Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of…

Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects' latent attributes are dichotomous--mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a…

This report describes the use of electroencephalography (EEG) to collect online learners' physiological information. Recent technological advancements allow the unobtrusive collection of live neurosignals while learners are engaged in online activities. In the context of multimodal learning analytics, we discuss the potential use of this new…

In this study, we developed machine learning algorithms to automatically score students' written arguments and then applied the cognitive diagnostic modeling (CDM) approach to examine students' cognitive patterns of scientific argumentation. We abstracted three types of skills (i.e., attributes) critical for successful argumentation practice:…

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…

Generalizing Boolean matrix decomposition to a larger class of matrix decomposition models is demonstrated, and probability matrix decomposition (PMD) models are introduced as a probabilistic version of the larger class. An algorithm is presented for the computation of maximum likelihood and maximum a posteriori estimates of the parameters of PMD…

A latent class model for handling the items in Birenbaum and Tatsuoka's study is described. A method to derive the optimal scoring rule when multiple choice test items are used is illustrated. Remedial training begins after a determination is made as to which of several erroneous algorithms is being used. (Author/DWH)

Six alternately weighted algorithms for diagnosing alcohol dependency in accordance with the Diagnostic and Statistical Manual of Mental Disorders (4th ed.) based on statistical, unit, rational and random criterion weighting systems, were used to predict an array of concurrent validators and six-month drinking outcomes in a regional clinical…

Presents a description and evaluation of a microcomputer-based system designed to diagnose errors in basic algorithmic procedures made by students in a college remedial mathematics class. The paper-and-pencil diagnostic test is described, and results of pretests and posttests for the computer group and control group are discussed. (five…

How teachers can diagnose difficulties with computation is discussed, with emphasis on how to develop diagnostic tests. How to use such tests to plan remediation is considered in terms of several types of errors. (MN)

A study was conducted to investigate learner control of instruction in contrast to response sensitive branching algorithms with respect to two specific types of instructional decisions: (1) whether a student should enter and study a particular instructional module given his score on an associated diagnostic pretest; and (2) when a student should…

A newly introduced approach, rule space, can represent large numbers of erroneous rules of arithmetic operations quantitatively and can predict the likelihood of each erroneous rule. The new model challenges the credibility of the traditional right-or-wrong scoring procedure. (Author/PN)

This paper reports on a project which has developed the general latent class model as a framework for representation of item responses. This framework can be used to represent data in applications such as mastery tests and other kinds of achievement tests, where there is reason to believe that current foundations are deficient. Methods of…