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Application of qualitative and quantitative methods to enrich understanding of emotional and motivational aspects of learning
Available online at www.sciencedirect.com
International Journal of Educational Research 47 (2008) 79–83 www.elsevier.com/locate/ijedures
Editorial
Application of qualitative and quantitative methods to enrich understanding of emotional and motivational aspects of learning For several years now, there has been an increased interest in emotional and motivational aspects of learning in education. Numerous studies have shown that emotions like anxiety, anger, boredom, pleasure, hope, and satisfaction are related to learning and achievement (Gla¨ser-Zikuda, Fuß, Laukenmann, Metz, & Randler, 2005; Pekrun, Go¨tz, Titz, & Perry, 2002). In spite of interest in integrating affective and motivational aspects of learning, less is known about affective factors in school, among different subjects, and in specific learning situations. The interaction between the learner and contextual factors is not only important in research on emotions, flow, and well-being; it is an important issue in research on motivation understood as a dynamic construct that is both a psychological and social phenomenon (Pintrich & Schunk, 1996; Volet & Ja¨rvela¨, 2001). Although all of these issues have been actively discussed, there is still a lack of empirical evidence on how motivation interacts with the learning process in different learning contexts. Recent discussion in motivation research stresses the social nature and origin of motivation and emphasizes that both the individual and the social context should be targets for data collection and analysis (Pintrich, 2003). New approaches in motivation research (Dai & Sternberg, 2004; Volet & Ja¨rvela¨, 2001) hint at the importance studying motivation in context. In these studies the dynamic interplay of students’ motivation and cognition as well as students’ perceptions, as thoughts, beliefs, and feelings are taken into account (Pintrich, 1989). Generally, there seems to be a growing consensus that learning processes cannot be understood without taking emotional and motivational variables into account. Perhaps because of an increased desire ‘‘to unfold and make transparent’’ the complex and interactive learning situations in instructional settings in education, methodological developments in research on emotion and motivation have received attention (Pekrun et al., 2002; Perry, 2002). Many researchers have argued that, in order to understand motivational and emotional processes in learning, we need new and multidimensional methods to capture these processes (Ainley & Hidi, 2002; Ja¨rvela¨ & Volet, 2004). Different qualitative approaches have been used as well as combinations of multiple methods including quantitative and qualitative approaches. In the past methodological disputes between qualitative and quantitative research often have been conducted on a too abstract, less theoretically based level, and there have been very few studies addressing these issues. Strong arguments against polarizing qualitative and quantitative research have been presented, and a continuum in making inferences with both kinds of data has been suggested (Ercikan & Roth, 2006). After overcoming the controversy regarding combining qualitative and quantitative methodologies (Creswell, 1995; Denzin & Lincoln, 1998), the application of multiple methods for ‘‘triangulation’’ is currently well accepted (Flick, 2004; Johnson & Onwuegbuzie, 2004). Since it was already in the 1970s introduced into the social sciences by Denzin (1978), the term triangulation has become very popular. Studies may incorporate multiple methods for investigation (methodological triangulation), various data sources in a study (data triangulation), the work of different researchers (investigator triangulation), and the use of multiple theoretical perspectives for the interpretation of results (theory triangulation). Kelle (2001) identifies the various meanings of ‘‘triangulation’’. He distinguishes three meanings or models of triangulation: (1) triangulation as the mutual validation of results obtained by different methods (the validity model), (2) triangulation as a model obtaining a larger, more complete picture of the 0883-0355/$ – see front matter # 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijer.2007.11.009
80
Editorial / International Journal of Educational Research 47 (2008) 79–83
phenomenon (the complementarity model), and (3) triangulation in its original trigonometrical sense, indicating that a combination of methods is necessary in order to gain a clear picture of the relevant phenomenon at all (the trigonometry model). The combination of qualitative and quantitative methods has become almost a commonplace in methodology and research textbooks in the social sciences (Banister, Burman, Parker, Taylor, & Tindall, 1994; Denzin & Lincoln, 1998; Mayring, Huber, Gu¨rtler, & Kiegelmann, 2007). Recently, these ideas were further integrated into what is known as the ‘‘mixed-model approach’’ (Tashakkori & Teddlie, 1998). The selection of a specific research method is no longer an ideological matter, but rather a rational choice with respect to the subject of investigation. The approach of mixed methodology (Tashakkori & Teddlie, 1998) criticises the ‘‘paradigm wars’’ between (post)positivism and constructivism/phenomenology, between the ‘‘QUANs and the QUALs’’ as unproductive. The authors developed a classification based on a multilevel model which differentiates between three steps of methodological decisions in the research process regarding the research question (a more explorative, qualitative oriented or more confirmatory quantitative oriented), the data collection (qualitative and/or quantitative) and the strategy of data analysis (qualitative and/or quantitative) (cf. Tashakkori & Teddlie, 2003). The papers presented in this special issue take these new developments in methodology into account. They are innovative in two ways: first, through the special focus on emotional and motivational factors in learning, and second, through the combination of qualitative and quantitative analysis in the sense of mixed methodology. To tie together the articles in this special issue each paper meets the following conditions: (1) each article focuses on methodological considerations, especially on the application of qualitative methods, on the combination of different qualitative methods, or on the combination of qualitative and quantitative methods; (2) each article attempts to study affective aspects of learning in real context; (3) the articles offer perspectives on affective aspects of learning in different educational areas (e.g. secondary school, university), disciplines (e.g. science, language), and situations (e.g. single lessons, student–teacher interactions). One of the important motivational constructs supporting sustained participation and engagement is the experience of flow (Csikszentmihalyi, 1990; Csikszentmihalyi & Lefevre, 1989). The complex experience of flow and a wide range of indicators representing the phenomenon are illustrated in Mary Ainley, Laura Enger and Gregor Kennedy’s paper in this special issue. In their investigation the authors compare a range of indicators using a variety of data types to capture the complexity of the flow experienced by students working on a short writing activity. The presented study has two aims. First, the authors want to determine the level of agreement between two frequently used methods of measuring flow, the four-channel model based on measures of challenge and skill (with an emotionally based indicator for anxiety, boredom, flow and apathy), and a combined measure of a cognitive- and action-based indicator (absorption, timelessness and effort). The second aim is to identify relationships between end-of-task assessments of flow and actual on-task experiences including ratings of challenge, skill, interest, and perceived control. One of their interesting results is that, contrary to their expectations, the boredom group identified by the four-channel model matched flow status based on absorption, timelessness and effort measures. The analysis of the qualitative data based on students’ reports of their skills and task challenges on completion of the task shows that students reflect feelings of how successful they have been. This finding is important for further studies in the school context because demands form teachers and students’ achievement goals influence emotional experiences very much. As it is a main goal of education to develop and support students’ willingness to learn and to perform it is generally an important issue for educational science and psychology to consider how emotions are influenced in school and instruction, and how they interact with contextual and personal factors. Well-being is a theory focusing on emotions and it can be defined as an indicator of a learning environment (Diener, 1984; Veenhoven, 1991). It may not directly enhance student achievement but enables students to move towards their academic and social goals and a qualitatively good school life (Hascher, 2003). Tina Hascher’s paper in this special issue shows two ways of assessing well-being in school: a quantified well-being questionnaire data and a semi-structured diary data about emotional situations in school. Her paper shows that combining both approaches can provide a more context-sensitive understanding of emotions in school. Tina Hascher highlights in her paper the crucial role of subjective well-being, in human life, for successful mastery of everyday challenges and subjective goals, and therefore in learning situations. As the empirical focus on well-being in school is quite new, the author presents a large quantitative study and describes the development of a questionnaire on subjective well-being in school. Furthermore, in the qualitative part of the study Hascher tries to obtain insight into student emotions by examining diaries. She asks students to report on specific feelings in different
Editorial / International Journal of Educational Research 47 (2008) 79–83
81
situations in school and finds more negative than positive emotions with a high inter- and intra-individual variation of causes reported by students. It is interesting that besides aspects of the learning context, teacher behaviour seems to have the highest influence on student emotions. This is shown in the study of Michaela Gla¨ser-Zikuda and Stefan Fuß, as well. Michaela Gla¨ser-Zikuda and Stefan Fuß present a study on the impact of teacher competencies on student emotions based on the combination of a large sample with case analyses, selected by criterion sampling. Quantitative data from two questionnaires and qualitative data from student interviews, as well as content analytical methods are compared and integrated in this mixed method approach. Gla¨ser-Zikuda and Fuß analyze the relation between specific teacher competencies (including clarity of instruction, teacher motivational and diagnostic competencies, individual reference norm orientation and teacher care) and student well-being and anxiety. The study focuses on Physics instruction because, on the one hand this school subject is not favoured very much by students, and on the other hand Physics instruction is generally highly teacher-oriented. The authors find highly significant correlations between all teacher competencies and students’ positive emotions. Quality of instruction, and particularly teacher competencies in Physics seem to be highly important in influencing positive student emotions, which are considered preconditions for a sustainable learning process. Finally, the authors compare qualitative and quantitative methods and results, illustrating that qualitative results support the quantitative data. Investigating emotion and motivation in real-life learning environments in increasingly social and interactive situations is expected to provide more realistic information on the conditions and dynamic features that contribute to students’ engagement in these contexts. This issue has been addressed in the paper by Sanna Ja¨rvela¨, Hanna Ja¨rvenoja, and Marjaana Veermans. Their paper illustrates a methodological approach where the dynamics of motivation in socially shared learning is analyzed. In their study, higher education students’ motivation was analyzed in immediate contexts of collaborative learning tasks by applying quantitative and qualitative methods. Focusing not only on the individuals, but on the whole system that changes in interaction, the authors compare the motivational orientations and task-specific goals of the students. The reasons for the students’ achievement of learning goals were searched by observing how they regulate their motivation in socially shared learning. The data was collected in a collaborative university course in two sub-samples using video-taped observation in single case analyses. Their results show that socially shared learning tasks may stimulate new strategies for motivation regulation. In general, the results show that motivation regulation is crucial in the social context, because students’ engagement is constantly shaped and reshaped as the activity unfolds. The researchers conclude that empirical research should not only study individuals, but also focus on collective interactions, and a process of collaboration targeting a group of shared regulators. Nancy Perry, Lynda Hutchinson, and Carolyn Thauberger focus on motivation in the context of self-regulation in teacher education. They examine how a university course for student teachers can support self-regulated learning. In particular, the study highlights specific strategies for scaffolding in teaching self-regulated learning. Scaffolding is a very important factor to ensure that students acquire self-regulated abilities, and the domain and strategy knowledge they need. Self-regulated learning as a highly demanding process involves not only metacognitive and strategic processes, but motivational and emotional ones, as well. This may be the reason why many learners (students and others) are not able to regulate their learning in ways that are academically successful. Furthermore, many teachers are unsure or not enough trained in how to support students’ self-regulation. In their study, Perry and her colleagues conducted a content analysis of protocols of student teachers and quantitatively processed data. The results of the study indicate that students showed more self-regulation in their learning when the instruction allowed them to choose their learning processes and products. Furthermore, it was supporting if students were able to choose the criteria by which processes and products are evaluated. It was also positive if students had a certain control of the degree of challenge in the tasks, if they had the opportunity to work collaboratively with and if they got feedback from peers. Summing up, the five papers point to the importance of a supportive learning environment and a higher quality of teacher competencies and instruction. Taking all these aspects more into account may lead to positive student emotions and a higher and sustainable student motivation to learn. The articles presented in this special issue involve different empirical approaches to investigating emotional and motivational aspects of learning. The studies apply not only quantitative approaches such as surveying with questionnaires; they also employ a range of methods for investigating the aspects of emotion, flow, well-being, and motivation in learning and instruction. Different types of qualitative methods such as interviews, diaries, videotaped
82
Editorial / International Journal of Educational Research 47 (2008) 79–83
classroom observation, and experience sampling are applied. Most of the studies use a multi-method approach characterized by triangulation, multiple qualitative methods, or a combination of quantitative and qualitative methods. We hope that this special issue contributes to the current discussion on the combination of research methods among researchers in educational sciences. The discussion has included researchers from adjacent disciplines such as psychology and sociology. Qualitative and mixed method approaches have been represented, and we believe that discussion regarding the appropriateness and potential of combined research methods should receive more attention. This special issue also represents an increased interest in social and contextual aspects of learning, which requires more dynamic approaches to investigations. This need is obvious for analyses which focus on individuals or groups of individuals who are in continuous mutual interactions within their social environment. References Ainley, M., & Hidi, S. (2002). Dynamic measures for studying interest and learning. In Pintrich, P. R., & Maehr, M. L. Eds. New directions in measures and methods. Vol. 12 (pp.43–76). Amsterdam: Elsevier Science. Banister, P., Burman, E., Parker, I., Taylor, M., & Tindall, C. (1994). Qualitative methods in psychology. A research guide. Buckingham: Open University Press. Creswell, J. W. (1995). Research design: Qualitative and quantitative approaches. Thousand Oaks, CA: Sage. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper & Row. Csikszentmihalyi, M., & Lefevre, J. (1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56(5), 815–822. Dai, D. Y., & Sternberg, R. (Eds.). (2004). Motivation, emotion and cognition: Integrative perspectives on intellectual functioning and development. Mahwah: Erlbaum. Denzin, N. K. (1978). The research act. New York: McGraw Hill. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (Vol. 1–3). Thousand Oaks: Sage. Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575. Ercikan, K., & Roth, W.-M. (2006). What good is polarizing research into qualitative and quantitative? Educational Researcher, 35(5), 14–23. Flick, U. (2004). Triangulation in qualitative research. In U. Flick, E. v. Kardorff, & I. Steinke (Eds.), A companion to qualitative research (pp. 178– 183). London: Sage. Gla¨ser-Zikuda, M., Fuß, S., Laukenmann, M., Metz, K., & Randler, Ch. (2005). Promoting students’ emotions and achievement – conception and evaluation of the ECOLE-approach. In Efklides, A., & Volet, S. Eds. Special issue feelings and emotions in the learning process. Learning and instruction. Vol. 15 (pp.481–495). . Hascher, T. (2003). Well-being in school – why students need social support. In P. Mayring & C. von Rho¨neck (Eds.), Learning emotions – the influence of affective factors on classroom learning (pp. 127–142). Bern u.a Lang. Ja¨rvela¨, S., & Volet, S. (2004). Motivation in real-life, dynamic and interactive learning environments: Stretching constructs and methodologies. European Psychologist, 9(4), 193–197. Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed method research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. Kelle, U. (2001). Sociological explanations between micro and macro and the integration of qualitative and quantitative methods. Forum: Qualitative Social Research [On-line Journal], 2 (1). Available at: http://www.qualitative-research.net/fqs-texte/1-01/1-01hrsg-e.htm [date of access: October 4, 2007]. Huber, G. L., Gu¨rtler, L., & Kiegelmann, M. (Eds.). (2007). Mixed methodology in psychological research. Rotterdam: Sense Publishers. Pekrun, R., Go¨tz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. Perry, N. (2002) (Ed.). Using qualitative methods to enrich understandings of self-regulated learning (Special issue). Educational Psychologist, 37. Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686. Pintrich, P. R., & Schunk, D. H. (1996). Motivation in education: Theory, research, and application. Englewood Cliffs, NJ: Prentice Hall. Pintrich, P. R. (1989). The dynamic interplay of student motivation and cognition in a college classroom. In C. Ames & M. Maehr (Eds.), Advances in motivation and achievement. Vol. 6: Motivation enhancing environments (pp. 117–160). Greenwich, CT: JAI. Tashakkori, A., & Teddlie, Ch. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Thousand Oaks, CA: Sage. Tashakkori, A., & (2003). Handbook of mixed methods in the social and behavioral research. Thousand Oaks: Sage. Veenhoven, R. (1991). Questions on happiness: Classical topics, modern answers, blind spots. In F. Strack, I. Argyle, & N. Schwarz (Eds.), Subjective well-being (pp. 7–26). Oxford: Pergamon Press. Volet, S., & Ja¨rvela¨, S. (Eds.). (2001). Motivation in learning contexts: Theoretical advances and methodological implications. London: Pergamon/ Elsevier.
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Michaela Gla¨ser-Zikuda* Institute for Educational Science, University of Jena, Am Planetarium 4, D-07737 Jena, Germany Sanna Ja¨rvela¨1 Department of Educational Sciences and Teacher Education, Research Unit for Educational Technology, P.O. Box 2000, FIN-90014 University of Oulu, Finland *Corresponding author. Tel.: +49 3641 945 351; fax: +49 3641 945 352 1 Tel.: +358 8 553 3657; fax: +358 8 553 374 E-mail addresses: [email protected] (M. Gla¨ser-Zikuda) [email protected] (S. Ja¨rvela¨) 12 October 2007
International Journal of Educational Research 47 (2008) 79–83 www.elsevier.com/locate/ijedures
Editorial
Application of qualitative and quantitative methods to enrich understanding of emotional and motivational aspects of learning For several years now, there has been an increased interest in emotional and motivational aspects of learning in education. Numerous studies have shown that emotions like anxiety, anger, boredom, pleasure, hope, and satisfaction are related to learning and achievement (Gla¨ser-Zikuda, Fuß, Laukenmann, Metz, & Randler, 2005; Pekrun, Go¨tz, Titz, & Perry, 2002). In spite of interest in integrating affective and motivational aspects of learning, less is known about affective factors in school, among different subjects, and in specific learning situations. The interaction between the learner and contextual factors is not only important in research on emotions, flow, and well-being; it is an important issue in research on motivation understood as a dynamic construct that is both a psychological and social phenomenon (Pintrich & Schunk, 1996; Volet & Ja¨rvela¨, 2001). Although all of these issues have been actively discussed, there is still a lack of empirical evidence on how motivation interacts with the learning process in different learning contexts. Recent discussion in motivation research stresses the social nature and origin of motivation and emphasizes that both the individual and the social context should be targets for data collection and analysis (Pintrich, 2003). New approaches in motivation research (Dai & Sternberg, 2004; Volet & Ja¨rvela¨, 2001) hint at the importance studying motivation in context. In these studies the dynamic interplay of students’ motivation and cognition as well as students’ perceptions, as thoughts, beliefs, and feelings are taken into account (Pintrich, 1989). Generally, there seems to be a growing consensus that learning processes cannot be understood without taking emotional and motivational variables into account. Perhaps because of an increased desire ‘‘to unfold and make transparent’’ the complex and interactive learning situations in instructional settings in education, methodological developments in research on emotion and motivation have received attention (Pekrun et al., 2002; Perry, 2002). Many researchers have argued that, in order to understand motivational and emotional processes in learning, we need new and multidimensional methods to capture these processes (Ainley & Hidi, 2002; Ja¨rvela¨ & Volet, 2004). Different qualitative approaches have been used as well as combinations of multiple methods including quantitative and qualitative approaches. In the past methodological disputes between qualitative and quantitative research often have been conducted on a too abstract, less theoretically based level, and there have been very few studies addressing these issues. Strong arguments against polarizing qualitative and quantitative research have been presented, and a continuum in making inferences with both kinds of data has been suggested (Ercikan & Roth, 2006). After overcoming the controversy regarding combining qualitative and quantitative methodologies (Creswell, 1995; Denzin & Lincoln, 1998), the application of multiple methods for ‘‘triangulation’’ is currently well accepted (Flick, 2004; Johnson & Onwuegbuzie, 2004). Since it was already in the 1970s introduced into the social sciences by Denzin (1978), the term triangulation has become very popular. Studies may incorporate multiple methods for investigation (methodological triangulation), various data sources in a study (data triangulation), the work of different researchers (investigator triangulation), and the use of multiple theoretical perspectives for the interpretation of results (theory triangulation). Kelle (2001) identifies the various meanings of ‘‘triangulation’’. He distinguishes three meanings or models of triangulation: (1) triangulation as the mutual validation of results obtained by different methods (the validity model), (2) triangulation as a model obtaining a larger, more complete picture of the 0883-0355/$ – see front matter # 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijer.2007.11.009
80
Editorial / International Journal of Educational Research 47 (2008) 79–83
phenomenon (the complementarity model), and (3) triangulation in its original trigonometrical sense, indicating that a combination of methods is necessary in order to gain a clear picture of the relevant phenomenon at all (the trigonometry model). The combination of qualitative and quantitative methods has become almost a commonplace in methodology and research textbooks in the social sciences (Banister, Burman, Parker, Taylor, & Tindall, 1994; Denzin & Lincoln, 1998; Mayring, Huber, Gu¨rtler, & Kiegelmann, 2007). Recently, these ideas were further integrated into what is known as the ‘‘mixed-model approach’’ (Tashakkori & Teddlie, 1998). The selection of a specific research method is no longer an ideological matter, but rather a rational choice with respect to the subject of investigation. The approach of mixed methodology (Tashakkori & Teddlie, 1998) criticises the ‘‘paradigm wars’’ between (post)positivism and constructivism/phenomenology, between the ‘‘QUANs and the QUALs’’ as unproductive. The authors developed a classification based on a multilevel model which differentiates between three steps of methodological decisions in the research process regarding the research question (a more explorative, qualitative oriented or more confirmatory quantitative oriented), the data collection (qualitative and/or quantitative) and the strategy of data analysis (qualitative and/or quantitative) (cf. Tashakkori & Teddlie, 2003). The papers presented in this special issue take these new developments in methodology into account. They are innovative in two ways: first, through the special focus on emotional and motivational factors in learning, and second, through the combination of qualitative and quantitative analysis in the sense of mixed methodology. To tie together the articles in this special issue each paper meets the following conditions: (1) each article focuses on methodological considerations, especially on the application of qualitative methods, on the combination of different qualitative methods, or on the combination of qualitative and quantitative methods; (2) each article attempts to study affective aspects of learning in real context; (3) the articles offer perspectives on affective aspects of learning in different educational areas (e.g. secondary school, university), disciplines (e.g. science, language), and situations (e.g. single lessons, student–teacher interactions). One of the important motivational constructs supporting sustained participation and engagement is the experience of flow (Csikszentmihalyi, 1990; Csikszentmihalyi & Lefevre, 1989). The complex experience of flow and a wide range of indicators representing the phenomenon are illustrated in Mary Ainley, Laura Enger and Gregor Kennedy’s paper in this special issue. In their investigation the authors compare a range of indicators using a variety of data types to capture the complexity of the flow experienced by students working on a short writing activity. The presented study has two aims. First, the authors want to determine the level of agreement between two frequently used methods of measuring flow, the four-channel model based on measures of challenge and skill (with an emotionally based indicator for anxiety, boredom, flow and apathy), and a combined measure of a cognitive- and action-based indicator (absorption, timelessness and effort). The second aim is to identify relationships between end-of-task assessments of flow and actual on-task experiences including ratings of challenge, skill, interest, and perceived control. One of their interesting results is that, contrary to their expectations, the boredom group identified by the four-channel model matched flow status based on absorption, timelessness and effort measures. The analysis of the qualitative data based on students’ reports of their skills and task challenges on completion of the task shows that students reflect feelings of how successful they have been. This finding is important for further studies in the school context because demands form teachers and students’ achievement goals influence emotional experiences very much. As it is a main goal of education to develop and support students’ willingness to learn and to perform it is generally an important issue for educational science and psychology to consider how emotions are influenced in school and instruction, and how they interact with contextual and personal factors. Well-being is a theory focusing on emotions and it can be defined as an indicator of a learning environment (Diener, 1984; Veenhoven, 1991). It may not directly enhance student achievement but enables students to move towards their academic and social goals and a qualitatively good school life (Hascher, 2003). Tina Hascher’s paper in this special issue shows two ways of assessing well-being in school: a quantified well-being questionnaire data and a semi-structured diary data about emotional situations in school. Her paper shows that combining both approaches can provide a more context-sensitive understanding of emotions in school. Tina Hascher highlights in her paper the crucial role of subjective well-being, in human life, for successful mastery of everyday challenges and subjective goals, and therefore in learning situations. As the empirical focus on well-being in school is quite new, the author presents a large quantitative study and describes the development of a questionnaire on subjective well-being in school. Furthermore, in the qualitative part of the study Hascher tries to obtain insight into student emotions by examining diaries. She asks students to report on specific feelings in different
Editorial / International Journal of Educational Research 47 (2008) 79–83
81
situations in school and finds more negative than positive emotions with a high inter- and intra-individual variation of causes reported by students. It is interesting that besides aspects of the learning context, teacher behaviour seems to have the highest influence on student emotions. This is shown in the study of Michaela Gla¨ser-Zikuda and Stefan Fuß, as well. Michaela Gla¨ser-Zikuda and Stefan Fuß present a study on the impact of teacher competencies on student emotions based on the combination of a large sample with case analyses, selected by criterion sampling. Quantitative data from two questionnaires and qualitative data from student interviews, as well as content analytical methods are compared and integrated in this mixed method approach. Gla¨ser-Zikuda and Fuß analyze the relation between specific teacher competencies (including clarity of instruction, teacher motivational and diagnostic competencies, individual reference norm orientation and teacher care) and student well-being and anxiety. The study focuses on Physics instruction because, on the one hand this school subject is not favoured very much by students, and on the other hand Physics instruction is generally highly teacher-oriented. The authors find highly significant correlations between all teacher competencies and students’ positive emotions. Quality of instruction, and particularly teacher competencies in Physics seem to be highly important in influencing positive student emotions, which are considered preconditions for a sustainable learning process. Finally, the authors compare qualitative and quantitative methods and results, illustrating that qualitative results support the quantitative data. Investigating emotion and motivation in real-life learning environments in increasingly social and interactive situations is expected to provide more realistic information on the conditions and dynamic features that contribute to students’ engagement in these contexts. This issue has been addressed in the paper by Sanna Ja¨rvela¨, Hanna Ja¨rvenoja, and Marjaana Veermans. Their paper illustrates a methodological approach where the dynamics of motivation in socially shared learning is analyzed. In their study, higher education students’ motivation was analyzed in immediate contexts of collaborative learning tasks by applying quantitative and qualitative methods. Focusing not only on the individuals, but on the whole system that changes in interaction, the authors compare the motivational orientations and task-specific goals of the students. The reasons for the students’ achievement of learning goals were searched by observing how they regulate their motivation in socially shared learning. The data was collected in a collaborative university course in two sub-samples using video-taped observation in single case analyses. Their results show that socially shared learning tasks may stimulate new strategies for motivation regulation. In general, the results show that motivation regulation is crucial in the social context, because students’ engagement is constantly shaped and reshaped as the activity unfolds. The researchers conclude that empirical research should not only study individuals, but also focus on collective interactions, and a process of collaboration targeting a group of shared regulators. Nancy Perry, Lynda Hutchinson, and Carolyn Thauberger focus on motivation in the context of self-regulation in teacher education. They examine how a university course for student teachers can support self-regulated learning. In particular, the study highlights specific strategies for scaffolding in teaching self-regulated learning. Scaffolding is a very important factor to ensure that students acquire self-regulated abilities, and the domain and strategy knowledge they need. Self-regulated learning as a highly demanding process involves not only metacognitive and strategic processes, but motivational and emotional ones, as well. This may be the reason why many learners (students and others) are not able to regulate their learning in ways that are academically successful. Furthermore, many teachers are unsure or not enough trained in how to support students’ self-regulation. In their study, Perry and her colleagues conducted a content analysis of protocols of student teachers and quantitatively processed data. The results of the study indicate that students showed more self-regulation in their learning when the instruction allowed them to choose their learning processes and products. Furthermore, it was supporting if students were able to choose the criteria by which processes and products are evaluated. It was also positive if students had a certain control of the degree of challenge in the tasks, if they had the opportunity to work collaboratively with and if they got feedback from peers. Summing up, the five papers point to the importance of a supportive learning environment and a higher quality of teacher competencies and instruction. Taking all these aspects more into account may lead to positive student emotions and a higher and sustainable student motivation to learn. The articles presented in this special issue involve different empirical approaches to investigating emotional and motivational aspects of learning. The studies apply not only quantitative approaches such as surveying with questionnaires; they also employ a range of methods for investigating the aspects of emotion, flow, well-being, and motivation in learning and instruction. Different types of qualitative methods such as interviews, diaries, videotaped
82
Editorial / International Journal of Educational Research 47 (2008) 79–83
classroom observation, and experience sampling are applied. Most of the studies use a multi-method approach characterized by triangulation, multiple qualitative methods, or a combination of quantitative and qualitative methods. We hope that this special issue contributes to the current discussion on the combination of research methods among researchers in educational sciences. The discussion has included researchers from adjacent disciplines such as psychology and sociology. Qualitative and mixed method approaches have been represented, and we believe that discussion regarding the appropriateness and potential of combined research methods should receive more attention. This special issue also represents an increased interest in social and contextual aspects of learning, which requires more dynamic approaches to investigations. This need is obvious for analyses which focus on individuals or groups of individuals who are in continuous mutual interactions within their social environment. References Ainley, M., & Hidi, S. (2002). Dynamic measures for studying interest and learning. In Pintrich, P. R., & Maehr, M. L. Eds. New directions in measures and methods. Vol. 12 (pp.43–76). Amsterdam: Elsevier Science. Banister, P., Burman, E., Parker, I., Taylor, M., & Tindall, C. (1994). Qualitative methods in psychology. A research guide. Buckingham: Open University Press. Creswell, J. W. (1995). Research design: Qualitative and quantitative approaches. Thousand Oaks, CA: Sage. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper & Row. Csikszentmihalyi, M., & Lefevre, J. (1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56(5), 815–822. Dai, D. Y., & Sternberg, R. (Eds.). (2004). Motivation, emotion and cognition: Integrative perspectives on intellectual functioning and development. Mahwah: Erlbaum. Denzin, N. K. (1978). The research act. New York: McGraw Hill. In N. K. Denzin & Y. S. 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Michaela Gla¨ser-Zikuda* Institute for Educational Science, University of Jena, Am Planetarium 4, D-07737 Jena, Germany Sanna Ja¨rvela¨1 Department of Educational Sciences and Teacher Education, Research Unit for Educational Technology, P.O. Box 2000, FIN-90014 University of Oulu, Finland *Corresponding author. Tel.: +49 3641 945 351; fax: +49 3641 945 352 1 Tel.: +358 8 553 3657; fax: +358 8 553 374 E-mail addresses: [email protected] (M. Gla¨ser-Zikuda) [email protected] (S. Ja¨rvela¨) 12 October 2007