Digital learning solutions for improving adults’ basic skills

ABSTRACTOne of the primary goals of adult basic education programs is to help individuals develop the reading, writing, and math skills necessary to access stable employment opportunities. In the US, this need has been largely overlooked by the burgeoning educational technology sector. To understand how learning technologies might support these learners and what challenges program providers might face as they implement technology platforms, we conducted a study of 5 learning technology products across 11 states in the US. The study involved the participation of 14 program sites, 105 instructors, and 1596 adult learners. We documented the intended use of each product, ensured that educators got the appropriate training on the products, and collected pre- and post-scores on literacy and numeracy assessments. Overall, the effects on learning estimated for math were slightly larger than for reading. Moderate to large statistically reliable positive impacts were found for one product within one program site, and a moderate to large statistically significant negative effect was found for another product also within a single site. We offer recommendations for future research on the use of technology to support teaching and learning in this important area of education.KEYWORDS: Digital learningadult basic educationmath and Readingbasic literacy and numeracy AckowledgementsThis research was made possible through the collaboration and support of many individuals and organizations. First, we would like to thank all of our colleagues on the SRI Education research team for their dedication and professionalism throughout the course of the project. We are also especially grateful for the cooperation of the Adult Basic Education program administrators, instructors, and adult learners and their willingness to participate in the research and data collection. We also acknowledge the contributions of the project's esteemed advisory board as well as the leadership and staff of each product vendor and their valuable insights and collaboration throughout this study. In addition, we thank our research partners in this work, Digital Promise and Mockingbird Education. Finally, we thank The Joyce Foundation for their support and commitment to this important area of study.Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 Candidate high-capacity ABE program sites were recommended by advisory board members or during interviews with product vendors.2 We examined the equivalence of learners’ pretest scores, elapsed time (days) between pretest and posttest, age, and gender. When a difference between treatment and comparison learners for any baseline measure had an effect size greater than 0.25, we used propensity score matching (with R MatchIt) to improve the equivalence between the groups.3 Impacts were estimated for only 6 of the 12 sites that provided achievement data. Sites were excluded from the impact analysis for reasons including: (1) an insufficient number of eligible learners available for analysis; (2) the site failed to provide information about the tests; or (3) the site did not have a viable comparison group because the product was implemented in a new course. Learners were included in the impact analysis if they used the product for 10 or more hours based on the products’ user logs. For a site to be included, we needed to identify at least 25 eligible learners in both the product and non-product groups.4 An effect size is commonly computed by taking the mean difference in test scores between the treatment and comparison groups and dividing it by the pooled standard deviation for the total sample.Additional informationFundingThis work was supported by Joyce Foundation.Notes on contributorsRobert MurphyRobert Murphy, Ph.D., is a senior education researcher. Murphy’s research focuses on research and evaluation of innovative educational and workforce training programs and technologies. His research combines studies of program implementation and impacts on program participants integrating qualitative methods with experimental and rigorous quasi-experimental designs.Shuai WangShuai Wang, Ph.D., is an Associate Professor at Shanghai Jiao Tong University. Prior to this, he was an Education Researcher at SRI International. The overriding goal of Dr. Wang’s research is to develop and evaluate diverse digital teaching and learning approaches, ultimately facilitating educational policy formulation and implementation.Marie BienkowskiMarie Bienkowski, Ph.D, is the director of STEM at a public charter school network in the southwestern US where she develops new K-8 STEAM + CS programs, working to create a coherent and interdisciplinary student experience across all grades. From 1987 to 1998 she conducted research in computer science, specializing in artificial intelligence, at SRI International in Menlo Park, California, USA. From 1998 until 2019 she was at SRI International in an educational research position.Ruchi BhanotRuchi Bhanot, Ph.D., works in the tech industry as a product manager building edtech products for use in workforce development. She applies ML and data science to create innovative solutions that support the environment learners experience building new skills.