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. 2018 Sep;17(3):ar40.
doi: 10.1187/cbe.17-04-0061.

Error-Discovery Learning Boosts Student Engagement and Performance, while Reducing Student Attrition in a Bioinformatics Course

Christopher J Lee  1   2 , Brit Toven-Lindsey  3 , Casey Shapiro  3 , Michael Soh  3 , Sepideh Mazrouee  2 , Marc Levis-Fitzgerald  3 , Erin R Sanders  4   5
Affiliations

Error-Discovery Learning Boosts Student Engagement and Performance, while Reducing Student Attrition in a Bioinformatics Course

Christopher J Lee et al. CBE Life Sci Educ. 2018 Sep.

Abstract

We sought to test a hypothesis that systemic blind spots in active learning are a barrier both for instructors-who cannot see what every student is actually thinking on each concept in each class-and for students-who often cannot tell precisely whether their thinking is right or wrong, let alone exactly how to fix it. We tested a strategy for eliminating these blind spots by having students answer open-ended, conceptual problems using a Web-based platform, and measured the effects on student attrition, engagement, and performance. In 4 years of testing both in class and using an online platform, this approach revealed (and provided specific resolution lessons for) more than 200 distinct conceptual errors, dramatically increased average student engagement, and reduced student attrition by approximately fourfold compared with the original lecture course format (down from 48.3% to 11.4%), especially for women undergraduates (down from 73.1% to 7.4%). Median exam scores increased from 53% to 72-80%, and the bottom half of students boosted their scores to the range in which the top half had scored before the pedagogical switch. By contrast, in our control year with the same active-learning content (but without this "zero blind spots" approach), these gains were not observed.

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Figures

FIGURE 1.
FIGURE 1.
Stages of the error-discovery learning process. Students answer challenge problems by writing text on their laptops or smartphones and then briefly discuss their answers in pairs, before assessing their individual answers against the correct answer and against known conceptual errors that have been previously observed on that question (see the text for more details). Optional stages that can be performed outside class (online) are highlighted in yellow.
FIGURE 2.
FIGURE 2.
Increasing student engagement in answering target problems in class. Cumulative distribution functions for the number of questions answered by each student in class, before (2008, 2009) and after (2011–2015) the switch to EDL, for undergraduates (UG, solid lines) and graduate students (G, dashed lines). Thus, a point (x,y) on the graph for a given year means that y percent of students answered x questions (or fewer) that year. For 2008 and 2009, in the absence of individual counts for each student, we simply plotted the mean number of questions answered per student.
FIGURE 3.
FIGURE 3.
COPUS analysis of class-time usage. The total fraction of instructor time spent on lecturing, answering questions, and so on, as defined specifically by COPUS before (2008, 2009) and after (2011, 2013, 2015) the switch to EDL.
FIGURE 4.
FIGURE 4.
Undergraduate exam cognitive rigor versus student performance. Independent assessment of exam cognitive rigor (%HOCS, rated as the percent of questions requiring HOCS in Bloom’s taxonomy) before (2008, 2009) and after (2011–2015) the switch to EDL, versus the mean undergraduate exam score each year.
FIGURE 5.
FIGURE 5.
Undergraduate exam scores distributions. The percentage of students scoring above a given exam score before (2008, 2009) and after (2011–2015) the switch to EDL. Thus, a point (x,y) on the graph for a given year means that y percent of students obtained an exam score of x or higher that year.
FIGURE 6.
FIGURE 6.
Rates of student attrition from the course. Total rates of attrition before (2003–2009) and after (2011–2015) the switch to EDL for undergraduates (A) and graduate students (B). Note that the large error bars in some years (e.g., undergraduates in 2008–2009) are due to having attrition rate data from only a small sample of students in those years (PTE request data, etc.; see Methods).
FIGURE 7.
FIGURE 7.
Undergraduate evaluations of course workload. The mean and SD (error bars) of student-reported course workload before (2004–2006) and after (2011–2015) the switch to EDL.
FIGURE 8.
FIGURE 8.
Increasing student engagement in self-assessment and detecting conceptual errors. The number of in-class questions for which students completed key steps such as independently formulating and articulating their own answers (problems answered, green line), assessing the correctness of their answers (self-assessments, blue line), and identifying conceptual errors (errors detected, red line), both before (2008, 2009) and after (2011–2015) the switch to EDL.
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