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…

Knowledge tracing is a well-known problem in AI for education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their…

The higher the level of development of higher education, the larger its contribution to socioeconomic development. In order to predict the trend of higher education development in a country more accurately, a new methodology is employed in this study. A weakening buffer operator-based GM (1, 1) model is constructed using Kazakhstan's gross…

In Ireland as elsewhere, the value of putting evidence and scientific advice at the centre of public policy-making, has rarely been more evident. The prominence of the science-policy interface has renewed interest in the prospects for evidence based policy (EBP) in education. Notwithstanding the political rhetoric around EBP in education, little…

Replication provides a confrontation of psychological theory, not only in experimental research, but also in model-based research. Goodness-of-fit (GOF) of the original model to the replication data is routinely provided as meaningful evidence of replication. We demonstrate, however, that GOF obscures important differences between the original and…

Education in organic chemistry is highly reliant on molecular representations. Students abstract information from representations to make sense of submicroscopic interactions. This study investigates relationships between differing representations: bond-line structures, ball-and-stick, or electrostatic potential maps (EPMs), and predicting partial…

Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…