Academic Monitoring in Higher Education

When a student is falling behind, someone on your campus usually knows. An instructor has marked the absences, the LMS (learning management system) shows the missing work, and the tutoring center has no record of a visit. Academic monitoring brings those records together, so that what each office knows reaches a person who can act on it.

Software helps, but a monitoring program starts with a weekly review of the data and a named person who contacts each flagged student.

Key takeaways

• Academic monitoring is how a college collects and acts on data about student progress, such as attendance, grades and center visits, before problems grow.
• Attendance, missing assignments and visits to support centers often change before grades do, so they warn you first.
• Every warning sign needs a named person who reviews it and contacts the student.
• You judge the program first by what happens after each flag, such as whether the student got help and returned the next term.

What is academic monitoring?

In higher education, academic monitoring is the process of collecting data about each student’s progress and engagement, reviewing it on a regular schedule, and acting on what it shows before a student fails or withdraws from a course.

For a long time, monitoring meant watching grades, which only change after a student has fallen behind. Today it also covers three measures that move earlier: class attendance, LMS activity and center visits. When all three drop together, you usually still have weeks of the term left to act.

How monitoring relates to early alerts

Monitoring is the regular reading of student data across the term. An early alert is a flag about one student, raised by a faculty member or by an automatic rule, and sent to the person who will follow it up. Our guide to identifying at-risk students explains how early alerts reach the right person and how faculty hear what happened.

Why academic monitoring matters

Academic monitoring matters because it lets your staff make the first move instead of waiting for a struggling student to ask. Retention, funding and fair access to support all depend on how early that move comes.

Retention

Every student who leaves before finishing takes their remaining tuition with them and lowers your college’s graduation rate. Students often leave after weeks of missed classes and unfinished work, so a program that spots that pattern gives your staff time to step in. Our guide to student retention strategies covers the advising and support programs that help those students stay once you have contacted them.

Funding

By 2020, more than half of the states tied part of their public college funding to student outcomes through performance-based funding models, according to the State Higher Education Executive Officers Association (SHEEO). The share of funding at stake varies widely by state. In those states, a record of which flagged students stayed and passed their courses shows the outcomes the state pays for.

Equity

Students who struggle for reasons outside class, such as long work hours or family duties, are often the ones who do not come in on their own. Vincent Tinto’s book Leaving College ties retention to a strong sense of community on campus, in class and outside it. A student who never visits a center or an advisor may lack that connection and be drifting away even while their grades hold.

What to monitor

The most useful data is usually already on your campus, spread across offices that rarely compare notes. Start with the sources you have and add others as the process grows.

Academic data

Your student information system (SIS) holds enrollment, course load, grade history and academic standing, including probation. Your LMS shows logins and submitted work in each course. Submissions change early, so a student who stops turning in work can be flagged well before midterm.

Engagement data

Engagement data covers class attendance, check-ins at tutoring and learning centers, advising appointments and attendance at campus events. A student who never visits the center, or whose visits stop partway through the term, deserves a second look when attendance in the same course is slipping too.

Financial and administrative signals

A hold on a student’s account or a change in financial aid can block registration and push a student toward leaving. Falling short of Satisfactory Academic Progress, the grade and pace standard for keeping federal aid, can do the same. These signals sit with the business office and financial aid, so agree in advance on who may see them and who acts on them.

Concerns people report

Some of what puts a student at risk is never typed into a system. An instructor may notice that a student seems overwhelmed, and residence life staff may hear about a crisis at home. Give faculty and staff one simple referral form, and those concerns can join the rest of your data.

Measures to watch

Once you collect the data, these measures help you decide which students and courses need attention first:

• Pace to degree, which shows whether a student is earning credits fast enough to finish on time.
• DFW rate, the share of students in a course who earn a D or an F, or withdraw. It matters most in the large introductory courses that many majors require, often called gateway courses.
• Engagement, a combined measure of LMS activity, attendance and use of support services.
• Sudden change in any of these, because a sharp drop in LMS activity or grades says more than one low score.

A four-step monitoring workflow

A monitoring program repeats the same cycle all term: collect the data, review it, contact the student and record what happened. Give each step a named owner before the term starts.

Collect

Pull the data from each system on a schedule your team can keep, automatically where you can, so nobody has to retype it. A weekly pull is enough for most signals while the program is new.

Review

Before the term starts, agree on the patterns that count as a flag, for example two missed classes in a row in one course or a week with no LMS login. That way every reviewer makes the same call. A pattern that shows up in two sources, like falling attendance and falling LMS activity in the same course, is a stronger case than one low quiz score.

Intervene

Each flag goes to the person best placed to act on it, and who that is depends on what the flag shows:

• Advisors for course load, registration and degree planning.
• Success coaches for barriers outside class, such as work hours or money worries.
• Center staff for tutoring in the course where the student is struggling.

Faculty can raise a flag too, and they add context the data cannot show. Whoever makes the contact, the first message works best when it names the class the student has missed and the service that can help.

In a short video walk-through, the Center for Student Success at Southern University at Shreveport shows its referral process in Accudemia, the academic center management software we make. A faculty member submits a referral and a success coach invites the student in. When the student completes an academic plan, the faculty member is told which actions were agreed, and the referral closes.

Measure

After each contact, record what happened, for instance whether the student came to tutoring, met an advisor or returned to class. Record false alarms as well, because they show you which flags to adjust.

Choosing tools for academic monitoring

Test each tool against the data and the process you already have. These questions are worth asking in any demo:

• Can it bring in the data you plan to watch, such as class enrollments from your SIS, or will staff enter it by hand?
• Can you limit each kind of flag to the roles your Family Educational Rights and Privacy Act (FERPA) policy allows?
• Does every contact with the student leave a record that the next staff member can read?
• Can it report at the end of the term on what happened to flagged students?

How AccuClass fits in

If class attendance is your earliest warning sign, our class attendance software, AccuClass, covers that one signal. It raises alerts automatically by rule.

Attendance comes in through the instructor’s roll call, sign-in stations or the AccuClass mobile app. You set the rule, for example a number of absences in one class. When a student triggers it, the alert goes to the student and the instructor, and you can add other recipients, such as an advisor.

Once a week, administrators receive a summary of every flagged student, so nobody has to run a report. AccuClass covers class attendance only, so the follow-up still needs an owner in your advising office or center.

How Accudemia fits in

Accudemia covers the records your support centers keep. It imports your classes and student enrollments from your SIS, logs each visit through sign-in kiosks and appointments, and lets tutors add comments and an assessment of the student’s progress to each session log.

In Accudemia, referrals come from tutors and instructors who are worried about a student, and Accudemia also calls them early alerts. Each referral goes by email to the follow-up person you assign and appears on a dashboard your staff check. If the follow-up person changes, the new owner can read every earlier comment in the referral’s history.

Accudemia’s built-in visit reports and its referrals summary show which referred students came in for a visit. Grades and assignment data stay in your SIS and LMS, so your team reads those systems alongside Accudemia.

To see visits, referrals and session notes kept together for each student, request an Accudemia demo and bring the warning signs you plan to watch.

Rolling out monitoring in phases

Start with one group of students and widen the program once the process holds up, because a small start shows how much staff time each flag takes.

Pilot

For the first term or two, run the process with one group, such as first-year students or a single college. Build a small team from advising, your support centers and institutional research, and agree on a few measures and who reviews each one. Keep the group small enough that your team can contact every flagged student within the same week.

Expand

Once the pilot shows which contacts help, adjust your flags and add colleges and support services one at a time. Train the faculty and staff in each new group as it joins, so every group starts with a process that already works. This is also the stage to connect your tools to the SIS and LMS where that saves staff time.

Scale

In the second year, the process runs campus-wide. Predictive models become worth considering at this stage, once your own records cover several terms and you can check a model against them. Comparing results across courses can also show which courses or policies hold students back.

Privacy, bias and faculty buy-in

Before the first flag goes out, decide who may see what, how you will check flags for bias, and how faculty will hear back.

FERPA

FERPA is the federal law that protects the privacy of student education records. It lets a college share records with school officials who have a legitimate educational interest, without the student’s consent, and those officials can include faculty. Your college’s annual FERPA notice defines who counts as a school official and what counts as a legitimate educational interest.

Check each monitoring role against that notice and against the US Department of Education’s FERPA regulations and guidance. It also helps to tell students what you monitor and why, for example at orientation, so a message from an advisor does not come as a surprise.

Bias in predictive models

Because predictive models score each student’s risk from past data, they can repeat any inequities that data reflects. Each term, check how flags fall across student groups. Use a risk score to decide whom to contact first, and leave decisions about the student to the people who meet them.

Faculty buy-in

Some faculty will see monitoring as surveillance of their students. Explain what you collect, who sees it and what a flag leads to, which is usually a conversation with an advisor or coach. Faculty also keep raising concerns when they can see where each one goes. Tell them when someone has contacted the student, and share a summary at the end of each term.

Measuring the results of academic monitoring

The first test of a monitoring program is what happened to the students it flagged, such as whether they used a support service after someone contacted them and whether they stayed enrolled. Your contact records answer that within a term. Over several years, the program should also move these campus measures, compared with the years before it started:

• Course success, for example a lower DFW rate in the gateway courses you targeted.
• Retention, especially from the first year to the second.
• Time to degree, or how long your students take to graduate.

To estimate the return, take the net tuition kept from students who would otherwise have left, subtract what the program costs, and divide the result by that cost. Count the tuition for every term those students stay. You can only estimate which students would have left, so state your assumptions whenever you present the figure.

Long-term reporting is easier when your centers record every visit. At Santa Barbara City College, which runs its centers on Accudemia, the Institutional Research office draws on those records each semester when it reports how the centers affect student retention and success.

Your first term of academic monitoring

The first version of your monitoring program can run this term on the systems you already have, in three steps:

1. Pick two or three warning signs you can pull without new software, for example absences in gateway courses, missing LMS work and center visits.
2. Name who reviews each one and how often, such as the advising office every Monday.
3. Record each contact and what came of it.

After one term, those records show how much staff time each kind of flag takes and which contacts led somewhere. Bring that evidence to any decision about adding or changing a tool.