Predicting what we will remember and forget is crucial for daily functioning. We were interested in whether evaluating something as likely to be remembered or forgotten leads to enhanced memory for "both" forms of information relative to information that was not judged for memorability. We presented participants with lists of words to…

Retirement incentives are frequently used by school districts facing financial difficulties. They provide a means of either decreasing staff size or replacing retiring senior teachers with less expensive junior teachers. We analyze a one-time retirement incentive in a large school district paid to teachers willing to retire at the end of the…

Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean…

University leaders do not have sufficient tools easily available to guide their decision-making related to institutional financial wellbeing. Currently, many financial indicators used by leaders of tuition-dependent higher education institutions are not focused on vital metrics such as liquidity and cash earnings. The twofold purpose of this…

Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…

Since 2018, institutions of higher education have been aware of the "enrollment cliff" which refers to expected declines in future enrollment. This paper attempts to describe how prepared institutions in Ohio are for this future by looking at trends leading up to the anticipated decline. Using IPEDS data from 2012-2022, we analyze trends…

Research on fostering learning about percentages within intelligent tutoring systems (ITSs) is limited. Additionally, there is a lack of data-driven approaches for improving the design of ITS to facilitate learning about percentages. To address these gaps, we first investigated whether students' understanding of basic mathematical skills (eg,…

We study the external validity of instrumental variable estimation. The key assumption we impose for external validity is conditional external unconfoundedness among compliers, which means that the treatment effect and target selection are independent among compliers conditional on covariates. We study this assumption with a case study about the…

Large language models (LLMs) provide cost-effective but possibly inaccurate predictions of human behavior. Despite growing evidence that predicted and observed behavior are often not "interchangeable," there is limited guidance on using LLMs to obtain valid estimates of causal effects and other parameters. We argue that LLM predictions…

With a focus on the evaluation of conceptual product designs presented in the form of renderings, this paper describes an eye-tracking study in which gaze metrics, design ratings, and overall rankings of designs were measured. To explore the rationales behind fixational eye movements and numeric evaluation outcomes, additional qualitative data…

How well can individuals' parental background and previous life experiences predict their mid-life socioeconomic status (SES) attainment? This question is central to stratification research, as a strong power of earlier experiences in predicting later-life outcomes signals substantial intra- or intergenerational status persistence, or put simply,…

Social support and psychological distress should be regularly measured when conducting assessments with college students. However, the psychological impact of stressors experienced by college students is subjective and major differences exist between first- and continuing-generation college students where first-generation students typically…

The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…

The study provides a detailed methodological approach, cross-industry standard process for data mining, for predicting at-risk students with an imbalanced class. The objective was to identify the best machine learning model for predicting students at risk of failing the course during weeks 2-8 of the semester. We encountered issues in the dataset,…

Short-form development is an important topic in psychometric research, which requires researchers to face methodological choices at different steps. The statistical techniques traditionally used for shortening tests, which belong to the so-called exploratory model, make assumptions not always verified in psychological data. This article proposes a…