A card-sorting tool to measure expert versus novice thinking in scientific research.

Undergraduate research and laboratory experiences provide a wide range of benefits to student learning in science and are integral to imbed authentic research experiences in biology labs. While the benefit of courses with research experience is widely accepted, it can be challenging to measure conceptual research skills in a quick and easily scalable manner. We developed a card-sorting task to differentiate between novice and expert conceptualization of research principles. There were significant differences in the way faculty/postdocs, graduate students, and undergraduate students organized their information, with faculty/postdocs more likely to use deep feature sorting patterns related to research approach. When provided scaffolding of group names reflecting expert-like organization, participant groups were better able to sort by that organization, but undergraduate students did not reach expert levels. Undergraduates with Advanced Placement experience were more likely to display expert-like thinking than undergraduates without Advanced Placement Biology experience and non-PEER (persons excluded because of their Ethnicity or Race) students displayed more expert-like thinking than PEER students. We found evidence of undergraduates in various stages of development toward expert-like thinking in written responses. This card-sorting task can provide a framework for analyzing student's conceptualizations of research and identify areas to provide added scaffolding to help shift from novice-like to expert-like thinking.

Aggregate Trends of Apolipoprotein E on Cognition in Transgenic Alzheimer's Disease Mice.

BACKGROUND: Apolipoprotein E (APOE) genotypes typically increase risk of amyloid-ß deposition and onset of clinical Alzheimer's disease (AD). However, cognitive assessments in APOE transgenic AD mice have resulted in discord. OBJECTIVE: Analysis of 31 peer-reviewed AD APOE mouse publications (n = 3,045 mice) uncovered aggregate trends between age, APOE genotype, gender, modulatory treatments, and cognition. METHODS: T-tests with Bonferroni correction (significance = p < 0.002) compared age-normalized Morris water maze (MWM) escape latencies in wild type (WT), APOE2 knock-in (KI2), APOE3 knock-in (KI3), APOE4 knock-in (KI4), and APOE knock-out (KO) mice. Positive treatments (t+) to favorably modulate APOE to improve cognition, negative treatments (t-) to perturb etiology and diminish cognition, and untreated (t0) mice were compared. Machine learning with random forest modeling predicted MWM escape latency performance based on 12 features: mouse genotype (WT, KI2, KI3, KI4, KO), modulatory treatment (t+, t-, t0), mouse age, and mouse gender (male = g_m; female = g_f, mixed gender = g_mi). RESULTS: KI3 mice performed significantly better in MWM, but KI4 and KO performed significantly worse than WT. KI2 performed similarly to WT. KI4 performed significantly worse compared to every other genotype. Positive treatments significantly improved cognition in WT, KI4, and KO compared to untreated. Interestingly, negative treatments in KI4 also significantly improved mean MWM escape latency. Random forest modeling resulted in the following feature importance for predicting superior MWM performance: [KI3, age, g_m, KI4, t0, t+, KO, WT, g_mi, t-, g_f, KI2] = [0.270, 0.094, 0.092, 0.088, 0.077, 0.074, 0.069, 0.061, 0.058, 0.054, 0.038, 0.023]. CONCLUSION: APOE3, age, and male gender was most important for predicting superior mouse cognitive performance.