Revista de Psicodidáctica ISSN: Universidad del País Vasco/Euskal Herriko Unibertsitatea.


Save this PDF as:
 WORD  PNG  TXT  JPG

Tamaño: px
Comenzar la demostración a partir de la página:

Download "Revista de Psicodidáctica ISSN: Universidad del País Vasco/Euskal Herriko Unibertsitatea."

Transcripción

1 Revista de Psicodidáctica ISSN: Universidad del País Vasco/Euskal Herriko Unibertsitatea España Villardón-Gallego, Lourdes; Yániz, Concepción; Achurra, Cristina; Iraurgi, Ioseba; Aguilar, M. Carmen Learning Competence in University: Development and Structural Validation of a Scale to Measure Revista de Psicodidáctica, vol. 18, núm. 2, 2013, pp Universidad del País Vasco/Euskal Herriko Unibertsitatea Vitoria-Gazteis, España Available in: How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative

2 ISSN: eissn: UPV/EHU DOI: /RevPsicodidact.6470 Learning Competence in University: Development and Structural Validation of a Scale to Measure Lourdes Villardón-Gallego, Concepción Yániz, Cristina Achurra, Ioseba Iraurgi, and M. Carmen Aguilar University of Deusto (Spain) Abstract This research focused on designing and validating a scale to assess the level of learning competence in university students. Learning competence refers to the acquisition, selection and integrated mobilization of the knowledge, skills and attitudes required for continuous, life-long learning. The development of this competence is a basic training goal, because it constitutes an essential element of life-long learning. Learning competence comprises four dimensions: self-knowledge as apprenticeship, construction of knowledge, self-management of learning, and knowledge transfer. To validate the theoretical model of the construct, were conducted exploratory and confirmatory analyses. The results largely confirmed this structure (.86 reliability of the full scale and between.57 and.83 reliability of the sub-scales as well as the adequacy of the structural model chosen: GFI =.94, RMSEA =.039); thus, it is possible to conclude that the Learning Competence Scale (LCS) is a valid 17-item tool for measuring this competence. Keywords: Learning competence, learning strategies, self-management of learning, life-long learning, structural validity. Resumen Esta investigación se centra en el diseño y validación de una escala para evaluar el nivel de la competencia para aprender de los estudiantes universitarios. La competencia para aprender se refiere a la adquisición, selección y movilización integrada de los conocimientos, habilidades y actitudes necesarios para aprender de manera continuada a lo largo de la vida. El desarrollo de esta competencia es un objetivo formativo fundamental por su influencia en el desarrollo personal y profesional. Se parte de que la competencia para aprender está compuesta de cuatro dimensiones: conocimiento personal como aprendiz, construcción del conocimiento, autogestión del aprendizaje y transferencia del conocimiento. Se han realizado análisis exploratorios y confirmatorios para validar el modelo teórico del constructo. Los resultados confirman en buena medida esta estructura (fiabilidad 0.86 de la escala total y entre.57 y.75 de las sub-escalas, así como la adecuación del modelo estructural elegido: GFI =.94, RMSEA =.039), lo que permite considerar la Escala de Competencia de Aprendizaje (LCS) como un instrumento válido de 17 ítems para medir esta competencia. Palabras clave: Competencia para aprender, estrategias de aprendizaje, autogestión del aprendizaje, aprendizaje a lo largo de la vida, validez estructural. Acknowledgement: This research was supported by a grant from the Spanish Ministry of Science and Innovation, Reference EDU (Spanish Government). Correspondence: Lourdes Villardón Gallego, Department of Teaching and Curriculum Development. University of Deusto, Apdo. 1, Bilbao (Spain), telephone number: (ext. 2360),

3 358 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR Introduction Learning competence is essential for people who must function effectively and manage in the 21st century. A knowledge-oriented society (Caprile & Serrano, 2011; Castells, 1997; Longworth 2003; Sahlberg & Boce 2010; Varela-Petito, 2010; Vázquez, 2009) demands continuous adaptation to different ways of working, communicating, receiving information, relating to one another and managing one s time, including the time devoted to leisure. Permanent learning is necessary to engage in these activities (Fernández-March, 2006; Monereo & Pozo, 2001; Yániz & Villardón, 2006), as implied by the concept of life-long learning. This type of learning is defined as all learning activities undertaken throughout life, with the aim of improving knowledge, skills, and competence within a personal, civic, social, and/or employment-related perspective (European Commission, 2001, p. 9). Such learning is also considered a means of promoting active citizenship, employability and, therefore, the economic and social development of a country (Bolhius, 2003; Carneiro, 2007; Edwards 2010; Lüftenegger, Schober, Schoot, Wagner, & Finsterwald, 2011). The European Commission (2005) defines learning competence as the disposition and ability to initiate and continue one s own learning, to regulate this learning, and to manage one s time and information effectively, both individually and within a group. Numerous studies have investigated constructs that are linked to the learning competence, such as learning strategies (Gargallo, Suárez-Rodríguez, & Pérez-Pérez, 2009; Suárez & Fernández, 2005), the self-management of learning (Solzbacher, 2006; Suárez & Fernández, 2011), learning styles (López-Aguado, 2010; Villar dón, Elexpuru, & Yániz, 2007) and performance (García-Ros & Pérez-González, 2011; Masui & De Corte, 2005). However, these studies reflect a certain degree of confusion regarding the concept. A detailed analysis of the instruments that are used enables us to confirm that multiple items are related to different constructs. Learning strategies are usually defined as the organized, conscious, and intentional tasks that an individual performs to meet a learning goal in a given context (Gargallo, Suárez, & Ferreras, 2007). These strategies involve designing, assessing and adjusting plans to complete tasks under certain conditions (Gargallo, 2000; Monereo & Castelló, 1997). According to López-Aguado (2010), learning strategies are linked to meta-cognition; learning strategies require decisions to be made on the course of action that will lead to the completion of a task, and they are therefore geared towards achieving a goal, following procedures conditioned by the learning situation. An in-depth analysis of the content of the tools

4 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL used in Spain for measuring learning strategies indicates that these tools do not merely collect data pertaining to a greater number of strategies than have been conceptualized thus far. Rather, these tools also gather information on emotional states that are linked to learning situations such as anxiety, and information on learning achievements such as the capacity to select information or to transfer learning to other situations. These characteristics apply to the Escala de Estrategias de Aprendizaje (ACRA) by Román and Gallego (1994), which is a learning-strategy scale that includes a dimension of intrinsic motivation. The Cuestionario de Evaluación de Estrategias de Aprendizaje de los Estudiantes Universitarios (CEVEAPEU), by Gargallo et al. (2009), has items that pertain to outcomes and emotional states concerning learning. The Escala de Evaluación de las Estrategias Motivacionales de los Estudiantes (EMMA), by Suárez and Fernández (2005), a scale that measures motivational strategies, includes items pertaining to emotional states. The Cuestionario de Estrategias de Aprendizaje y Motivación (CEAM), by Ayala, Martínez and Yuste (2004), considers the building relationships dimension of building relationship as strategy and collects data related to the construction of knowledge a learning outcome. The Cuestionario de Evaluación del Procesamiento Estratégico de la Información para Universitarios (CPEI-U), by Castellanos, Palacio, Cuesta and García (2011) is intended to measure the strategic processing of information and includes a dimension related to attitudes toward studying and several items pertaining to self-esteem and motivation. López-Aguado (2010) creates the Cuestionario de Estrategias de Trabajo Autónomo (CETA), a questionnaire that aims to gather information on individual learning strategies, based on three theoretical dimensions: the competences for learning, the competences for the adequate use of the new information and communication technologies, and the competences for collaborative work. With the exception of the aforementioned CPEI-U and the Escala de Evaluación de las Estrategias Motivacionales de los Estudiantes- Versión Secundaria (EMMA-VS) in a study subsequent to the one published in 2005 (Suárez & Fernández, 2011), these tools lack a confirmed structure based on a theoretical model of the construct resulting from confirmatory factor analysis. With regard to measuring the self-management of learning, Muis, Winne and Jamieson-Noel (2007) assessed the conceptual similarities between three questionnaires: the Learning and Study Strategies Inventory (LASSI), by Weinstein (1987), the Motivated Strategies for Learning Questionnaire (MSLQ;), by Pintrich, Smith, García, & McKeachie (1993) and the Meta-cognitive Awareness Inventory (MAI), by Schraw &

5 360 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR Dennison (1994). The authors concluded that each of these tools assigned greater importance to a different dimension of self-management: LASSI focused primarily on encoding processes, MAI focused on metacognitive processes and MSLQ focused on motivational processes. Therefore, the compatibility of the design and validation of data collection tools with the learning-related constructs appears to be important. Hence, this research sought to design a scale to measure learning competence by providing a conceptual clarification of the construct and differentiating it from other related constructs, such as learning strategies or emotional states concerning learning. It is accept the definition of competence that was provided by Yániz and Villardón (2006, p. 23) as complex know-how resulting from comprehensive identification and mobilization of knowledge, skills and attitudes that generate an efficient outcome when performing a task, solving a problem or meeting a goal. The learning competence refers to the acquisition, selection and integrated mobilization of the knowledge, skills and attitudes required for continuous, life-long learning. The learning competence integrates the concept of self-regulated learning, which has been used (García- Ros & Pérez-Gonzalez, 2011; Zimmerman, 2000; Zimmerman & Kitsantas, 2007) to define learning whose main features are selfmotivation and the use of goal-oriented strategies, both cognitive and meta-cognitive, focusing in the integration of the diverse elements, in effective implementation and in transference. The acquisition process for the learning competence consists of learning to learn, controlling one s learning and self-managing this learning (Solzbacher, 2006); it means that learners commit to building their own knowledge on the basis of past experiences, so that they are able to apply their knowledge and skills in a variety of contexts (European Commission, 2005). Learning competence encompasses processes that include assessing specific learning needs, establishing goals, choosing specific strategies and follow-up learning that is focused on goal acquisition (Schulz & Stamov, 2010). The theoretical model that is proposed in this research includes the following dimensions of learning competence: the self-management of learning, the construction of knowledge, self-knowledge as apprenticeship and knowledge transfer. The dimension of Self-management of learning refers to an individual s capacity to establish learning goals, create plans to achieve them, regulate the development of the processes and evaluate those (Wirth & Leutner, 2008). Self-management is based on an open attitude toward knowledge (inquisitiveness) and a sustained effort that provides and/or draws from personal interest. This dimension is supported by metacognition (Pozo & Mateos, 2010).

6 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL Therefore, it requires deliberation and flexibility in choosing one s resources as well as the ability to plan and evaluate actions and procedures (López-Aguado, 2010). Construction of knowledge. Cognitive approaches to the study of human learning have placed greater emphasis on the constructive nature of the knowledge acquisition process. All new knowledge is generated on the basis of previous knowledge and thus extends it. This perspective has contributed to the highlighting of self-structuring and self-directed activity, which is necessary for true learning (Gómez & Coll, 1994). A sound background of knowledge and skills supported by personalized procedures or strategies is essential for building knowledge in a permanent manner. Particularly important are the procedures or strategies concerned with the selection and organization of information that is relevant to the knowledge to be acquired. Self-knowledge as apprenticeship. Having a well-adjusted selfconcept provides a sound basis for establishing learning goals, generating realistic expectations, selecting effective strategies, maintaining a satisfactory level of motivation and ensuring continuous improvement through life-long learning (Bornholt, 2000). Self-knowledge is closely linked to the capacity to evaluate one s actions when completing a task and to compare these actions with the intended outcomes and to the ability to assess those outcomes on the basis of external criteria (Kostons, van Gog, & Paas, 2012). Knowledge transfer. The concept of competence entails the effective use of knowledge in different situations and contexts. The transfer dimension is defined as the capacity to learn in new situations in which knowledge and skills are both the gateways to new tasks and the methods for adapting to the demands of new tasks. To achieve this transference, students must perceive the similarities and differences between tasks and build mental patterns of the relationships that exist between them (Singley & Anderson, 1989; Tuomi-Gröhn & Engeström, 2003). The aim of this research was to create a valid tool for measuring learning competence based on a theoretical model that proposes that this competence is formed by four dimensions. Participants Method Our sample group consisted of 487 undergraduate students (144 males and 343 females) from 5 faculties at the University of Deusto (Bilbao, Spain). The average age of the participants was (SD = 2.09), ranging from 18 to 43. First-year undergraduate students accounted for 35.1% of the sample, and second-year students

7 362 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR for 64.9%. With respect to distribution by school, 31% of the participants belonged to the Faculty of Economics and Business Administration, 24.4% to the Faculty of Social and Human Sciences, 16.6% to the Law Faculty, 6.8% to the Engineering Faculty and 21% to the Faculty of Psychology and Education. Survey instrument To gather data on learning competence, we designed a scale by following the theoretical model that is proposed for competence. The survey instrument was based on the CEVEAPEU, by Gargallo et al. (2009), and the EEMA, by Suárez and Fernández (2005). Both instruments have shown high internal consistency. CEVEAPEU has shown a minimum reliability of.82 in each of its sub-scales, and EMMA sub-scales have shown a reliability ranging from.74 to.81. In order to design the tool, 20 items were first chosen. These items were judged by four experts, who classified each item in one of the four theoretical dimensions of the proposed model. The experts also appraised the transparency, consistency and accuracy of the items. The items accepted were only those located in the same dimension by at least three of the four experts. Subsequently an 18-item scale was designed, categorized by the following dimensions: self-management of learning, construction of knowledge, self-knowledge as apprenticeship, and knowledge transfer. The statements required that each participant express his degree of agreement using a Likert rating scale with five response options, from 1 (strongly disagree) to 5 (strongly agree). Procedure Lecturers were contacted and informed about the research and asked to cooperate by arranging for the survey to be administered to groups during class time. The students were informed about the nature of the research and told that their participation was voluntary. They responded to the scale items using a computer-based application. The survey was administered by purpose-trained professionals between April and May Statistical analysis To describe the level of learning competence in the sample, frequencies (n), proportions, central tendencies (means-m) and deviations (standard deviation-sd) were measured. For the analysis of the items in the LCS, the M, SD, asymmetry (As), Kurtosis (K) and the correlation coefficient between the item and the rest of the scale (r) were measured, as well as the value of Cronbach s alpha coefficient if the item was removed. The mean differences among Faculties (F test with p value) were also calculated.

8 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL Analyses of the reliability and standard validity of the instrument were conducted. Reliability was tested using Cronbach s alpha. To analyze the standard validity, the LCS scores were analyzed with the CEVEAPEU questionnaire by Gargallo et al. (2009). In order to avoid overlapping, the items that were included in the LCS were eliminated. The suitability of the correlation matrix was verified to ensure that it is factorized on the basis of the Kaiser-Meyer-Olkin test and the Bartlett sphericity test. parallel analysis PA (Timmerman & Lorenzo-Seva, 2011) and minimum average partial method MAP (Velicer, 1976) tests were carried out as extraction criteria for the advisable number of factors according to the configuration of the correlation matrix. Also, the multivariate normality was analyzed with the Mardia test (Mardia, 1970) To validate the instrument based on the theoretical model underlying learning competence, a variety of analytical strategies were used. Several confirmatory factor analyses (CFAs) with covariance structural techniques using EQS (Bentler, 1995; Bentler & Wu, 1995) were conducted. Maximum likelihood estimation was used to estimate the parameters. In all cases, the chi-squared test (χ 2 ) was used to evaluate the goodness of fit of the corresponding model and indicated that the probability that the variation between the sampling variance and covariance matrix and the matrix resulting from the hypothesized model was random; in the event of non-compliance with the multivariate normality, estimations would be carried out by application of robust methods (Satorra & Bentler, 2001; Satorra, 2003). Because χ 2 is sensitive to variations in sample size (Schermelleh- Engel, Moosbrugger, & Müller, 2003), additional measurements of the goodness of fit of the model were used (Hu & Bentler, 1999) such as the Standardized Root Mean Square Residual (SRMR), the Root Mean Square Error of Approximation (RMSEA) and 90% Confidence Interval of RMSEA, which considers values <.05 to be adequate and those <.08 to be acceptable; the Goodness-of-Fit (GFI) and Comparative Fit (CFI) Indexes, with values >.90; and the Akaike information criterion (AIC) to compare the models with different estimated parameters for which lower values would indicate higher parsimony and would be eligible. This final model will be graphically presented and will indicate the parameters of structural relationships using standardized factor coefficients and estimation errors. A significance level of p <.05 was chosen for a 95% reliability interval to interpret the results. Results Table 1 shows the descriptive analysis of the set of items in the LCS. The full average scale value

9 364 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR Table 1 Descriptive Statistics and Internal Consistency Analysis of the Learning Competence Scale (n = 487) M SD As K r α F p 1 I know my strengths and weaknesses in a subject area I know where to obtain material for my courses subjects I am capable of selecting information to revise for my subjects I use what I learned at the university in my everyday life When I complete academic tasks well, I am aware of it I can easily manage in the library and find the material that I need I use what I learn in one subject for other subjects When I take an exam, I know whether I did well or not I can separate relevant from irrelevant information I can recognize basic material on the Internet I persevere until I achieve the goals that I set for myself I use different strategies to direct my learning I am interested in learning from different situations I keep trying when performing difficult or uninteresting tasks I achieve a deep understanding of the topics that I learn about I manage my own learning I modify my learning strategies if they do not meet my expectations I show competence for learning by myself Full scale

10 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL is 3.75 on a possible range of 1 to 5, with an average of a minimum of 3.27 and a maximum of 4.05 for the items in this scale. The asymmetry in the score distribution is never greater than 1, although the asymmetry in all cases is either negative or to the left, which indicates a tendency toward higher values of the scale. Via tests of analysis of variance (Values F and p, Table 1), we analyzed the mean, in each of the items, of the participants in the study in relation to the university degree they were pursuing. In two of the 18 items (IT06 and IT14) statistically significant differences were found. Post-hoc tests established that students in Economic Sciences had a lower average than the rest of participants. With regard to the internal consistency of the scale, the correlation coefficients between the items and the full scale are between.31 and.60, and the average value is.47. Likewise, removing any of the items would not improve the alpha coefficient of the full scale, which is.86. For the standard validity, we analyzed the LCS scores with the CEVEAPEU questionnaire by Gargallo et al. (2009) that measures learning strategies. The correlation is significant, positive and high (.72, p <.001). Both the KMO test (=.885) and Bartlett s test of sphericity (χ 2 = ; p <.001) that were conducted on the correlation matrix indicated that the item factorization for the LCS was adequate. Parallel Analysis (PA) and Minimum Average Partial Method (MAP) tests conducted on the correlation matrix showed that a single factor should be retained. In order to validate the dimensional construct of the LCS, four structure models were tested: 1) the existence of a single factor, supported by the results of both PA and MAP; 2) a model with four independent dimensions: 3) a model with four interrelated dimensions; and 4) a model of four dimensions subsumed under a second order general factor. A series of Confirmatory factor analysis, (CFA) was performed on the theoretically defined structure models, whose goodness-offit indexes are shown in Table 2. Because no multivariate normality exists in the data (the Mardia standardized estimator of the multivariate Kurtosis equals > 1.96), maximum-likelihood robust estimators were used in order to adjust the measurement models. In all cases, the Satorra- Bentler Chi-Square (χ 2, as a measurement of overall fit) has proven to be statistically significant and thus indicates that the empirical model is not a good fit with the theoretical model. Nevertheless, in large samples (n > 100), the χ 2 value tends to increase because of the error in model specification (Jöreskog & Sörbom, 1989).

11 366 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR Table 2 Structural Models of the LCS on the Basis of a Confirmatory Factor Analysis (n=487). Maximum-Likelihood Robust Estimatorf Model (M) of LCS Goodness-of-Fit Indexes χ 2 χ 2 /gl AIC GFI CFI SRMR RMSEA 90% CI M1 Single factor [ ] M2 Four independent factors [ ] M3 Four correlated factors [ ] M4 Four factors subsumed by a second-order factor [ ] χ 2 - Chi-squared χ 2 /gl - Normed chi-square: chi for degrees of freedom AIC - Akaike information criterion GFI - Goodness-of-fit index CFI - Comparative fit index SRMR - Standardized Root Mean Square Residual RMSEA - Root mean squared error of approximation 90% CI - 90% Confidence Interval Accordingly, additional indexes are often needed to determine the necessary adjustments to the model. Looking at the normed χ 2 value (χ 2 /gl) in Table 2, it can be observed that none of the models is below 1 (which would indicate over-adjustment), and models 1 (single factor) and 2 (four independent factors) show a value over 4 (which would indicate that the model needs further adjustment). The values for the GFI index do not conform to the fit criterion in model 1 (M1) and in model 2 (M2). Nevertheless, models M3 (four related factors) and M4 (four factors subsumed by a second-order factor) show a GFI value of.91 and.92, which is over the minimum decision value for a good fit (.90). For the SMRM and RM- SEA, the value is not acceptable (>.08) in M2 (.21 and.09, respectively). The value is acceptable both in M3 and M4. Therefore, M3 and M4 are considered better fit. However, both the ratio χ/gl (3.35 vs. 3.38) and the Akaike information criterion (AIC = vs ), as well as the scaled difference chi-square tests between both models are statistically significant [χ 2 (2) = 10.16, p =.006], showing that the third model is superior to the fourth model. There-

12 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL IT04 IT Knowledge Transfer.88 IT IT05 IT Self-knowledge as apprenticeship IT IT03 IT09 IT Construction of Knowledge IT11 IT12 IT IT16 IT Self-management of learning.75 IT IT IT18 Goodness of Fit Indexes 2 = ; p <.001 (112) 2 /gl = 2.29 GFI =.94 CFI =.93 RMSEA =.039 (.030 a.048) Standardized =.041 Figure 1. Confirmatory factor analysis of the Learning Competence scale (n = 487).

13 368 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR fore, among the four models initially proposed, the model of four interrelated factors is retained. The results of the multipliers of Lagrange (LM Test) and the Wald Test (TW) on the model of four interrelated factors show that the model can be improved by the relocation of certain items in dimensions other than the ones to which they had been initially assigned. The indications of both TW and LMT were followed until we reached a final model (M5-Figure 1) that shows better fit (Normed Chi-Square = 2.29, GFI =.94, CFI =.93; RMSEA =.039). The scaled difference chisquare test between both models (M5 vs. M3) is statistically significant [χ 2 (17) = , p =.001]. In the final model, items 13, 15 and 18 are relocated to the dimension of self-management of learning, item 6 disappears from the scale and item 1 weighs in two different dimensions, construction of knowledge and self-knowledge as apprenticeship. Although item 1 weighs more in the dimension of construction of knowledge, it was kept in both dimensions because of the theoretical content of the item. Furthermore, the factor loadings (lambda values) are always greater than.45, except for item 1, which is lower in both dimensions (λ 12 =.17; λ 13 =.34). The construct reliability (CR), the percentage of variance extracted (VE) and Cronbach s alpha coefficient of each dimension were as follows: knowledge transfer (CR =.51; VE = 23.07%; α =.57), self-knowledge as apprenticeship (CR =.49; VE = 27.6%; α =.59), self-management of learning (CR =.79; VE = 26.3%; α =.83), and construction of knowledge (CR =.63; VE = 22.7%; α =.69). Discussion The results of this research partly agree with those proposed by Jornet, García-Bellido and González-Such (2012), who developed a procedure to design tools for the evaluation of the learning competence, identifying three dimensions (attitudes towards selfimprovement, understanding of scientific language and knowledge and resources for improvement) among whose components we find those identified in this study. Thus, the components that these authors named attitudes towards learning, self-regulation, commitment and meta-cognition can be theoretically identified with the self-management of learning dimension in this study. Identification, reasoning, conceptualization and critical thinking correspond to the dimension of construction of knowledge, whereas the self-awareness described by Jornet-Meliá et al. (2012) is equivalent to self-knowledge as apprenticeship. Finally, problem-solving and extension of interests correspond to knowledge transfer.

14 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL In recent years, many studies have focused in aspects or dimensions of the learning competence, such as perceived self-efficacy, self-regulation of learning, cognitive and affective learning strategies, and emotional states as related to learning (Cabanach et al., 2006; Sáiz, Montero, Bol, & Carbonero, 2012; Suárez & Fernández, 2011). These studies have helped progress in the conceptualization of the learning competence as an integration of diverse elements that facilitates an efficient life-long performance in the diverse learning experiences (Salmerón & Gutiérrez, 2012). This research sought to define this construct along with its dimensions and produce a tool able to measure them. The findings, based on the factorial weight of all items and in the proposal of the analysis of paralell and map, allow the following proposal to be made: that the combination of items explains a general dimension that, because of its content, would refer to the construct named learning competence. However, this study sought to assess a construct composed of four dimensions. In order to do that, four conceptually acceptable structural models were tested. The confirmatory tests that were carried out indicated that the model of four interrelated factors should be retained, because it shows a better fit and is also more parsimonious. On this model initially retained, the improvement analysis drove a re-specification that achieves a better fit. The resulting final model involved the transposition of three items (number 13, 15 and 18) from the dimension of construction of knowledge to the dimension of self-management of learning, and the weighing of item 1 in two different dimensions, construction of knowledge and self-knowledge as apprenticeship, as well as the deletion of item 6, which had little weight in any of the dimensions. Although the initial proposal included items 13, 15 and 18 in construction of knowledge, their inclusion in self-management of learning is understandable, and it also adds to it the aspect of motivation for learning and that of learning as an accomplishment, making this dimension closer to that which other authors have named self-regulated learning (Salmerón & Gutiérrez, 2012). Item 1, referring to the awareness of one s own strengths and weaknesses when learning a subject, shares weight in two dimensions, although it weighs less in the dimension in which it had been theoretically included (self-knowledge as apprenticeship) than in the dimension of construction of knowledge. It was kept in both dimensions because of its content. However, it would be advisable to review the formulation of the item in case it influenced the result, given that it links self-awareness to performance in academic subjects.

15 370 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR The dimension construction of knowledge has been included in the final proposal constituted by items concerning the selection and organization of information, which makes sense given that construction of knowledge requires the ability to transform information in knowledge by a process involving its selection and organization. However, this dimension should be reviewed in order to add items regarding the transformation of information into knowledge. The dimensions of self-knowledge as apprenticeship and construction of knowledge should also be reviewed in order to add items so that they are composed of at least four items. Ultimately, despite the re-specification carried out to account for the weight of the items in the diverse dimensions, the final model fits properly with the theoretical construct that was the goal of our study. As mentioned, it would be advisable to review the content of the questionnaire in light of the results obtained, as well as replicate this instrument with a larger sample reflecting a greater diversity of Faculties. References Ayala, C. L., Martínez, R., & Yuste, C. (2004). CEAM: Cuestionario de Estrategias de Aprendizaje y Motivación. Madrid: EOS. Bentler, P. M. (1995). EQS. Structural Equations Program Manual. Encino, CA: Multivariate Software. Bentler, P. M., & Wu, E. J. (1995). EQS for Windows User s Guide. Encino, CA: Multivariate Software. Bolhuis, S. (2003). Towards processoriented teaching for self-directed lifelong learning: a multidimensional perspective. Learning and Instruction, 13(3), doi: /S (02) Bornholt, L. J. (2000). Social and personal aspects of self-knowledge: a balance of individuality and belonging. Learning and Instruction, 10(5), doi: / S (00) Cabanach, R., Valle, A., Gerpe, M., Rodríguez, S., Piñeiro, I., & Rosario, P. (2006). Diseño y validación de un cuestionario de gestión motivacional. Revista de Psicodidáctica, 14(1), Caprile, M., & Serrano, A. (2011). The move towards the knowledge-based society: a gender approach. Gender, Work and Organization, 18(1), Carneiro, R. (2007). The big picture: Understanding learning and metalearning challenges. European Journal of Education, 42(2), Castells, M. (1997). The rise of the network society. Cambridge, Mass: Blackwell.

16 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL Castellanos, S., Palacio, M. E., Cuesta, I., & García, E. (2011). Cuestionario de Evaluación del Procesamiento de la Información para Universitarios (CPEI-U). Revista Electrónica de Metodología Aplicada, 16(2), Comisión de las Comunidades Europeas (2005). Recomendación del Parlamento y del Consejo sobre las competencias clave del aprendizaje permanente. Brussels. Retrieved from Crue/procbolonia/documentos/antecedentes/9. Competencias_clave_ para_aprendizaje_permanente. pdf Edwards, R. (2010). Reflexivity: towards a theory of lifelong learning. International Journal of Lifelong Education, 21(6), doi: / European Commission (2001). Making a European area of lifelong learning a reality. Brussels. Retrieved from pdf/ MitteilungEng.pdf Fernández-March, A. (2006). Metodologías activas para la formación de competencias. Educatio Siglo XXI, 24, García-Ros, R., & Pérez-González, F. (2011). Validez predictiva e incremental de las habilidades de autorregulación sobre el éxito académico en la universidad. Revista de Psicodidáctica, 16(2), Gargallo, B. (2000). Procedimientos. Estrategias de evaluación. Su naturaleza, enseñanza y evaluación. Valencia: Tirant lo Blanch. Gargallo, B., Suárez, J., & Ferreras, A. (2007). Estrategias de aprendizaje y rendimiento académico en estudiantes universitarios. Revista de Investigación Educativa, 25(2), Gargallo, B., Suárez-Rodríguez, J. M., & Pérez-Pérez, C. (2009). El cuestionario CEVEAPEU. Un instrumento para la evaluación de las estrategias de aprendizaje de los estudiantes universitarios. RELIEVE, 15, 2. Retrieved from uv.es/relieve/v15n2/relieve v15n2_5.htm Gómez, C., & Coll, C. (1994). De qué hablamos cuando hablamos de constructivismo?. Cuadernos de Pedagogía, 221, Hu, L., & Bentler, P. M. (1999). Cutoff criterion for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), Jornet, J. M, García-Bellido, R., & González-Such, J. (2012). Evaluar la competencia para aprender a aprender: una propuesta metodológica. Profesorado. Revista de Currículum y Formación de Profesorado, 16, Retrieved from rev161art7.pdf Jöreskog, K. G., & Sörbom, D. (1989). Lisrel 7: A guide to the Program and Applications (2nd Ed). Chicago, Ill.: SPSS. Kostons, D., van Gog, T., & Paas, F. (2012). Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning. Learning and Instruction, 22, doi: / j.learninstruc Longworth, N. (2003). El aprendizaje a lo largo de la vida. Barcelona: Paidós. López-Aguado, M. (2010). Diseño y análisis del Cuestionario de Estrategias de Trabajo Autónomo (CETA) para estudiantes universitarios. Revista de Psicodidáctica, 15(1),

17 372 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR Lüftenegger, M., Schober, B., Schoot, R., Wagner, P., & Finsterwald, M. (2011). Lifelong learning as a goal. Do autonomy and self-regulation in school result in well prepared pupils?. Learning and Instruction, 22(1), doi: / j.learninstruc Mardia, K., V. (1970). Measures of multivariate skewnees and kurtosis with applications. Biometrika, 57(3), Masui, C., & De Corte, E. (2005). Learning to reflect and to attribute constructively as basis components of self-regulated learning. British Journal of Educational Psychology, 75(3), doi: / x Monereo, C., & Castelló, M. (1997). Las estrategias de aprendizaje. Cómo incorporarlas a la práctica educativa. Barcelona: Edebé. Monereo, C., & Pozo, J. I. (2001). En qué siglo vive la escuela?, el reto de la nueva cultura educativa. Cuadernos de Pedagogía, 298, Muis, K. R., Winne, P. H., & Jamieson- Noel, D. (2007). Using a multitraitmultimethod analysis to examine conceptual similarities of three selfregulated learning inventories. British Journal of Educational Psychology, 77(1), Pozo, I., & Mateos, M. (2010). Aprender a aprender; Hacia una gestión autónoma y metacognitiva del aprendizaje. In J. I. Pozo, & M. P. Pérez Echeverría (Eds.), Psicología del aprendizaje universitario: La formación en competencias (pp ). Madrid: Morata. Pintrich, P. R., Smith, D. A., García, T., & Mckeachie, W. J. (1993). Reliability and predictive validity of the Motivational Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), Román, J. M., & Gallego, S. (1994). ACRA. Escalas de estrategias de aprendizaje. Madrid: TEA. Salmerón, H., & Gutiérrez, C. (2012). La competencia para aprender a aprender y el aprendizaje autorregulado. Posicionamientos Teóricos. Revista de Currículum y Formación del Profesorado, 16(1), 5-13 (enero-abril 2012). Retrieved from ugr.es/~recfpro/rev161art1.pdf Sahlberg, P., & Boce, E. (2010). Are teachers teaching for a knowledge society?. Teachers and Teaching: Theory and Practice. 16(1), Sáiz, M. C., Montero, E. Bol, A., & Carbonero, M. A. (2012). Un análisis de competencias para aprender a aprender en la universidad. Electronic Journal of Research in Educational Psychology, 10(1), Retrieves from investigacionpsicopedagogica. org/revista/new/contadorarticulo. php?629. Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), Satorra, A. (2003). Power of chisquare Goodness-of-fit test in structural equation models: the case of non-normal data. In H. Yanai, A. Okada, K. Shigemasu, Y. Kano, & J. J. Meulman (Eds.), New Developments of Psychometrics (pp ). Tokio: Springer Verlag. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating

18 LEARNING COMPETENCE IN UNIVERSITY: DEVELOPMENT AND STRUCTURAL the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), Schulz, M., & Stamov, C. (2010). Informal workplace learning: An exploration of age differences in learning competence. Learning and Instruction, 20(5), doi: / j.learninstruc Singley, M. K., & Anderson, J. R. (1989). Transfer of cognitive skill. Cambridge, MA: Harvard University Press. Solbazcher, C. (2006). Improving learning competence in schools: what relevance does empirical research in this area have for teacher training?. European Journal of Teaching Education, 29(4), Suárez, J. M., & Fernández, A. P. (2005). Escalas de evaluación de las estrategias motivacionales de los estudiantes. Anales de Psicología, 21(1), Suárez, J. M., & Fernández, A. P. (2011). Evaluación de las estrategias de autorregulación afectivomotivacional de los estudiantes: Las EEMA-VS. Anales de Psicología, 27(2), Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality Assessment of Ordered Polytomous Items with Parallel Analysis. Psychological Methods, 16(2), Tuomi-Gröhn, T., & Engeström, Y. (2003). Conceptualizing transfer: From standard notions to developmental perspectives. In T. Tuomi- Gröhn, & Y. Engeström (Eds.), Between school and work: New perspectives on transfer and boundarycrossing (pp ). New York: Pergamon. Varela-Petito, G. (2010). Facing the Knowledge Society: Mexico s Public Universities. Higher Education Policy, 23(3), Vázquez, M. A. (2009). La universidad del siglo XXI en la sociedad de la comunicación y del conocimiento. Sevilla: Universidad de Sevilla. Secretariado de Publicaciones. Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), Villardón, L., Elexpuru, I., & Yániz, C. (2007, July). Autonomía, condición indispensable de la competencia para aprender. Datos preliminares de un estudio. Paper presented at the Seminario Internacional El desarrollo de la autonomía en el aprendizaje. Red Estatal de Docencia Universitaria (REDU), Barcelona, Spain. Retrieved from Weinstein, C. E. (1987). LASSI (Learning and Study Strategies Inventory). Clearwater, FL: H&H Publishing Company. Wirth, J., & Leutner, D. (2008). Selfregulated learning as a competence: Implications of theoretical models for assessment methods. Journal of Psychology, 216(2), Yániz, C., & Villardón, L. (2006). Planificar desde competencias para promover el aprendizaje. Bilbao: University of Deusto. Zimmerman, B. J. (2000). Attaining self-regulation: a social cognitive perspective. In M. Boekaerts, P. Pintrich, & M. Zeodmer (Eds.), Handbook of Self-Regulation, (pp ). San Diego: Academic Press. Zimmerman, B. J., & Kitsantas, A. (2007). Reliability and validity of Self-efficacy for Learning Form (SELF) scores of college students. Journal of Psychology, 215(3),

19 374 LOURDES VILLARDÓN-GALLEGO, CONCEPCIÓN YÁNIZ, CRISTINA ACHURRA, IOSEBA IRAURGI, AND M. CARMEN AGUILAR Lourdes Villardón-Gallego is Professor of Didactics and Curricular Development in the Faculty of Psychology and Education in the University of Deusto. She is main researcher in the research team on Competences and Values Development, which has been recognized by the Basque University System (2007 and 2009). She has published extensively on development and the assessment of competences and values, with a special focus in the Higher Education level. Her main areas of interest are development and life-long learning. Concepción Yániz is a lecturer of the Department of Didactics and Curriculum Development at the Faculty of Psychology and Education in the University of Deusto and Director of the Doctoral Program in Innovation and Life-Long Learning. She is a researcher in the research team on Competences and Values Development. Her main areas of interest are development and life-long learning. Cristina Achurra is a lecturer in the Master s Degree in Secondary Education at the Faculty of Psychology and Education in the University of Deusto. She is a researcher in the research team on Competences and Values Development. Her main areas of interest are the active methodologies for competence development. Ioseba Irargui is a lecturer of the Department of Personality, Psychological Assessment and Treatment at the Faculty of Psychology and Education in the University of Deusto. He is main researcher in the research team on Clinical and Health Evaluation, and he manages the clinical area in Deusto Psych i+d. His main areas of interest are the assessment of health outcomes and the development of assessment tools. María del Carmen Aguilar-Rivera is a lecturer and Doctor in Psychopedagogy by the Pontifical Catholic University of Argentina. She is currently a part of the research team on Competences and Values Development in the University of Deusto. Her main areas of interest and research are learning processes and projects of life. Received date: Review date: Accepted date:

20 ISSN: eissn: UPV/EHU DOI: /RevPsicodidact.6470 La competencia para aprender en la universidad: desarrollo y validación de un instrumento de medida Lourdes Villardón-Gallego, Concepción Yániz, Cristina Achurra, Ioseba Iraurgi, y M. Carmen Aguilar Universidad de Deusto (España) Resumen Esta investigación se centra en el diseño y validación de una escala para evaluar el nivel de la competencia para aprender de los estudiantes universitarios. La competencia para aprender se refiere a la adquisición, selección y movilización integrada de los conocimientos, habilidades y actitudes necesarios para aprender de manera continuada a lo largo de la vida. El desarrollo de esta competencia es un objetivo formativo fundamental por su influencia en el desarrollo personal y profesional. Se parte de que la competencia para aprender está compuesta de cuatro dimensiones: conocimiento personal como aprendiz, construcción del conocimiento, autogestión del aprendizaje y transferencia del conocimiento. Se han realizado análisis exploratorios y confirmatorios para validar el modelo teórico del constructo. Los resultados confirman en buena medida esta estructura (fiabilidad.86 de la escala total y entre.57 y.75 de las sub-escalas, así como la adecuación del modelo estructural elegido: GFI =.94, RMSEA =.039), lo que permite considerar la Escala de Competencia de Aprendizaje (LCS) como un instrumento válido de 17 ítems para medir esta competencia. Palabras clave: Competencia para aprender, estrategias de aprendizaje, autogestión del aprendizaje, aprendizaje a lo largo de la vida, validez estructural. Abstract This research focused on designing and validating a scale to assess the level of learning competence in university students. Learning competence refers to the acquisition, selection and integrated mobilization of the knowledge, skills and attitudes required for continuous, life-long learning. The development of this competence is a basic training goal, because it constitutes an essential element of life-long learning. Learning competence comprises four dimensions: self-knowledge as apprenticeship, construction of knowledge, self-management of learning, and knowledge transfer. To validate the theoretical model of the construct, were conducted exploratory and confirmatory analyses. The results largely confirmed this structure (.86 reliability of the full scale and between.57 and.83 reliability of the sub-scales as well as the adequacy of the structural model chosen: GFI =.94, RMSEA =.039); thus, it is to conclude that the Learning Competence Scale (LCS) is a valid 17-item tool for measuring this competence. Keywords: Learning competence, learning strategies, self-management of learning, life-long learning, structural validity. Agradecimientos: Esta investigación forma parte del proyecto financiado por el Ministerio de Ciencia e Innovación del Gobierno de España en convocatoria competitiva Referencia EDU Correspondencia: Lourdes Villardón Gallego, Departamento de Didáctica y Desarrollo del Curricular. Universidad de Deusto, Apdo. 1, Bilbao (España), teléfono: (ext. 2360),

Sitemap11.22.63 | ACDC - No Bull. | Alfie Allen