COVID-19, Low-income Stressors

1. We are solving the gap between low-income adult learners and aligned skills with employers.

2. We propose nudging low-income adult learners through a randomized encouragement design using big data through web-scraping and mentoring in accessing alternative digital credentials and gains in employment outcomes. 

3. If this were scaled, low-income adults learners will increase in employment alignment and ability to navigate stressors of having children at home during COVID-19.

COVID-19, Low-Income Adult Learners’ Work from Home Stress and Its Impact on Enrollment in Alternative Digital Credentials Aligned with Employer Needs

The goal of this study is to causally determine the effect of nudging low-income adult learners aligned with employer needs. During COVID-19, more parents are working from home and navigating children's schedules of distance education at home (Gouëdard, Pont, Viennet, 2020; Hanushek & Woessmann, 2020). We hypothesize that these stressors have negatively impacted enrollment in traditional degree programs that typically increase in enrollment during economic downturns, because parents are hesitant to commit to formal enrollment because of having children at home and other employment realities (National Clearinghouse, 2020). This research study will include survey, interview, and randomized encouragement treatment and control methods to determine the influence of work from home circumstances on enrollment patterns in traditional higher education degree programs versus enrollment in alternative non-accredited digital credentials that are designed to be embedded and aligned with employer needs.

We will send nudges to randomly assigned low-income adult learners and their mentors to improve their perceptions of alternative digital credentials with a direct alignment of localized employer needs to analyze the null hypothesis of H1 above. This treatment will be given in line with a theory of change that low-income adult learners are avoiding formal enrollment in traditional degree programs because of uncertainty surrounding COVID-19 and having children at home. These nudges will include recommendations for alternative digital credentials aligned with skills needed by employers based on web-scraped data by city/region. The recommended nudges will include Getsmarter.com and others as partners for mentoring aligned web-scraping skills by employer need by city/area. The null hypotheses of H2, H3, and H4 will be analyzed during year 2 and year 3 after H1 is fully understood after year 1.

Potential Sample Size

The target population includes all low-income adult learner parents in the United States. We plan to select a sample of low-income adult learners with children studying at home from a national dataset and randomly assign/encourage a statistically determined treatment group through power/estimated effect size analysis for the treatment group compared to a control group. 

Our initial exploratory research will help us identify further understanding of how COVID-19 has impacted low-income adult learners, their perception of alternative digital credentials and their enrollment decisions. This will help us clarify broader impacts on employer perceptions of alternative credentials compared to prior economic downturns.

Our outcomes of interest are shown in the logic model above. We will determine the effect of the treatment on employment outcomes (quantitative and qualitative measures: job acquired, promotions, salary, interview findings) compared to control group.

The Policy Problem That Motivates This Research

Unlike previous economic downturns, where adult learners typically increased enrollment in traditional higher education programs, this COVID-19 season has included a higher rate of children being educated through distance learning at home with parents. This and other stressors on low-income adult parents may be influencing adult learners to avoid traditional educational options. This may increase the uptake in alternative credentials by adult learners as well as the change in employer perceptions of alternative credentials compared to the 2008 recession (Jackson, 2020; Vandivier, 2020; West, Newby, Cheng, Erickson, & Clements, 2020).

During economic downturns, low-income and marginalized populations are often impacted by lay-offs, furloughs, and freezing of hiring and promotions at a higher rate (NPR, 2020). This study attempts to address how alternative credential options could explain the drop in enrollment in traditional higher education programs and demonstrate the nudge influence of alternative credentials through targeted encouragement aligned with local economic skills required by employers. 

Yes, we are exploring Idaho, Utah, and Mississippi. 

Full-time staff = 2

Part-time staff = 2

We look for individuals who are committed to helping low-income adult learners build confidence and alignment with reskilling for current and future economic changes. 

Our business model employs randomized control trials through encouragement nudges aligned with deep mentoring. 

We use predictive modeling, machine learning recommendations, mentor nudges, and other data integrations with our chat bot, portals, and personalized adaptive assessments for wrap-around alignment to skills based on big data for low-income adult learners. 

Some of our research shows a 5-8% lift in outcomes compared to a control group. 

Our goal is to grow to the demand of low-income adult learners in the states of Idaho and Mississipi.

We have been doing this kind of work for the past 10 years in Utah, the Philippines, and Mississipi.

We have several grants we've applied for and contracted a couple of them. 

We want to build into a gap we see regularly in inner-city Mississippi, rural Idaho, and other places.