Drivers Of Retention In Online Degree Programs
Drivers of Retention inOnline Degree ProgramsEmpower degree faculty and students to make the mostof Coursera’s for-credit learning experiences through newquantitative insightsBy Alexandra Urban, Eric Karsten, Alan Hickey
Table ofContentsPG. 3PG. 4PG. 5ExecutiveSummaryBackgroundFocusing onRetentionPG. 6PG. 9PG. dentBehaviorDriversPG. 17PG. 18LookingAheadTechnicalAppendix
Executive SummaryWe see a variety of factors driving student retention in degree programs offered on the Coursera platform. Thefollowing are actions universities can take to bolster their students’ likelihood of staying in their programs:Build on open content successRecommend relevant open courses on Coursera. Students who complete an open course are 12% more likely topersist in their degree. Those who take open courses that stack into the degree are 3% more likely to remain intheir programs.Set students up for a strong startProvide resources, office hours, and one-on-one question answering to help students submit their firstassessment successfully, which increases their retention in the program by 6%. Performance is a strongindicator of students’ later persistence, with first-term grades emerging as especially critical.Include staff gradingBoost motivation through expert grading and feedback. Having at least one staff-graded assessment drives a 6%increase in student retention.Encourage frequent learningUse techniques like short videos and smaller assessments throughout the course to help ensure students returnfrequently to the degree courses. Having students learn across more days leads to a 5% gain in retention and isa more significant driver than total learning time.Design hands-on projectsKeep students progressing with hands-on projects where they can apply their new skills. Across writing, coding,and creative projects, these opportunities drive 3% greater retention. A final project is a great opportunity toprovide this type of hands-on project and culminating experience for the course.Use practice opportunitiesInclude ungraded assessments for low-stakes testing and to further students’ understanding. Having practiceassessments drives a 2% increase in student degree retention.Drivers of Retention in Online Degree Programs3
BackgroundThe Coursera platform has become a hub for leading universities to build and offer their online degrees to aneager audience of individuals worldwide. From the activities of thousands of students enrolled in our universitypartners’ degree programs, we are able to derive new insights on how individuals are engaging with onlinefor-credit courses, what helps them succeed, and which activities most drive their retention. Throughout thisreport, we offer data-driven best practices for driving greater retention in online degree programs.Our university partners have incredible expertise to share with degree students around the world, and we atCoursera are here to help empower these faculty and staff to create high-quality, online learning experiences.These quantitative insights can help universities and students achieve their degree goals. Before exploring whatdrives retention, here’s a quick look at who is enrolling in our partner universities’ degree programs on Coursera.Degree Students on CourseraOnline degree students tend to be older than on-campus degree-seeking students, even in bachelor’s degrees.Degree students on Coursera tend to have more work experience and are more likely to be actively employed.We see a diverse, global audience attracted to these degree programs. In fact, existing degree students arejoining from more than 90 countries. In total, 17,960 unique individual students from 19 different degreeprograms comprised the sample for this 2030405060AgeFigure 1 Age Distribution of Degree Students on CourseraDrivers of Retention in Online Degree Programs4
Focusing onRetentionThe success metric that we seek to understand in this report is the first-year retention rate. We define first-yearretention as a student who is actively enrolled in coursework in the term that starts one year after their firstterm of enrollment. To understand the earliest indicators of retention likelihood, we centered on activities andbehaviors in each student’s first term of enrollment. For example, for students starting in the fall, we examinetheir degree course experiences in the first term until winter break and then assess if these same students areactive in the program the following fall term.First-year retention is both a university-recognized indicator of success and strongly correlated with final degreecompletion. Across our data, we see that students’ first term in a degree program is strongly predictive of theirsuccess throughout the program. Regardless of the degree program structure and graduation criteria, one-yearretention successfully differentiates students who are on track to graduate from those who are not. Thus, thisfirst term is the most crucial period to intervene and assist any struggling students. Plus, by focusing on firstyear retention, we can include a larger sample of students, including cohorts who are 18 months into a degreeprogram, many of whom are not yet eligible to graduate.Figure 2 First-Year Retention Captures the Variability of Students’ Overall Terms ActiveDrivers of Retention in Online Degree Programs5
QuantifyingDriversThroughout the report, we use controlled regressions to isolate the impact of potential drivers on first-yearpersistence. Since each potential driver is observed on a very different scale, we need to standardize. Thus, wequantify the impact of each driver as the percent difference in model predictions when the driver variable isperturbed from its 25th percentile to its 75th percentile, holding all control variables at their true distributionin the dataset. This result can effectively be thought of as the average impact that a driver has on first-yearretention when changed from the lower end of its distribution to the higher end. We believe that thesealterations fall in the range of reasonable actions that either instructors or learners can take to enhance thequality of the learning experience. For more complete details, see the Technical Appendix.Drivers of Retention in Online Degree Programs6
Open Content DriversLearners’ activity in open content on the Coursera platform caninfluence their later success in full degree programs. Completing OpenContent is a strong driver of degree retention but simply enrollingwithout completing is a detracker of later degree retention. On theother hand, stackable open content, meaning it shares activities withthe degree courses, has a very similar effect on later degree retentionwhether students are only enrolling or also completing.Open Course CompletionsOne of the strongest drivers of student degree retention is previoussuccessful learning experiences on the Coursera platform. If a learnerhas completed at least one open course before starting the degree, theyare 12% more likely to persist in the degree program. These learnerswith at least one open course completion have demonstrated themotivation to make it through an online course, familiarity with theCoursera platform, and prior experience making time to learn in theirbusy schedules. We have seen in previous analyses (Drivers of Qualityin Online Learning, 2020) that making learning a habit and buildinglearners’ confidence can increase course retention. Additionally, thisstrong driver highlights the benefits of Coursera’s robust ecosystem:with millions of open course completers, the Coursera community hasan abundance of learners poised to retain in online degree programs.These learners have already demonstrated their motivation andpersistence.Having students learn across more days leads to a 5% gainin retention and is a more significant driver than total learning time.Drivers of Retention in Online Degree ProgramsIf a learner hascompleted atleast one opencourse beforestarting thedegree, they are12% more likelyto persist in thedegree program7
Has Open Content CompletionsHas Stackable CompletionsHas Stackable EnrollmentsHas Open Content Paid EnrollmentsHas Open Content Enrollments-10%0%10%Figure 3 Open Content Engagement Effect on Degree RetentionStackable Open CoursesThe next largest driver from open courses is taking content that explicitly stacks into a degree program. For thisanalysis, we defined stackable open courses as those where at least 30% of the learning items are shared witha for-credit degree course. We see a 3% lift in later degree retention across both enrollees and completers ofstackable open courses. This stackability strategy allows learners to familiarize themselves with the university,their instructors, and the material. By starting open courses that stack into a full degree, learners can determineif a program aligns with their interests, prerequisite knowledge, and future goals. Often, completing the entireopen course may not be necessary for assessing these criteria of fit and focus. Through this open content,learners can try a preview before they start a full degree and increase their likelihood of retaining in the laterdegree program.Open Course EnrollmentsPrior enrollments in open content when the learner has never completed have a neutral or negative effecton later degree retention. A learner paying for access typically indicates an intention to progress throughthe material, take the assessments, and earn the final course certificate. However, more paid enrollmentswhen the learners do not go on to complete the open course are not beneficial for students’ later retentionin a degree program. When students are not paying but simply enrolling in open courses, we see a negativeeffect on later degree retention. This finding suggests that the selection of a few courses to make progressin prepares students better for the focus and persistence needed in their later degree terms. Taken together,we can see how simply enrolling in open content is insufficient preparation, when considering later degreepersistence. For a university partner, building stackable content into your degree can help not only boostinterest and application submissions but also strengthen the later retention of those who do enter your forcredit learning experience.Drivers of Retention in Online Degree Programs8
Course StructureDriversWhile different subject areas and skills often require varied forms of instruction and practice, pedagogyinsights can still inform how online degree courses are structured. We at Coursera keep our course designrecommendations broad and flexible so that instructors have the autonomy to create the materials theyknow will be best for the specific subject area and skills they are teaching. University faculty bring immersivesubject matter expertise, and we aim to complement that with online teaching knowledge and researchthey each can apply to their unique courses. In this section, we explore the quantitative trends on enhancingassessment design, creating powerful projects, and aligning coursework with students’ busy lives.Graded AssessmentsHas StaffHas Hands On ProjectHas Peer0.0%2.0%4.0%6.0%Figure 4 Different Graded Assessment Types Effects on RetentionDrivers of Retention in Online Degree Programs9
Assessments are the core of a degree program, with instructors,students, and staff often spending the majority of their time on thesummative learning experiences within each course. On the Courseraplatform, we see that degree courses with at least one staff-gradedassessment have 6% higher retention rates. Students are eager toreceive tailored expert feedback, with degree students on Courseraconsistently rating staff-graded assessments as one of the mostvaluable aspects of the coursework. Staff-graded assessments canprovide motivation to continue in a course and offer importantguidance to enhance students’ understanding. Even in large courses,this moment of personalized feedback in staff-graded assessmentsmimics the benefits of individualized tutoring and can havemeaningful implications for students’ retention one year later.Hands-onassessmentsemerge as a keydriver of studentretention,corresponding toa 3% liftFurthermore, hands-on assessments emerge as a key driver of student retention, corresponding to a 3%lift. These hands-on assessments can include programming, staff-graded, peer-review, graded discussionprompts, and team assessments, all of which are open-ended opportunities to demonstrate new skills. Withmany degree students on the Coursera platform looking for career impact, these hands-on assessmentsprovide the type of industry-relevant projects they desire.Sum Graded Points Stafffrom 0.5 to 0.85Sum Graded Points Peerfrom 0.1 to 0.4Sum Graded Points Quizfrom 0.25 to 0.8-4.0%-2.0%0.0%2.0%4.0%Figure 5 Course Grade Share by Assessment Types Affects RetentionHaving assessments reviewed by peers appears slightly beneficial for student retention. However, when a largepercentage of students’ grades come from peer-reviewed assessments, motivation decreases, and studentsare significantly less likely to persist. Quizzes show the same pattern; while they are useful for quick tests ofstudents’ knowledge, quizzes should not be the main contributor to students’ overall grades. Retention requiresa healthy mix of assessments and the inclusion of open-ended projects receiving expert feedback.Drivers of Retention in Online Degree Programs10
Practice Assessments% of Ungraded Items Practice Assignmentfrom 3.1% to 22.9%% of Ungraded Items Videofrom 44.9% to 64.5%% of Ungraded Items Readingfrom 19.3% to 32.6%-1.00%0.00%1.00%2.00%Figure 4 Ungraded Course Activities Effect on RetentionIt is important to explain new concepts but often even more useful toprovide low-stakes practice opportunities for students to test theirunderstanding of those concepts. Ungraded assessments show a 2%increase in student retention, while video lectures are more neutral,and too many readings can have a negative effect.It is important to understand that the metrics in this section areconstructed to be zero-sum. In other words, increasing the share ofpractice assessments means decreasing the share of video lecturesand readings. Thus, while creating engaging hands-on practiceassessments typically takes more time, this substitution instead ofmore readings can be incredibly valuable for degree students.Skills are built through these hands-on and practice activities. Just aswe saw in earlier research on the Drivers of Quality in Online Learning(2020), hands-on experiences and practice opportunities drivepersistence and stronger learning outcomes. Mirroring that earlierwork, more practice can help students continue in theirdegree program.Drivers of Retention in Online Degree ProgramsUngradedassessmentsshow a 2%increasein studentretention, whilevideo lecturesare moreneutral, and toomany readingscan have anegative effect11
Preparing for ProjectsGraded Points Centroidfrom 0.61 to 0.68Max Item Weightfrom 0.2 to 0.250.00%0.50%1.00%Figure 7 Course Grade Distribution Effect on RetentionFor the overarching course structure, it can be beneficial to have a culminating larger project towards the end.The “weekly centroid” metric is a measure of how many weeks into a course the bulk of the graded materialis located. A smaller number here means assessments are more evenly distributed while a larger numberindicates the presence of a larger final project. A final project can motivate students to persist and provideindustry-relevant assessments with the opportunity to apply different skills from across the course in a singlesubmission. On-the-job tasks rarely require only one skill in isolation, and these larger, end-of-course projectscan mimic the authentic activities that careers often demand. Additionally, these projects can become part of astudent’s public portfolio, showcasing to employers that they are capable of applying a skill in practice, not justunderstanding it in theory.While large, hands-on projects provide useful experience in real-world tasks, they can also be overwhelming.Course designers should not wait until the end of the course to introduce this larger project. Instead, instructorscan orient students to the broader goal at the start of the term and build in weekly scaffolding to help preparestudents for this final project. Providing milestones breaks up larger projects into more achievable pieces andensures more consistent time needed per week throughout the course. Adding submission points and feedbackthroughout the course can make the final submission more achievable and set up students for success.Drivers of Retention in Online Degree Programs12
Course PacingCourse Median Days Activefrom 5 to 12Avg Learning Minutes Per Weekfrom 206 to 384.6Video Minutes Centroidfrom 0.5 to 0.590.0%2.0%4.0%Figure 8 Course Learning Patterns Effect on RetentionAt the course level, when degree students, onaverage, are spending more days actively engagedin the materials, we see higher persistence.Designing degree courses to encourage frequentengagement helps students develop beneficiallearning patterns and return more often, increasingretention by more than 4%.Weighting lecture time towards the beginning ofthe course and more assessments toward the endis a solid design strategy. This method keeps theoverall learning time needed per week relativelyconsistent, while also scaffolding more at thebeginning and having more hands-on projectstowards the end.Drivers of Retention in Online Degree ProgramsDesigning degree coursesto encourage frequentengagement helps studentsdevelop beneficial learningpatterns and return moreoften, increasing retention bymore than 4%13
Student BehaviorDriversBase Courses EnrolledBase Is Required For RetentionBase Courses DroppedHas Drop After Deadline-5.0%0.0%5.0%10.0%Figure 9 Student Behavior Effects on RetentionPerformance and Course LoadStudent behavior is one of the largest drivers of student degree retention. However, with program and courselevel design decisions often directly affecting students’ behavior, these drivers are not beyond the control ofdegree educators.Taking on full course-loads (rather than part-time course-loads) in the first term in a program improvesyear-one persistence by about 8%. While it is difficult to separate out the effect of students with the mostdemanding schedules, universities should consider how to support students by designing achievablepathways through the degree.Dropping one course in a degree program is associated with a decrease in persistence of about 7%, anddropping after the drop deadline is associated with an 8% drop in persistence. Greater guidance on course loadand overall degree pathway planning is one way universities can assist their new degree students with this oftendaunting task of deciding what courses to take when.Drivers of Retention in Online Degree Programs14
Learning ActivityCount of Days Activefrom 8 to 18Grade Achievedfrom 86.4% to 96.3%Sum Learning Session Hoursfrom 11.53 to 39.450.0%1.0%2.0%3.0%4.0%5.0%Figure 10 Individuals’ Learning Patterns Effect on RetentionConsistent student engagement time per week corresponds to higherstudent retention rates. Most degree students on Coursera are workingfull-time and must balance their degree coursework with their home andfamily responsibilities as well. Creating consistent expectations of howmuch time per week they need to carve out of their busy schedules canhelp set up degree students for success.Student grades achieved are key indicators, suggesting interventions thattarget students falling behind may be especially useful at the beginning ofthe program. Raising students’ grades from 86% to 96% is associated witha roughly 3% increase in first-year persistence in online degree programs.While simply making the assessments easier is likely not the optimal path,providing additional clarity on expectations, grading criteria, examples,and common mistakes all are beneficial strategies.Mirroring similar research findings in the open content setting, degreestudents are more likely to retain with more days active, as opposed tomore time over fewer sessions. Making learning a habit is more importantthan carving out larger chunks of time to try and make progress, withmore days active linked to a 4% lift in retention. While open coursesand for-credit programs have certain differences, we see that setting uplearners for success in online settings revolves around clear expectations,consistent learning time, and chunkable pieces of material.Drivers of Retention in Online Degree ProgramsMaking learninga habit is moreimportant thancarving outlarger chunks oftime to try andmake progress,with more daysactive linkedto a 4% lift inretention15
Strong StartTurned In First Assignment Ontimefrom 0 to 1First Deadline Count of Days Activefrom 2 to 5First Deadline Sum Learning Session Hoursfrom 2.43 to 8.730.0%2.0%4.0%6.0%Figure 11 Beginning of Course Learning Patterns Effect on RetentionProviding support on the very first submission is crucial, so studentshave the opportunity to start strong. Turning in the first gradedassessment and passing drives a 6% increase in overall degreepersistence. As students are working towards this first deadline in theirdegree journey, the number of days active is more critical than learningtime. While it can be tempting to tell students they need to spend morehours on the platform, it is frequent and consistent learning behaviorthat correlates with long-term success. For-credit courses should bedesigned for shorter, recurring engagement so that learners returnfrequently, right from the start of the degree program.Overall, students need to get off to a strong start with both performanceand pacing, achieving high early grades and staying up to date with theirdegree deadlines. For universities, it is crucial to intervene early withat-risk students since the first term so strongly correlates with students’continued success in the full degree program.Drivers of Retention in Online Degree ProgramsTurning in thefirst gradedassessment andpassing drives a6% increase inoverall degreepersistence16
Looking AheadAt Coursera, we are focused on not only conducting research but also putting these new learnings into action.Looking ahead, we are committed to updating our Degree Student At-Risk models to incorporate theseadditional quantitative drivers of retention. Adding nuance to these machine learning models improves theaccuracy and can help alert partners to when and with whom they need to intervene. Human assistance forstruggling students is often crucial, and we at Coursera can aid in directing those resources to enable successfuldegree programs at scale.Additionally, new data dashboards will examine how stackable content builds into the overall degree learningjourney. By tracking how individuals are moving through open courses and into your degrees, we can unlockgreater insights for our partners on the metrics and power of stackability.The teams at Coursera want to empower our world-class educators to provide high-quality degree programs forstudents globally. These new insights can pave the way toward more efficient and effective designs, elevatingfaculty’s online teaching to new heights.Drivers of Retention in Online Degree Programs17
Technical AppendixOverviewThe Drivers of Retention in Online Degree Programs Report (DRODPR) analyzes data from 17,960 students in19 online degree programs hosted on the Coursera platform. We use regression analysis to exploit variation instudent behavior and partner course design to measure the impact of various drivers on the key outcome offirst-year student retention.Regression MethodologyTo reduce bias in the estimates presented in this paper, we maintain a standardized analysis methodologythroughout this report. The goal of this methodology is to describe the distribution of a particular driver ofretention and then identify the most actionable ways for instructors to influence it when designing or teachingin an online degree program.We analyze each driver using an appropriate dataset, which is specified to ensure we are drawing conclusionsfrom reliable and meaningful observations. The analysis for each metric of interest can happen at either thedegree enrollment level (using measurements for each learner in each program) or the course enrollment level(using measurements for each learner in each course). Tables of covariates used for each of these analyses areprovided in the next section of the appendix.Because of the vast diversity of learners and instructional design choices within degree programs hosted onCoursera, there are many potential confounding variables that make it challenging to estimate the true impact ofsome of these drivers on the outcome that we care about. There are five main categories of variables that we use: Student variables are generally variables that are fixed characteristics of a learner like age or nationality Course variables are generally variables that are fixed for a given course within a program, such as thenumber of staff-graded assessments Program drivers are captured within our analysis as program fixed effects Course Enrollment variables are those which arise from the interaction of a learner with course material. Forexample, this might be the number of assessments that a learner turns in Program Enrollment variables are those which arise from the interaction of a learner with a degree programoverall. These include features such as the number of courses that stack into a degree program that alearner took before enrolling in the programWith a large set of control variables (many of which are categorical and require the specification of many levels),we use double-lasso for principled covariate selection with a separate model for each driver to ensure that ourestimate of its impact is as reliable as possible. Because we are analyzing each driver in its own model, we makea couple of key assumptions in order to compare their relative impact on the same scale.Drivers of Retention in Online Degree Programs18
First, we assume that all drivers have a linear, additive effect on the outcome metric when controlling for theselected covariates. This is often an over-simplification but allows us to make relative comparisons betweendifferently-distributed metrics much more easily. Second, we report the impact of each driver as the percentdifference in model predictions when the driver variable is perturbed from its 25th percentile to its 75thpercentile, holding all control variables at their true distribution in the dataset. This can effectively be thoughtof as the average impact that a driver has on an outcome metric when it is changed from the lower end of itsdistribution to the higher end. We believe that these alterations fall in the range of reasonable actions thateither instructors or learners can take to enhance the quality of the learning experience.Detailed Table of Potential ControlsTable 1 Controls for Learner AnalysesCategoryVariableVariable DefinitionProgramDegree ProgramFixed Effect for the Degree ProgramProgramNumber of terms allowedmissingThe number of terms in a program that are allowed to bemissed by a learner.ProgramDistribution of Course LoadDistribution of course load within a program (quartilesand interquartile range of the number of courses enrolled)ProgramNumber of Terms per YearNumber of terms in a program including controls forrequired and non-required terms.Program EnrollmentTerms Since AdmittedThe number of terms between when the learner wasadmitted and when they started the degree program.Program EnrollmentSeasonWhether the term is in the spring, fall or summerProgram EnrollmentCourses EnrolledThe number of courses a learner enrolled in within a giventerm.Program EnrollmentCourses DroppedThe number of courses a learner dropped within a giventerm.Program EnrollmentCourses Dropped afterDeadlineWhether a student dropped a course after the dropdeadline.Program EnrollmentSupport TicketsWhether a learner files a support ticket of variousdifferent types during a given term.Program EnrollmentOpen Content EnrollmentsStudent has enrolled in open content on the Courseraplatform.Program EnrollmentOpen Content PaidEnrollmentsStudent has enrolled in paid enrollments in opencontent on the Coursera platform.Program EnrollmentOpen Content CompletionsStudent has completed open content on the Courseraplatform.Program EnrollmentStackable ContentEnrollmentsStudent has enrolled in open content on the Courseraplatform that stacks into the degree program.Drivers of Retention in Online Degree Programs19
Program EnrollmentStackable ContentCompletionsStudent has completed open content on the Courseraplatform that stacks into the degree programStudentCountry GroupStudent’s nationality grouped by regionStudentAgeStudent’s age at degree startStudentEducation LevelStudent prior educational backgroundStudentRegistration TimingWhether the learner was a long-time Coursera learner orregistered on the platform shortly before the start of theirdegree program.Table 2 Controls for Instructional Design AnalysesCategoryVariableVariable DefinitionAssignment TypesAssignment Type Indicators(7 variables)Indicator of whether the course contains at leastone assignment of the given type: quiz, staff-gradedassignment, peer-graded assignment, programmingassignment, plug-in, discussion promptAssignment TypesAssignment TypePrevalence (7 variables)Fraction of graded assignments in the course of thegiven type: quiz, staff-graded assignment, peergraded assignment, programming assignment, plug-in,discussion promptAssignment TypesAssignment Type GradingWeight (7 variables)The fraction of student grade attributable to assignmentsof the given type: quiz, staff-graded assignment, peergraded assignment, programming assignm
The Drivers of Retention in Online Degree Programs Report (DRODPR) analyzes data from 17,960 students in 19 online degree programs hosted on the Coursera platform. We use regression analysis to exploit variation in student behavior and partner course design to measure the impact of various drivers on the key outcome of first-year student retention.
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