Practical Programming Tasks for Better Understanding of Expectation in Computer Science Probability Courses
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| Title: | Practical Programming Tasks for Better Understanding of Expectation in Computer Science Probability Courses |
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| Language: | English |
| Authors: | Nili Beck, Yelena Stukalin, Sigal Levy |
| Source: | Teaching Statistics: An International Journal for Teachers. 2026 48(1):3-11. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 9 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Programming, Expectation, Computer Science Education, Probability, Efficiency, Algorithms, Experiential Learning, Problem Solving, Difficulty Level, Task Analysis, Design |
| DOI: | 10.1111/test.12402 |
| ISSN: | 0141-982X 1467-9639 |
| Abstract: | The concept of expectation is fundamental in computer science education, especially for analyzing the time efficiency of algorithms. However, the gap between theoretical knowledge and practical application can make it challenging for students to appreciate the importance of expectation in real-world contexts. This paper seeks to bridge that gap by highlighting the relationship between expectation and the population mean. To accomplish this, two experiments are presented, with tasks specifically designed to engage students in hands-on learning experiences in practical problem-solving. The first experiment focuses on the number of successes of the careless clerk, while the second experiment involves searching for all occurrences of a value in a randomly selected array, addressing the key question of the algorithm's expected time complexity. By presenting tasks that demonstrate the practical application of expectation, we aim to equip students with the tools needed to make informed decisions when designing and optimizing algorithms. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1494361 |
| Database: | ERIC |
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| Abstract: | The concept of expectation is fundamental in computer science education, especially for analyzing the time efficiency of algorithms. However, the gap between theoretical knowledge and practical application can make it challenging for students to appreciate the importance of expectation in real-world contexts. This paper seeks to bridge that gap by highlighting the relationship between expectation and the population mean. To accomplish this, two experiments are presented, with tasks specifically designed to engage students in hands-on learning experiences in practical problem-solving. The first experiment focuses on the number of successes of the careless clerk, while the second experiment involves searching for all occurrences of a value in a randomly selected array, addressing the key question of the algorithm's expected time complexity. By presenting tasks that demonstrate the practical application of expectation, we aim to equip students with the tools needed to make informed decisions when designing and optimizing algorithms. |
|---|---|
| ISSN: | 0141-982X 1467-9639 |
| DOI: | 10.1111/test.12402 |
