Motivating students to engage with a course, encouraging positive behavior, and inspiring them to take an active role in their educational process - particularly at the beginning of the course - are universal challenges in education. In this article, we share our experience implementing an early submission incentive policy in a Machine Organization and Assembly Language Programming course. This policy encourages students to complete and submit their weekly lab work early in exchange for bonus points. We examine the impact of this positive behavior reinforcement on overall student performance (final grade) and performance in specific components such as exams and programming assignments. Our results, based on data collected over four years and involving more than 1,400 students, indicate that students who participate in early submissions achieve a higher final grade and perform better on other assessments, such as programming assignments.

The ubiquitous use of online tools, contractors and homework sites, has made plagiarism a concerning topic in computer science education. With the introduction of ChatGPT, it poses a threat now more than ever. Many cheating detection tools, such as similarity checkers and style anomaly checkers, help instructors decide whether a student has plagiarized. However, these are not scalable to large classes. Similarity tools can produce high rates of suspected cheating and thus ineffectively use an instructor's time in weeding out the actual cheating cases, especially in the early weeks of CS courses where programs can be small and student solutions can be very similar. We developed a new approach using outlier detection to filter inconsistent performers based on their lab scores throughout the course and their midterm exam scores. Instructors can then manually analyze a manageable amount of students even with large class sizes. We performed our experiment on two large course offerings of CS1 (a total of 177 students) using our algorithm and compared it to a manual analysis performed by an experienced CS1 instructor. The detection approach identified 11 students in the first offering (Winter 2019) and 12 students in the second offering (Spring 2023). With an average precision of 83%, our tool produces a list of concerning students with high precision. This significantly helps teachers efficiently allocate their time and pursue cheating early in the term in order to address and prevent further issues.

Interactive real-time communication between people and machine enables innovations in transportation, health care, etc. Using voice or gesture commands improves usability and broad public appeal of such systems. In this paper we experimentally evaluate Google speech recognition and Apple Siri - two of the most popular cloud-based speech recognition systems. Our goal is to evaluate the performance of these systems under different network conditions in terms of command recognition accuracy and round trip delay - two metrics that affect interactive application usability. Our results show that speech recognition systems are affected by loss and jitter, commonly present in cellular and WiFi networks. Finally, we propose and evaluate a network coding transport solution to improve the quality of voice transmission to cloud-based speech recognition systems. Experiments show that our approach improves the accuracy and delay of cloud speech recognizers under different loss and jitter values.

This paper reports on our experience in achieving reusability and using component-based software engineering in the Algorithma 99 (Algorithm Animation) Project. We show how we extended and reused Algorithma 98 [2] into Algorithma 99 and how we prepared Algorithma 99 to be reused in Algorithma 2000 (to be implemented in Winter 2000). Component-based software engineering is not only confined to binary components, such as COM and CORBA, but is also applicable to software processes, architectures and design, and object-oriented libraries.