[version 1; peer review: awaiting peer review]
No competing interests were disclosed.
Learning management in university settings involves various elements that enable students to be aware and independent throughout their professional education.
This data report presents findings from a quantitative, correlational study aimed at evaluating learning management among university students in Latin America. The study employed the GAEU-1 Scale, an instrument designed to assess four core dimensions of learning management in university students: conscious motivational strategies, perception of academic performance, use of deep learning techniques, and overall self-regulation of the learning process. The dataset includes responses from 1316 undergraduate students, with 631 participants from Chile and 685 from Ecuador. Data collection adhered to ethical standards, and participants gave informed consent prior to participation.
The findings of this research help to elucidate the contribution of the four factors involved in the process of conscious learning among university students. For the majority of questionnaire items applied to the participants, between 27% and 58% of students reported that they moderately or strongly agreed with the statements presented.
This dataset offers valuable insights into the functioning and interaction of learning management components in diverse educational contexts. Researchers and educators may find this data valuable for exploring how learning management operates across different cultural settings, identifying predictors of academic performance, and designing interventions to enhance self-regulation skills in university students. The comprehensive nature of this dataset supports further empirical analysis and the development of evidence-based strategies for improving learning outcomes in higher education.
Learning management strategies are skills that enable students to monitor their progress and adjust their behaviour in response to the various challenges they encounter in university education. It involves contributions from multiple dimensions: emotional, behavioural, cognitive, and metacognitive (
The second factor is the perception of academic performance, which refers to the student’s internal evaluation of their progress and effectiveness in academic tasks. This self-awareness enables university students to mentally visualize and reflect on their learning behaviours, strategies, and outcomes across different subjects. It serves as a crucial component of self-monitoring, enabling students to identify strengths and areas for improvement. When students accurately assess their academic performance, they are better equipped to adjust their study habits, time management, and engagement strategies (
The third factor is the use of deep learning techniques, which enable university students to engage with academic content in a meaningful, reflective, and integrative manner. Unlike surface learning approaches, where students focus primarily on memorization for exams, deep learning involves critical analysis, synthesis of ideas, and the ability to connect theoretical knowledge to practical applications. These techniques promote a complex and strategic approach to learning, fostering long-term understanding and intellectual flexibility across subjects in the student’s academic career (
The fourth factor identified in the data is SRL, which represents a comprehensive and intentional process through which students take control of their own learning. This process involves setting academic goals, monitoring progress, selecting and adapting effective learning strategies, and maintaining motivation throughout the learning journey. SRL is not a passive activity; instead, it requires students to engage actively with their studies, reflect on their performance, and make adjustments to improve outcomes. In the university context, where independent study and complex problem-solving are essential, self-regulation becomes a critical skill for academic success (
This dataset is worth considering because it provides valuable insights into learning management within the university context. It includes data from two Latin American countries, offering a regional perspective on how this essential cognitive and motivational skill manifests among university students. Moreover, the data incorporates the four key factors that define learning management: conscious motivational strategies, perception of academic performance, deep learning techniques, and the self-regulation process itself, which are fundamental for understanding how students supervise and optimize their learning in higher education settings. This comprehensive approach makes a significant contribution to the academic discourse on learning control in diverse cultural and educational contexts.
The research design employed in this study followed a quantitative approach with a correlational scope, aiming to examine the relationships between key components of learning management among university students. This design was chosen to enable the objective measurement of variables and the identification of patterns and associations without manipulating the study environment.
Written informed consent was obtained from all participants prior to their involvement in the study. All participants were given detailed information regarding the purpose of the research, procedures involved, potential risks and benefits, and their rights to confidentiality and voluntary withdrawal at any point. The informed consent was prepared in accordance with the ethical standards of the institutional research committee and the Declarations of Helsinki and Nuremberg.
This research was conducted in Chile and Ecuador, two countries that share similar characteristics, including a predominantly urban population and relatively young populations. Both countries have a mainly Catholic population and capitalist elements within their economic systems, such as private ownership and market-based mechanisms. The mean years of education in Ecuador is approximately 10 years, while in Chile it is around 12 years. These features enable the data presented in this study to serve as a reference for understanding SRL among university students in other contexts that share similar characteristics, such as academic demands, cultural backgrounds, or educational systems. By identifying comparable patterns, researchers and educators can apply these findings to inform strategies that enhance learning autonomy and academic performance in diverse university settings.
The instrument employed in this database was the GAEU-1 Scale, a questionnaire comprising 19 self-report items designed to assess the learning management of university students. This scale measures SRL, conscious motivation strategies for learning, perception of academic performance, and deep learning techniques (
| GAEU-1 scale items | Factor |
|---|---|
| 1. I think that my academic performance is the same or better than that of my partners. | Academic performance perception |
| 2. Before studying, I organize the materials I will need (like notebooks, books, notes, or others). | Self-Regulated Learning |
| 3. To study, I look for a quiet, well-illuminated and comfortable place. | Self-Regulated Learning |
| 4. I focus my learning on an autonomous way without depending on someone to help me achieve it. | Self-Regulated Learning |
| 5. I believe that my academic performance is better than what my grades show. | Academic performance perception |
| 6. I live up to my academic university responsibilities in an efficient way. | Self-Regulated Learning |
| 7. In order to finish my academic assignments, I tell myself encouraging words to feel motivated. | Conscious learning motivation strategies |
| 8. When I go through academic difficulties, I tell myself I’m able to overcome them and move on. | Conscious learning motivation strategies |
| 9. I keep myself optimistic and willing to learn. | Conscious learning motivation strategies |
| 10. I keep myself motivated to learn because I have future plans. | Conscious learning motivation strategies |
| 11. I foresee my academic future positively and with motivation. | Conscious learning motivation strategies |
| 12. I consider that my academic performance is satisfactory. | Academic performance perception |
| 13. I organize my time to fulfil my academic university tasks. | Self-Regulated Learning |
| 14. I am able to find solutions to the problems that I go through during the process of learning at university. | Self-Regulated Learning |
| 15. I consider I have the ability to learn all the contents of my career’s contents. | Academic performance perception |
| 16. I use summaries, outlines, conceptual maps; I highlight ideas, I do critical reading, and I use other resources to learn significantly. | Deep learning techniques |
| 17. When I finish each task or content, I verify what I learned and what I need to review. | Deep learning techniques |
| 18. When the content or topic is difficult, I check it again. | Deep learning techniques |
| 19. I am used to investigating the contents of the subjects on my own without it being an obligation. | Deep learning techniques |
The demographic characteristics of the study participants are shown in
| Demographic variable | Mean ± SD or n (%) |
|---|---|
|
|
|
| Female | 746 (56.70%) |
| Male | 570 (43.30%) |
|
|
20.45 ± 2.04 |
| Female | 20.42 ± 1.98 |
| Male | 20.50 ± 2.12 |
|
|
|
|
|
|
| Chile | 631 (47.90%) |
| Ecuador | 685 (52.10%) |
|
|
|
| Single | 1273 (96.70%) |
| Married | 20 (1.50%) |
| Divorced | 1 (.10%) |
| Common-Law | 13 (1.00%) |
| Not specified | 9 (.70%) |
|
|
|
| Yes | 52 (4.00%) |
| No | 1248 (94.80%) |
| Not specified | 16 (1.20%) |
|
|
|
| Private | 561 (42.60%) |
| State | 317 (24.10%) |
| Municipal | 304 (23.10%) |
| Sponsored | 134 (10.20%) |
|
|
|
| Private | 1037 (78.80%) |
| State | 277 (21.00%) |
| Not specified | 2 (.20%) |
|
|
|
| Alone | 93 (7.10%) |
| With family members | 1086 (82.50%) |
| With friends | 83 (6.30%) |
| With partner/spouse | 37 (2.80%) |
| Not specified | 17 (1.30%) |
| GAEU-1 scale item | 1: Strongly disagree | 2: Moderately disagree | 3: Neither agree nor disagree | 4: Moderately agree | 5: Strongly agree |
|---|---|---|---|---|---|
|
|
58 (4.40%) | 111 (8.40%) | 439 (33.40%) | 464 (35.30%) | 244 (18.50%) |
|
|
31 (2.40%) | 92 (7.00%) | 217 (16.50%) | 454 (34.50%) | 522 (39.70%) |
|
|
14 (1.10%) | 56 (4.30%) | 196 (14.90%) | 427 (32.40%) | 623 (47.30%) |
|
|
15 (1.10%) | 59 (4.50%) | 227 (17.20%) | 496 (37.70%) | 519 (39.40%) |
|
|
29 (2.20%) | 73 (5.50%) | 360 (27.40%) | 509 (38.70%) | 345 (26.20%) |
|
|
8 (.60%) | 45 (3.40%) | 210 (16.00%) | 569 (43.20%) | 484 (36.80%) |
|
|
139 (10.60%) | 164 (12.50%) | 319 (24.20%) | 364 (27.70%) | 330 (25.10%) |
|
|
40 (3.00%) | 64 (4.90%) | 241 (18.30%) | 476 (36.20%) | 495 (37.60%) |
|
|
10 (.80%) | 29 (2.20%) | 149 (11.30%) | 484 (36.80%) | 644 (48.90%) |
|
|
10 (.80%) | 25 (1.90%) | 125 (9.50%) | 387 (29.40%) | 769 (58.40%) |
|
|
10 (.80%) | 35 (2.70%) | 145 (11.00%) | 392 (29.80%) | 734 (55.80%) |
|
|
25 (1.90%) | 67 (5.10%) | 291 (22.10%) | 535 (40.70%) | 398 (30.20%) |
|
|
28 (2.10%) | 105 (8.00%) | 316 (24.00%) | 521 (39.60%) | 346 (26.30%) |
|
|
4 (.30%) | 38 (2.90%) | 247 (18.80%) | 612 (46.50%) | 215 (31.50%) |
|
|
10 (.80%) | 45 (3.40%) | 188 (14.30%) | 511 (38.80%) | 562 (42.70%) |
|
|
33 (2.50%) | 97 (7.40%) | 258 (19.60%) | 423 (32.10%) | 505 (38.40%) |
|
|
45 (3.40%) | 128 (9.70%) | 363 (27.60%) | 471 (35.80%) | 309 (23.50%) |
|
|
15 (1.10%) | 50 (3.80%) | 225 (17.10%) | 491 (37.30%) | 535 (40.70%) |
|
|
76 (5.80%) | 139 (10.60%) | 405 (30.80%) | 427 (32.40%) | 269 (20.40%) |
The data presented in this data report article were collected as part of a research project approved by the Ethics Committee for Research Involving Human Beings at the Pontifical Catholic University of Ecuador, under ethical approval number 2019-58EO, on January 24, 2019. All procedures complied with internationally recognized ethical standards, as stated in the Helsinki and Nuremberg Declarations.
This dataset is available publicly via:
Mendeley Data: Learning Strategies in Higher Education: A Research Dataset.
This project contains the following underlying data:
Data base Learning Strategies English 2025_07.csv. (Contains data in an Excel file of the participants’ characteristics). Data base Learning Strategies English 2025_07.sav. (Contains data in a SPSS file of the participants’ characteristics). Data base Learning Strategies Spanish 2025_07.sav (Contains the data in Spanish).
Data are available under the terms of the
The authors express their sincere gratitude to the Chilean and Ecuadorian universities that collaborated on the development of this research project. Their support and participation were essential to the successful collection of data and the advancement of this study.