ISSN 0798 1015
ISSN 0798 1015
Vol. 41 (Number 02) Year 2020. Page 7
SAEZ, Fabiola 1; MELLA, Javier 2; LOYER, Solange 3; ZAMBRANO, Carolina 4 y ZAÑARTU, Natalia 5
Received: 07/08/2019 • Approved: 12/12/2019 • Published 31/01/2020
ABSTRACT: The aim of this study is to characterize and identify limitations and orientations of empirical and quantitative research on self-regulated learning in engineering students through a systematic review. The main findings are that research in self-regulated learning in engineering students is at an initial level of development, and there are a low number of experimental studies. Therefore, universities have the challenge of promoting self-regulated learning in engineering students with intra-curricular interventions. |
RESUMEN: El estudio tiene como objetivo caracterizar e identificar limitaciones y orientaciones de investigaciones empíricas cuantitativas sobre autorregulación del aprendizaje en estudiantes de ingeniería a través de una revisión sistemática. Los principales hallazgos muestran que la investigación sobre autorregulación del aprendizaje en estudiantes de ingeniería está en un nivel inicial de desarrollo y hay escasos estudios experimentales. Por lo tanto, las universidades tienen el desafío de promover la autorregulación del aprendizaje en estudiantes de ingeniería con intervenciones intra-curriculares. |
Engineering programs have high academic failure and drop out indicators, especially during the first semesters (Acevedo, Torres, & Tirado, 2015; García-Ros, Pérez-González, Cavas-Martínez & Tomás, 2018). This is due to the transition from high school to university, where students are exposed to a new culture that they don’t relate to and also that higher education requires having learning-autonomy (Gale & Parker, 2014; Graffigna et al., 2014).
Other aspects that have been studied in engineering programs are the way that students learn, depending on the course, and how they carry out their self-regulated processes (Capote, Rizo, & Bravo, 2017). Thus, evidence shows that 80% of engineering students have serious impairments in the use of self-regulated strategies, which results in low academic performances (Wisland, Duarte, & Yoshikazu, 2014). Studies also point out that students lack of study habits when starting college (Villalón, Medina, Sillero, Melchor, & Morales, 2017).
However, current demands of high-quality in Higher Education, require that university students develop self-regulated learning strategies and beliefs that will prepare them to solve problems, in the face of the challenges of autonomous learning (Capote, Rizo, & Bravo, 2017).
Improvements have been made in engineering education. There has been progress in student-centered approaches, but these changes haven’t been systemic and the studies have been mostly cross-sectional. Therefore, additional attention must be set on promoting more innovations (Borrego, Froyd, & Hall, 2010).
Hence, an important short-term goal in a university context is to promote new ways to learn, in order to face the challenges that come with autonomous learning (Villalón et al., 2017). From this perspective, SRL (Self Regulated Learnig) has proven to be a key factor for an effective performance and should therefore be included as a type of innovation in engineering circles (Capote et al., 2017). This helps students develop the ability to regulate their behaviors towards learning, learning difficulties and the way to overcome them (Lawanto et al., 2013).
Implementing interventions within engineering programs may prevent academic failure and early dropout (Acevedo et al., 2015). Interventions that emphasize the development and use of self-regulated learning strategies are crucial for the first semesters of engineering programs (Adams & Blair, 2019). In spite of the importance of promoting self-regulated learning in engineering students, it is still at an initial stage (Nelson, Shell, Husman, Fishman, & Soh, 2015).
A literature review is needed in order to have a clear notion of the current state of self-regulated learning research in engineering students. This will allow other researchers to have access to a more objective analysis of prior efforts, as well as the identification of gaps and the proposition of new directions for research (Borrego, Foster, & Froyd, 2014).
Thus, the following question arises: What are the characteristics, limitations and orientations of self-regulated learning research in engineering students? To answer this question this study has defined 3 objectives: (a) To characterize research on self-regulated learning for engineering students according to country of institutional affiliation of the main author, design, sample size, objective (s), variables (s), instrument (s) and result (s); (b) to describe the limitations of the studies and, (c) to identify recommendations for future research on self-regulated learning in engineering students.
This study considered preparation and writing guidelines for publications based on protocols, standards and stages suggested by specialized research in systematic review (Campbell, Taylor, Bates & O'Connor-Bones, 2018; Sánchez-Meca & Botella, 2010).
The two-stage process developed in this research is explained below. The first stage establishes a protocol for the search and selection of sources to analyze, and the second stage addresses protocols for the systematization of the information.
This stage guides the whole review process, making its reproduction feasible through a five-step analysis protocol (see figure 1).
Step 1. Identification: Identification of the articles produced through a systematic search in the electronic databases Web of Science and Scopus. To refine the search in both data bases, the keywords "Engineering", "Self-Regulated Learning" and "Higher Education”, related to the "AND" connector in English and Spanish were used for the time period going from 2007 to 2019. The last search date was held on June 16, 2019.
Step 2. Selection: When articles are found in both databases (they are duplicated), one of them is eliminated.
Step 3 Eligibility: In this phase, articles that did not contain the words "self-regulated learning" and "Engineering" in the title or abstract were not chosen.
Step 4. Inclusion: The inclusion criteria were: empirical quantitative and mixed research; Higher Education level; the sample or focus of the study must be university engineering students; and it must develop aspects of self-regulated learning theory. The exclusion criteria are the following: theoretical or empirical qualitative research; studies that are not for Higher Education levels (elementary or high school); articles that do not develop self-regulated learning theory; university students that are not from engineering programs.
Step 5. Bias evaluation: Three bias evaluation methods were used in order to ensure that the sample of articles went through a rigorous process: (1) The use of quality verification checklists or scales (available at https://amstar.ca/Amstar_Checklist.php), (2) the use of a third independent reviewer; (3) inclusion of the table of excluded articles with its exclusion justification.
Figure 1
Flow diagram of the search and
selection of analyzed sources
The following stage of the systematic review process considers the gathering of relevant information from each of the included articles. A matrix previously defined by the reviewers is used to gather all the relevant information for the analysis, synthesis and interpretation of the data. The matrix addresses the first research objective of this study, extracting the following information: Source, country, design, sample size, objective(s), variables(s), instrument(s) and result(s) reported in each study.
The country of Institutional affiliation of the main author of each study is shown in Table 1.
Table 1
Country of studies
Country |
Nº of studies |
% |
Region |
% |
|
Country |
Nº of studies |
% |
Region |
% |
United States |
10 |
48% |
North America |
52% |
Spain |
1 |
5% |
Europe |
10% |
|
Mexico |
1 |
5% |
Italy |
1 |
5% |
|||||
Colombia |
1 |
5% |
South America |
14% |
Turkey |
1 |
5% |
Asia |
24% |
|
Brazil |
1 |
5% |
Malaysia |
3 |
14% |
|||||
Chile |
1 |
5% |
|
China |
1 |
5% |
||||
Total of studies: 21 |
||||||||||
8 studies used correlational design, 4 studies used mixed design (non-experimental and case study, descriptive and case study, quasi-experiment and phenomenological analysis, quasi-experiment and qualitative aspects) and 1 study used an experimental design. Quasi-experimental designs were used in 2 studies and explanatory models in two others. In addition, there were also a pre-experiment type study, a descriptive study, a descriptive-correlational study and an instrumental study. In summary, 13 studies (62%) had non-experimental designs (exploratory, descriptive, correlational-causal, instrumental), 4 studies (19%) used experimental design (quasi-experimental, pre-experimental and experimental) and 4 studies (19%) had mixed designs (qualitative and quantitative designs).
The minimum sample size used in the studies was 15 students and the maximum was 1218. A sample size between 15 and 100 students was used in 9 investigations (43%); between 100 and 500 students also in 9 investigations (43%); between 500 and 1000 students in 2 investigations (10%) and a sample over 1000 students was used in one investigation (4%).
Of the 21 objectives formulated in the studies, 15 of them (71%) were to describe or study relationships between the different SRL variables with academic performance, perceived social support of peers, learning approaches, interpretation of the demands of the task, composition of gender in the classroom, virtual learning environments and teaching strategies. Only 6 objectives (29%) consider evaluating the impact of interventions that consider the improvement of SRL variables in engineering students.
50 variables were identified in the 21 studies, whereas 9 of these were used in more then one study (8 variables were considered in 2 studies and 1 variable was considered in 9 studies). These variables were grouped in 6 categories: (1) self-regulated learning, (2) forethought phase, (3) performance phase, (4) self-reflection phase, (5) performance and (6) others. Categories 2, 3 and 4 correspond to Zimmerman’s model (Zimmerman, 2000)
Table 2
Variables and categories used in studies
Category |
Studies that use the category |
Variables |
ID |
Studies that use the variable |
||
n |
% |
n |
% |
|||
Self-regulated learning |
12 |
57% |
Self-regulated learning |
3, 5, 10, 13, 14, 15, 17, 18, 19 |
9 |
14% |
Self-regulated learning skills in their online learning environment |
20 |
1 |
2% |
|||
Self-regulated learning strategies |
12 |
1 |
2% |
|||
Self-regulation strategies in reading |
9 |
1 |
2% |
|||
Forethought phase |
11 |
52% |
Academic goals |
9 |
1 |
2% |
Academic self-confidence |
16 |
1 |
2% |
|||
Extrinsic goal orientation |
6 |
1 |
2% |
|||
Goal-setting |
2 |
1 |
2% |
|||
Motivation |
4, 5 |
2 |
3% |
|||
Motivation for learning |
21 |
1 |
2% |
|||
Motivational Strategies |
11 |
1 |
2% |
|||
Perceived general self-efficacy |
18 |
1 |
2% |
|||
Planning strategies |
7 |
1 |
2% |
|||
Self-efficacy |
16 |
1 |
2% |
|||
Self-efficacy beliefs in the use of educational internet |
19 |
1 |
2% |
|||
Self-motivation |
1 |
1 |
2% |
|||
Performance phase |
10 |
48% |
Action control |
21 |
1 |
2% |
Cognitive |
4 |
1 |
2% |
|||
Critical thinking |
6, 16 |
2 |
3% |
|||
Environment structuring |
2 |
1 |
2% |
|||
Help seeking |
1, 2 |
2 |
3% |
|||
Learning approaches |
8, 13 |
2 |
3% |
|||
Learning strategies |
8, 21 |
2 |
3% |
|||
Metacognition |
6 |
1 |
2% |
|||
Monitoring strategies |
7 |
1 |
2% |
|||
Peer social support |
13 |
1 |
2% |
|||
Regulation strategies |
7 |
1 |
2% |
|||
Resource management |
4 |
1 |
2% |
|||
Search for help |
16 |
1 |
2% |
|||
Task interpretation |
12 |
1 |
2% |
|||
Task strategies |
2 |
1 |
2% |
|||
Time management |
1, 2 |
2 |
3% |
|||
Self-reflection phase |
2 |
10% |
Self-evaluation |
1, 2 |
2 |
3% |
Other |
9 |
14% |
Knowledge activation |
1 |
1 |
2% |
Previous knowledge |
9 |
1 |
2% |
|||
Academic integration |
16 |
1 |
2% |
|||
Affection |
5 |
1 |
2% |
|||
Discrimination |
16 |
1 |
2% |
|||
Effort |
16 |
1 |
2% |
|||
Effort investment |
1 |
1 |
2% |
|||
Information literacy |
19 |
1 |
2% |
|||
Learning experience |
3 |
1 |
2% |
|||
Peer learning |
6, 16 |
2 |
3% |
|||
Perceived benefit |
3 |
1 |
2% |
|||
Perception teacher support |
6 |
1 |
2% |
|||
Personality traits |
21 |
1 |
2% |
|||
Satisfaction |
3 |
1 |
2% |
|||
Student gender balance |
11 |
1 |
2% |
|||
Performance |
2 |
3% |
Academic performance |
3 |
1 |
2% |
Performance criteria |
12 |
1 |
2% |
|||
|
46 |
183% |
|
|
66 |
100% |
Self-regulated learning: (1) 3 phase SRL self-assessment instrument; (2) Escala de Evaluación de la Autorregulación del Aprendizaje a partir de Textos (ARATEX) (Text based Self-Regulated learning Scale); (3) Self-regulated learning survey: Engineering Design Questionnaires (EDQ); (4) Self-regulated learning skills scale; (5) Online Self-regulated Learning Questionnaire (OSLQ); (6) Student Perceptions of Classroom Knowledge Building scale (SPOCK); (7) Tracking of Supervised Learning Activities (TSLA) and (8) Ex-ante and Ex-post Survey (EAS & EPS).
Motivation: (1) Situational Motivation Scale (SIMS); (2) Motivated Strategies for Learning Questionnaire (MSLQ); (3) Questionnaire for the Evaluation of Academic Goals (CEMA). (4) Positive and Negative Affect Scale (PANAS); (4) Perceptions of instrumentality (PI). The instrument most used to measure motivation was MSLQ, which was used 9 times.
Strategies: (1) Cuestionario de Evaluación de las Estrategias de Aprendizaje de los Estudiantes Universitarios (CEVEAPEU) (University Students’ Learning Strategies Questionnaire); (2) Learning and Study Strategies Inventory (LASSI); (3) Learning strategies Scale; (4) Inquiry Learning Questionnaire (ILQ).
Self-efficacy: (1) Perceived General Self-Efficacy Scale; (2) Belief scale of self-efficacy in the use of educational internet; (3) Cooperative Institutional Research Program scale (CIRP).
Learning approaches: (1) Cuestionario de Procesos de Estudio (CPE) (Study Process Questionnaire); (2) Study Process Questionnaire (R-SPQ-2F).
Ten other instruments were used for specific variables that were used in just one study: (1) Learning Climate Questionnaire (LCQ) that measures the teacher's perceived support for autonomy; (2) Multidimensional Scale of Perceived Social Support (MSPSS) to measure peer social support; (3) Santiago and Einarson scale to measure academic integration; (4) Scale of perceived discrimination to measure this variable; (5) Information Literacy Scale; (6) Mini marker scale to measure personality traits; (7) Action Control Scale; (8) Learning experience questionnaire; (9) Perceived benefit questionnaire; (10) Satisfaction questionnaire.
Descriptive, correlational, instrumental, explanatory and causal results were identified, which is consistent with the designs and objectives proposed in the studies.
The descriptive studies showed the following results: (1) Engineering students have a low level of self-regulated learning (Hafizah et al., 2016); (2) Engineering students do not plan or monitor their learning process (Zambrano, 2016).
Correlational studies showed the following relationships: (1) Students with maladaptive profiles learned less than those who adopted learning profiles (Nelson et al., 2015); (2) High levels of student learning are related to the development of self-regulation strategies (Ernst & Clark, 2014); (3) Gender balance in the classroom between students and their instructors is associated with the SRL behaviors and attitudes adaptation (Stefanou et al., 2014); (4) Higher performing students had a greater awareness and use of the monitoring and fixation strategies associated with success in the design process (Lawanto, Butler, Cartier, Santoso, & Goodridge, 2013); (5) The dimensions of self-regulated learning, information processing, motivation and self-examination are related to the deep learning approach. The perceived social support of peers is associated with the information and motivation processes dimension (Hafzan et al., 2015); (6) Significant relationships between SRL and academic performance (Kosnin, 2007); (7) Higher performance students outperformed those with lower performance significantly in: goal setting, frequency of access to all course materials, and in the punctuality of task presentation (Lawanto et al., 2014).
Causal or explanatory correlational studies had the following results: (1) The initial grade average, along with motivational factors such as goal orientation, significantly predict the use of self-regulated learning strategies in the comprehension of academic texts (Gaeta, 2015); (2) The lack of academic integration decreases self-efficacy and academic confidence; and academic integration has a positive effect on self-efficacy, which in turn has positive effects on effort and critical thinking (Vogt, 2008); (3) The educational use of internet and self-regulation skills have been considered effective in information literacy (Gunes et al., 2015); (4) Direct effects of student personality, motivation for learning and action control on self-regulated learning strategies (Chi-Tung & Ruey-Gwo, 2011).
The only study with instrumental design had as a result that the adapted instrument (MSLQ-Colombia) had acceptable psychometric properties of construct validity, content validity and reliability (Ramírez-Echeverry et al., 2016).
The non-experimental designs had the following results: (1) Significant differences at the end of the course between Problem Based Learning (ABP) courses and courses with active learning lectures; also the use of PBL (Problem-Based Learning) promoted critical thinking, the search for help and elaboration, while active learning lectures tend to promote a more effective use of time and study environment (Lord et al., 2012); (2) Significant improvements in learning strategies and deep learning post-test scores (Gargallo et al., 2015).
Quasi-experimental designs had the following results: (1) The implementation of self-regulated learning self-assessment has a high and significant impact on the performance of freshmen students and students that require further elaboration to achieve deep learning, but it had a mild impact in older students (Zheng et al., 2016); 2) the discipline integration project increases the following: Commitment to SRL, intrinsic motivation, value of the task, and the use of critical thinking strategies, in comparison to students from courses that don’t have this project; 3) designs specifically tailored to implement self-regulated learning features in a web-enhanced active learning approach are effective in reinforcing professional knowledge and fostering SRL (Manganello, Falsetti & Leo, 2019).
The experimental design had as a result that learning analytics can be used to promote self- regulated learning in flipped classrooms, helping students to identify strategies that can increase their academic performance (Sedraz et al., 2018).
Of the 21 investigations included in this research, only 13 (62%) of them explicitly reported and informed the limitations of their work. The other 8 investigations did not inform it. This does not mean that the other investigations didn’t have any limitations, however, for analysis purposes only those that were reported by the authors were considered.
The limitations reported were grouped in the following 5 categories: (1) study design, (2) type of self-report instrument, (3) sample used, (4) measurement of achievement and student learning,(5) measurement and analysis of only some SRL variables.
Table 3
Limitations of studies
Category |
n |
% |
Limitation |
ID |
n |
% |
Study design |
5 |
38% |
Not controlling previous group differences by design |
3 |
1 |
8% |
Regarding student profile analysis, although it’s a highly interpretative descriptive methodology, it is not an inferential statistical technique of the data obtained and also does not control the differences between instructors or methodologies used in the classes |
5 |
1 |
8% |
|||
Application of pre-experimental designs that did not consider a control group |
8 |
1 |
8% |
|||
Exploratory type designs |
12 |
1 |
8% |
|||
Transversal non-longitudinal designs that condition the accuracy of the results and their generalization |
16 |
1 |
8% |
|||
Type of self-report instrument |
5 |
38% |
Self-report instruments may possibly have subjective biases, since they do not provide complete information of the student's self-regulated behaviors. Therefore, considering only this method of evaluation is not enough to examine in detail the strategies of self-regulation and it is insufficient to fully capture the complexity of real classroom situations and direct information of self-regulated behaviors |
12, 7, 6,4, 3 |
5 |
38% |
Sample used |
12 |
92% |
Sample is not representative because it does not examine the impact on students from other populations |
2, 3 |
2 |
15% |
Sample is not representative because it does not consider other courses at the university |
9, 4 |
2 |
15% |
|||
Sample is not representative because it does not consider other universities |
8, 5 |
2 |
15% |
|||
the second reason is the small size of the sample |
10, 12, 7 |
3 |
23% |
|||
and the third reason is the use of convenience sampling |
6, 16 |
2 |
15% |
|||
or arbitrary sampling of the participants |
14 |
1 |
8% |
|||
Measurement of achievement and student learning |
1 |
8% |
Not measuring students’ achievement or learning |
1 |
1 |
8% |
Measurement and analysis of only some of the SRL variables |
1 |
8% |
Measurement and analysis of only some SRL variables. |
9 |
1 |
8% |
Of the 21 studies included in this investigation, 14 of them (67%), report orientations or guidelines for future research, which were grouped in the following: (1) more rigorous experimental design, (2) longitudinal studies, (3) reliable instruments that measure the SRL process, (4) larger samples through random selection that takes into consideration other populations, (5) study of self-regulation profiles, (6) that universities take on the challenge of promoting SRL in engineering students with intra-curricular interventions.
Table 4
Guidelines of the studies
Category |
n |
% |
Guidelines |
ID |
n |
% |
More rigorous experimental design |
6 |
43% |
The design must be rigorous in terms of the participants. |
3 |
1 |
7% |
There should be a combination of methodologies applying mixed designs to better understand students’ SRL processes. |
7, 20, 4 |
3 |
21% |
|||
To perform quasi-experimental designs with control groups in order to compare the results and also to evaluate the impact of interventions in engineering students’ SRL. |
8, 17 |
2 |
14% |
|||
Longitudinal studies |
4 |
29% |
Follow-up objectives involving longitudinal studies were proposed to evaluate the use of SRL strategies in future courses. |
7, 20, 3 |
3 |
21% |
To verify the causality of the variables. |
16 |
1 |
7% |
|||
Reliable instruments that measure the SRL process |
4 |
29% |
To collect data with more reliable instruments and to use technology to register it. |
3 |
1 |
7% |
To use instruments that evaluate the use of the strategy in real time, complementing the evidence generate by the "self-report", that is, measure through direct observations of students’ behavior to identify SRL behaviors. |
14, 12, 6 |
3 |
21% |
|||
Larger samples through random selection that takes into consideration other populations, |
5 |
36% |
To increase the representativeness of the participants considering other populations. |
9, 4 |
2 |
14% |
To use random and not by convenience samples. |
6 |
1 |
7% |
|||
To increase the sample size to have more diverse contexts in the understanding of SRL and thus be able to generalize the results. |
10, 8, 12, 7, 20 |
5 |
36% |
|||
Study of self-regulation profiles |
2 |
14% |
The development of a comparative study to examine SRL processes in first-year engineering students and in students from higher years. |
12, 5 |
1 |
7% |
That universities take on the challenge of promoting SRL in engineering students with intra-curricular interventions |
8 |
57% |
That universities promote SRL in engineering students with intra-curricular interventions, especially in the first year. |
21, 17, 14, 12, 20, 16 |
6 |
43% |
That the interventions include teachers in the promotion of SRL. |
9, 5 |
2 |
14% |
The three objectives of the study are discussed below:
(a) Characterization of research on self-regulated learning in engineering students: Research of self-regulated learning in engineering students is led by the United States with more than 48% of the total research. In Latin America, only four investigations were found, representing 19% of the total research. This is consistent with findings in previous studies, regarding the low amount of research in engineering in Latin America (Hernández & Camargo, 2017). The most frequent design observed in the studies is correlation, followed by a much lower number of quasi-experimental studies. This may be seen as a weakness in the type of design in terms of the low amount of interventions carried out in the classroom. This is relevant because it is precisely in the classroom where self-regulated learning must be encouraged so that students become aware of their learning process (Merchán and Hernández, 2018).
(b) Limitations of the studies: The most frequently reported limitation was regarding the representativeness of the sample. This was followed by the design of the study and the use of only self-report instruments.
(c) Guidelines: The main recommendations are as follows: Universities should develop action plans to promote SRL in engineering students with intra-curricular interventions; there should be more studies with quasi-experimental designs and larger samples, and finally, more studies with longitudinal designs are needed.
It is reasonable to conclude that research in self-regulated learning in engineering students is at an initial level of development. Another interesting fact is that research in the field of engineering education focuses mainly in the areas of active learning, curriculum and others, but not on psychological aspects of learning, such as self-regulated learning (Borrego et al. al., 2014; Borrego et al., 2015; Jesiek et al., 2011).
Este trabajo fue financiado por el Fondo FAA 02/2019 de la Dirección de Investigación de la Universidad Católica de la Santísima Concepción, Chile.
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Appendix 1
Exclusion criteria for articles that were not included in the systematic review
Articles eliminated in the eligibility stage by exclusion criteria:
Level of studies other than Higher Education
1 |
Han, S. (2017). Korean Students’ Attitudes toward STEM Project-Based Learning and Major Selection. Educational Sciences: Theory & Practice, 17(2). DOI: 10.12738/estp.2017.2.0264 |
2 |
Barak, M. (2012). From ‘doing’to ‘doing with learning’: Reflection on an effort to promote self-regulated learning in technological projects in high school. European Journal of Engineering Education, 37(1), 105-116. 10.1080/03043797.2012.658759 |
3 |
Lawanto, O., Santoso, H. B., & Liu, Y. (2012). Understanding of the relationship between interest and expectancy for success in engineering design activity in grades 9-12. Educational Technology & Society, 15(1), 152-161 |
|
Articles eliminated in the eligibility stage by exclusion criteria: Does not develop the self-regulated learning theory, does not measure self-regulation variables |
4 |
Gonzalez Hernandez, W. (2016). Modelling as a Competency for the Training of Computer Professionals. REVISTA DIGITAL DE INVESTIGACION EN DOCENCIA UNIVERSITARIA-RIDU, 10(2), 59-71. Doi: 10.19083/ridu.10.493 |
5 |
Yan, C. (2014). Research-oriented English teaching of engineering majors at regional Chinese engineering colleges based on the idea of steps. World Trans. on Engng. and Technol. Educ, 12(4), 753-758. |
6 |
Sáiz Manzanares, M. C., & Bol Arreba, A. (2014). Learning based on assessment with rubrics: a study in higher educatio. Suma Psicológica, 21(1), 28-35. |
7 |
Cubero, S. N. (2015). A fun and effective self-learning approach to teaching microcontrollers and mobile robotics. International Journal of Electrical Engineering Education, 52(4), 298-319. Doi:10.1177/0020720915585798 |
8 |
Zheng, W., Shih, H. R., Lozano, K., & Mo, Y. L. (2011). Impact of nanotechnology on future civil engineering practice and its reflection in current civil engineering education. Journal of Professional Issues in Engineering Education and Practice, 137(3), 162-173. DOI: 10.1061/(ASCE)EI.1943-5541.0000034 |
Articles eliminated in the eligibility stage by exclusion criteria: Only qualitative design
9 |
Pedrosa, D., Cravino, J., Morgado, L., & Barreira, C. (2017). Self-regulated learning in higher education: strategies adopted by computer programming students when supported by the SimProgramming approach. Production, 27(SPE). |
Appendix 2
Article information Matrix
1. PhD in Psychology, Professor at Fundamentals of Pedagogy Department, Universidad Católica de la Santísima Concepción, Chile (UCSC). fsaez@ucsc.cl
2. Professor at Science Department, Universidad Técnica Federico Santa María, Chile. Javier.mellan@usm.cl
3. Professor at Civil Engineering Department, Universidad Católica de la Santísima Concepción, Chile (UCSC). sloyer@ucsc.cl
4. PhD Student in Education, Department of Research Methodology and Educational Informatics, Universidad de Concepción, Chile. carolinazambrano@gmail.com
5. PhD Psychology, School of Psychology, Universidad de San Sebastián, Chile. natalia.zanartuc@docente.uss.cl
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