Data from a moderately-selective state university in the Midwest is used to cross-examine the most appropriate data analytical techniques for predicting versus explaining college student persistence decisions. The current research provides an overview of the relative benefits of models specializing in prediction versus explanation with particular…

Higher education institutes experience lower success rates in online learning environments compared to traditional learning. Students' engagement within the learning management system (LMS) is one of the main factors affecting students' academic performance and retention. This quantitative correlational-predictive study examined if, and to what…

Even with extensive retention research dating from the 1960s, community colleges still struggle to identify the reasons why students do not return to college. Data mining has allowed these retention models to evolve to identify new patterns among student populations and variables. The purpose of this study was to create a predictive model for…

Knowledge tracing is a fundamental area of educational data modeling that aims at gaining a better understanding of the learning occurring in tutoring systems. Knowledge tracing models fit various parameters on observed student performance and are evaluated through several goodness of fit metrics. Fitted parameter values are of crucial interest in…

This work asks one simple question: "how reliable is the method used by the National Centre for Vocational Education Research (NCVER) to estimate projected rates of VET program completion?" In other words, how well do early projections align with actual completion rates some years later? Completion rates are simple to calculate with a…

This paper outlines the development of practical measures of productive persistence using digital learning system data. Practical measurement refers to data collection and analysis approaches originating from improvement science; productive persistence refers to the combination of academic and social mindsets as well as learning behaviours that…

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…

Predictive analytics in education can offer a benefit as long as educators heed the differences between how the tools are used in industry and how they should be used differently in schooling. Perhaps most important, teachers already know a great deal about their students--far more than an investor knows about a stock or a baseball scout about an…

The prevalence of school shootings and other campus violence incidents have called attention to the increasing number of college students who are living with Mental Health Disorders (MHDs). There is a substantial amount of literature on MHDs among college students who are attending 4-year universities. However, the literature is lacking research…

Many researchers who are interested in studying students' online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students' SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students' online…

This case study uses structural equation modeling to examine the predictive validity of the Read Aloud Profile-Together, a measure of the distinct behaviors of parents and children during shared book reading, in relation to preschool children's early reading competency. Using secondary data analysis, this case study includes 800 parent-child pairs…

For identifying children with four major kinds of verbal learning disabilities viz. reading disability, speech and language comprehension disability, writing disability and mathematics disability, the present task was undertaken to construct and standardize verbal learning disabilities checklist. This checklist was developed by keeping in view the…

Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for…

With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…

Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…