Rating scales are ubiquitous in the social sciences, yet may present practical difficulties when response formats change over time or vary across surveys. To allow researchers to pool rating data across alternative question formats, the article provides a generalization of the ordered logit model that accommodates multiple scale formats in the…

Purpose: Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested k-fold cross-validation. The second…

Given the frequent presence of slipping and guessing in item responses, models for the inclusion of their effects are highly important. Unfortunately, the most common model for their inclusion, the four-parameter item response theory model, potentially has severe deficiencies related to its possible unidentifiability. With this issue in mind, the…

A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. In two experiments, we used category learning as a representative domain to test two factors thought to influence the acquisition of conceptual knowledge: the number of training examples (set size)…

The unique orthographic complexities of Japanese, which utilizes multiple types of scripts (morphographic kanji and syllabic hiragana and katakana) for the same spoken language, place unique demands on early learners. Much research has centered on the average ability of Japanese readers, but given the varying challenges of these scripts, attention…

The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both…

We explore how different components of an Automatic Short Answer Grading (ASAG) model affect the model's ability to generalize to questions outside of those used for training. For supervised automatic grading models, human ratings are primarily used as ground truth labels. Producing such ratings can be resource heavy, as subject matter experts…

Psychology research often focuses on interactions, and this has deep implications for inference from non-representative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This…

Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these…

Background: College students need the ability to generalize and apply solutions through reflective practice. University faculty need professional development to use authentic cases to prepare students for the future. Purpose: This study was to explore the experiences of faculty through a year-long professional development program that included a…

The purpose of this paper is to highlight issues related to students' personal inferences that arise when students verbally explain their justification for calculus statements. We conducted clinical interviews with three undergraduate students who had taken first-semester calculus but had not yet been exposed to formal proof writing activities…

Autistic individuals have significant social-communication challenges that commonly persist into adulthood and impact academic, social, and vocational pursuits. More than two decades of research have established video behavior modeling as a successful, cost-effective, and time-efficient intervention tool for autistic children; however, less…

Background: Competency models provide useful frameworks for organizing learning and assessment programs, but their construction is both time intensive and subject to perceptual biases. Some aspects of model development may be particularly well-suited to automation, specifically natural language processing (NLP), which could also help make them…

Background: The reciprocal internal/external frame of reference (RI/E) combines two models of academic self-concept formation, namely the reciprocal effects model (REM) and the internal/external frame of reference (I/E) model. The REM assumes reciprocal relations between achievement and academic self-concept. The I/E model assumes contrast effects…

Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as…