Work in progress: How do students respond to active learning? A coding guide for a systematic review of the literature

Caroline Elizabeth Crockett, Kevin A. Nguyen, Prateek Shekhar, Robert Matthew De Monbrun, Sneha Tharayil, Robyn Rosenberg, Cindy Waters, Maura Borrego, Cynthia J. Finelli

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


This work in progress paper presents an example of conducting a systematic literature review (SLR) to understand students' affective response to active learning practices, and it focuses on the development and testing of a coding form for analyzing the literature. Specifically, the full paper seeks to answer: (1) what affective responses do instructors measure, (2) what evidence is used to study those responses, and (3) how are course features connected with student response. We conducted database searches with carefully-defined search queries which resulted in 2,365 abstracts from 1990 to 2015. Each abstract was screened by two researchers based on meeting inclusion criteria, with an adjudication round in the case of disagreement. We used RefWorks, an online citation management program, to track abstracts during this process. We identified over 480 abstracts which satisfied our criteria. Following abstract screening, we developed and tested a manuscript coding guide to capture the salient characteristics of each paper. We created an initial coding form by determining what paper topics would address our research questions and reviewing the literature to determine the most frequent response categories. We then piloted and tested the reliability of the form over three rounds of independent pair-coding, with each round resulting in clarifications to the form and mutual agreement on terms' meanings. This process of developing a manuscript coding guide demonstrates how to use free online tools, such as Google Forms and Google Sheets, to inexpensively manage a large SLR team with significant turnover. Currently, we are in the process of applying the coding guide to the full texts. When complete, the resulting data will be synthesized by creating and testing relationships between variables, using each primary source as a case study to support or refute the hypothesized relationship.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
StatePublished - Jun 23 2018
Externally publishedYes
Event125th ASEE Annual Conference and Exposition - Salt Lake City, United States
Duration: Jun 23 2018Dec 27 2018

All Science Journal Classification (ASJC) codes

  • General Engineering


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