The simple average of student growth scores is often used in accountability systems, but it can be problematic for decision making. When computed using a small/moderate number of students, it can be sensitive to the sample, resulting in inaccurate representations of growth of the students, low year-to-year stability, and inequities for…

The purpose of this quantitative correlational-predictive study was to determine if and to what extent teachers' Leader-Member Exchange (LMX) and organizational citizenship behavior (OCB) individually and combined predict teachers' Class Reading Growth in grades K-5 at a school district in a southeastern coastal state. The theoretical foundations…

Questionnaire designers use readability measures to ensure that questions can be understood by the target population. The most common measure is the Flesch-Kincaid Grade level, but other formulas exist. This article compares six different readability measures across 150 questions in a self-administered questionnaire, finding notable variation in…

Learning analytics (LA) is considered a promising field of study as it's helping to improve learning and the context in which it occurs. A learner's performance can be defined as how well students are learning in terms of knowledge and skills development and can be analyzed based on students' outcomes and engagement in the course. We have…

The Dunning-Kruger effect refers to low performing individuals' tendency to overestimate their performance. The metacognitive deficit is thought to be responsible for this inflated self-assessment observed among the low performers. The present study investigated whether the Dunning-Kruger effect occurs in college students during online distance…

Teamwork skills are commonly evaluated by human assessors, which can be logistically challenging and resource intensive. Technological advancements provide an opportunity for a new assessment method -- virtual behavioural simulations with self-scoring algorithms. This study explores whether a rule-based algorithm can match human assessors at…

Academic achievement is an important factor strongly related to positive educational experiences that facilitate subsequent learning. Therefore, identifying students who need support at an early stage and promptly providing appropriate intervention play a crucial role in preventing learning deficits. This study examined the longitudinal change in…

The important role of willingness to communicate (WTC) in facilitating second language (L2) learning and use has been widely endorsed. However, few studies have examined how teacher support in an L2 class may predict students' L2 WTC. Such a relationship may also be mediated by learners' L2 anxiety, a typical predictor of L2 WTC, and moderated by…

A frequent concern about constructivist instruction is that it works well, mainly for students with higher domain knowledge. We present findings from a set of two quasi-experimental pretest-intervention-posttest studies investigating the relationship between prior math achievement and learning in the context of a specific type of constructivist…

The attentional boost effect refers to the observation that when simultaneously performing a scene memory task and a target detection task, participants better remember scenes that appear at the same time as the detection target than scenes that coincide with distractors. The attentional boost effect is thought to result from a transient increase…

Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…

The default risk, one of the main risk factors for bonds, should be measured and reflected in the bond yield. Particularly, in the case of financial companies that treat bonds as a major product, failure to properly identify and filter customers' workout status adversely affects returns. This study proposes a two-stage classification algorithm for…

The sudden outbreak of COVID-19, which has presented great challenges to pedagogy, has catalyzed the transition of teaching and learning to the online mode. Uncovering the key factors that facilitate positive learning outcomes in online learning environments has thus gathered importance. To bring these factors to light, this study aims to…

The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's…

While open and distance education gains growing recognition over time, it also faces increasing drop-out rates. Consequently, the development of predictive models for early identification of students at-risk for drop-out could be critical to promote ongoing engagement. This study aims to gain insights into the dropout prediction problem in a…