This study aims to conduct a comparative study of Bagging and Boosting algorithms among ensemble methods and to compare the classification performance of TreeNet and Random Forest methods using these algorithms on the data extracted from ABIDE application in education. The main factor in choosing them for analyses is that they are Ensemble methods…

Correct specifications of hierarchical attribute structures in analyses using diagnostic classification models (DCMs) are pivotal because misspecifications can lead to biased parameter estimations and inaccurate classification profiles. This research is aimed to demonstrate DCM analyses with various hierarchical attribute structures via Bayesian…

This study examined the efficiency of using the same rating scale categories in measuring affective constructs for students with distinctive levels of achievement. Data used in this study came from the Trends in Mathematics and Science Study (TIMSS) 2011, as a case, on the three scales that were designed to measure eighth graders' attitudes…

Inspired by a "whole child" framing, the current study takes a "whole classroom" perspective to consider classroom practice. Study aims included: (1) presenting a systematic video-based observational coding strategy to concurrently consider practice domains that have implications for learning--cognitive instruction, classroom…

The current investigation used latent profile analysis (LPA) to categorise students according to their strategies for managing mathematics homework. A sample of 918 Chinese students in grades 7-9 rated their use of seven homework management strategies; namely, arranging the environment, managing time, monitoring motivation, managing emotion,…

We present two types of validity evidence to support inferences and decisions about use of easyCBMs in relation to state testing programs. The first type involves the use of Benchmarks in reading to use in making predictions of performance on the Smarter Balanced (SB) test. These predictions can be made both well in advance (several months) or…

Educational Data Mining (EDM) is an important tool in the field of classification of educational data that helps researchers and education planners analyse and model available educational data for specific needs such as developing educational strategies. Trends International Mathematics and Science Study (TIMSS) which is a notable study in…

Reclassification can be an important juncture in the academic experience of English Learners (ELs). Literature has explored the potential for reclassification to influence academic outcomes like achievement, yet its impact on social-emotional learning (SEL) skills, which are as malleable and important to long-term success, remains unclear. Using a…

The present study illustrates the utility of applying multilevel mixture models in educational research, using data on the homework behavior of 1,812 Swiss eighth-grade students in French as a second language. A previous person-centered study identified 5 homework learning types characterized by different patterns of high or low homework time and…

English learners' (ELs) day-to-day experiences in school change when reclassified as fully English proficient. Prior research, however, is mixed on how reclassification influences outcomes. Many studies also do not or cannot explore key long-term outcomes or identify impacts over time. In this study, I leverage longitudinal student data in a…

The term 'Plant Blindness' describes people tending to overlook plants, even though they have an enormous importance for life on earth, especially because of their ability to photosynthesise. Many attempts have been made to counteract plant blindness in biology education. However, so far there is no comprehensive tool to assess the degree of plant…

The purpose of this study was to apply Fautley's model-- [Faultley, Martin. 2005. "A New Model of the Group Composing Process of Lower Secondary School Students." "Music Education Research" 7 (1): 39-57. doi:10.1080/14613800500042109]of the group composing process to the analysis of a collaborative composing project in a lower…

Schools often track students to classes based on ability. Proponents of tracking argue it is a low-cost tool to improve learning since instruction is more effective when students are more homogeneous, while opponents argue it exacerbates initial differences in opportunities without strong evidence of efficacy. In fact, little is known about the…

Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth…

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…