Detection of metal ions in solution has been performed without employing H[subscript 2]S or any other sulfide materials. The new method is free from the interference from anions. Identification of Na[superscript +], K[superscript +], and NH[subscript 4][superscript +] has been made possible directly from an aqueous extract of the sample mixture. A…

Understanding software modelers' difficulties and evaluating their performance is crucial to Model-Driven Engineering (MDE) education. The software modeling process contains fine-grained information about the modelers' analysis and thought processes. However, existing research primarily focuses on identifying obvious issues in the software…

Requiring students to create weed collections is a common technique for teaching weed identification. Data compiled over 18 years from students' weed collections in a college-level course included over 350 species of plants. Almost half of the specimens belonged to the Asteraceae or Poaceae. The 30 most frequently collected species accounted for…

Field-Based Practicum (FBP) about Pterydophyta diversity material in Low plant botany learning is very important, even though the facts in the field show that FBP is still minimally carried out. This research aims to utilize the potential diversity of Pterydophyta in the Tuban-Lamongan Pantura area as a support for FBP about low plant botany…

Autism spectrum disorder (ASD) is characterized by impairments in many functional areas requiring long-term interventions to promote autonomy. This study aims to map The Sensory Profile™ 2 (SP-2), one of the most widely used assessment tools in children with ASD, with the International Classification of Functioning, Disability and Health for…

This non-experimental causal-comparative study aims to explore the possible effect of expertise on learning experience design (LXD) deviation identification and the classification of these deviations in alignment with provided learning experience design constructs within a learning technology. Additionally, this study challenges Nielsen's (1993)…

Background: In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have…

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish…

Purpose: Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation…

Across the United States, suspension bans have become a popular policy response to address excessive and inequitable use of suspension in schools. However, there is little research that examines what strategies school staff employ when suspension is no longer permitted. I examine the effect of New York City's suspension ban on the use of a…

Preschool children considered at risk for future reading difficulties experience unique and complex combinations of risk factors. In this exploratory study, we used latent profile analysis (LPA) to investigate the underlying classifications of children identified as at-risk for reading difficulties (N = 281) along selected cognitive,…

In this study, an early warning system predicting first-year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first-year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental…

The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by…

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a…

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…