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We conducted an across-semester quasi-experimental study that compared students' outcomes under frequent and infrequent testing regimens in an introductory computer science course. Students in the frequent testing (4 quizzes and 4 exams) semester outperformed the infrequent testing (1 midterm and 1 final exam) semester by 9.1 to 13.5 percentage points on code writing questions. We complement these performance results with additional data from surveys, interviews, and analysis of textbook behavior. In the surveys, students report a preference for the smaller number of exams, but rated the exams in the frequent testing semester to be both less difficult and less stressful, in spite of the exams containing identical content. In the interviews, students predominantly indicated (1) that the frequent testing regimen encourages better study habits (e.g., more attention to work, less cramming) and leads to better learning, (2) that frequent testing reduces test anxiety, and (3) that the frequent testing regimen was more fair, but these opinions were not universally held. The students' impressions that the frequent testing regimen would lead to better study habits is borne out in our analysis of students' activities in the course's interactive textbook. In the frequent testing semester, students spent more time on textbook readings and appeared to answer textbook questions more earnestly (i.e., less "gaming the system'' by using hints and brute force).
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LDA Topic Analysis of a Cybersecurity Textbook
We perform a Latent Dirichlet Allocation (LDA) topic analysis of Matt Bishop’s widely used 1440-page textbook on cybersecurity. To our knowledge, our work is the first time such analysis has been carried out on any singleauthored work of this length. This topic analysis might be useful for creating a supplemental interactive guide to the textbook to help readers explore the book more efficiently for particular learning objectives.
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- Award ID(s):
- 1753681
- PAR ID:
- 10160115
- Date Published:
- Journal Name:
- The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), November 16--20, 2020, online, (May 31, 2020), submitted
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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