Limiting the number of revisions while providing error-flagging support during tests

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Abstract

Error-flagging support provided during tests leads to higher scores, as reported in literature. Although many beneficial factors contribute to higher scores, one undesirable contributing factor is that students abuse error-flagging feedback to find the correct answer through trial and error even when the test is not multiple-choice in nature. A limit can be placed on the number of revisions allowed per problem to foil the trial and error approach. A follow-up study was conducted to examine whether limiting the number of revisions allowed per problem yielded the benefits of error-flagging feedback while alleviating its shortcomings. The study also considered the effects of error-flagging feedback on partial scores. The findings are: even with a limit placed on the number of revisions per problem, students revised more often and scored higher with rather than without error-flagging. When students solved problems incorrectly without revisions, their solution qualified for more partial credit when error-flagging support was provided. When a limit was placed on the number of revisions and students solved problems correctly with revisions, they did so with fewer revisions when error-flagging feedback was provided than when it was not. When students solved problems incorrectly with revisions, even with a limit placed on the number of revisions, they revised more often with error-flagging than without, scored more partial credit, but did not take more time than when error-flagging was not provided. A limit on the number of revisions may discourage students from using error-flagging feedback as a substitute for their own judgment. Overall, students solved problems faster with error-flagging feedback, even though revisions prompted by such feedback can cost time. © 2012 Springer-Verlag.

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APA

Kumar, A. N. (2012). Limiting the number of revisions while providing error-flagging support during tests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7315 LNCS, pp. 524–530). https://doi.org/10.1007/978-3-642-30950-2_68

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