Curriculum research is an important tool for understanding complex processes within a degree program. In particular, stochastic graphical models and simulations on related curriculum graphs have been used to make predictions about dropout rates, grades, and degree completion time. There exists, however, little research on changes in the curriculum…

Greater financial literacy is critically needed among young adults in the United States, but many financial literacy education courses have been less effective than hoped for by educators and researchers. Additionally, many have not been designed around established curricula or learning science principles, rendering findings difficult for…

The current study was designed to examine the relationship between cognitive ability and player experience in shaping how players think within, and acclimate to, video games as complex systems. Specifically, researchers examined the relationship between player's cognitive ability and gameplay outcomes within a video game (i.e., The Deed). Outcomes…

Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge…

Recent work describes methods for systematic, data-driven improvement to instructional content and calls for diverse teams of learning engineers to implement and evaluate such improvements. Focusing on an approach called "design-loop adaptivity," we consider the problem of how developers might use data to target or prioritize particular…

Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from…

Language learners are underserved if there are unlearned meanings of a word that they think they have already learned. For example, "circle" as a noun is well known, whereas its use as a verb is not. For artificial-intelligence-based support systems for learning vocabulary, assessing each learner's knowledge of such atypical but common…

We propose a model that employs convolutional neural networks (CNN) to evaluate sociomoral reasoning maturity, a key social ability, necessary for adaptive social functioning. Our model is used in a serious game to evaluate learners. It uses pre-annotated textual data (verbatims) and a coding scheme (SoMoral) applied by experts in psychology.…

In this investigation we propose new machine learning methods for automated scoring models that predict the vocabulary acquisition in science and social studies of second grade English language learners, based upon free-form spoken responses. We evaluate performance on an existing dataset and use transfer learning from a large pre-trained language…

Linguistic alignment--the contingent reuse of our interlocutors' language at all levels of linguistic structure--pervades human dialogue. Here, we design unique measures to capture the degree of linguistic alignment between interlocutors' linguistic representations at three levels of structure: lexical, syntactic, and semantic. We track these…

Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data--often highdimensional and longitudinal--to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a…

In the learning support for repetitive motions having an operating instrument, it is necessary for learners to control not only their own body motions but also an instrument corresponding to the body. This study focuses on the repetitive motion learning using single operation instrument without the movement in space; i.e. jump-rope and hula-hoop.…

We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude…

As higher education institutions develop fully online course programs to provide better access for the non-traditional learner, there is increasing interest in identifying students who may be at risk of attrition and poor performance in these online course programs. In our study, we investigate the effectiveness of an online orientation course in…

Classification evaluation metrics are often used to evaluate adaptive tutoring systems-- programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may…