Bayesian Knowledge Tracing (BKT) is a well-established model for formative assessment, with optimization typically using expectation maximization, conjugate gradient descent, or brute force search. However, one of the flaws of existing optimization techniques for BKT models is convergence to undesirable local minima that negatively impact…

The multiple-choice (MC) item format has been widely used in educational assessments across diverse content domains. MC items purportedly allow for collecting richer diagnostic information. The effectiveness and economy of administering MC items may have further contributed to their popularity not just in educational assessment. The MC item format…

Previous research has been limited regarding the measurement of computational thinking, particularly as a learning progression in K-12. This study proposes to apply a multidimensional item response theory (IRT) model to a newly developed measure of computational thinking utilizing both selected response and open-ended polytomous items to establish…

This study investigates two latent constructs (Engagement and Value) of dialogic interaction to examine the epistemic climate. Since the new reform movement emphasizes creating generative learning environments, it is important to examine whether a classroom promotes students' knowledge generation or limit students' epistemic growth through rote…

Learning progressions (LPs) are "descriptions of the successively more sophisticated ways of thinking about a topic that can follow one another as children learn about and investigate a topic over a broad span of time" (National Research Council, 2007). One challenge that arises in LP research is the collection of evidence to ensure that…

Vocabulary proficiency diagnosis plays an important role in the field of language learning, which aims to identify the level of vocabulary knowledge of a learner through his or her learning process periodically, and can be used to provide personalized materials and feedback in language-learning applications. Traditional approaches are widely…

Knowledge tracing (KT), the task of tracking the knowledge state of each student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines Item Response Theory (IRT) with a deep learning model, provides superior performance. It can express the abilities of each student…

This work presents an evaluation of a unified Calculus 1 course in a large Brazilian federal university. By course unification we mean having all the sections following the same weekly schedule, under the same syllabus and common assessment. We analyse two sets of data: pass rates relative to the 9 semesters prior to the unification and to the…

New knowledge tracing models are continuously being proposed, even at a pace where state-of-the-art models cannot be compared with each other at the time of publication. This leads to a situation where ranking models is hard, and the underlying reasons of the models' performance -- be it architectural choices, hyperparameter tuning, performance…

This study employed a mixed-methods approach to examine how rater performance develops during a semester-long rater certification program for an English as a Second Language (ESL) writing placement test at a large US university. From 2016 to 2018, we tracked three groups of novice raters (n = 30) across four rounds in the certification program.…

This research uses Rasch model analysis to identify the reliability and separation index of an integrated mathematics test instrument with a cultural architecture structure in measuring students' mathematical thinking abilities. The study involved 357 students from six eighth-grade public junior high schools in Bima. The selection of schools was…

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…

Integrative learning is the ability to connect, apply, and/or synthesize. A highly valued skill for the knowledge economy and combating false narratives, integrative learning represents the cognitive heart of the liberal arts, demonstrating students' ability to make interdisciplinary connections and apply their learning to their lives and the…

This study describes how we developed a learning progression (LP) for systems thinking in ecosystems and collected preliminary validity evidence for the LP. In particular, the LP focuses on how middle and high school students use discipline-specific systems thinking concepts (e.g. feedback loops and energy pyramid) to analyze and explain the…

This study investigated the effects of retrieval practice on the cognitive and metacognitive learning outcome in a psychology lecture at university. In a within-subjects design, N = 180 students completed an intermediate knowledge test in the 9th session and a final test in the 13th session of the semester. Both tests assessed students'…