Abridged ACRA Scale of Learning Strategies for University Students. – – Electronic Escalas de Estrategias de Aprendizaje [ACRA. Learning Strategy. Pereira S, Ramirez J. Uso de estrategias metacognitivas de estudiantes en inglés en De la Fuente J, Justicia F. Escala de estrategias de aprendizaje ACRA. Evaluación de estrategias de aprendizaje mediante la escala ACRA abreviada para estudiantes universitarios. Palabras clave: Estrategias de.

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Psychometric Studies of the Learning Strategies Scale for University Students

The aim of this study is twofold. Second, we aim to propose a methodological alternative based on the Canonical non-symmetrical correspondence analysis CNCAas an alternative to the methods traditionally used in Psychology and Education.

Male participants with high scores on learning support strategies are positively related to high attention, clarity, and emotional repair. For women, high scores on cognitive, control, and learning support LS are related to high emotional attention, whereas dimensions such as study habits and learning support are related to adequate emotional repair.

Participants in the 18—19 and 22—23 years age groups dd similar behavior.

High scores on learning support strategies are related to high values on three dimensions of the PEI, and high values of study habits show high values for clarity and low values for attention and repair. The 20—21 and older than 24 years age groups behaved similarly.

High scores on learning support strategies are related to low values on clarity, and study habits show high values for clarity and repair. This article presents the relationship between PEI and LS in university students, the differences by gender and age, and CNCA as an alternative method to techniques used in this field to study this association.

Over the last few years, university education has been facing important changes that affect both students and lecturers. Hence, university didactic has changed from a system focused on the lecturer, to a system that focuses on the students, thus forcing the students to acquire a relevant role in the teaching-learning escwla.

One aprsndizaje the implications is that lecturing, studying, tutorials, seminars, essays, practical classes, or preparation for exams and evaluations are timed. This system implies that the students need to acquire new forms of learning and developing learning strategies LSs.

For this reason, it is important to know the variables that intervene in the learning of students of higher education and the possible relationships that exist between them. Thus, teachers will be able to improve their didactic methodologies to help students to learn more effectively. There is a large body of evidence pointing to the important role of Aprendizwje in predicting academic achievement in college e.

Students experience emotions they cannot always control. These affective reactions that appear suddenly in response to a characteristic situation or a stimulus, predispose the individual to a number of different biological consequences that must be considered aprendixaje the learning process. Put simply, when a student is determined to solve a problem and is successful then they will experience positive emotions and feelings, whereas if they fail they will show negative emotions.

Given that students play the main role in higher education, the influence of emotions and cara is of great interest in academic performance. Many of the competences included in the studied subjects refer to aspects related to the emotional and social development of the students.

Although the programs do not allude to the development of emotional competences, the intention is to educate people to increase their capacity to adapt to any circumstance in the present society, which should include social and emotional training. In this regard, Mayer and Salovey published the first article related to the issue of emotional intelligence EI.

Since then, scientific evidence has shown how EI is linked to different positive indicators of the human being in general and in higher education in particular Salovey and Mayer, ; Mayer and Salovey,; Parker et al. Emotional intelligence, following the model of Mayer and Saloveyp. According to this model, EI involves a set of abilities related to the emotional processing of information.

More specifically, EI is part of a model with four interrelated branches: In the university context, this concept is related to complementary aptitudes that are different from academic intelligence or purely cognitive abilities, but have a great impact on the general development of the students Costa and Faria, ; Nathanson et al. This construct has been evaluated with execution and self-reporting scales, depending on which of the theoretical models of EI is used Matthews et al.

However, the EI has shown to be beneficial for a number of relevant variables related to the educational context. To be more specific, EI is related to different variables such as better physical and mental health Martins et al. The presence of emotional abilities or competences in the academic context is relevant given its impact on the general development of the students Costa et al. Several studies have suggested that academic performance is better if the students have emotional skills and are more actively involved in their own education Schutte et al.


Relatively little research, however, has been carried out to analyze the relationship between PEI and LS.


A number of studies with Iranian university students from different fields of study have found that students both male and female that are emotionally intelligent use more LS Hasanzadeh and Shahmohamadi, ; Zafari and Biria, ; Soodmand et al. However, some studies concerning the relationship between PEI and LS with Spanish speaking participants have found a more complex pattern of results. Conventional methods of analysis for the study of relationships between variables are usually correlation or regression analyses Balluerka et al.

Nevertheless, sometimes problems with data can generate confusion in the interpretation of analyses. For this reason, we present an alternative statistical procedure that complements the traditional techniques, known as the Canonical non-symmetrical correspondence analysis CNCA Willems and Galindo-Villardon, The present study had three main objectives: We propose the following hypotheses: The gender distribution of the university students was Of the whole sample, The biggest groups were within Health Science With respect to their academic year, This instrument was designed to assess how people reflect upon their moods and provides an indicator of the levels of perceived EI.

Respondents are asked to rate their degree of agreement on each of the 24 items on a five-point Likert-type scale ranging from 1 very much agree to 5 very much disagree. The scale is composed of three sub-factors: Attention to Feelings, assessed by the first eight items, is the degree to which people believe they pay attention to their own feelings i. It includes 44 items with a Likert scale with four possible answers. However, the distribution of the responses shows that it would be convenient to use the information by grouping the answers few-nothing and enough-a lot together.

It has been designed to include information about LSs and techniques in the university population Justicia and De la Fuente, ; De la Fuente and Justicia, ; Gargallo et al.

Abridged ACRA is divided into three dimensions: Cognitive and control learning strategies, Learning support strategies, and Study habits. The dimension of Cognitive and control learning strategies includes 25 items that refer to selection, organization, highlighting, awareness of the functionality of the strategies, elaboration strategies, planning and control of the answer during evaluation, repetition, and re-reading i. The dimension Support Strategies includes 14 motivational and affective items, such as intrinsic motivation, anxiety control, non-distraction conditions, need for social support, timing, and scheduling i.

Finally, the Study Habits dimension includes five items that involve understanding and study habits i. The research data were collected using questionnaires, and the confidentiality and anonymity of the participants was guaranteed. The study was carried out in accordance with the Declaration of Helsinki and ethical guidelines of the American Psychological Association, and all the participants signed a consent form prior to their participation in the study.

We used a multi-stage sampling method. In the first stage we used a stratified probabilistic sampling method according to the different knowledge areas Arts and Humanities, Science, Health Sciences, Social and Legal Sciences, and Architecture.

In the second stage we used a simple random sampling method for each stratum. The first considers commonality and the second uses factorial procedures such as minimum rank factor analysis, but more recently the GLBalgebraic GLBa procedure has been developed from an algorithm that introduces a vector to weight the items by importance.

The differences between the quantitative variables of the two categories were analyzed with non-parametric Mann—Whitney U -test for independent samples and in the case of more than two groups we used the Kruskal—Wallis test. In Psychology and Education, the traditional ways to analyze relationships between studied variables are correlation and regression analyses Moreno et al.

However, it is sometimes the case that the distribution is not homogeneous and we can find a modal category or even the Simpson paradox Simpson, The Simpson paradox can lead to error in the interpretation of the results, produce a change in the relationship between the variables, and even change the direction of the relationship between the variables when the sample is divided into sub-samples Malinas and Bigelow, Moreover, we can decrease the multiple correlations by increasing the simple correlations.

The size of the sample affects statistical significance through the standard error which becomes smaller as we include more participants in the sampleso any difference between the variables can be statistically significant if we have access to a sample that is sufficiently large. Thus, we can find statistically significant differences and small coefficients, even close to zero, that cannot capture the actual direction of the relationship, which could be reflected aprendizqje dot clouds without any linearity.


In our current work, we present an alternative statistical procedure to the traditional techniques such as regression analysis. In order to explore our results obtained by both procedures, and to analyze whether the dimensions of PEI are related to the LS scales used by the students, we carried out a series of hierarchical regression analyses. We first included gender and age as co-variables. We then introduced the dimensions of the TMMS as the predictive variables.

We began with two matrices of data, one of which contained the information secala LS Cognitive and control learning strategies, Learning Support strategies, and Studying habits for all participants.

CNCA has the advantage that, when we have co-linearity, it allows us to reach the aimed objective and it without affecting the coefficients. Our procedure selects the lineal combination of the dimensions of the PEI that maximizes the dispersion of the values of different LS. Thus, the different LS can be escxla through a model in which the explanatory variable is a lineal combination escaala the dimensions that evaluate the PEI.

The results are presented with an ordering diagram where the LS are represented by dots and the dimensions of the PEI are represented by vectors. The angle that the respective items form between them, which evaluate different aspects of the PEI, allows us to estimate the degree of co-variation between the different aspects.

Estartegias angles indicate a strong relationship; straight angles indicate independence between both aspects; and obtuse angles indicate an inverse relationship. To evaluate the relationship between each one of the dimensions of the PEI for each of the LS, we only have to draw a perpendicular line to the vector that links the dimension-PEI and the coordinates origin.

The points that represent the different LS, and which projections over the vector dimension-PEI are closer to the end of the arrow are taken to indicate that aperndizaje have higher percentages with respect to that dimension. This is the first study to use the CNCA method to obtain a relationship between two instruments within the Psychology and Education contexts.

Emotional repair has a greater average with respect to other dimensions of the PEI, and cognitive and learning control strategies has the ara mean value in LS dimensions. Two subscales of the Abridged ACRA are consistent, but the study habits scale presents weak coefficients that are less than 0. We observed that the aprehdizaje among the dimensions of PEI are greater than those of LS, emphasizing the covariances between emotional clarity and repair and attention. We next present ed differences in the studied variables.

The dimensions of the Ecala were analyzed separately, adjusting a model for each of them.


Thus, these results demonstrate that the students that reported higher levels of use of cognitive and control LS strategies presented a higher perceived ability to understand and regulate their emotions.

For the dimension of LS, study habits and emotional attention were not significant. We clearly observed statistically significant results when the part that explains these variables is small, as a consequence of the sample size. Further, the distribution of the scale used to evaluate LS is not homogeneous given that the students generally use LS and hence report this in the questionnaire, although they do not use all the LSs to the same degree, as this depends on gender and age.

In order to address these problems, we introduced an alternative statistical procedure, CNCA. The first two axes explain the total variability, both for male and female participants. Regarding participants, we can highlight the small angle that is formed between the repair and clarity dimensions of the PEI, which indicates that when the students score high on one dimension, they also score high on the other.

In the case of female participants, we can observe that clarity is not well-represented due to the short longitude of the vector. However, for the attention and emotional repair dimensions, the graph shows an inverse relationship between both aspects. When we analyze the dimensions of LS, we observe that both males and females are perfectly differentiated, sharing no similarities, since there is a substantial difference between their points.