Optimizing Community College Academic Plans for Seamless Transfer: Comparing Manual vs. Algorithm-Assisted Approaches

المفاهيم الأساسية

Manually developing an optimal academic plan for community college students transferring to multiple universities can be error-prone and time-consuming. Algorithm-assisted optimization can potentially reduce unnecessary excess credits and improve the usability of the academic planning process.

الملخص

The study examined the challenges community college students face when manually developing an optimal academic plan to transfer to multiple universities. The authors designed a low-fidelity prototype that lists the minimal set of community college courses an optimization algorithm would output based on the user's selected articulation agreements.

The experiment compared the performance of 24 community college transfer students using either the prototype or the standard ASSIST articulation agreement reports to create an optimal academic plan. The results showed that participants using the prototype made significantly fewer optimality mistakes, were faster in creating their plan, and provided higher usability ratings compared to the ASSIST users.

The authors argue that while manual academic planning can be error-prone, an optimization algorithm could potentially help community college students transfer with fewer unnecessary excess credits and improve the overall usability of the academic planning process. However, the authors note that future research is needed to move beyond a proof of value and towards actually implementing an optimization algorithm.

الإحصائيات

The ASSIST users took an average of 11.29 minutes to manually create an optimal academic plan and made an average of 3.33 optimality mistakes (i.e., excluding necessary courses or including unnecessary excess courses).

اقتباسات

"On average, the ASSIST users took 11.29 minutes to manually create an optimal academic plan and made 3.33 courses worth of optimality mistakes (i.e., excluding necessary courses or including unnecessary excess courses)."
"Optimization software can potentially help students transfer with fewer unnecessary excess community college credits, which may consequently reduce students' time to transfer. Furthermore, optimization software can potentially save time from manually developing an optimal academic plan."

الرؤى الأساسية المستخلصة من

Optimal Academic Plan Derived from Articulation Agreements: A Preliminary Experiment on Human-Generated and (Hypothetical) Algorithm-Generated Academic Plans

by David V. Ngu... في arxiv.org 05-02-2024

https://arxiv.org/pdf/2307.04500.pdf

استفسارات أعمق

How can an optimization algorithm be designed to balance competing priorities, such as maximizing GPA, minimizing course difficulty, and fulfilling degree requirements?

Designing an optimization algorithm to balance competing priorities in academic planning involves creating a weighted system that assigns values to each priority based on their importance. For example, GPA maximization could be assigned a higher weight if the student's goal is to apply for competitive graduate programs. Minimizing course difficulty could be another factor, especially if the student prefers a lighter workload. Fulfilling degree requirements is crucial for timely graduation and could be given a high weight as well.
The algorithm would then analyze the available course options, considering prerequisites, course availability, and scheduling constraints. It would generate multiple academic plans that meet the specified criteria and assign a score to each plan based on how well it balances the competing priorities. The student can then choose the plan that aligns best with their goals and preferences.

What are the potential unintended consequences of implementing an optimization algorithm for academic planning, and how can they be mitigated?

One potential unintended consequence of implementing an optimization algorithm is the risk of oversimplifying the academic planning process. Students may become overly reliant on the algorithm and neglect critical thinking skills necessary for decision-making. To mitigate this, it's essential to provide students with guidance on how to interpret and use the algorithm's recommendations effectively.
Another consequence could be a reduction in personalized academic planning. While the algorithm can provide efficient solutions, it may not account for individual preferences, career goals, or learning styles. To address this, advisors should supplement the algorithm's recommendations with personalized guidance and counseling sessions.

How can the insights from this study on the challenges of manual academic planning be applied to improve the design of articulation agreement databases and academic advising practices more broadly?

The insights from this study highlight the complexity and potential errors in manual academic planning, especially in the context of non-uniform transfer requirements. To improve the design of articulation agreement databases, they should be user-friendly, with clear language, intuitive formatting, and centralized access. Providing training on how to navigate and interpret these databases can also enhance their usability.
In terms of academic advising practices, advisors should be aware of the challenges students face in developing optimal academic plans. They can use the findings to streamline the advising process, offer targeted support to students navigating transfer requirements, and advocate for policy changes that promote smoother transitions between institutions. Additionally, incorporating technology solutions like optimization algorithms can complement advisors' efforts and provide students with more efficient planning tools.