scholarly journals Attitudinal and Mathematical Assessments as Measures of Student Success in a College General Chemistry II Course

2019 ◽  
Vol 119 (2) ◽  
pp. 17
Author(s):  
Larry Kolopajlo

This study reports results from administering unannounced attitudinal and mathematical assessments to 118 students, at the beginning of the term, in second-semester general chemistry classes (designed for science majors) at Eastern Michigan University. Testing was conducted during the 2010-2011 school year. The question to be answered was: which is more important in determining student course grades, attitudes toward chemistry and mathematics, or mathematical skill? The hypothesis was that attitudes and mathematical skill equally affect final course grade. A modified Wiebe instrument was selected to evaluate student attitudes toward chemistry and mathematics. To evaluate student mathematical skills, this study employed a mathematics assessment developed and performed at the University of Minnesota, and hence will be called the Minnesota Mathematics Assessment or MMA—a 20-question, multiple choice quiz designed for second-semester general chemistry students. Results were inter-correlated to determine what factors influenced student success. This study found a strong correlation between mathematics attitudes and chemistry attitudes, with a Pearson product-moment correlation coefficient (r) of 0.50. Between course grade vs. either chemical attitudes or mathematics attitudes, the r values were 0.25 and 0.23 respectively, showing weak correlations. The correlation of course grade versus total MMA score gave an r value of 0.35, a moderate correlation. Comparison of the current study's MMA results with those of a previous Minnesota study demonstrates that the MMA is reproducible. The correlation coefficient found for course grade vs. total MMA score was comparable to that found in the Minnesota study. Analysis of the 20-question MMA data resulted in a 10-question subgroup whose r = 0.41. Although some gender attitude differences were found, these did not correlate with course grade.

2018 ◽  
Vol 22 (3) ◽  
pp. 497-521 ◽  
Author(s):  
Yu (April) Chen ◽  
Sylvester Upah

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.


2019 ◽  
Vol 96 (8) ◽  
pp. 1600-1608 ◽  
Author(s):  
Viveka L. Perera ◽  
Tianlan Wei ◽  
Debra A. Mlsna

2013 ◽  
Vol 90 (11) ◽  
pp. 1433-1436 ◽  
Author(s):  
Kevin M. Esterling ◽  
Ludwig Bartels

2021 ◽  
Vol 40 (2) ◽  
Author(s):  
Rebecca Campbell ◽  
Benjamin Blankenship

Institutions are redesigning gateway courses—lower-division courses known to create student success bottlenecks—to influence persistence and completion goals. These initiatives, student success course redesigns (SSCR), are specialized versions of course design institutes (CDIs). This investigation into SSCRs uses content analysis to examine the implementation plans created during a SSCR. Results demonstrated that the majority of the strategies planned focused on the Learning key performance indicator (KPI), and the minority of the planned-for strategies focused on the Monitoring Student Performance KPI. A more granular analysis of the Learning strategies revealed five themes: Content, Assessment, Pedagogy, Syllabus, and Student Success. Additional results indicated the majority of planned strategies would occur out of class, and disciplinary differences between science, technology, engineering, and mathematics (STEM) and non-STEM faculty for pedagogical and content design changes. Results also demonstrated a need for more faculty to utilize actionable language for course redesign strategies. Moreover, the implementation plans provided useful assessment feedback of the CDI itself.


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