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Targeted Support for Virtual Students

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As we begin to think about school post-pandemic, one of the lingering questions that remains is the possibility of students continuing on with virtual learning. Virtual schooling was around well before the planet became familiar with terms like “social distancing,” and “Zoom fatigue,” but in reality only a small (albeit growing) percentage of students were taking one or more virtual courses. Even fewer students were doing school completely online, with most full-time virtual enrollments occurring at the secondary level.

There are several reasons for a lack of large-scale adoption. In no particular order, they include things like a perceived lack of socialization opportunities, the fact that public schools serve a necessary role for working parents to provide supervision and nutrition, and the general idea that learning is a social endeavor best done in person.  Further, data tends to support the notion that many students are simply not successful in an online environment, with reports showing high failure rates and persistence issues with students in online courses, particularly those taking multiple online courses at the same time (Miron et al, 2021). Finally, despite Barbour, Siko, Gross, and Waddell (2013) describing a lack of preparation of teachers for online learning almost a decade ago, little progress has been made in this area since then. In particular, teachers lack the necessary skills to increase student engagement in online settings (Harris et al, 2020), a necessary skill given the somewhat lackluster quality of content provided by some commercial vendors.

While there is nuance in the data that can paint a slightly better picture, simply put, kids didn’t do very well in school over the past 15 months, and most students and parents are looking forward to some normalcy for the 2021-22 school year. Yet, many schools are thinking of continuing or expanding their virtual options next year, creating virtual academies using their own staff to provide a virtual option for K-12 students in their district. Why? 

It depends. There are some families that are still uncomfortable with the thought of returning to in-person learning at this time. Members of their household may be vulnerable to infection. The vaccine likely won’t be available to young children until later this fall. They may not be satisfied with the steps a district has taken to ensure their child’s safety. The list goes on and on. And then, there is a group of students who actually THRIVED during the past year and a half. For some students, online learning was a wonderful experience. We know a lot of kids struggled with online learning, but we also know that a lot of students struggle with conventional in-person learning, too.

So, we have an intersection of students who may want or need a virtual option, but have the likely potential of not being successful. What kind of supports can we offer them?

Addressing Weaknesses in Online Learning Skills

Luckily, there is some research on the “soft skills” that successful virtual students seem to possess (Farid, Plaisent, Bernard, & Chitu, 2014). Even better, researchers have developed several instruments that can rate students on these skills and determine whether the student will succeed in an online course, often with prediction rate over 80%. Now, the goal of such an instrument is not to steer students away from taking an online class if they happen to score low; rather, the goal should be to provide supports in areas where they are weak.

One such tool is the Educational Success Prediction Instrument, or ESPRI (Roblyer & Marshall, 2003; Roblyer et al, 2008). There are several versions of the instrument, with the simplest being just 25 questions (click here for a copy).  It looks at a student’s beliefs in four different areas: organization skills, technology self-efficacy, academic risk-taking (i.e., how a student takes risks in learning and a willingness to possibly be wrong), and their beliefs about achievement.

As you consider virtual options for the fall, one course of action would be to have students take this (or other) inventory, then sit down with the student and family to go over their responses.  In areas where the student scored low, provide targeted support on how to improve that area. For example, if a student scored low on the achievement and risk-taking sections, the counselor could direct them to some pre-coursework on goal setting and growth mindset. Another course of action would be to limit the number of courses a student takes if they score low on any part of the inventory; if they succeed, then allow them to take more. Otherwise, the alternatives are a) tell the student, “Sorry, but online learning isn’t for everyone,” or b) let them take multiple courses with the high probability that they’ll be unsuccessful, neither of which should be appealing.

Fortunately, members of the instructional technology department have done work in this area (Siko, 2014), and can work with schools to analyze data from these instruments and help create content to support students to be successful in online courses.

Jason Siko is an instructional technology consultant for Wayne RESA. Learn more about the instructional technology department and how it can serve your needs at resa.net/edtech.

References

Barbour, M.K., Siko, J., Gross, E., & Waddell, K. (2013). Virtually unprepared: Examining the preparation of K-12 online teachers. In R. Hartshorne, T. Heafner, & T. Petty (Eds.), Teacher education programs and online learning tools: Innovations in teacher preparation (pp. 60-81). Hershey, PA: IGI Global.

Farid, A., Plaisent, M., Bernard, P., & Chitu, O. (2014). Student online readiness assessment tools: A systematic review approach. The Electronic Journal of e-Learning, 12(4), 375-382.  Retrieved from www.ejel.org  

Harris, L., Dargusch, J., Ames, K., & Bloomfield, C. (2020). Catering for ‘very different kids’: distance education teachers’ understandings of and strategies for student engagement. International Journal of Inclusive Education.  DOI: 10.1080/13603116.2020.1735543

Molnar, A. (Ed.), Miron, G., Barbour, M.K., Huerta, L., Shafer, S.R., Rice, J.K., Glover, A., Browning, N., Hagle, S., & Boninger, F. (2021). Virtual schools in the U.S. 2021. Boulder, CO: National Education Policy Center. Retrieved from http://nepc.colorado.edu/publication/virtual-schools-annual-2021  

Roblyer, M.D., Davis, L., Mills, S.C., Marshall, J., & Pape, L. (2008). Toward practical procedures for predicting and promoting success in virtual school students. The American Journal of Distance Education, 22(2), 90-109.

Roblyer, M.D., & Marshall, J.C. (2003). Predicting success of virtual high school students: Preliminary results from an educational success prediction instrument. Journal of Research on Technology in Education, 35(2), 241-255. 

Siko, J. (2014). Testing the waters: An analysis of the student and parent experience in a secondary school’s first blended course offering. International Journal of E-Learning & Distance Education, 29(2), 1-19. Retrieved from https://eric.ed.gov/?id=EJ1102008

 

  • Online Learning