Learning Management in Higher Education: A Data Note

Carlos Ramos-Galarza; Jennifer Obregón; Nancy Lepe-Martínez; Milenko Del Valle

doi:10.12688/f1000research.167353.1

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Data Note

Learning Management in Higher Education: A Data Note

[version 1; peer review: awaiting peer review]

Carlos Ramos-Galarza ¹, Jennifer Obregón¹, Nancy Lepe-Martínez², Milenko Del Valle³

PUBLISHED 01 Aug 2025

Author details Author details

¹ Facultad de Salud y Bienestar, Pontificia Universidad Catolica del Ecuador, Quito, Pichincha, Ecuador
² Universidad Catolica del Maule Facultad de Ciencias de la Educacion, Talca, Maule Region, Chile
³ University of Antofagasta, Antofagasta, Antofagasta Region, Chile

Carlos Ramos-Galarza
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Jennifer Obregón
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Nancy Lepe-Martínez
Roles: Conceptualization, Data Curation, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Milenko Del Valle
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

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Abstract

Background

Learning management in university settings involves various elements that enable students to be aware and independent throughout their professional education.

Method

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.

Results

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.

Conclusions

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.

Keywords

Self-Regulated Learning, Higher education, University students, Motivational strategies, Academic performance perception, Deep learning techniques

Corresponding author: Carlos Ramos-Galarza

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 Ramos-Galarza C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Ramos-Galarza C, Obregón J, Lepe-Martínez N and Del Valle M. Learning Management in Higher Education: A Data Note [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:756 (https://doi.org/10.12688/f1000research.167353.1) First published: 01 Aug 2025, 14:756 (https://doi.org/10.12688/f1000research.167353.1) Latest published: 01 Aug 2025, 14:756 (https://doi.org/10.12688/f1000research.167353.1)

Introduction

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 (Dettori & Persico, 2010; Zimmerman, 2015). The data presented in this paper conceptualize learning management as comprising four interrelated factors that represent core processes involved in the practical and autonomous academic functioning of university students. The first of these factors is conscious motivational strategies for learning, which refer to the deliberate use of internal dialogue, affirmations, and goal-oriented thinking that students employ to sustain engagement, persistence, and a positive attitude toward academic tasks (Méndez et al., 2025; Siqueira et al., 2020).

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 (Karimi, 2010).

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 (Hermida, 2014; Han, 2022).

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 (Mikroyannidis et al., 2014; Winne and Hadwin, 2010).

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.

Materials and methods

Research design

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.

Informed consent

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.

Study scenario

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.

Measuring instrument

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 (Ramos-Galarza et al., 2023). The scale items and their corresponding factors are presented in Table 1.

Table 1. GAEU-1 scale items and their corresponding factor.

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

Data description

The demographic characteristics of the study participants are shown in Table 2, including variables such as gender, age, nationality, and civil status. Table 3 presents the frequency and percentage of responses for the 19 items from the GAEU-1 scale. The statistical analyses were conducted with the latest version of SPSS software.

Table 2. Demographic data of the participants (N = 1316).

Demographic variable	Mean ± SD or n (%)
Gender
Female	746 (56.70%)
Male	570 (43.30%)
Age	20.45 ± 2.04
Female	20.42 ± 1.98
Male	20.50 ± 2.12
	t₍₁₃₁₄₎ = -.77, p = .44
Nationality
Chile	631 (47.90%)
Ecuador	685 (52.10%)
Civil Status
Single	1273 (96.70%)
Married	20 (1.50%)
Divorced	1 (.10%)
Common-Law	13 (1.00%)
Not specified	9 (.70%)
Children
Yes	52 (4.00%)
No	1248 (94.80%)
Not specified	16 (1.20%)
Type of pre-university institution
Private	561 (42.60%)
State	317 (24.10%)
Municipal	304 (23.10%)
Sponsored	134 (10.20%)
Type of university
Private	1037 (78.80%)
State	277 (21.00%)
Not specified	2 (.20%)
Living Arrangement
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%)

Table 3. Descriptive statistics of the GAEU-1 scale item responses.

GAEU-1 scale item	1: Strongly disagree	2: Moderately disagree	3: Neither agree nor disagree	4: Moderately agree	5: Strongly agree
1	58 (4.40%)	111 (8.40%)	439 (33.40%)	464 (35.30%)	244 (18.50%)
2	31 (2.40%)	92 (7.00%)	217 (16.50%)	454 (34.50%)	522 (39.70%)
3	14 (1.10%)	56 (4.30%)	196 (14.90%)	427 (32.40%)	623 (47.30%)
4	15 (1.10%)	59 (4.50%)	227 (17.20%)	496 (37.70%)	519 (39.40%)
5	29 (2.20%)	73 (5.50%)	360 (27.40%)	509 (38.70%)	345 (26.20%)
6	8 (.60%)	45 (3.40%)	210 (16.00%)	569 (43.20%)	484 (36.80%)
7	139 (10.60%)	164 (12.50%)	319 (24.20%)	364 (27.70%)	330 (25.10%)
8	40 (3.00%)	64 (4.90%)	241 (18.30%)	476 (36.20%)	495 (37.60%)
9	10 (.80%)	29 (2.20%)	149 (11.30%)	484 (36.80%)	644 (48.90%)
10	10 (.80%)	25 (1.90%)	125 (9.50%)	387 (29.40%)	769 (58.40%)
11	10 (.80%)	35 (2.70%)	145 (11.00%)	392 (29.80%)	734 (55.80%)
12	25 (1.90%)	67 (5.10%)	291 (22.10%)	535 (40.70%)	398 (30.20%)
13	28 (2.10%)	105 (8.00%)	316 (24.00%)	521 (39.60%)	346 (26.30%)
14	4 (.30%)	38 (2.90%)	247 (18.80%)	612 (46.50%)	215 (31.50%)
15	10 (.80%)	45 (3.40%)	188 (14.30%)	511 (38.80%)	562 (42.70%)
16	33 (2.50%)	97 (7.40%)	258 (19.60%)	423 (32.10%)	505 (38.40%)
17	45 (3.40%)	128 (9.70%)	363 (27.60%)	471 (35.80%)	309 (23.50%)
18	15 (1.10%)	50 (3.80%)	225 (17.10%)	491 (37.30%)	535 (40.70%)
19	76 (5.80%)	139 (10.60%)	405 (30.80%)	427 (32.40%)	269 (20.40%)

Ethical approval

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.

Data availability statement

This dataset is available publicly via:

Mendeley Data: Learning Strategies in Higher Education: A Research Dataset. https://doi.org/10.17632/7vgyndhb87.5 (Ramos-Galarza et al., 2025).

Underlying data

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).

Extended data

• GAEU-1 Scale Questionnaire.docx (This is the instrument administered in the present study, designed to assess four learning strategies within the university context: conscious motivational strategies, perception of academic performance, use of deep learning techniques, and overall self-regulation of the learning process).
• Informed Consent.docx (This document was completed by the participants and includes all ethical provisions required for research involving human subjects).

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

Acknowledgements

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.

References

Dettori G, Persico D: Fostering self-regulated learning through ICT. New York: IGI Global. Information Science Reference; 2010. Publisher Full Text
Han X: Investigation on deep learning model of college English based on multimodal learning method. Comput. Intell. Neurosci. 2022; Article 7001392. PubMed Abstract | Publisher Full Text | Free Full Text
Hermida J: Facilitating deep learning: Pathways to success for university and college teachers. Apple Academic Press; 2014. Publisher Full Text
Karimi FK: Factors contributing to the academic performance of students in a private university in Kenya: A case of Daystar University. Int. J. Learn. 2010; 17(8): 63–76. Publisher Full Text
Méndez I, Ruiz-Esteban C, Martínez-Ramón JP, et al.: Profiles of intrinsic motivation and motivational learning strategies in Spanish university students. Curr. Psychol. 2025; 44(6): 4649–4657. Publisher Full Text
Mikroyannidis A, Connolly T, Law EL-C, et al.: Self-regulated learning in formal education: Perceptions, challenges and opportunities. Int. J. Technol. Enhanc. Learn. 2014; 6(2): 145–163. Publisher Full Text
Ramos-Galarza C, Garíca-Cruz P, Lepe-Martínez N, et al.: Validation of the GAEU-1 Acale to Assess the Learning Management of University Students. Emerg. Sci. J. 2023; 7: 253–263. Publisher Full Text
Ramos-Galarza C, Obregón J, Lepe-Martínez N, et al.: Learning Strategies in Higher Education: A Research Dataset. (Version 5). [Data set]. Mendeley Data. 2025. Publisher Full Text
Siqueira MAM, Gonçalves JP, Mendonça VS, et al.: Relationship between metacognitive awareness and motivation to learn in medical students. BMC Med. Educ. 2020; 20(1): 393. PubMed Abstract | Publisher Full Text | Free Full Text
Winne PH, Hadwin AF: Self-regulated learning and socio-cognitive theory. McGaw BJ, Baker E, Peterson P, editors. International Encyclopedia of Education. Elsevier; 3rd ed. 2010; pp. 503–508. Publisher Full Text
Zimmerman BJ: Self-regulated learning: Theories, measures, and outcomes. Wright JD, editor. International encyclopedia of the social & behavioral sciences. 2nd ed.Elsevier; 2015; pp. 541–546. Publisher Full Text

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Version 1

VERSION 1 PUBLISHED 01 Aug 2025

Author details Author details

¹ Facultad de Salud y Bienestar, Pontificia Universidad Catolica del Ecuador, Quito, Pichincha, Ecuador
² Universidad Catolica del Maule Facultad de Ciencias de la Educacion, Talca, Maule Region, Chile
³ University of Antofagasta, Antofagasta, Antofagasta Region, Chile

Carlos Ramos-Galarza
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Jennifer Obregón
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Nancy Lepe-Martínez
Roles: Conceptualization, Data Curation, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Milenko Del Valle
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

version 1

Published: 01 Aug 2025, 14:756

https://doi.org/10.12688/f1000research.167353.1

Copyright

© 2025 Ramos-Galarza C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Ramos-Galarza C, Obregón J, Lepe-Martínez N and Del Valle M. Learning Management in Higher Education: A Data Note [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:756 (https://doi.org/10.12688/f1000research.167353.1)

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[1] Dettori G, Persico D: Fostering self-regulated learning through ICT. New York: IGI Global. Information Science Reference; 2010. Publisher Full Text

[2] Han X: Investigation on deep learning model of college English based on multimodal learning method. Comput. Intell. Neurosci. 2022; Article 7001392. PubMed Abstract | Publisher Full Text | Free Full Text

[3] Hermida J: Facilitating deep learning: Pathways to success for university and college teachers. Apple Academic Press; 2014. Publisher Full Text

[4] Karimi FK: Factors contributing to the academic performance of students in a private university in Kenya: A case of Daystar University. Int. J. Learn. 2010; 17(8): 63–76. Publisher Full Text

[5] Méndez I, Ruiz-Esteban C, Martínez-Ramón JP, et al.: Profiles of intrinsic motivation and motivational learning strategies in Spanish university students. Curr. Psychol. 2025; 44(6): 4649–4657. Publisher Full Text

[6] Mikroyannidis A, Connolly T, Law EL-C, et al.: Self-regulated learning in formal education: Perceptions, challenges and opportunities. Int. J. Technol. Enhanc. Learn. 2014; 6(2): 145–163. Publisher Full Text

[7] Ramos-Galarza C, Garíca-Cruz P, Lepe-Martínez N, et al.: Validation of the GAEU-1 Acale to Assess the Learning Management of University Students. Emerg. Sci. J. 2023; 7: 253–263. Publisher Full Text

[8] Ramos-Galarza C, Obregón J, Lepe-Martínez N, et al.: Learning Strategies in Higher Education: A Research Dataset. (Version 5). [Data set]. Mendeley Data. 2025. Publisher Full Text

[9] Siqueira MAM, Gonçalves JP, Mendonça VS, et al.: Relationship between metacognitive awareness and motivation to learn in medical students. BMC Med. Educ. 2020; 20(1): 393. PubMed Abstract | Publisher Full Text | Free Full Text

[10] Winne PH, Hadwin AF: Self-regulated learning and socio-cognitive theory. McGaw BJ, Baker E, Peterson P, editors. International Encyclopedia of Education. Elsevier; 3rd ed. 2010; pp. 503–508. Publisher Full Text

[11] Zimmerman BJ: Self-regulated learning: Theories, measures, and outcomes. Wright JD, editor. International encyclopedia of the social & behavioral sciences. 2nd ed.Elsevier; 2015; pp. 541–546. Publisher Full Text

Learning Management in Higher Education: A Data Note

Abstract

Background

Method

Results

Conclusions

Keywords

Introduction

Materials and methods

Research design

Informed consent

Study scenario

Measuring instrument

Table 1. GAEU-1 scale items and their corresponding factor.

Data description

Table 2. Demographic data of the participants (N = 1316).

Table 3. Descriptive statistics of the GAEU-1 scale item responses.

Ethical approval

Data availability statement

Underlying data

Extended data

Acknowledgements

References

Comments on this article Comments (0)

Open Peer Review

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Open Peer Review

Reviewer Status

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