• Keine Ergebnisse gefunden

Aims and research questions

3.3 Research questions of the empirical studies

In Study 1, entitled Der Wert der Mathematik im Klassenzimmer—Die Bedeutung relevanzbezogener Unterrichtsmerkmale für die Wertüberzeugungen der Schülerinnen und Schüler [The value of mathematics in the classroom: The importance of a relevance-oriented learning environment for students’ value beliefs], the effect of instructional strategies and the value classmates attribute to learning mathematics on students’ mathematics-related value beliefs was investigated. Results of this study thereby contribute to filling a gap in research framed in EVT on the role of students’ socializers (teachers, peers) and their beliefs and behaviors for students’

development of all four value beliefs. Unlike prior studies of students’ motivation in the classroom, in this dissertation the social complexity of classroom was taken into account by simultaneously investigating teacher- and peer-related influences, and changes in value beliefs were investigated using a longitudinal design (see Frenzel et al., 2010, for an exception). The following research questions were investigated in Study 1:

1) How are relevance-oriented teaching strategies in mathematics (stressing practical applicability, introducing new topics with everyday examples, demonstrating links with other academic subjects) and students’ perception of the value their classmates attribute to learning mathematics associated with students’ mathematics-related value beliefs (intrinsic, attainment, utility values, and cost)?

2) Do relevance-oriented teaching strategies and students’ perception of the value their classmates attribute to learning mathematics lead to a change in students’

mathematics-related value beliefs after six months?

AIMS AND RESEARCH QUESTIONS

43 To address the first research question pretest data from the MoMa intervention study were analyzed in multiple linear regression models distinguishing between the individual level and the class level. To address the second research question, data from the first and the last measurement points (six months after the pretest) of the MoMa intervention were examined in multiple hierarchical linear regression models controlling for students’ initial value beliefs as well as for the intervention.

In Study 2, entitled Short intervention, sustained effects: Promoting students’ mathematics-related competence beliefs, effort, and achievement, investigation was made into the effects of the MoMa relevance interventions on students’ competence beliefs, effort, and achievement, thereby extending relevance intervention research to secondary school classrooms in Germany. Unlike in previous relevance intervention studies, the effectiveness of two intervention approaches (one previously established and one newly developed) was systematically compared. Through implementation at the class level, the interventions were first adapted to students’ genuine classroom setting. The short- and long-term effects of the relevance interventions on a broad range of previously neglected outcomes including students’ motivation, behavior, and achieve-ment in mathematics were analyzed. In a previous investigation, the interventions had been found to foster students’ value beliefs (Gaspard, Dicke, Flunger, Brisson et al., 2015). Study 2 addresses the following research questions:

1) How do two relevance interventions (writing a text or evaluating quotations about the relevance of mathematics) influence students’ mathematics-related competence beliefs (self-concept, homework self-efficacy) and their effort to learn mathematics as rated by teachers six weeks after the intervention?

2) Are the intervention effects stable?

3) Do the interventions promote students’ test-based achievement in mathematics five months after the intervention?

To answer the three research questions, separate multiple linear hierarchical regression models were run for each of the outcomes six weeks and five months after the interventions. Two dummies representing the intervention conditions at the class level were simultaneously regressed on students’ competence beliefs, effort, and achievement, controlling for students’

initial values.

In Study 3, entitled Who sticks to the instructions—and does it matter? Antecedents and effects of students’ fidelity to a classroom-based relevance intervention, investigation was made into the processes underlying the effects of the MoMa relevance interventions. Little is known about the mechanisms through which relevance interventions work or do not work. Knowledge about the characteristics of students who respond well and those who respond less well to relevance interventions is needed to find ways to optimize relevance interventions so as to reach a maximum number of students. By systematically analyzing the criteria “positive argumentation”, “personal connections”, and “in-depth reflection”, the third study also makes a

INTRODUCTION AND THEORETICAL FRAMEWORK

44

unique contribution to understanding the elements through which a change in students’

perception of relevance can be triggered. In Study 3 the following research questions were addressed:

1) How did students respond to the writing tasks in the MoMa relevance interventions?

2) Which individual student characteristics and classroom-related perceptions predicted students’ responsiveness to the writing tasks?

3) How does the degree of students’ responsiveness relate to the effects of the interventions on students’ utility value beliefs six weeks and five months after the intervention?

To address these research questions, students’ essays produced during the MoMa interventions were coded according to the three fidelity criteria which were first investigated descriptively and then combined into a responsiveness index. Students’ individual characteristics and classroom-related perceptions at the pretest were regressed on the responsiveness index using multiple linear regression models. Finally, complier-average causal effects analyses (e.g., Sagarin et al., 2014) were conducted to compare the effects of the relevance interventions on the utility value beliefs of the responsive students and the nonresponsive students six weeks and five months after the intervention.

REFERENCES

45 References

Acatech & Körber-Stifung (2014). MINT-Nachwuchsbarometer 2014. München/Hamburg.

Acatech & Körber-Stifung (2015). MINT-Nachwuchsbarometer 2015. München/Hamburg.

Allen, V. L., & Feldman, R. S. (1976). Studies on the role of tutor. In V. L. Allen (Ed.), Children as teachers. Theory and research on tutoring (pp. 113–130). New York: Academic Press.

Assor, A., Kaplan, H., & Roth, G. (2002). Choice is good, but relevance is excellent: Autonomy-enhancing and suppressing teacher behaviours predicting students' engagement in schoolwork. British Journal of Educational Psychology, 72, 261–278. https://doi.org/10.1348 /000709902158883

Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.

Biesta, G. (2007). Why "what works" won't work: Evidence-based practice and the democratic deficit in educational research. Educational Theory, 57(1), 1–22. https://doi.org/10.1111 /j.1741-5446.2006.00241.x

Brophy, J. E. (1999). Teaching. Lausanne, Switzerland: PCL.

Canning, E. A., & Harackiewicz, J. M. (2015). Teach it, don’t preach it: The differential effects of directly-communicated and self-generated utility value information. Motivation Science, 1(1), 47–71. https://doi.org/10.1037/mot0000015

Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. https://doi.org/10.3102 /0013189X032001009

Craven, R. G., Marsh, H. W., Debus, R. L., & Jayasinghe, U. (2001). Diffusion effects: Control group contamination threats to the validity of teacher-administered interventions. Journal of Educational Psychology, 93(3), 639–645. https://doi.org/10.1037/0022-0663.93.3.639 Durik, A. M., Hulleman, C. S., & Harackiewicz, J. M. (2015). One size fits some: Instructional

enhancement to promote interest. In K. A. Renninger, M. Nieswandt, & S. Hidi (Eds.), Interest in mathematics and science learning (pp. 49–62). Washington, DC: American Educational Research Association.

Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983).

Expectancies, values and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–146). New York:

Freeman.

Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Maciver, D.

(1993). Development during adolescence—The impact of stage-environment fit on young adolescents experiences in schools and in families. American Psychologist, 48(2), 90–101.

https://doi.org/10.1037//0003-066x.48.2.90

INTRODUCTION AND THEORETICAL FRAMEWORK

46

Eccles, J. S., Vida, M. N., & Barber, B. (2004). The relation of early adolescents' college plans and both academic ability and task-value beliefs to subsequent college enrollment. Journal of Early Adolescence, 24(1), 63–77. https://doi.org/10.1177/0272431603260919

Fahrenberg, J., Myrtek, M., Pawlik, K., & Perrez, M. (2007). Ambulatory assessment: Monitoring behavior in daily life settings. European Journal of Psychological Assessment, 23(4), 206–213.

https://doi.org/10.1027/1015-5759.23.4.206

Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28(4), 717–741. https://doi.org/10.1007/s10648-015-9348-9

Gaspard, H., Dicke, A.-L., Flunger, B., Brisson, B. M., Häfner, I., Trautwein, U., & Nagengast, B.

(2015). Fostering adolescents’ value beliefs for mathematics with a relevance intervention in the classroom. Developmental Psychology, 51(9), 1226–1240. https://doi.org/10.1037 /dev0000028

Gaspard, H., Dicke, A.-L., Flunger, B., Häfner, I., Brisson, B. M., Trautwein, U., & Nagengast, B.

(2016). Side effects of motivational interventions? Effects of an intervention in math classrooms on motivation in verbal domains. AERA Open, 2(2), 1–14. https://doi.org /10.1177/2332858416649168

Häfner, I., Flunger, B., Dicke, A.-L., Gaspard, H., Brisson, B. M., Nagengast, B., & Trautwein, U.

(2017). Robin Hood effects on motivation in math: Family interest moderates the effects of relevance interventions. Developmental Psychology, 53(8), 1522–1539. https://doi.org /10.1037 /dev0000337

Haney, P., & Durlak, J. A. (1998). Changing self-esteem in children and adolescents: A meta-analytical review. Journal of Clinical Child Psychology, 27(4), 423–433.

Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Priniski, S. J., & Hyde, J. S. (2016). Closing achievement gaps with a utility-value intervention: Disentangling race and social class.

Journal of Personality and Social Psychology, 111(5), 745–765. https://doi.org/10.1037 /pspp0000075

Harackiewicz, J. M., Hulleman, C. S., Rozek, C., Katz-Wise, S. L., & Hyde, J. S. (2010, March).

Parents’ understanding of the utility value of STEM courses for high school students. Paper presented at the 13th Biennial SRA Conference (Society for Research on Adolescence).

Philadelphia, PA.

Hoogerheide, V., van Wermeskerken, M., Loyens, S. M. M., & van Gog, T. (2016). Learning from video modeling examples: Content kept equal, adults are more effective models than peers.

Learning and Instruction, 44, 22–30. https://doi.org/10.1016/j.learninstruc.2016.02.004 Hulleman, C. S., & Cordray, D. S. (2009). Moving from the lab to the field: The role of fidelity and

achieved relative intervention strength. Journal of Research on Educational Effectiveness, 2(1), 88–110. https://doi.org/10.1080/1934574080253925

REFERENCES

47 Hulleman, C. S., Godes, O., Hendricks, B. L., & Harackiewicz, J. M. (2010). Enhancing interest and performance with a utility value intervention. Journal of Educational Psychology, 102(4), 880–895. https://doi.org/10.1037/A0019506

Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science, 326(5958), 1410–1412. https://doi.org/10.1126/science.1177067 Hulleman, C. S., Kosovich, J. J., Barron, K. E., & Daniel, D. B. (2017). Making connections:

Replicating and extending the utility value intervention in the classroom. Journal of Educational Psychology, 109(3), 387–404. https://doi.org/10.1037/edu0000146

Husman, J., Nelson, K., & Cheng, K. (2017, August). Intervening to promote engineering student success in technical, nonmajor, required barrier courses. Poster presented at the 17th Biennial EARLI Conference (European Association for Research on Learning and Instruction). Tampere, Finland.

Institut der deutschen Wirtschaft. (2017). MINT-Frühjahrsreport 2017: MINT-Bildung:

Wachstum für die Wirtschaft, Chancen für den Einzelnen [Education in STEM: Economic growth, opportunities for the individual]. http://www.mintzukunftschaffen.de /uploads/media/FINAL_MINT-Fruehjahrsreport_2017.pdf

Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children's self-competence and values: Gender and domain differences across grades one through twelve.

Child Development, 73(2), 509–527. https://doi.org/10.1111/1467-8624.00421

Jang, H., Reeve, J., & Deci, E. L. (2010). Engaging students in learning activities: It is not autonomy support or structure but autonomy support and structure. Journal of Educational Psychology, 102(3), 588–600. https://doi.org/10.1037/a0019682

Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Journal of Educational and Behavioral Statistics, 27(4), 385–409.

https://doi.org/10.3102/10769986027004385

Juvonen, J., Espinoza, G., & Knifsend, C. (2012). The role of peer relationships in student academic and extracurricular engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 387–492). New York: Springer.

Karabenick, S. A., Albrecht, J., & Rausch, N. (2017, August). Examining the self-generation of subjective task value: A mixed-methods investigation. Paper presented at the 17th Biennial EARLI Conference (European Association for Research on Learning and Instruction).

Tampere, Finland.

Kindermann, T. A. (2007). Effects of naturally existing peer groups on changes in academic engagement in a cohort of sixth graders. Child Development, 78(4), 1186–1203.

https://doi.org/10.1111/j.1467-8624.2007.01060.x

INTRODUCTION AND THEORETICAL FRAMEWORK

48

Kindermann, T. A., McCollam, T. L., & Gibson, E. (1996). Peer networks and students’ classroom engagement during childhood and adolescence. In J. Juvonen & K. R. Wentzel (Eds.), Social motivation: Understanding children’s school adjustment (pp. 279–312). Cambridge, England:

Cambridge University Press.

Klein, F., & Schimmack, R. (1907). Vorträge über den Mathematischen Unterricht an den Höheren Schulen. Mathematische Vorlesungen an der Universität Göttingen. Leipzig: B. G. Teubner.

Kunter, M., & Baumert, J. (2006). Who is the expert? Construct and criteria validity of student and teacher ratings of instruction. Learning Environments Research, 9(3), 231–251.

https://doi.org/10.1007/s10984-006-9015-7

Kyndt, E., Raes, E., Lismont, B., Timmers, F., Cascallar, E., & Dochy, F. (2013). A meta-analysis of the effects of face-to-face cooperative learning: Do recent studies falsify or verify earlier findings? Educational Research Review, 10, 133–149. https://doi.org/10.1016 /j.edurev.2013.02.002

Lazarides, R., & Ittel, A. (2012). Instructional quality and attitudes toward mathematics: Do self-concept and interest differ across students' patterns of perceived instructional quality in mathematics classrooms? Child Development Research, 2012, 1–11. https://doi.org/10.1155 /2012/813920

Lazarides, R., & Rubach, C. (2017). Instructional characteristics in mathematics classrooms:

Relationships to achievement goal orientation and student engagement. Mathematics Education Research Journal, 29(2), 201–217. https://doi.org/10.1007/s13394-017-0196-4 Lou, Y., Abrami, P. C., Spence, J. C., Poulsen, C., Chambers, B., & d'Apollonia, S. (1996).

Within-class grouping: A meta-analysis. Review of Educational Research, 66(4), 423–458.

https://doi.org/10.3102/00346543066004423

Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory. Journal of Educational Psychology, 90(2), 312–

320. https://doi.org/10.1037/0022-0663.90.2.312

Murrah, W. M., Kosovich, J. J., & Hulleman, C. S. (2017). A framework for incorporating intervention fidelity in educational evaluation studies. In G. Roberts, S. Vaughn, T. Beretvas,

& V. Wong (Eds.), Treatment fidelity in studies of educational intervention (pp. 39–60). New York: Routledge.

Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K. T., & Trautwein, U. (2011). Who took the "x" out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22(8), 1058–1066. https://doi.org/10.1177/0956797611415540

REFERENCES

49 O'Mara, A. J., Marsh, H. W., Craven, R. G., & Debus, R. L. (2006). Do self-concept interventions make a difference? A synergistic blend of construct validation and meta-analysis.

Educational Psychologist, 41(3), 181–206. https://doi.org/10.1207/s15326985ep4103_4 Pekrun, R., & Linnenbrink-Garcia, L. (2012). Academic emotions and student engagement. In S. L.

Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). New York: Springer.

Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: Standardized observation can leverage capacity. Educational Researcher, 38(2), 109–119. https://doi.org/10.3102/0013189X09332374

Ramani, G. B., Zippert, E., & Daubert, E. (2016). The influence of same- and cross-age peers on children's literacy and mathematical development. In K. R. Wentzel & G. B. Ramani (Eds.), Handbook of social influences in school contexts. Social-emotional, motivation, and cognitive outcomes (pp. 96–112). New York, London: Routledge Taylor & Francis Group.

Reeve, J., Vansteenkiste, M., Assor, A., Ahmad, I., Cheon, S. H., Jang, H.,. . . Wang, C. K. J. (2014). The beliefs that underlie autonomy-supportive and controlling teaching: A multinational investigation. Motivation and Emotion, 38(1), 93–110. https://doi.org/10.1007/s11031-013-9367-0

Reiss, K., Sälzer, C., Schiepe-Tiska, A., Klieme, E., & Köller, O. (Eds.). (2016). PISA 2015: Eine Studie zwischen Kontinuität und Innovation. Münster: Waxmann. Retrieved from http://www.pisa.tum.de/fileadmin/w00bgi/www/Berichtband_und_Zusammenfassung_20 12/PISA_2015_eBook.pdf

Renn, O., Duddeck, H., Menzel, R., Holtfrerich, C.-L., Luca, K., Fischer, W.,. . . Pfenning, U. (2012).

Stellungnahmen und Empfehlungen zur MINT-Bildung in Deutschland auf der Basis einer europäischen Vergleichsstudie. Berlin.

Robinson, D. R., Schofield, J. W., & Steers-Wentzell, K. L. (2005). Peer and cross-age tutoring in math: Outcomes and their design implications. Educational Psychology Review, 17(4), 327–

362. https://doi.org/10.1007/s10648-005-8137-2

Rosenzweig, E. Q., & Wigfield, A. (2016). STEM Motivation interventions for adolescents: A promising start, but further to go. Educational Psychologist, 51(2), 146–163.

https://doi.org/10.1080/00461520.2016.1154792

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.

Sarbin, T. R. (1976). Cross-age tutoring and social identity. In V. L. Allen (Ed.), Children as teachers. Theory and research on tutoring (pp. 27–40). New York: Academic Press.

Schmiedek, F. (2016). Methods and Designs. In T. Strobach & J. Karbach (Eds.), Cognitive training.

An overview of features and applications (pp. 9–18). Cham: Springer International.

INTRODUCTION AND THEORETICAL FRAMEWORK

50

Schunk, D. H. (1987). Peer models and children's behavioral change. Review of Educational Research, 57(2), 149–174. https://doi.org/10.2307/1170234

Schunk, D. H., & Zimmerman, B. J. (2007). Influencing children's self-efficacy and self-regulation of reading and writing through modeling. Reading & Writing Quarterly, 23(1), 7–25.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Belmont, CA: Wadsworth Cengage Learning.

Topping, K. (2000). Tutoring (5th ed.). Educational Practice Series. Lausanne, Switzerland: PCL.

Trautwein, U., Marsh, H. W., Nagengast, B., Lüdtke, O., Nagy, G., & Jonkmann, K. (2012). Probing for the multiplicative term in modern expectancy-value theory: A latent interaction modeling study. Journal of Educational Psychology, 104(3), 763–777.

https://doi.org/10.1037/A0027470

Turner, J. C., Warzon, K. B., & Christensen, A. (2011). Motivating mathematics learning: Changes in teachers' practices and beliefs during a nine-month collaboration. American Educational Research Journal, 48(3), 718–762. https://doi.org/10.3102/0002831210385103

Turner, R. H. (2006). Role theory. In J. H. Turner (Ed.), Handbooks of sociology and social research: Handbook of sociological theory (pp. 233–254). Dordrecht: Springer.

Wang, M.-T. (2012). Educational and career interests in math: A longitudinal examination of the links between classroom environment, motivational beliefs, and interests. Developmental Psychology, 48(6), 1643–1657. https://doi.org/10.1037/A0027247

Wentzel, K. R. (2005). Peer relationships, motivation, and academic performance at school. In A.

J. Elliott & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 279–296). New York and London: Guilford.

Wentzel, K. R., & Brophy, J. E. (2014). Motivating students to learn (4th ed.). New York: Routledge.

Wigfield, A., Tonks, S., & Klauda, S. (2009). Expectancy-value theory. In K. R. Wentzel & A.

Wigfield (Eds.), Handbook of motivation at school (pp. 55–75). New York: Routledge.

Woolley, M. E., Rose, R. A., Orthner, D. K., Akos, P. T., & Jones-Sanpei, H. (2013). Advancing academic achievement through career relevance in the middle grades: A longitudinal evaluation of CareerStart. American Educational Research Journal, 50(6), 1309–1335.

https://doi.org/10.3102/0002831213488818

Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81(2), 267–301. https://doi.org/10.3102 /0034654311405999

51

52

53