Implementation of a culturally responsive positive behavioral interventions and supports (PBIS) framework is associated with positive outcomes for secondary students when implemented schoolwide. Yet, educators often report more implementation challenges in secondary school as compared to elementary school settings. Difficulties obtaining student…

Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…

Purpose: In the early 1980s, researchers and speech-language pathologists (SLPs) collaborated to develop the Systematic Analysis of Language Transcripts (SALT). Research and development over the ensuing decades has culminated into SALT Solutions, a set of tools to assist SLPs to efficiently complete language sample analysis (LSA) with their…

Students cannot experience the full potential benefits of an intervention unless they are receiving the intervention. This is the second installment in a three-part series on intervention fidelity designed to highlight the importance of ensuring classroom supports are implemented as intended. This article provides guidance related to measuring and…

This qualitative case study sought to discover how school-level data teams can intentionally use effective data practices to identify and implement high-leverage interventions that support all students, including Black and Latinx boys, in attaining the necessary academic requirements for high school graduation. The researcher analyzed data from…

Early Warning Systems (EWS) are research-based analytics that use statistical models to assess dropout risk. School leaders use this analytic to consolidate data about a student and provide actionable data to craft an intervention. Little is currently known about the processes involved in school implementation or data use. By analyzing Montana EWS…

Purpose: The aim of this study was to map the use of implementation science frameworks, models, and theories in intervention research targeting learning needs in the classroom. Method: A scoping review was conducted. Electronic database and manual searches were conducted. Two reviewers independently completed screening, data extraction, and…

This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…

Evaluation is a critical component of continuous improvement in education. Robust evaluations enable engaged parties to determine program and intervention impact on key outcomes, identify areas for improvement, and guide future actions. Additionally, as educational systems increasingly focus on data-driven decisionmaking, evaluation becomes even…

Purpose: The purposes of this tutorial are (a) to describe a method of language sample analysis (LSA) referred to as SUGAR (Sampling Utterances and Grammatical Analysis Revised) and (b) to offer step-by-step instructions detailing how to collect, transcribe, analyze, and interpret the results of a SUGAR language sample. Method: The tutorial begins…

Data-based individualization (DBI) is a systematic approach to intensifying and individualizing interventions for students who require more support. Diagnostic data represent the third step in the DBI process. When progress monitoring data indicate that a student is not making adequate progress in an intervention, educators use diagnostic data to…

Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication…

The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and…

English language learning students in China often feel challenged to learn English due to lack of motivation and confidence, pronunciation and grammar difference, lack of practice and people to communicate with etc., which affects students mental health. Adopting Big data and AI will help in overcoming these limitations as it provides personalized…

Through time-series graphs, teachers often evaluate progress monitoring data to make both low- and high-stakes decisions for students. The construction of these graphs--specifically, the presence of an aimline and the data points per x- to y-axis ratio (DPPXYR)--may impact decisions teachers make. The purpose of this study was to evaluate the…