Teaching Scientific Literature Analysis: A Systematic Adoption of Skill-Building Methods to Enrich Research Training for Undergraduate Students

Abstract

Teaching scientific literature analysis skills is a critical step in research training. Here I describe a 6-week skill-building module on understanding scientific literature, incorporated into a 10-week undergraduate honors research practice course in Neuroscience. Key pedagogical components include: 1) student-centered active-learning, skill-building and community-building activities; 2) persistent adoption of a proven CREATE method and a novel curate scientific summary (CSS) method for teaching scientific literature analysis skills; 3) collaborative class organization consisting of persistent learning pods (PLPs) to facilitate student-driven participation and peer learning; and, 4) role play of a real research lab. Skill development was assessed using a self-assessment survey (SAS) and longitudinal evaluation of the CREATE and CSS methods application by the PLPs to analyze primary research articles (PRAs) over four weeks. Outcomes demonstrate alleviation of pre-existing student anxiety to read complex scientific literature and advancement of critical-thinking and collaborative skills. Specifically, the SAS responses indicate that student perception about reading scientific literature transformed from being a daunting task to an enjoyable activity; this enhanced their confidence in evaluating scientific literature. PLPs fostered student engagement, peer instruction, and community building, and contributed to skill development. Weekly assessment of CREATE and CSS application highlighted marked improvements in students' abilities to analyze and critique complicated scientific material. Role playing a research lab setting with a focused research theme facilitated integrative understanding of a frontier topic in Neuroscience. The outlined innovative approach can be adopted in Course-based Undergraduate Research Experience (CURE) and should help contribute to systematizing didactic practices to train neuroscientists.

Keywords: CREATE method; knowledge graphs; research practice; scientific literature analysis; undergraduate pedagogy.

Figure 1

Curated Scientific Summary schema. A, E, and T are biomolecules such as proteins and arrows indicate functional associations between them, reported in the PRAs.

Figure 2

Key results of SAS. a) Pie charts showing the variety and proportion of student responses to the question “Reading a research article is …” before and after the 6-week module on literature analysis. The categories were based on the affect grouping (see results) of evident responses and percentages indicate proportion of responses in each category. b) Histogram showing the level of efforts students input for the module. c) Histogram showing student-evaluation of their skill levels before and after the module.

Figure 3

Progressive improvement in student skills to comprehend and critique PRAs. Top panels show concept maps and bottom panels show an integrated analysis of experiment 1 methods and results. Left columns indicate work produced in week 3 (1^st week of PRA analysis) and right columns indicate work produced in week 6 (4^th week of PRA analysis). Red callouts indicate missing or inadequate evaluations. Green callouts highlight greater details and improvements. Top and bottom figure panels are work from two different PLPs. Left and right panels are produced by the same PLP and demonstrate progressive improvements.

Figure 4

An excerpt of CSS highlighting student misconception of protein associations in the PRA (see article info) in week 3 (1^st attempt), which was corrected by week 4 (2^nd attempt). Red font indicates misconceptions and corresponding corrections are in the right column.

Figure 5

Knowledge graphs of consolidated metadata from the CSS. a) Alluvial diagram showing protein associations consolidated from the 24 articles analyzed by all the PLPs over four weeks. Left column indicates activator proteins, middle column indicates effector proteins and right column indicates effector-modulated target proteins. b) Protein-protein interaction network generated in STRING software using the list of proteins consolidated in the CSS. The annotations at the bottom indicate the network statistics from STRING.