As educators, we have the responsibility to identify students who are at risk of failing and dropping out. We must do everything we can to support these learners to prevent this from happening. But one paradox that often faces institutions is that in order to know which students are at risk, we first have to allow them to fail in some way.
However, this blog and video will show you that you do not have to allow your students to fail before you identify them as at risk of failing or dropping out.
Watch this short video to learn how your institution can identify students who are at risk of failing and what you can do to create a plan to help them succeed.
Don't want to watch the video? No problem! Keep scrolling to read the video summary below.
The Importance of Providing High-Quality Education
There is an organization in the San Francisco Bay area that is relentlessly pursuing the goal of fighting poverty. That organization is called the Tipping Point Community and it takes a comprehensive approach to poverty alleviation by investing in four areas:
Their CEO was quoted in an interview with Rocketship Public Schools as saying, “Of these four investment areas, we know that providing high-quality education is one of the best ways to break the cycle of poverty for good. When an individual graduates from college, he or she doubles their lifetime earnings, and paves the way for future generations of their families to pursue the path to and through college.”
Many other change agents, including Bill Gates, concur that higher education is the best way to permanently break the cycle of poverty. However, there are many persons who catch this vision for improving their lives through higher education, enroll, then soon afterward drop out. Frequently these persons have taken out a student loan and then have no greater capacity to re-pay it and actually find themselves in worse financial condition than before they enrolled.
The Paradox – Failing Students
As mentioned earlier, we educators have the responsibility to identify these persons who are at risk of dropping out and should do everything we can to support these learners to prevent this from happening. But educators are facing a difficult paradox – in order to know which students are at risk, we first have to allow them to fail in some way.
Behavioral Data as an Alternative Early Indicator
However, behavioral data is typically the first early indicator that a student is on a path to dropping out. Learners who do not attend or log into courses within the first three days of a term start are at a much higher risk of dropping out. Learners who minimally engage such as responding to introductory discussion board questions with very short responses are also at a higher risk of dropping out. Certainly, students who score poorly on early assignments are getting off to a bad start from which they may not recover. One type of behavioral data that schools may also use is engagement metrics from their learning management system such as frequency or duration of login.
Warning Signs that a College Student is Headed Toward Dropping Out
One of the institutions we serve is Georgia State University in Atlanta. Their Vice President for Student Success, Tim Renick, was recently interviewed by the Market Watch publication about their award-winning student success program. He listed the following eight warning signs that a college student is headed toward dropping out:
- Not accepting help
- Taking too few credits
- Choosing unnecessary courses
- Struggling in their major
- Being placed in remedial classes
- Running out of money
- Skipping class
- Dealing with adversity
All eight of these factors are a form of behavioral data.
Demographic Data is Predictive but Not Malleable
In an effort to identify these at risk students before they are allowed to fail, some schools have turned to demographic data in addition to behavioral data. It is true that demographic factors such as whether or not the student is a first-generation college student, socio-economic status, and ethnicity can be very predictive. But, when used as a retention predictor these factors are not always accurate and more importantly, they are not malleable or changeable – there is nothing a school can do to help a student regarding these demographic factors.
Wouldn’t it be great if there was a way to identify students at risk of failing even before the term begins using factors for which remediation can be provided? Wouldn’t it be great if there was a category of data in addition to behavioral and demographic data that could be collected very early in the student experience that was really predictive of learner success and retention?
Top 5 Reasons Why Students Drop Out
Earlier in this blog, I mentioned that Bill Gates recognized that the problem of college student dropouts was a significant problem in our country. To determine why college students drop out, the Bill and Melinda Gates Foundation surveyed thousands of persons who had started college but did not finish. The top five reasons were:
- Conflicts with work schedule
- Affordability of tuition
- Lack of support from family
- Lack of belief that a college degree is valuable
- Lack of discipline
Studying Non-Cognitive Data
You may think of non-cognitive data as “grit.” We call it non-cognitive data because it is independent of one’s intelligence. Most persons do not drop out of college because they were not smart enough. Non-cognitive data is also independent of demographic or behavioral data. It consists of one’s traits, attributes, skills, situation, and habits that form their learning persona. This can include but is not limited to characteristics of:
- And more
A Tool That Can Help: SmarterMeasure
Did you know that there is an assessment that measures these constructs? Did you think it was even possible to quantify grit? Well, there is and since 2002 the SmarterMeasure Learning Readiness Indicator assessment has been taken by almost six million students from over one thousand institutions.
The assessment begins by looking at the learner internally. We call this section individual attributes. It measures things like motivation, procrastination, willingness to ask for help, and locus of control. While looking at the learner internally, we also identify their dominant learning style which is based on the multiple intelligences model. This determines whether they are visual, verbal, social, solitary, etc.
Then we look at the learner externally. We call this section Life Factors. It looks at their availability of time, support from family and employers, having an appropriate place to study, health, and finances.
Next, we look at the learner’s skill set. We look at their technology skills, their on-screen reading skills, and their keyboarding skills.
We have found that the non-cognitive data measured by the assessment works very well with the demographic and behavioral data that schools are already collecting. In fact, the non-cognitive data is typically the WHY that explains the WHAT that is being observed with behavioral data.
For example, if a student has not logged in during the first week of the course, there must be a reason. That reason is typically a non-cognitive factor on which the student scored low in SmarterMeasure.
Identifying At Risk Students Before Allowing Them to Fail
Colleges position the SmarterMeasure assessment at different points in students' experiences. Many institutions position it as a part of the admissions process. This is not to use the assessment as a gatekeeper, but rather to identify the students who are at risk of failing or dropping out even before they take their first class. This enables the institution to provide the resources needed to hopefully retain the student thereby improving the life of the student and their family for years to come. Overall, institutions do not have to allow their students to fail before they identify them as at risk.
Don't Let the Learning Stop Here
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