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Empirical Evaluation of a Differentiated Assessment of Data Structures: The Role of Prerequisite Skills

Begum, M., Haglund, P., Korhonen, A. , Lonati, V., Monga, M., Strömbäck, F. & Tilanterä, A. (2023). Empirical Evaluation of a Differentiated Assessment of Data Structures: The Role of Prerequisite Skills. Informatics in Education, 23(1), doi: 10.15388/infedu.2024.05


There can be many reasons why students fail to answer correctly to summative tests in advanced computer science courses: often the cause is a lack of prerequisites or misconceptions about topics presented in previous courses. One of the ITiCSE 2020 working groups investigated the possibility of designing assessments suitable for differentiating between fragilities in prerequisites (in particular, knowledge and skills related to introductory programming courses) and advanced topics. This paper reports on an empirical evaluation of an instrument focusing on data structures, among those proposed by the ITiCSE working group. The evaluation aimed at understanding what fragile knowledge and skills the instrument is actually able to detect and to what extent it is able to differentiate them. Our results support that the instrument is able to distinguish between some specific fragilities (e.g., value vs. reference semantics), but not all of those claimed in the original report. In addition, our findings highlight the role of relevant skills at a level between prerequisite and advanced skills, such as program comprehension and reasoning about constraints. We also suggest ways to improve the questions in the instrument, both by improving the distractors of the multiple choice questions, and by slightly changing the content or phrasing of the questions. We argue that these improvements will increase the effectiveness of the instrument in assessing prerequisites as a whole, but also to pinpoint specific fragilities.

Publication Type: Article
Additional Information: Open access article under the CC BY license.
Publisher Keywords: data structures and algorithms, prerequisite skills, prerequisite knowledge, misconceptions, CS2, computer science education, qualitative content analysis
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
SWORD Depositor:
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