Metacognitive Strategies, Learning Strategies and Learning Approaches in University Students

Session Information

27 SES 05.5 PS, General Poster Session

General Poster Session, Chaired by Convenors of NW 27

Time:
2014-09-03
12:30-14:00
Room:
Poster Area D (between B014 - B018)
Chair:

Contribution

The results that we enclose are based on a three years research[1]. The research question that guides the work we present here is this: Do students with better metacognitive strategies have better learning strategies than their peers and do they also prefer deep learning approaches?

The Bologna process of convergence, developed by EU countries, involves, among other things, reconfiguring the roles of teachers and students. Underlying learning theories defend a university pedagogy focused on learning/learner-centered (student-centered learning, learning paradigm) (Attard, Di Ioio, Geven & Santa, 2010, Biggs, 2005; Kember, 2009; Monereo and Pozo, 2003; Samuelowicz & Bain, 2001).

In this model student learning is the key element of the process, but there is also recognition of the changed role of the teacher (Attard et al., 2010). In this model the teacher must act as a mediator, as a designer of learning environments that promote the independent learning of students -which requires teaching skills-, compared to traditional models where the teacher is focused on the knowledge of content and conveying that knowledge to students.

The development of this model also requires a change in the role of the student, from being a “receiver” and” repeater” of the knowledge transmitted by the teacher, to being a subject actively involved in the learning process. In this model students must inquire, question, develop, investigate, make personal contributions, they must be actively involved making the learning process significant (Machemer and Crawford, 2007). They must able to lead the process, establish their own learning paths, self-regulate and self-evaluate (Hannafin, 2012).

This is a model that demands self-regulated learners (Attard et al., 2010).

 A self-regulated learner (Pintrich, 2000, Zimmerman, 2002) effectively manages the learning strategies, including cognitive and affective-motivational components of support ("to want"), metacognitive components ("to make decisions and to evaluate") and cognitive components ("to be able to”). These are the three components of the model of Weinstein, Husman and Dierking (2000) -"will", "self-regulation" and "skill"- on which researchers basically agree (Yip, 2012).  It is essential that students develop metacognitive strategies so that they can act as self-regulated learners.

In this context, it is relevant to verify whether the domain of metacognitive strategies also involves better management of other learning strategies (motivational, affective, contextual control strategies, information search strategies and information processing strategies) as well as a deep learning approach. This is the objective that we address in this work.

To pursue this objective we are collecting data from students of three universities in the city of Valencia (Spain). If the results confirm our assumptions, we can offer to other Spanish and European universities relevant data and training proposals of interest. We know that the learning-centered model helps the student to improve their learning strategies, to increase the deep approach to learning and to improve their performance (Gargallo, Garfella Perez & Fernandez, 2010). If, as we think, metacognitive strategies are critical in the process, the emphasis should be on developing metacognitive strategies in order to promote the strategic learning and the deep approach to learning.

In addition, there is convincing evidence that learning strategies influence student achievement (Pintrich, 1995; Gargallo, Suárez-Rodríguez & Pérez-Pérez, 2009), and also learning approaches: (Gargallo, 2008; Valle, Gonzalez Cabanach Núñez, Suárez Piñeiro & Rodríguez, 2000), so the interest of this work is clear.

[1] It is the "Learning-centered methodologies at the university. Design, implementation and assessment” , approved by the Spanish Economy and Competitiveness’ Ministry into the National Basic Research Program, 2001 (2013-2015) (Financing Plan E, PGE), directed by Professor Ph.D. Bernardo Gargallo (code EDU2012-32725).

 

Method

The study is based on a survey design. The sample included 686 students from different degrees and masters programs from three universities in the city of Valencia (Spain): 360 students were from the University of Valencia, 193 were from the Poytechnical University of Valencia, and 103 from the Catholic University of Valencia. The sampling method is purposeful sampling, since participants were selected from a sampling of teachers who apply innovative learning centered methods. Participants belong to three branches of knowledge: Education, Health and Engineering. In the first year of research different data were collected from students in order to diagnose their learning process and to make comparisons and analyze relationships between constructs, among other things. The information was collected from two questionnaires. To evaluate the learning strategies we used the LSUSQ (Gargallo, Suárez-Rodríguez & Pérez-Pérez, 2009). This 88-item questionnaire is constructed using the Likert-scale format with five possible answers for each item, ranging from "strongly disagree" to "strongly agree". The questionnaire is divided into two scales and six subscales, which are used in this study. The first scale, of affective, support and control strategies (α = 0.776) consists of four subscales: motivational strategies (α=.692), affective components (α=.678), metacognitive strategies (α=.766) and context control strategies, social interaction and use of resources strategies ( α=.768). The second scale, of strategies related to information processing (α=.859) consists of two subscales: search and selection of information strategies (α=.660) and processing and use of information strategies (α=.841). In addition, in this study metacognitive strategies were used to classify students into three groups according to their management, using the percentile scores: a "low" group, made up of students placed below the 25th percentile, an "average" group, consisting of students located between the 25th and 75th, and a "high" group, consisting of students located above the 75th percentile. The reliability of the questionnaire is α= .897. Learning approaches were assessed by means of the R-SPQ-2 (Biggs, Kember & Leung, 2001). It comprises 20 items, divided into two subscales: one surface approach sub-scale (α = .795) and another deep approach sub-scale (α = .812); each of them consists of 10 items for evaluating motives and strategies. The information was gathered through on-line questionnaires. Statistical analyses, performed by SPSS 19.0, were descriptive and ANOVA.

Expected Outcomes

The descriptive results show that all of the students present a positive average value in the five learning strategies. The affective component shows lower scores than the others. Regarding learning approaches, the surface approach shows lower scores than the deep approach. Groups have been established based on the scores in metacognitive strategies and it has been found that the group with the highest level has a higher value in the scores of the five learning strategies analyzed. This group also shows lower levels in surface approach and higher levels in deep approach. The group of students with lower levels in metacognitive strategies has lower levels in learning strategies, higher levels in surface approach and lower levels in deep approach. The group of students with average levels is between the other two groups in all of the strategies. The ANOVA test shows significant differences between the three groups in all variables (p <.001), with a value of partial eta squared between medium and high. The post-hoc tests show significant differences between the three groups in the five learning strategies scales. These differences favor the medium group compared to the low group and the high group compared to both of them. A greater mastery of metacognitive strategies also reflects a greater mastery of learning strategies. Significant differences in the deep approach to learning were found favoring students of the average group versus students of the low group and also favoring students of the high group versus students of the other two groups. This also happened in the surface learning approach. The more metacognitive strategies the students had the more their surface learning approach decreased and their deep approach to learning increased. Metacognitive strategies appear to be critical in the learning process, so teachers should work to empower their students in these strategies.

References

Attard, A., Di Ioio, E., Geven, K. & Santa, R. (2010). Student centered learning. An insight into theory and practice.Bucarest: Partos Timisoara. Biggs, J. (2005). Calidad del aprendizaje universitario. Madrid: Narcea. Biggs, J., Kember, D. & Leung, D.Y.P. (2001). The revised two-factor Study Process Questionnaire: R-SPQ-2. British Journal of Educational Psychology, 71, 133-149. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. Gargallo, B., Almerich, G., Suárez, J., García, E., Pérez, C. & Fernández, A. (2013) Enfoques de aprendizaje en estudiantes universitarios excelentes y medios. Su evolución a lo largo del primer año de carrera. Bordón, 65, 1-22. Gargallo, B. (2008) Estilos de docencia y evaluación de los profesores universitarios y su influencia sobre los modos de aprender de sus estudiantes. Revista Española de Pedagogía, 241, 425-446. 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, 1-31. Hannafin, M. (2012). Student-Centered Learning. En N.M. Seel (Ed.), Encyclopedia of the Sciences of Learning (pp. 3211-3214). Nueva York: Springer. Kember, D. (2009). Promoting student-centred forms of learning across an entire university. Higher Education, 58, 1-13. Machemer, P.L. & Crawford, P. (2007). Student perceptions of active learning in a large cross-disciplinary classroom. Active Learning in Higher Education, 8 (1), 9-30. Monereo, C. & Pozo, J.I. (2003). La universidad ante la nueva cultura educativa. Enseñar y aprender para la autonomía. Madrid: Síntesis. Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. En M. Boekaerts, P. Pintrich & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 451-502). California. Academic Press Samuelowicz, K. & Bain, J.D. (2001). Revisiting academics’ beliefs about teaching and learning, Higher Education, 41, 299-325. Yip, M.C.W. (2012). Learning strategies and self-efficacy as predictors of academic performance: a preliminary study. Quality in Higher Education, 18 (1), 23-34. Valle, A., González Cabanach, R., Núñez, J., Suárez, J.M., Piñeiro, I. & Rodríguez, S. (2000). Enfoques de aprendizaje en estudiantes universitarios, Psicothema, 12 (3), 368-375. Weinstein, C.E., Husman, J. & Dierking, D. (2002). Self-Regulation Interventions with a focus on learning strategies. En M. Boekaerts, P.R. Pintrich & M. Zeinder, Handbook of Self-regulation (pp. 727-747). San Diego: Academic Press. Zimmerman, B.J. (2002). Becoming a self-regulated learner: an overview. Theory into Practice, 41, 64-70.

Author Information

Bernardo Gargallo López (presenting / submitting)
University of Valencia
Department of Theory of Education
Valencia
University of Valencia, Spain
Universidad de Valencia
Tª de la Educación
Valencia
Universidad Católica de Valencia San Vicente Mártir
Facultad de Psicologia, Magisterio y Ciencias de la Educación, Departamento de Lengua y Literatura
Alzira
University of Valencia, Spain
Polytechnic University of Valencia, Spain

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