Computerized classification of student answers offers the possibility of instant feedback and improved learning. However, there are tradeoffs between formative assessment and ease of classification with different question types. Open response (OR) questions provide greater insight into student thinking and understanding than more constrained multiple choice (MC) questions, but development of automated classifiers is more difficult, often requiring training a machine learning system with many human-classified answers. Here we explore a novel intermediate-constraint question format called WordBytes (WB) where students assemble one-sentence answers to two different college evolutionary biology questions by choosing, then ordering, fixed tiles containing words and phrases. We found WB allowed students to construct hundreds to thousands of different answers, with multiple ways to express correct answers and incorrect answers with different misconceptions. WB offers the possibility of more rapid development of classifiers, as we found humans could specify rules for an automated grader that could accurately classify answers as correct/incorrect with Cohen’s kappa of 0.89 or higher, near the measured intra-rater reliability of two human graders and the performance of machine classification of OR answers (Nehm et al. 2012). Finer-grained classification to identify specific misconception had much lower accuracy (Cohen’s kappa < 0.70), which could be improved either by using a machine learner or human rules, but both required inspecting and classifying many student answers. We thus find that the intermediate constraints of our WB format allows the possibility of accurate grading of the correctness without the labor-intensive step of collecting hundreds of student answers.
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