TABLE 3.

Best-fit models for each of four student outcomes with binomial outcomes (logistic regressions)^a

Model name	Variable	Estimate	SE	p value	AICc difference	AICc weight
A. Likelihood of getting an “A” (n = 894)
A1	Semester: Spring	−0.162	0.202	0.422	0.000	0.457
	Semester: Summer	0.651	0.261	0.013
A2	Class size category	−0.163	0.238	0.492	1.600	0.210
	Semester: Spring	−0.137	0.205	0.505
	Semester: Summer	0.558	0.294	0.058
A3	Class size category	−0.425	0.209	0.042	1.900	0.179
B. Likelihood of getting a “C,” “D,” or “F” (n = 894)
B1	Semester: Spring	0.585	0.177	0.001	0.000	0.730
	Semester: Summer	−0.522	0.288	0.070
C. Likelihood of withdrawal (n = 1082)
C1	Semester: Spring	0.059	0.215	0.785	0.000	0.719
	Semester: Summer	−1.452	0.405	0.000
D. Likelihood of failing first exam (n = 1014)
D1	Semester: Spring	0.135	0.222	0.543	0.000	0.390
	Semester: Summer	−1.858	0.644	0.004
D2	Class size category	0.422	0.264	0.110	0.500	0.297
	Semester: Spring	0.089	0.215	0.680
	Semester: Summer	−1.566	0.664	0.018
D3	Class size category	0.708	0.260	0.006	1.900	0.151

^aFor each outcome, all uni- and multivariable models that are <2 of the lowest AICc are shown.