Over the past decade, statisticians have developed methods to improve generalizations from nonrandom samples using propensity score methods. While these methods contribute to generalization research, their effectiveness is limited by small sample sizes. Small area estimation is a class of model-based methods that address the imprecision due to…

In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively…

Language can be used to express broad, unquantified generalizations about both categories (e.g., "Dogs bark") and individuals (e.g., "Daisy barks"). Although these two classes of statements are commonly assumed to arise from the same linguistic phenomenon--"genericity"--the literature to date has not offered a direct…

A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models.…

Capacities for abstract thinking and problem-solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in…

Statistical learning allows us to discover myriad structures in our environment, which is saturated with information at many different levels--from items to categories. How do children learn different levels of information--about regularities that pertain to items and the categories they come from--and how does this differ from adults? Studies on…

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)…

In education, machine learning (ML), especially deep learning (DL) in recent years, has been extensively used to improve both teaching and learning. Despite the rapid advancement of ML and its application in education, a few challenges remain to be addressed. In this thesis, in particular, we focus on two such challenges: (i) data scarcity and…

Most social scientists agree that case studies are useful for "theory building," but ethnographic methods papers often look to survey research for case selection strategies. This is due to a common but untenable distinction between theoretical and empirical generalization, which obscures how theoretically inclined ethnographers make…

Children achieve better long-term language outcomes than adults. However, it remains unclear whether children actually learn language "more quickly" than adults during real-time exposure to input--indicative of true superior language learning abilities--or whether this advantage stems from other factors. To examine this issue, we…

In this paper, we present data from two iterative teaching experiments involving students' constructions of four basic counting problems. The teaching experiments were designed to leverage the generalizing activities of relating and extending to provide students with opportunities to reflect on initial combinatorial activity when constructing…

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…

In five experiments, we established and explored the contrast diversity effect--the effect of diversity of negative evidence on inductive inferences drawn from a single observation of a target exemplar. In Experiments 1 through 3, we show that increasing the diversity of negative evidence in a contrasting category led people to infer that a target…

In recent times, Educational Data Mining and Learning Analytics have been abundantly used to model decision-making to improve teaching/learning ecosystems. However, the adaptation of student models in different domains/courses needs a balance between the generalization and context specificity to reduce the redundancy in creating domain-specific…

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…