This research study is the first time of applying the thinking of semi-supervised learning into CDM. Also, we used the validating test to choose the appropriate parameters for the ANNs instead of using typical statistical criteria, such as AIC, BIC.

This report presents the results of a mode comparability study conducted through simulations to evaluate how scores from MAP Growth administered on the constraint-based engine (CBE) compare to those administered on the current MAP Growth engine known as COLO.

Important educational policy decisions, like whether to shorten or extend the school year, often assume that growth in achievement is linear through the school year.  This research examines this untested assumption using data from seven million students in kindergarten through 8th grade across the fall, winter, and spring of the 2016-17 school year.

In this National Institute of Statistical Sciences (NISS) Ingram Olkin “Statistics Serving Society” Forum, experts from around the country share statistical and data-analytic challenges they have faced as they have reported on and researched issues around the impact of COVID-19 in U.S. schools. Read more on the forum and presenters on the NISS webpage.

How have COVID-19 school closures impacted student academic growth and achievement? New research using fall 2020 MAP Growth assessment data for 4.4 million students provides new insights, key findings, and actionable recommendations.

This technical brief provides additional detail on the samples, methodology, and results from analyses highlighted in the brief, Learning during COVID-19: Initial findings on students’ reading and math achievement and growth.

How have COVID-19 school closures impacted student academic growth and achievement? New research using fall 2020 MAP Growth assessment data for 4.4 million students provides new insights, key findings, and actionable recommendations.