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Improving Online Course Completion Rates

Online community college student course completion rates are lower than their equivalent face-to-face courses.  According to a large California Community College Study1, rates are 11 to 14% lower, and they are even lower for subpopulations such as part-timers, minorities, and students with disabilities. This translates into lost time, money, and resources for both community colleges and their students. At Linn-Benton Community College where we work, 687 more students would have completed 2404 credits for a savings of $240,450 in the last three years if face-to-face and online course completion rates were the same. 

The Linn-Benton Community College elearning department has had success with several pilot projects in improving online course completion rates.

  • Writing 122 went from 70% passing to 90% passing, and two winter 2017 sections were 100%.
  • Math 65 went from 54% passing to 88% passing
  • Communications 218 went from 73% passing to 100% passing.

Three categories of strategies were used. First, good fit strategies help the student decide the first week or before the official drop date whether taking this online class is a good fit for them at this time in their life. Second, strong instructor presence strategies encourage practices that an instructor uses to help students complete a course, including good course design. Third, strong institutional support strategies were implemented, which included evaluating and applying institutional policies and practices designed to help distance learning students complete courses. 

Some but not all of the strategies in the good fit category are:

  • A precourse email: Students are informed of time and materials expectations and notified that they may be dropped from the course if they don’t complete the first-week requirements.
  • A time/date assignment: Instructors provide the estimated time to complete all assignments, and students map when they’ll complete each one. This is due the first week.
  • Emphasis on student login by Monday of the first week.
  • Substantial assignment due by Wednesday/Thursday of the first week.
  • Frequent assignments and assignment due dates, i.e., at least 3 due dates per week.

After the pre-course email and through the first week, some students make a determination that taking this class at this point in their life is not a good fit and decide to drop the course. 

On Thursday or Friday of the first week, the eLearning department meets with the faculty to look at the learning analytics of the students in their course. Here, institutional data is available to the instructor, often for the first time, as decisions are made about the best way to support individual students. Hopefully, in the next year, a dashboard combining LMS and institutional data will be available. We look at LMS data, such as login frequency, timely completion of assignments, grades on assignments, and quantity of interactions in the system. We also look at SIS data such as previous online course failures and cumulative GPA. At Linn-Benton, you are four times as likely to fail an online class if you have a GPA of less than 2.0, and more than four times as likely to fail another online course if you failed a previous one. If you have both of these characteristics you are more than 60% likely to fail an online course. 

These are combined with the instructor’s holistic view of the student to determine intervention strategies. Most of the time, the instructor contacts a subset of struggling students to prompt them to complete their work. If they do not complete the work or respond to the instructor, they are administratively dropped from the course during the first week. 

Potentially, more students will drop the first week, so more students are allowed to automatically start from the waitlist than normal. However, students are not allowed to add the course after the first day of the term 

After the drop date, increased emphasis is put on strong instructor presence. Some but not all of he strategies in strong instructor presence are:

  • Frequent assignments and assignment due dates, i.e., at least 3 times a week, which provides students with frequent feedback (automatic or personalized).
  • Required high scores on low stakes quizzes with multiple tries, i.e., a student must receive 100% on a reading quiz before moving to the next assignment.
  • Frequent communication (email, text, and calling) by the instructor with students who are at risk. Some automation through software is used. For example, an automatic message might pop up in the LMS or be sent by e-mail, saying, “I notice that you received a failing grade on the last test…,” or “I noticed that you haven't logged in very much this week. Please contact me immediately so we can discuss what would help you.” Positive messages are also sent: “I noticed you improved your grade from a ‘C’ to a ‘B’ on the last assignment. Congratulations, and keep up the good work!”
  • Required contact with other resources, such as the library or tutoring center.

The pilot courses which have adopted these strategies have been very successful. Writing 122 is one example. A redesign emphasized two major changes: First, a Course Orientation block was included, which requires students to complete seven steps before they start working with the course content. This includes a getting started reading that outlines course participation expectations, a time/date assignment, a brief piece of writing where students outline their goals for the course, and quiz that emphasizes student workload expectations. The quiz is one of several where students must receive a 100% score to continue (and have multiple chances to do so). Students who do not complete the orientation assignments are dropped from the course. 

After the orientation block, each week displays a calendar of suggested due dates, providing a visual of how to break assignments into small pieces. Weeks have similar, predictable structures, starting with a subject reading, then an activity that links the reading to students’ own experiences, then a reading or activity on writing process, and finally ending with response assignments that incorporate both content readings and process lessons. Deadlines have remained at twice a week, but the visual schedule shows a (usually) one-hour-per-day breakdown for all assignments. This blends with further chunking of large assignments. Instead of turning in three large papers, students now submit small pieces of the essay along the way, receiving brief feedback at each instance instead of one large download of feedback after the assignment is complete. This encourages a dialogue between student and instructor that helps improve the student’s writing (and achievement of course outcomes) while also increasing the student’s feeling of connection to the course. 

Finally, students are given multiple methods of contact throughout the course: they can receive weekly text-message reminders, view weekly 2-minute-or-less instructor videos, receive prompts directly from the LMS, and see forum posts and e-mails. Through steady and predictable contact and content, student completion rates have risen dramatically in the two years since the redesign. Thanks to the automation available in the LMS, this has not led to an increase in faculty workload and has, in fact, allowed more time to work with individuals who are struggling. 

Incorporating all of these strategies has been accomplished by adding several new strategies each term to minimize workload on the instructor. 

Ongoing documentation and research about the process can be found here. 

We are interested in hearing about other successes or questions. Please email Steve Smith ( or Jenn Kepka ( 

Steve Smith is the Director of eLearning and the iLearn campus at Linn-Benton Community College. Jenn Kepka is a writing instructor at Linn-Benton Community College. 

1 Johnson, H., Mejia, M. C., & Cook, K. (2015). Successful Online Courses in California’s Community Colleges.  Retrieved from

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