Accusations of entrenched political partisanship have been launched against both conservatives and liberals. But is feeling superior about one's beliefs a partisan issue? Two competing hypotheses exist: the rigidity-of-the-right hypothesis (i.e., conservatives are dogmatic) and the ideological-extremism hypothesis (i.e., extreme views on both sides predict dogmatism). We measured 527 Americans' attitudes about nine contentious political issues, the degree to which they thought their beliefs were superior to other people's, and their level of dogmatism. Dogmatism was higher for people endorsing conservative views than for people endorsing liberal views, which replicates the rigidity-of-the-right hypothesis. However, curvilinear effects of ideological attitude on belief superiority (i.e., belief that one's position is more correct than another's) supported the ideological-extremism hypothesis. Furthermore, responses reflecting the greatest belief superiority were obtained on conservative attitudes for three issues and liberal attitudes for another three issues. These findings capture nuances in the relationship between political beliefs and attitude entrenchment that have not been revealed previously.
This study examined whether students who left biomedical fields of study during college did so primarily because they became disenchanted with those fields or because they felt attracted to alternative fields of study. We identified 1,193 students intending to pursue biomedical fields of study early in college, collected data about their beliefs and performance throughout college, and interviewed them near graduation about their future plans. Descriptively, we examined the topics students discussed as affecting their attrition decisions. Predictive research aims were to determine how academic performance, interest, and demographic factors predicted students' likelihood of overall attrition and likelihood of reporting distinct reasons for attrition. Among the 192 students who left biomedical fields, 62.5% described leaving only in terms of feeling disenchanted, whereas 37.4% expressed that they left at least in part due to feeling attracted toward nonbiomedical fields. Most students who left biomedical fields expressed changing plans for reasons related to interest; this was especially prevalent among students who reported leaving due to attraction toward nonbiomedical fields. Predictive analyses showed that interest in biology and grades at the end of an introductory biology course predicted the likelihood of overall attrition and likelihood of leaving due to feeling disenchantment, whereas underrepresented ethnic minority status predicted these outcomes positively. Interest and course grades also predicted the likelihood of students leaving due to feeling attraction toward other fields, but interest was a stronger predictor relative to grades. Results highlight distinct types of attrition that may have implications for policies to promote STEM retention. Educational Impact and Implications StatementThis study shows that students reflect on their attrition from biomedical fields of study during college in distinct ways. Results of interviews demonstrated that many students reported leaving biomedical fields because they felt disenchanted with them; however, 37.4% of students reported leaving biomedical fields at least in part due to feeling attracted to positive features of nonbiomedical fields. Both biology grades and interest affected students' likelihood of leaving biomedical fields; these two factors predicted leaving due to disenchantment equally strongly, whereas interest played a stronger This article was published Online First January 9, 2020.
We tested the long-term effects of a utility-value intervention administered in a gateway chemistry course, with the goal of promoting persistence and diversity in STEM. In a randomized controlled trial (N = 2,505), students wrote three essays about course content and its personal relevance or three control essays. The intervention significantly improved STEM persistence overall (74% vs. 70% were STEM majors 2.5 y later). Effects were larger for students from marginalized and underrepresented racial/ethnic groups, who were 14 percentage points more likely to persist in STEM fields in the intervention condition (69% vs. 55%). Mediation analysis suggests that the intervention promoted persistence for these students by bolstering their motivation to attain a STEM degree and by promoting engagement with course assignments. This theory-informed curricular intervention is a promising tool for educators committed to retaining students in STEM.
Analytics of student learning data are increasingly important for continuous redesign and improvement of tutoring systems and courses. There is still a lack of general guidance on converting analytics into better system design, and on combining multiple methods to maximally improve a tutor. We present a multi-method approach to data-driven redesign of tutoring systems and its empirical evaluation. Our approach systematically combines existing and new learning analytics and instructional design methods. In particular, our methods involve identifying difficult skills and creating focused tasks for learning these difficult skills effectively following content redesign strategies derived from analytics. In our past work, we applied this approach to redesigning an algebraic modeling unit and found initial evidence of its effectiveness. In the current work, we extended this approach and applied it to redesigning two other tutor units in addition to a second iteration of redesigning the previously redesigned unit. We conducted a one-month classroom experiment with 129 high school students. Compared to the original tutor, the redesigned tutor led to significantly higher learning outcomes, with time mainly allocated to focused tasks rather than original full tasks. Moreover, it reduced over-and under-practice, yielded a more effective practice experience, and selected skills progressing from easier to harder to a greater degree. Our work provides empirical evidence of the effectiveness and generality of a multi-method approach to data-driven instructional redesign. CCS CONCEPTS• Applied computing → E-learning; Computer-managed instruction; Interactive learning environments.
Researchers often invoke the metaphor of a pipeline when studying participation in careers in science, technology, engineering, and mathematics (STEM), focusing on the important issue of students who “leak” from the pipeline, but largely ignoring students who persist in STEM. Using interview, survey, and institutional data over 6 years, we examined the experiences of 921 students who persisted in biomedical fields through college graduation and planned to pursue biomedical careers. Despite remaining in the biomedical pipeline, almost half of these students changed their career plans, which was almost twice the number of students who abandoned biomedical career paths altogether. Women changed plans more often and were more likely than men to change to a career requiring fewer years of post-graduate education. Results highlight the importance of studying within-pipeline patterns rather than focusing only on why students leave STEM fields.
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