Factors in Information Literacy Education

Abstract

Information literacy has long been discussed in the field of library science but is only recently becoming applied in specific academic disciplines. This article assesses student learning of information literacy skills analyzing data collected from three semesters of the Introduction to Comparative Politics course. Variables such as major discipline, gender, class year, and grades on several performance indicators are used to identify key patterns in successful information literacy learning among students. Questions that drive this research include: How do major disciplines approach information literacy differently. Is information literacy discipline specific? Does gender affect information literacy aptitude? Do upper-division students still need information literacy education? Which students are most deficient in their pretest knowledge of information literacy? What types of exercises are effective in teaching information literacy? Through analysis of our data, we address these questions and isolate the most significant factors in student learning of information literacy skills. Our data suggest that information literacy knowledge is content sensitive. Not only is information literacy significantly associated with several performance indicators, information literacy appears to be discipline specific.

Keywords:

• information literacy
• political science learning outcomes
• teaching comparative politics

Notes

Note: ∗p ≤ .05; ∗∗p ≤ .01; ∗∗∗p ≤ .001 (two-tailed).

Note: ∗ = Social Science major; ∗∗ = Social Science major, but specifically Political Science major. Cell entries in first column represent the student's major. Cell entries in the second and third column represent the mean scores of majors on the pretest and posttest. Cell entries in the final column represent the mean net gain or amount of improvement across time from the pretest student mean by major to the posttest student mean by major. Highlighted entries represent those majors with means or gains in the top half of the overall distribution of declared majors.

Note: ∗p ≤ .10; ∗∗p ≤ .05; ∗∗∗p ≤ .01 (two-tailed). The dependent variables are the student-level pretest score, posttest score, and paper grade. Assignments were graded on a 100-point scale. Entries are unstandardized regression coefficients. Standard errors are in parentheses. Posttest is excluded from the first model because it accounts for an exercise subsequent to the initial pretest.

∗ = Variable operationalized as both a dependent and independent variable throughout analysis.

It should be noted that there were only two business majors in the sample who completed both the present and the posttest. Findings concerning business major thus should be treated with caution.