Test-taking behavior is a potential source of construct irrelevant variance for test scores in international large-scale assessments where test-taking effort, motivation, and behaviors in general tend to be confounded with test scores. In an attempt to disentangle this relationship and gain further insight into examinees' test-taking processes,…

Game-based learning (GBL) is increasingly recognized as an effective tool for teaching diverse skills, particularly in science education, due to its interactive, engaging, and motivational qualities, along with timely assessments and intelligent feedback. However, more empirical studies are needed to facilitate its wider application in school…

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…

This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…

Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers. Using nearly 1,000 word-to-word transcriptions of fourth- and fifth-grade English language arts classes, we apply novel text-as-data methods to develop automated measures of teaching to complement classroom observations…

The purpose of the study is to analyze the affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), as one of the data mining methods. Structural Equation Modeling, one of the regression-based methods, is quite popular for…

Although educators frequently use assessment to identify who needs supplemental instruction and if that instruction is working, there is a lack of research investigating assessment that informs what instruction students need. The purpose of the current study was to determine if a brief (approximately 20 min) task that reflects a common middle…

Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers, but conventional classroom observations are costly, prone to rater bias, and hard to implement at scale. Using nearly 1,000 word-to-word transcriptions of 4th- and 5th-grade English language arts classes, we apply novel…

Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…

Stealth assessment has been successfully embedded in educational games to measure students' learning in an unobtrusive and supportive way. This study explored the possibility of applying stealth assessment in a digital reading platform and sought to identify potential in-system indicators of students' digital learning outcomes. Utilizing the user…

Self-report data have contributed to a rich understanding of learning and motivation; yet, self-report measures present challenges to researchers studying students' experiences in small-group contexts. Rather than using self-report data alone, we argue that fusing self-report and observational data can yield a broader understanding of students'…

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

The purpose for this correlational, cross-sectional, explanatory was to explain the influence of the length of the school day on the total percentage of students who scored Proficient or Advanced Proficient (TPAP) on the New Jersey Ask (NJ ASK) in Language Arts and Mathematics in Grades 6-8 in for student populations with low, median, and high…

The purpose of this study was to examine the effectiveness of dropout predictors across time. Two state-level high school graduation panels were selected to begin with the seventh and ninth grades but end at the same time. The first panel (seventh grade) contained 29,554 students and used sixth grade predictors. The second panel (ninth grade)…