BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/item for each student given their responses to previous questions, effectively reducing test length. Existing CAT methods use item response theory (IRT) models to relate student ability to their responses to questions and static question selection algorithms designed to reduce the ability estimation error as quickly as possible; therefore, these algorithms cannot improve by learning from large-scale student response data. In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. BOBCAT is agnostic to the underlying student response model and is computationally efficient during the adaptive testing process. Through extensive experiments on five real-world student response datasets, we show that BOBCAT outperforms existing CAT methods (sometimes significantly) at reducing test length.

Question Selection in Template-Based Test Paper Models

The success of any educational program depends on its evaluation system. Examinations are a part of learning process which acts as an element in evaluation. For the smooth conduct of examinations of various universities and academic institutions, the test paper generation process would be helpful. However, examination test paper composition is a multi-constraint concurrent optimization problem. Question selection plays a key role in test paper generation systems. Also, it is the most significant and time-consuming activity. The question selection is handled in traditional test paper generation systems by using a specified test paper format containing a listing of weightages to be allotted to each unit/module of the syllabus.

The Case for Campaign Analysis: A Method for Studying Military Operations

Military operations lie at the center of international relations theory and practice. Although security studies scholars have used campaign analysis to study military operations for decades, the method has not been formally defined or standardized, and there is little methodological guidance available for scholars interested in conducting or evaluating it. Campaign analysis is a method involving the use of a model and techniques for managing uncertainty to answer questions about military operations. The method comprises six steps: (1) question selection, (2) scenario development, (3) model construction, (4) value assignment, (5) sensitivity analysis, and (6) interpretation and presentation of results. The models that scholars develop to direct analysis are significant intellectual contributions in their own right, and can be adapted by other scholars and practitioners to guide additional analyses. Careful model construction can clarify, but does not obviate, the uncertainty of conflict. To manage uncertainty in parameter values, scholars can use the “input distribution approach” to propagate uncertainty in inputs through to a model's output. Replications and extensions of Wu Riqiang's 2020 analysis of Chinese nuclear survivability and Barry Posen's 1991 analysis of the North Atlantic Treaty Organization's prospects against the Warsaw Pact illustrate the six steps of campaign analysis, the value of transparent models and the input distribution approach, and the potential of campaign analysis to contribute to policy and theory.

Software Tool for Test Paper Generation

In this chapter, the authors discuss the features of the tool which is developed using the algorithms designed and implemented as part of the research work carried out. They have named it a test paper generation system (TPGS). At some places, they have used question paper generation system (QPGS) instead of its alias TPGS. The main modules of this tool are (1) test paper template generation, (2) question conflict detection, (3) test paper template-based question selection, (4) syllabus coverage evaluator for test paper, (5) and answer paper evaluator.

Why Physical Inactivity Level Has Increased in the Iranian Population During the Past Decade? A Delphi Technique

Background: Considering the well-established benefits of physical activity in the health domain, the economy, and environment, physical activity programs should be considered as an important agenda in public health policymaking. Objectives: The objective of this qualitative study was to identify “Why the prevalence of physical inactivity has increased during the past decade in Iran, despite having various national physical activity documents? "using a Delphi technique. Methods: The “Delphi technique” process in this study consisted of literature review, selecting the research question, selection and recruitment of panel members, pilot study and conduction of round one (using an open question), round 2 (using a 5-point Likert scale), and round three (final ranking). Results: Statements with the highest scores in round three were recognized as the most important causes of the decreased level of physical activity based on the experts’ responses in Iran. Conclusions: The absence of executive support, coordination mechanisms, and appropriate infrastructure for conducting physical activity were the top challenges in improving the physical activity level in Iran.