Item response theory (IRT) models are often compared with respect to predictive performance to determine the dimensionality of rating scale data. However, such model comparisons could be biased toward nested-dimensionality IRT models (e.g., the bifactor model) when comparing those models with non-nested-dimensionality IRT models (e.g., a…

The world's move to a global economy has an impact on the high rate of student academic failure. Higher education, as the affected party, is considered crucial in reducing student academic failure. This study aims to construct a prediction (predictive model) that can forecast students' time to graduation in developing countries such as Indonesia,…

This case is designed to be used in business analytics courses; particularly those that emphasize predictive analytics. Students are given background information on money laundering and data from People's United Bank, a regional bank in the northeast United States. The students must develop their hypothesis, analyze the data, develop and optimize…

We evaluate the feasibility of developing predictive models of rater behavior, that is, "rater-specific" models for predicting the scores produced by a rater under operational conditions. In the present study, the dependent variable is the score assigned to essays by a rater, and the predictors are linguistic attributes of the essays…

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…

Educational stakeholders would be better informed if they could use their students' formative assessments results and personal background attributes to predict the conditions for achieving favorable learning outcomes, and conversely, to gain awareness of the "at-risk" signals to prevent unfavorable or worst-case scenarios from happening.…

Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement can help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interactions on the MOOC open up avenues for studying…

Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be…

Educational institutions increasingly recognize the role that student belonging plays in retention. Many studies in this area focus on helping students improve a sense of belonging before they matriculate or identifying belonging as a reason for their departure. This study measures students' sense of belonging at key transition points during the…

Physics education research (PER) has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferences or causal relationships observed in various data sets. This research introduces a modern…

The 3-year graduation rate is a rarely measured metric in higher education compared to its 4- or 6- year graduation rate counterparts. For the first time in college (FTIC) students to graduate in three years, they must come with certain skills, abilities, plans, supports, or motivations. This project considers two distinct but interrelated ways of…

This paper reports the findings of a research using marks of students in learning activities of an online module to build a predictive model of performance for the final assessment of the module. The objectives were (1) to compare the performances of students of two cohorts in terms of continuous learning assessment marks and final learning…

Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models…

Dropout remains a persistent challenge within high school education. In this paper, we present a case study on automatically detecting whether a student is at-risk of dropout within a diverse school district in Texas. We predict whether a student will drop out in a future school year from data on students' discipline, attendance, course-taking,…

Organizational commitment is a major determinant of organizational effectiveness and desirable employee attitudes and behaviours. Highly committed academic staff are the backbone of universities since they play an important role in the success of their institutions. This study investigated factors associated with organizational commitment among…