Affective Strategies, Learning Strategies and Learning Approaches in University Students

Session Information

22 SES 05 C PS, Interactive Poster Session

Interactive Poster Session

Time:
2015-09-09
11:00-12:30
Room:
338. [Main]
Chair:
Christine Teelken

Contribution

The research question that guides the work we present here is this: Do students with better affective strategies have better learning strategies than their peers?

The results that we enclose are based on a three years research[1].

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 Ioiro, 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 professor (Attard et al., 2010). Professors must act as mediators, as designers 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).  

In this context, it is relevant to verify whether the domain of affective strategies (called “affective components” in the LSUSQ (Gargallo, Suárez-Rodríguez & Pérez-Pérez, 2009), which include physical and emotional state, and control of the anxiety) also involves better management of other learning strategies (motivational, meta-cognitive, contextual control strategies, information search strategies and information processing strategies). 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 and to improve their performance (Gargallo, Garfella Perez & Fernandez, 2010). If, as we think, affective strategies are critical in the process, the emphasis should be on developing these affective strategies in order to promote the strategic learning.

This is a relevant topic from a pedagogical point of view because learning strategies influence academic performance, which has been proven in different studies: Camarero, Martin & Herrero (2000), Cano & Justicia (1993), Gargallo, Garfella, Pérez, y Fernández (2010), Gargallo, Suarez-Rodríguez & Pérez-Pérez (2009), Pintrich, Smith, Garcia & Mackeachie (1991), Valle & Rodriguez (1998).  This is because they are one of the most powerful explicative constructs of the learning processes of students, 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 800 students from different degrees and masters programs from three universities in the city of Valencia (Spain): 558 students were from the University of Valencia, 125 were from the Poytechnical University of Valencia, and 117 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 second 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 one on-line 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 affective components 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 total questionnaire is α= .897. Statistical analyses, performed by SPSS 22.0, were descriptive, MANOVA and ANOVA.

Expected Outcomes

As we have said before, we used the total score of the subscale of affective components to classify students into three groups, which we have described above. Next, a MANOVA test was carried out to verify the possible statistically significant differences between the three groups in the other five subscales of learning strategies (motivational strategies, meta-cognitive strategies, contextual control strategies, information search strategies and information processing strategies) The results showed statistically significant differences (Wilks’ Lambda= .928, F8= 8.014, p=.000) considering all of the learning strategies as a whole, with a small effect size (partial eta-squared=.037). Afterwards, we carried out one-way ANOVA to verify the possible significant differences existing between the three groups in the five subscales of learning strategies analyzed. We found statistically significant differences (p<.001) in all strategies. The results show that the average value of the scores in these five learning strategies increases according to the three established groups. The low group in affective components has a lower average than the average and high groups in all the strategies, while the high group also presents a higher average score than the average group. Then, post-hoc tests (Scheffe) were performed to analyze between which groups significant differences existed. These differences were found in all strategies between the three groups, favoring the high group compared to the average and the low group. We also found significant differences between the average and the low group, favoring the average group. Affective components appear to be critical in the learning process, so teachers should promote these strategies in

References

Attard, A., Di Iorio, 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. Camarero, F., Martín, F & Herrero, J. (2000). Estilos y estrategias de aprendizaje en estudiantes universitarios, Psicothema, 12:4, 615-622. Cano, F. & Justicia. F. (1993). Factores académicos, estrategias y estilos de aprendizaje, Revista de Psicología General y Aplicada, 46: 1, 89-99. Gargallo, B., Garfella, P.R., Pérez, C. y Fernández, A. (2010). Modelos de enseñanza y aprendizaje. Ponencia presentada en el XXIX Seminario Interuniversitario de Teoría de la Educación "Formación y participación de los estudiantes en la universidad". Madrid, Universidad Complutense, Noviembre. www.ucm.es/info/site/docu/29site/ponencia3.pdf 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 Pintrich, P.R., Smith, D. A. F., García, T. & Mackeachie, W.J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor: Universidad de Michigan. Technical Report No. 91-B-004. 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. & Rodríguez, A. (1998) Estrategias de aprendizaje y rendimiento académico, Boletín de Psicología, 60, 27-53. 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

University of Valencia
Department of Research Methods and Educational Diagnosis
Valencia
University of Valencia, Spain
University of Valencia, Spain
University of Valencia, Spain
Catholic University of Valencia, Spain
University of Valencia, Spain
Polytechnic University of Valencia, Spain

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