These psychodynamic factors illustrate societalattitudes reflecting a cultural divide between scien-tists and non-scientists identified more than 50years ago (Snow 1961). The pervasive negativeattitudes toward math can be attributed to cultur-ally embedded conceptions that math is difficultand accessible to only a few extraordinary individ-uals (Belbase 2013). Restrictive images of math,science, and research are perpetuated through sto-ries told by peers and parents, school experiences,and media representations (Hannover and Kessels2004; Murtonen et al. 2008). These attitudes arereinforced or even exacerbated through a steadystream of negative messages about research meth-ods courses, with sometimes exaggerated claimsabout difficulty, amount of work, and the caveatthat success depends on proficiency in math.Researchers have attributed students’ scienceaversion to the disconnect between the culture ofscience and students’ images of themselves(Hannover and Kessels 2004; Taconis and Kessels2009). Through a process of self-to-prototypematching (Niedenthal, Cantor, and Kihlstrom1985), students compare themselves to their idea ofa typical scientist; those whose self-concepts donot correspond to their perceptions of scientistsexperience self-to-prototype mismatch. Science islargely perceived by students as dull, abstract, andhard to understand. Furthermore, successfulengagement with science culture requires a “cer-tain way of being” in addition to particular person-ality traits (Taconis and Kessels 2009:1130).Congruence between student self-concepts andperceptions of science culture is associated withsuccess in math and science courses, while lack ofcongruence is associated with reduced interest inthose courses (Lee 1998). The perceived matchbetween self and prototype not only influences stu-dents’ affinity for the course but also their intendedcareer choice (Hannover and Kessels 2004).The concept of self-to-prototype matching canbe extended to explain students’ attitudes towardresearch methods courses. Influenced by mediaimages, societal attitudes, and personal percep-tions, social science students compare their self-concepts to their idea of a typical researcher, withmany students experiencing self-to-prototype mis-match as a result. I argue that research self-conceptis an important previously unidentified core con-cept in understanding perceived learning inresearch methods courses.Learning approach, a cognitive factor, predictsperceived learning. A surface learning approach(resistance) is associated with lower levels of per-ceived learning, while a deep learning approach(leaning in) is associated with higher levels of per-ceived learning. The extent of correspondencebetween self and prototype appears to inform stu-dents’ choice of learning approach. Students withmore positive math self-concepts, those who cansee themselves conducting research, and those whoconsider the course useful are more likely to take adeep learning approach. They lean in and adoptlearning behaviors and strategies that result inhigher levels of perceived learning. In contrast, stu-dents with negative math self-concepts, thoseaverse to performing research, and those who con-sider the course useless adopt a surface learningapproach. Since their goal is simply to pass thecourse, they adopt resistant learning strategies,which result in lower levels of perceived learning.The course instructor, an environmental factor,has a substantial impact on students’ perceptions oflearning. Characteristics of good instructorsinclude being student centered, professional, andenthusiastic. Students appreciate instructors whomthey feel are approachable and accessible. As far as
Psychodynamic factors rooted in societal attitudes, identified over 50 years ago, illustrate a cultural divide between scientists and non-scientists, contributing to pervasive negative attitudes toward math and research. The concept of self-to-prototype matching is extended to explain students' attitudes toward research methods courses, suggesting that research self-concept, influenced by media images and societal attitudes, plays a crucial role in understanding perceived learning in these courses. Learning approach, whether deep or surface, is influenced by the extent of correspondence between students' self-concepts and their idea of a typical researcher, with positive math self-concepts and perceived course usefulness associated with a deep learning approach and higher perceived learning