What learning analytics would be most useful in a community college, and how could those data help students make more informed decisions throughout their educational journey?
At the institution where I worked, we served as an open-enrollment college that accepted students with a wide range of academic backgrounds and life experiences. Placement measures such as the Texas Success Initiative assessment helped determine where students should begin, but those scores only told part of the story.
A large percentage of the student population was first-generation, nontraditional, or both. Many had never attended college, did not have family members who could guide them through the process, or were balancing school with employment, family responsibilities, and financial pressures.
The challenge was not always that students could not understand the course material. Often, they simply did not know the many small but important details involved in navigating higher education. Registration deadlines, degree requirements, grade point average expectations, financial aid rules, scholarship conditions, syllabus responsibilities, and program prerequisites can become overwhelming when no one has explained how the system works.
For that reason, the most useful learning analytics would be those that help identify risk early while also making the student’s academic pathway more visible.
Early-alert data
Attendance, grades, missing assignments, course participation, and sudden changes in performance could all contribute to an early-alert system.
If a student stops attending class, misses several assignments, or experiences a significant decline in grades, advisors and faculty could intervene before the situation becomes difficult to recover from. The purpose should not be to label the student as unsuccessful. It should be to identify when support may be needed.
These data become especially useful when combined with human judgment. A dashboard may show that a student has missed two classes, but a conversation may reveal transportation problems, work conflicts, childcare issues, confusion about the course, or a misunderstanding of institutional procedures.
Analytics can identify the signal. People still need to understand the cause.
Greater transparency for students
Treca Stark of Prince George’s Community College has noted that community colleges operate with limited financial and human resources while carrying multiple responsibilities related to academic preparation, workforce development, and open access. These constraints make it understandable that institutional leaders would explore how analytics could help staff use their time more effectively.
One of the best uses of analytics would be to provide students with greater transparency throughout their college journey.
In previous roles, I worked with many students who did not understand the requirements of their degree plan, how many credit hours they had completed, when they might graduate, or what prerequisites were required for a program they hoped to enter. This confusion often created frustration for the student and additional work for advisors and support staff.
A well-designed dashboard could make this information visible in one place. It might show:
- Completed and remaining degree requirements
- Current grade point average
- Credits earned and credits still needed
- Progress toward graduation
- Financial aid status
- Registration holds
- Upcoming deadlines
- Program prerequisites
- Recommended next steps
This would not remove the need for advising. It would allow the student and advisor to begin the conversation with the same information.
Course recommendations
Another useful approach would be a recommendation system similar to the model associated with Austin Peay State University. Such a system could examine a student’s academic profile, intended program, completed coursework, and the prior success of students with similar backgrounds.
The system could then recommend courses in which the student is more likely to succeed and that keep the student moving efficiently toward completion.
This could be particularly valuable for students who are unsure which course to take next or who unknowingly register for courses that do not apply to their degree.
However, recommendations should remain advisory rather than deterministic. A student should not be restricted because an algorithm predicts a lower probability of success. The system should inform a decision, not make the decision on the student’s behalf.
Why these analytics matter
The greatest value of learning analytics is not simply that they produce more data. Their value lies in whether they make the educational process easier to understand and support timely action.
For community college students, useful analytics could help answer practical questions:
- Am I currently on track?
- What do I need to complete next?
- Am I at risk of losing financial aid?
- Which course should I take?
- How close am I to graduation?
- Who should I contact when I need help?
When students can see their progress clearly, they are better positioned to take responsibility for it. At the same time, faculty and staff can focus their limited time on students who need direct support.
The burden of responsibility becomes more evenly shared. The institution provides clear, timely information, while the student gains a better opportunity to understand and manage the path ahead.
Final thought
The most useful learning analytics for an open-enrollment community college would combine early-alert indicators, degree-progress information, and carefully designed course recommendations.
These tools would not replace advisors, faculty members, or student support staff. They would strengthen their work by making important information easier to see and act upon.
The central question is not whether a college can collect more data. It is whether that data can help students feel less confused, more informed, and better supported as they move toward completing their education.