacional: Estándares Básicos de Competencias en Lenguaje, Matemáticas, Ciencias y Ciudadanas. 2006. Junca GA : Desempeño académico en las pruebas saber 11. Panorama Económico. 2019;27(1):8–38. 10.32997/2463-0470-vol.27-num.1-2019-2615 Timarán-Pereira R Caicedo-Zambrano J Hidalgo-Troya A : Árboles de decisión para predecir factores asociados al desempeño académico de estudiantes de bachillerato en las pruebas Saber 11°. Revista de Investigación, Desarrollo e Innovación. Feb. 2019;9(2):363–378. 10.19053/20278306.v9.n2.2019.9184 Hernández R Fernández C Baptista P : Metodología de la investigación. McGraw Hill; 6th ed. 2014. Creswell JW : Educational research planning, conducting and evaluating quantitative and qualitative research. 4th ed. 2012. Renzetti CM Follingstad DR Campe MI : Mixed methods in the context of quasi-experimental research designs. The Routledge International Handbook of Domestic Violence and Abuse. 2021; pp.734–748. Felder RM Silverman LK : Estilos de aprendizaje y de enseñanza en la educación de ingeniería. Ing. Educación. 1988;78(7):674–681. Hervás R Castejón L : Evaluación de preferencias individuales para el aprendizaje y la orientación educativa, aplicación del Inventario de Estilos de aprendizaje de Dunn, Dunn y Price. Memorias del II Congreso Internacional de Estilos de Aprendizaje. 2006;6(2). Velasco S : Preferencias perceptuales de estilos de aprendizaje en cuatro escuelas primarias: Comparaciones y sugerencias para la formación y actualización de docentes. Rev. Mex. Investig. Educ. 1996;1(2):283–313. Chalisa D Johnson G Price G : Comparing students with high and low preferences for tactile learning. Education Chula Vista. 2000;120(3). Gamboa MC Briceño JJ Camacho JP : Caracterización de estilos de aprendizaje y canales de percepción de estudiantes universitarios. Opción. 2015;31(3):509–527. Gamboa MC Vera-Monroy SP Mejía-Camacho A : Perception channels and cognitive styles: opponents, followers or learning allies? Heliyon. Feb. 2021;7(2):e06242. 33665424 10.1016/j.heliyon.2021.e06242 PMC7907214 Honey P Mumford A : Using your learning styles. Chartered Institute of Personnel and Development;1986. Alonso C : Analysis and diagnosis of learning styles in university students. Editorial of the Complutense University;1992; vol.I. Gamboa MC García Y Ahumada V d R : Diseño de Ambientes de Enseñanza-Aprendizaje: Consideraciones con base en la PNL y los Estilos de Aprendizaje. Universidad Nacional Abierta Y a Distancia;2017. Accessed: Mar. 02, 2021. Reference Source Alonso C Gallego DJ Honey P : Los estilos de aprendizaje: procedimientos de diagnóstico y mejora. Mensajero;1994; vol.55. Harrison GM Vallin LM : Evaluating the metacognitive awareness inventory using empirical factor-structure evidence. Metacogn. Learn. Apr. 2018;13(1):15–38. 10.1007/s11409-017-9176-z Abdelrahman RM : Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students. Heliyon. Sep. 2020;6(9):e04192. 32944661 10.1016/j.heliyon.2020.e04192 PMC7481518 Huertas AP Vesga GJ Galindo M : Validación del instrumento ‘inventario de habilidades metacognitivas (Mai)’ con estudiantes colombianos. Praxis & Saber. 2014;5(10):55–74. 10.19053/22160159.3022 Carrasco A Donoso JA Duarte-Atoche T : Diseño y validación de un cuestionario que mide la percepción de efectividad del uso de metodologías de participación activa (CEMPA). El caso del Aprendizaje Basado en Proyectos (ABPrj) en la docencia de la contabilidad. INNOVAR. Revista de Ciencias Administrativas y Sociales. 2015;25(58):143–158. 10.15446/innovar.v25n58.52439 Vera-Monroy SP Monsalve-Silva S Arcos-Villacis JS : Aprendizaje Basado en Retos como herramienta para desarrollar habilidades sociales en los estudiantes. Revolución en la formación y la capacitación para el siglo XXI. Serna E , editor. 1st ed. 2021; vol.I: pp.374–385. Deligiannidi K Howard-Jones P : The neuroscience literacy of teachers in Greece. Procedia Soc. Behav. Sci. Feb. 2015;174:3909–3915. 10.1016/J.SBSPRO.2015.01.1133 Dekker S Lee NC Howard-Jones P : Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Front. Psychol. Oct. 2012;3(OCT):33784. 10.3389/FPSYG.2012.00429/BIBTEX Varas-Genestier P Ferreira RA : Neuromitos de los profesores chilenos: orígenes y predictores. Estudios pedagógicos (Valdivia). 2017;43(3):341–360. 10.4067/S0718-07052017000300020 Abdelrahman RM : Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students. Heliyon. Sep. 2020;6(9):e04192. 32944661 10.1016/J.HELIYON.2020.E04192 PMC7481518 Ijirana Aminah S Supriadi : Critical thinking skills of chemistry education students in team project-based STEM-metacognitive skills learning during the Covid-19 pandemic. J. Technol. Sci. Educ. 2022;12(2):397–409. 10.3926/JOTSE.1697 Ausubel DP : The psychology of meaningful verbal learning. Grune & Stratton;1963. Novak JD : Learning, Creating, and Using Knowledge: Concept maps as facilitative tools in schools and corporations Invited Papers. J. E-Learn. Knowl. Soc. 2010;6(3):21–30. Piaget J : The origins of intelligence in children. W W Norton & Co.;1952. Vygotsky LS : Mind in Society. Harvard University Press;1978. 10.2307/J.CTVJF9VZ4 Bruner JS : Toward a Theory of Instruction. Cambridge: Harvard University Press;1966. Vera-Monroy SP Rodriguez S Figueredo MA : Evaluating the effect of a blended collaborative/game-based learning strategy for skill reinforcement on undergraduate engineering. Int. J. Math. Educ. Sci. Technol. Aug. 2024; vol.55(7): pp.1727–1743. 10.1080/0020739X.2023.2295892 Hamari J Koivisto J Sarsa H : Does gamification work? - A literature review of empirical studies on gamification. Proceedings of the Annual Hawaii International Conference on System Sciences. 2014;3025–3034. 10.1109/HICSS.2014.377 Vera-Monroy SP Mejia-Camacho A Gamboa MC : C=OCARBOHIDRATOS: efecto del juego sobre el aprendizaje. Educación Química. Feb. 2020;31(1):23. 10.22201/fq.18708404e.2020.1.68522 Mester JP : Transfer of learning: Issues and research agenda. National Research Council Report;2005. Chi MTH Vanlehn KA : Educational Psychologist Seeing Deep Structure From the Interactions of Surface Features. Educ. Psychol. 2010;47(2):177–188. 10.1080/00461520.2012.695709 Alvarado Lagunas E Ramírez DM Téllez EA : Percepción de la calidad educativa: caso aplicado a estudiantes de la Universidad Autónoma de Nuevo León y del Instituto Tecnológico de Estudios Superiores de Monterrey. Revista de la Educación Superior. Oct. 2016;45(180):55–74. 10.1016/J.RESU.2016.06.006 Díaz-Iso A García-Olalla A : Una revisión sistemática del concepto de actividad extracurricular en Educación Superior. Educación XXI. 2020;23(2):307–335. 10.5944/educXX1.25765 Monsalve-Silva S Bermúdez-Aponte JJ Vera-Monroy SP : Extended data - Implementation of didactic strategies for assurance of learning with students at risk of academic failure in general chemistry. Zenodo. 2025. Monsalve-Silva S Bermúdez-Aponte JJ Vera-Monroy SP : Implementation of didactic strategies for assurance of learning with students at risk of academic failure in general chemistry.Dataset.2025. 10.5256/f1000research.182065.r403820 Reviewer response for version 1 Asli Sare 1 2 Referee https://orcid.org/0000-0002-3754-4613 Science & Technology, Al-Qasemi Academic College, Baqa El-Garbiah, Israel Science Education, The Institute of Applied Research, Galilee Society, Shefa-'Amr, Israel

Competing interests: No competing interests were disclosed.

 12 9 2025 Copyright: © 2025 Asli S 2025 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

The article is, for the most part, clearly written and logically structured, so I would rate the clarity and accuracy of presentation as partly rather than a full yes. The introduction does a good job linking the institutional problem to the research interventions, but it is overly long and at times repeats ideas. Trimming this section and tightening the focus would keep the reader engaged and highlight the central research question more effectively. The literature cited is relevant to the topic and grounded in both national and international work, but some of it is dated. Bringing in more recent studies from 2022–2024—especially on gamification and academic leveling in STEM—would both strengthen the theoretical base and broaden the paper’s global relevance.

Regarding the study design, I consider it appropriate and technically sound, though with minor reservations. The mixed-methods, quasi-experimental approach is well suited to an educational intervention where random assignment is impractical. The use of historical control data alongside a defined at-risk experimental group provides a solid comparative framework. However, the absence of a power calculation or sample size justification is a weakness. Including such a calculation—or at least explaining why the chosen sample size is sufficient—would reassure readers that the study was capable of detecting meaningful effects.

When it comes to the level of methodological detail, I rate it as partly sufficient for full replication. The description of the two interventions is thorough enough for another instructor to reproduce them, and the open sharing of datasets and instruments via Zenodo is excellent. However, the rationale for the choice of statistical tests could be explained more explicitly. While it is mentioned that non-parametric tests were chosen due to non-normality, linking each test choice directly to the research questions and data characteristics would be helpful. It is also unclear whether any data were missing and, if so, how these were handled—this is an important detail for anyone attempting to replicate the analysis.

On the statistical analysis itself, I also answer partly. The selection of non-parametric tests is correct for the data distribution, and the results are clearly reported in terms of p-values. However, the absence of effect sizes means we cannot judge the magnitude of the improvements, which limits the practical interpretability of the results. Additionally, the paper refers to a possible “synergistic effect” when both strategies are combined, but no formal interaction test is presented. Including such a test would make this claim much stronger.

For the question about whether all underlying data are available, my answer is a confident yes. The data and instruments are hosted on Zenodo, have functioning DOIs, and are released under a CC-BY license, which is excellent for transparency and reproducibility.

Finally, regarding whether the conclusions are supported by the results, I would rate this as partly. The conclusion that the leveling course had the most impact is strongly supported by statistically significant results. The gamified kit’s effect is positive, but smaller, and this should be described with a bit more nuance. The claim that these strategies could be applied successfully in other subjects is plausible but not tested in this study, so it should be framed as a recommendation for future research rather than as an established fact.

In short, my main recommendations for improving the paper are:

Streamline the introduction and remove repeated points.

Update the literature with recent (2022–2024) international studies on similar interventions.

Provide a power calculation or clear justification for the sample size.

Clarify how missing or incomplete data were handled.

Report effect sizes for all key comparisons.

Run an interaction test before claiming synergy between strategies.

Reframe the scalability claim as a future research direction.

 With these revisions, the manuscript would not only be methodologically stronger but also have greater appeal to an international readership, while still maintaining its strong local relevance.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Environmental Engineering, Agrobiology, Science Education for Sustainability, and Educational Methods in STEM

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Vera Monroy Sulma Universidad de La Sabana, Chia, Chía, Colombia

Competing interests: No competing interests were disclosed.

 25 10 2025

We thank the reviewer for these insightful suggestions. We implemented the following targeted revisions responding solely to this review:

Introduction: The Introduction was revised and condensed to remove repeated ideas and maintain a sharper focus on the central research question. Recent international studies (2022–2024) on academic leveling and gamification in STEM education were incorporated to update the theoretical framework and reinforce the global relevance of the study.

Methodology: A new Statistical Approach and Justification section was created to clarify the rationale behind the selection of non-parametric tests and their alignment with the research questions. The total number of participants and the size of each subgroup (reinforcement course, gamified kit, and combined strategies) were reported to increase methodological transparency.

Results: The discussion of p-values (Tables 6–7 and 9) was expanded to provide a clearer interpretation of statistical significance. In Table 10, Cohen’s d was added to report the effect size and strengthen the interpretation of the results.

Conclusions: The effect of the gamified kit was described with greater nuance, emphasizing its smaller magnitude relative to the reinforcement course. The claim about scalability of the strategies was reframed as a recommendation for future research, rather than a generalizable conclusion.

Once again, we appreciate the reviewers’ thorough feedback and remain attentive to any further comments.

 10.5256/f1000research.182065.r403822 Reviewer response for version 1 Trčková Kateřina 1 Referee https://orcid.org/0000-0001-8158-0307 University of Ostrava, Ostrava, Czech Republic

Competing interests: No competing interests were disclosed.

 4 9 2025 Copyright: © 2025 Trčková K 2025 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

The article by Monsalve-Silva, Bermudez-Aponte, and Vera-Monroy presents a study on the implementation of didactic strategies aimed at students at risk of academic failure in general chemistry at Universidad de La Sabana. Two interventions were tested: a reinforcement course and a gamified kit for unit conversions. The mixed-methods quasi-experimental design, using 411 control students (2020–2023) and 60 experimental students (2023–2024), revealed improvements in academic performance, especially among students with low Saber 11 scores, as well as positive student perceptions. The topic is relevant, the interventions are innovative, and the paper provides valuable empirical evidence for assurance of learning processes in STEM higher education.

Regarding the study design, it is overall appropriate and technically sound, but only partly satisfactory. The quasi-experimental mixed-methods approach is suitable for the problem, and the instruments used are valid. However, the lack of precise participant numbers in each intervention group (reinforcement course, gamified kit, both) weakens the technical rigor and complicates interpretation of results. The description of methods and analyses is also only partly sufficient to allow full replication. While the authors describe the instruments (VAK, CHAEA, MAI) and statistical tests (Mann-Whitney, Spearman, Kolmogorov-Smirnov), replication would require additional detail. In particular, the gamified kit is shown only through illustrations, not full materials; the number of participants per subgroup is not reported; and the interpretation of p-values is not explained in sufficient depth.

The statistical analysis and its interpretation are likewise only partly appropriate. The choice of non-parametric tests is correct given the distribution of the data, but interpretation is limited. Effect sizes (e.g., Cohen’s d, r) are not reported, and the significance of p-values is not clearly explained. In Table 9, none of the p-values reach conventional significance, yet the text suggests positive effects. The combined effect of both strategies is under-discussed, although the data indicate potentially interesting interactions.

In contrast, all underlying source data have been made openly available on Zenodo under a CC-BY license, ensuring reproducibility. This is a strong aspect of the study and fully meets the requirements for data accessibility. As for the conclusions, they are supported by the results but only with some reservations. The claim that the reinforcement course significantly improves outcomes is well substantiated, and the positive effect of the gamified kit is reasonably supported. However, broader generalizations—such as the assertion that these strategies can be scaled across all subjects—go beyond the presented data and should be phrased more cautiously.

Additional issues should also be addressed. The grading scale is not explained; international readers may be confused unless it is stated clearly that in Colombia grades range from 0–5, with 5 as the highest and 3.0 as the minimum passing threshold. Tables would benefit from indicating the number of participants (N) per subgroup, and figure captions could provide more informative notes. More international references on remediation and gamification in higher education would help situate the study in a global context. Finally, the perspectives of instructors are missing; only student perceptions are reported, yet teacher views on feasibility and workload are critical for evaluating implementation.

In summary, the study is scientifically valid, well-motivated, and presents meaningful findings. Nevertheless, to improve transparency, replicability, and international readability, the authors should provide subgroup sample sizes, clarify the grading scale, report effect sizes, and interpret p-values with more rigor. For these reasons, my recommendation is: Approved with reservations.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

My expertise lies in chemistry education and science didactics, with a particular focus on the design, implementation, and evaluation of innovative teaching strategies in chemistry. I have experience in developing and assessing active learning approaches, laboratory instruction, and educational interventions aimed at improving student engagement and performance in STEM subjects.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Vera Monroy Sulma Universidad de La Sabana, Chia, Chía, Colombia

Competing interests: No competing interests were disclosed.

 25 10 2025

We thank the reviewer for these precise and valuable suggestions. In response, several modifications were made to improve methodological clarity, statistical transparency, and contextual understanding:

Participants and subgroups: The total number of students and the size of each subgroup (reinforcement course, gamified kit, and combined interventions) were added to the Methodology section.

Procedures and implementation: Additional detail was included regarding the classification of at-risk students, implementation period and frequency, and timing of the applied instruments to ensure reproducibility.

Grading scale: A specific subsection explaining the Colombian 0–5 grading system (with 3.0 as the minimum passing threshold) was incorporated to enhance international readability.

Statistical analysis: A new Statistical Approach and Justification section now elaborates on the rationale for the non-parametric tests used, expands the interpretation of p-values, and includes calculation of Cohen’s d to report effect sizes.

Tables: Tables 6–10 were updated to include the number of participants (N) per subgroup and to refine the discussion of p-values and observed effects.

Instructor perspectives: A subsection summarizing teachers’ perceptions of feasibility and student engagement was incorporated to complement student perceptions.

Conclusions: The statements about replicability and scalability were rewritten to present a more cautious interpretation aligned with the evidence obtained.

 These revisions directly address the reviewer’s concerns, increasing the transparency, rigor, and international accessibility of the study while preserving its applied focus within STEM higher education.