Research suggests that online learners with disabilities, those at risk of dropping out, and those taking courses for credit recovery benefit from additional assistance and instructional support. These learners can benefit from online courses however those courses and the accompanying instruction need to be responsive to the unique needs of these learners.
Appropriately supporting students in online and blended learning environments requires a great deal of instructional planning and preparation. The intent of this document is to supply educational teams content that will provide support for the planning, implementation, and evaluation of programs and services for students with disabilities enrolled in online and blended learning environments.
This project explores student behavioral, textual, and limited demographic data retrieved from Michigan Virtual School for the 2014-2015 and 2015-2016 academic years. The primary method of analysis was deep learning (DL) however a variety of other data mining techniques were explored, including text analysis, to improve prediction accuracy. DL was also compared to machine learning (ML), and results indicate that DL was slightly better than other ML models; also the inclusion of textual content improved the overall predictive accuracy in identifying at-risk students. Factors affecting the predictive power of the analyses are discussed as well as recommendations and considerations for using this and similar predictive models in practice to identify at-risk students.
An adequate, sustainable force of educators with strong preparation for working with students with disabilities has been difficult to secure in traditional settings; that shortage exists in online settings as well. While there are nascent understandings about instructor work with students with disabilities in K-12 online settings, understanding about course design for diverse learners, including those with disabilities, is lacking.
The second report in the Credit Recovery series—Examining Credit Recovery Learning Profile from Time-Series Clustering Analysis—examines student learning behaviors in the first part of Algebra 2 courses. The ways that students engaged in coursework is targeted with two types of behavioral indicators, namely students’ attempted scores and the number of minutes spent in the learning management system (LMS) on a weekly basis.
This report, Meeting the Needs of Students with Disabilities in K-12 Online Learning: An Introduction to the Analysis of the iNACOL Program, Course, and Teacher Standards, is part of a series of four reports and includes the introductory information and methodology for the review process. The other three reports in the series are the reviews of the iNACOL National Standards for Quality Online Teaching, iNACOL National Standards for Quality Online Courses, and iNACOL National Standards for Quality Online Programs as well as implications, conclusion, and suggestions for further research for each specific set of standards.
This report begins discussion on the topic of credit recovery by testifying to the concept that students who have different reasons for taking online courses perform differently. Specifically, the underperformance of credit recovery students was hypothesized; the contextual information was also explored, including enrollment patterns, demographic factors, and the learning environment which focused on instructors who taught the courses.
Access for All is designed to provide an overview of different disability groups in order to better understand the needs of each group, some common accommodations for students in each group, and considerations for each group related to online and blended learning environments. Also provided are the terminology and acronyms commonly associated with disabilities and special education, a synopsis of disability law, and a thorough description of individualized education plans and 504 plans. In better understanding the needs of students with disabilities, it is hoped that virtual school educators will be better prepared to help all their students reach optimum success.
Report #4: Teaching This report includes a summary of the need for this research and a summary of the methodology but focuses primarily on the findings specific to the iNACOL Teaching Standards.
Report #3: Courses The purpose of this report is to describe the findings of an expert panel aiming to offer improvement suggestions for the online course standards.
Report #2: Programs The purpose of this report is to share findings from an expert panel about improving the program standards’ applicability to online learning.
Report #1: Overview This report is part of a series of four reports and includes the introductory information and methodology for the review process. The other three reports in the series are the reviews of the iNACOL National Standards for Quality Online Teaching, iNACOL National Standards for Quality Online Courses, and iNACOL National Standards for Quality Online Programs as well as implications, conclusion, and suggestions for further research for each specific set of standards.
This report describes what we currently know about high school dropout and retention, what solutions have been proposed, and how online learning might impact the retention rate. Drawing on existing work from Michigan Virtual School, data are provided to discuss performance of credit recovery students and conditions under which such students succeed and struggle in online learning environments.