To the Department of Computer Science, Old Dominion University

The undisclosed inclusion of machine learning in CS350

We, the undersigned students of Old Dominion University, respectfully submit this petition to address a significant pedagogical unfairness within the current offering of CS350: Introduction to Software Engineering, and its role as a co-requisite for CS410

Students enrolled in CS350 are not reasonably informed about the inclusion of machine learning in the course. Specifically, the development of a training model capable of classifying computer science articles according to ACM standards.

Machine learning is not included in the following;

• Any prerequisite course for CS350
• The official course description
• The course syllabus
• Prior publicly available CS350 material

Figure 1. The course description of CS350

 

 

 

 

Important: Software development process models include methods for how teams achieve projects, it has nothing in relation with machine learning.

Figure 2. Course material that requires LM knowledge

 

 

 

 

 

 

Many students are not adequately prepared to work with LMs or training models.

Prior CS350 courses did not require the implementation of machine learning concepts as a fundamental part of the course project.

Machine learning is not aligned with the scope of CS350

Introducing machine learning as a major part of the course project, which relies on knowledge outside of the scope of the course, as stated when registering, creates a substantial disparity in student success. Students in previous iterations of CS350 did not have this element of machine learning as a required part of their course project, which creates pedagogical unfairness between iterations of CS350.

CS361, Advanced Data Structures and Algorithms, is a course that is listed as an optional prerequisite; however, it does not include machine learning. Therefore, CS361 does not adequately prepare students for the machine learning subject matter in CS350.

CS350, at its core, is a course with the objective of teaching software engineering. The inclusion of machine learning in the course project is a large disconnect from the core focus of CS350. Students who wish to be able to complete their ACM classifier are expected to do tasks outside of the scope of this course.

Harmful consequences due to CS350 being a co-requisite of CS410

CS410 and CS411W, I.E., Professional Workforce Development I and II, are back-to-back classes that prepare students in a simulated business environment as a development team. While in some organizations, machine learning may be an integrated part of a model, machine learning is a specialization. If there is no required machine learning course as a part of the CS curriculum at ODU, it should not be expected of students to be able to implement machine learning functionality as a part of CS350.

To build on the previous sentiment, due to CS350 being a co-requisite of CS410, there are large consequences to failing this class;

• Students who fail CS350 are unable to advance to CS411W, even if they pass CS410
• Being unable to progress to CS411W causes a logical setback for teams, in my case, specifically, three team members are at risk of not being able to progress, halving the team that is to progress to the next semester
• Teams in CS411W that lose a substantial amount of team members lose the synergy and cooperation built in the previous semester, which is a major setback.
• Teams that take CS410 together should be able to move forward with their team, since that is what is expected of the course, as well as students putting in time and effort into something that they may lose access to, due to circumstances outside of their control
• Students may be delayed for an entire academic year due to the pedagogical unfairness 350, in a class where a major part of the course project is not disclosed to students before registering.

Withdrawing from the course and doing another course under a different professor is not a solution, as all presently offered CS350 courses use the same template of the ACM Classifier course project, which includes the state machine learning integration.

Requested actions

We respectfully request that the Department of Computer Science take the following actions to restore fairness to students and prevent unnecessary struggle:

• Update the official course listing to clearly state that students will be required to implement a machine learning model as a part of the course project, if machine learning is to stay as a part of the curriculum
• Remove CS350 as a co-requisite for CS410, and allow students to take CS350 with CS411W if they were to fail, so that CS350 does not gatekeep progression due to factors outside of students' control.

We do not want to devalue the critical role that CS350 may play in software engineering; however, it is important that we, as a community, advocate for fairness and transparency between our university and its students.

Call to action:

If you want to make this statement heard, share it with anybody it may relate to. Please also help raise awareness by sending this to the Computer Science Department of ODU.

Conclusion

We believe these requested actions represent a reasonable, constructive solution that ensures transparency, fairness, and academic integrity in the CS curriculum.

We ask the department to review and address these concerns promptly.