Hans J. Vos

Optimizing conditions for computer-assisted anatomical learning

An experiment evaluated the impact of two typical features of virtual learning environments on anatomical learning for users of differing visuo-spatial ability. The two features studied are computer-implemented stereopsis (the spatial information that is based on differences in visual patterns projected in both eyes) and interactivity (the possibility to actively and continuously change ones view of computer-mediated objects). Participants of differing visuo-spatial ability learned about human abdominal organs via anatomical three-dimensional (3D) reconstructions using either a stereoptic study phase (involving stereopsis and interactivity) or using a biocular study phase that involved neither stereopsis nor interactivity. Subsequent tests assessed the acquired knowledge in tasks involving (a) identification of anatomical structures in anatomical 2D cross-sections (i.e. typical Computed Tomography pictures) in an identification task, and (b) localization of these cross-sections in a frontal view of the anatomy in a localization task. The results show that the stereoptic group performed significantly better on both tasks and that participants of low visuo-spatial ability benefited more from the stereoptic study phase than those of high visuo-spatial ability.

A comparison of parallelism in interface designs for computer-based learning environments

In this paper we discuss an experiment that was carried out with a prototype, designed in conformity with the concept of parallelism and the Parallel Instruction theory (the PI theory). We designed this prototype with five different interfaces, and ran an empirical study in which 18 participants completed an abstract task. The five basic designs were based on hypotheses of the PI theory that for solving tasks on screens all task relevant information must be in view on a computer monitor, as clearly as possible. The condition with two parallel frames and the condition with one long web page appeared to be the best design for this type of task, better than window versions that we normally use for our computer simulations on the web. We do not only describe the results of the abstract task in the five conditions, but we also discuss the results from the perspective of concrete, realistic tasks with computer simulations. The interface with two parallel frames is the best solution here, but also the interface with long web pages (‘virtual parallelism’) is a great favourite in practice when doing realistic tasks.

Testlet-Based Adaptive Mastery Testing

In mastery testing, the problem is to decide whether a test taker must be classified as a master or a nonmaster. The decision is based on the test taker’s observed test score. Well-known examples of mastery testing include testing for pass-fail decisions, licensure, and certification. A mastery test can have both fixed-length and variable-length forms. In a fixed-length mastery test, the performance on a fixed number of items is used for deciding on mastery or nonmastery. Over the last few decades, the fixed-length mastery problem has been studied extensively by many researchers (e.g., De Gruijter & Hambleton, 1984; van der Linden, 1990). Most of these authors derived, analytically or numerically, optimal rules by applying (empirical) Bayesian decision theory (e.g., DeGroot, 1970; Lehmann, 1986) to this problem. In the variable-length form, in addition to the action of declaring mastery or nonmastery, the action of continuing to administer items is available also (e.g., Kingsbury and Weiss, 1983; Lewis & Sheehan, 1990; Sheehan and Lewis, 1992; Spray & Reckase, 1996).

Adaptive Mastery Testing Using a Multidimensional IRT Model

Mastery testing concerns the decision to classify a student as a master or as a nonmaster. In the previous chapter, adaptive mastery testing (AMT) using item response theory (IRT) and sequential mastery testing (SMT) using Bayesian decision theory were combined into an approach labeled adaptive sequential mastery testing (ASMT). This approach is based on the one-parameter logistic model (1PLM; Rasch, 1960) and three-parameter logistic model (3PLM; Birnbaum, 1968). In the present chapter, ASMT is applied to a multidimensional IRT (MIRT) model.

A comparison of parallelism in interface designs for computer-based learning environments: Comparison of parallelism in interface designs

In this paper we discuss an experiment that was carried out with a prototype, designed in conformity with the concept of parallelism and the Parallel Instruction theory (the PI theory). We designed this prototype with five different interfaces, and ran an empirical study in which 18 participants completed an abstract task. The five basic designs were based on hypotheses of the PI theory that for solving tasks on screens all task relevant information must be in view on a computer monitor, as clearly as possible. The condition with two parallel frames and the condition with one long web page appeared to be the best design for this type of task, better than window versions that we normally use for our computer simulations on the web. We do not only describe the results of the abstract task in the five conditions, but we also discuss the results from the perspective of concrete, realistic tasks with computer simulations. The interface with two parallel frames is the best solution here, but also the interface with long web pages (‘virtual parallelism’) is a great favourite in practice when doing realistic tasks.

Comparing Optimal Individual and Collective Assessment Procedures

This paper focuses on the comparison between the optimal cutoff points set on single and multiple tests in predictor-based assessment, that is, assessing applicants as either suitable or unsuitable for a job. Our main result specifies the condition that determines the number of predictor tests, the collective assessment rule (aggregation procedure of predictor tests’ recommendations) and the function relating the tests’ assessment skills to the predictor cutoff points.

A Backward Induction Computational Procedure to Sequential Mastery Testing

In a sequential mastery test, the decision is to classify a student as a master, a non-master, or to continue testing and administering another random item. Sequential mastery tests are designed with the goal of maximizing the probability of making correct classification decisions (i.e., mastery and non-mastery) while at the same time minimizing test length. The purpose of this paper is to derive optimal rules for sequential mastery tests. The framework of Bayesian sequential decision theory is used; that is, optimal rules are obtained by minimizing the posterior expected losses associated with all possible decision rules at each stage of testing. The main advantage of this approach is that costs of testing can be explicitly taken into account. Techniques of backward induction (i.e., dynamic programming) are used for computing optimal rules that minimize the posterior expected loss at each stage of testing. This technique starts by considering the final stage of testing and then works backward to the first stage of testing. For given maximum number of items to be administered, it is shown how the appropriate action can be computed at each stage of testing for different number-correct score.