Towards Developing a Quantitative Literacy/Reasoning Assessment Instrument

Self Cite

117

We seek to understand how the experiences of groups that differ in gender, ethnicity, and sexual orientation produce college-level educational performances that differ from the experiences of the dominant majority group. We employ two datasets: a National Database of 24,701 participants and a Paired-Measures Database with 3,323 participants. Both datasets provide demographic information, socioeconomic conditions of status as first-generation student, English as a first language, and interest in majoring in science, and competency scores on understanding science as a way of knowing obtained from the Science Literacy Concept Inventory. The Paired-Measures Database includes additional selfassessed competence ratings that enabled quantifying affective confidence. We meld the ways of knowing of ethics, numeracy, and social justice, especially the social justice concept of Othering, to interpret our data. Two of three competing hypotheses about self-assessment encourage Othering. Our data strongly support the third-that all groups are good at self-assessment and merit equal respect. Women and men are equally competent in science literacy. Women, on average, are more accurate in their self-assessments whereas men, on average, are overconfident. Those with minority sexual orientations register higher competence than the binary-sexual majority but are less confident of their competency. Minority ethnicities, on average, produce significantly lower science literacy scores. With one exception (Middle Eastern), groups produce mean self-assessed competence ratings that are remarkably accurate predictors of their mean competence scores. The three socioeconomic conditions exert significant and unequal impacts across ethnic groups, with Hispanic, Middle Eastern and Pacific Islander data providing some unique results.

Section: Ways Of Knowingmentioning

confidence: 99%

Paired Measures of Competence and Confidence Illuminate Impacts of Privilege on College Students

Watson¹,

Nuhfer²,

Moon³

et al. 2019

Self Cite

117

“…The learning outcomes desired of students were assessed with the Quantitative Literacy and Reasoning Assessment (QLRA) distributed by The Science Education Resource Center (SERC) 3 and described in Gaze et al (2014). The assessment was built by "leaders and experts in the QLR field with extensive backgrounds working with SIGMAA-QL, the National Numeracy Network (NNN), and assessment to create an online resource for assessing QLR."…”

Section: Discussionmentioning

confidence: 99%

“…The third track is a calculus track that experienced little change at this time from the previous implementation with students who were mainly from STEM disciplines. It is the belief of the authors and many others that the landscape of the traditional calculus track should and will take a more quantitative reasoning approach (Gaze 2014).…”

Section: North Carolina Community College System Redesignmentioning

confidence: 99%

Implementing Quantitative Literacy at Southwestern Community College, North Carolina

Todd¹,

Wagaman

2015

“…There are some other well-known quantitative literacy instruments where similar studies have been conducted, including the Quantitative Literacy/ Reasoning Assessment (QLRA) instrument (Gaze et al 2014) and the Quantitative Reasoning Test, Version 9 (QR-9; Sundre 2008). Similar to HEIghten, the QLRA is intended to be used across multiple campuses, and similar to the QR-9, HEIghten is intended to measure quantitative literacy skills that students may have obtained through a general education curriculum.…”

Section: Study Purpose Research Questions and Rationalementioning

confidence: 99%

Preliminary Evaluation of the Psychometric Quality of HEIghtenTM Quantitative Literacy

Roohr¹,

Lee²,

Xu³

et al. 2017

Quantitative literacy has been identified as an important student learning outcome (SLO) by both the higher education and workforce communities. This paper aims to provide preliminary evidence of the psychometric quality of the pilot forms for HEIghten quantitative literacy, a next-generation SLO assessment for students in higher education. We evaluated the psychometric quality of the test items (e.g., item analyses), individual-and group-level reliability, the relationship with student performance and related variables (e.g., grade point average) as well as student perceptions, and differences across college-related and demographic subgroups. Our study used data from a pilot test administered to over 1,500 students at 23 higher education institutions in the United States. Results showed that (a) overall, items were functioning well, but a small portion of items should be dropped due to unsatisfactory performance; (b) correlations across sub-areas of the assessment were very high indicating that the assessment may be unidimensional; (c) reliability estimates similar to existing SLO assessments were found at both individual and group levels; (d) assessment scores correlated positively with high school and college GPA, number of math college courses, self-rated quantitative literacy skills, and college admissions scores; (e) students had positive perceptions about the assessment; and (f) performance differences were found across institution type, college majors, gender, racial/ethnic groups, and language groups, but not across credit-hour categories. Implications for operational test development and understanding of quantitative literacy performance are discussed. Zhen Wang is a Senior Psychometrician at the Educational Testing Service (ETS). She holds a Ph.D. in Educational Measurement and Research Methodology from the University of British Columbia. Currently, she is the statistical coordinator for ETS' HEIghten® Outcomes Assessment Suite, Major Field Test, Success Navigator, Proficiency Profile, and iSkills projects. She also works on the research projects related to automated scoring and higher education. Her research interests include item response theory, structural equation models, hierarchical linear models, rater models, and equating and scaling techniques.