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Intelligent learning environments can be designed to support the development of learners’ cognitive skills, strategies, and metacognitive processes as they work on complex decision-making and problem-solving tasks. However, the complexity of the tasks may impede the progress of novice learners. Providing adaptive feedback to learners who face difficulties requires learner modeling approaches that can identify learners’ proficiencies and the difficulties they face in executing required skills, strategies, and metacognitive processes. This paper discusses a multilevel hierarchical learner modeling scheme that analyzes and captures learners’ cognitive processes and problem-solving strategies along with their performance on assigned tasks in a game-based environment called UrbanSim that requires complex decision making for dealing with counterinsurgency scenarios. As the scenario evolves in a turn-by-turn fashion, UrbanSim evaluates the learners’ moves using a number of performance measures. Our learner modeling scheme interprets the reported performance values by analyzing the learners’ activities captured in log files to derive learners’ proficiencies in associated cognitive skills and strategies, and updates the learner model. We discuss the details of the learner modeling algorithms in this paper, and then demonstrate the effectiveness of our approach by presenting results from a study we conducted at Vanderbilt University. [ABSTRACT FROM AUTHOR]

This paper develops a structural diagnosis approach for fault detection and isolation in hybrid systems. Hybrid systems are characterized by continuous behaviors that are interspersed with discrete mode changes in the system, making the analysis of behaviors quite complex. In this paper, we address the mode detection problem in hybrid systems as the first step in diagnoser design. The proposed method uses analytic redundancy methods to detect the operating mode of the system even in the presence of system faults. We define hybrid minimal structurally overdetermined (HMSO) sets for hybrid systems. For residual generation, we develop the HMSO selection problem, formulated as a binary integer linear programming optimization problem to minimize the number of selected HMSOs and reduce online computational costs of the diagnosis algorithm. The proposed structural approach does not require preenumeration of all possible modes in the diagnoser design step. Therefore, our approach is feasible for hybrid systems with a large number of switching elements, implying that the system can have a large number of operating modes. The case study demonstrates the effectiveness of our approach. We discuss the results of our case study, and present directions for future work. Note to Practitioners—Developing feasible approaches for online monitoring, fault detection, and fault isolation of complex hybrid and embedded systems, such as automobiles, aircraft, power plants, and manufacturing processes, is essential in securing their safe, reliable, and efficient operation. Frequent changes in the operational modes of these systems because of operator actions, such as changing gears in an automobile, or environmental changes, such as driving on a wet or icy road make the fault detection and isolation task in these systems challenging. It is important to detect and isolate faults in all the operating modes, and at the same time, not mistake a mode change as a fault in the system. In this paper, we propose an approach that exploits the equation structure of hybrid systems behavior to combine mode detection and diagnosis in nonlinear hybrid systems. The proposed algorithm is scalable and efficient. We demonstrate its effectiveness using a case study of a reverse osmosis subsystem in an advances life support system for long duration manned space missions. Important challenges that can affect the success of our approach include the need for sufficiently detailed hybrid models that capture nominal and faulty behavior, and a sufficient number of sensors to make simultaneous mode detection and fault isolation possible. [ABSTRACT FROM AUTHOR]

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and the choice to adopt a variety of strategies and approaches to solving these problems. To help students overcome their difficulties and become effective learners and problem solvers, we have to develop methods that can track and interpret students’ open-ended learning and problem-solving behaviors. The complexity of the problems and the open-ended nature of the solution processes pose considerable challenges to accurately interpret and evaluate student behaviors and performance as they work on the system. In this paper, we develop a framework that combines model-driven strategy detection with data-driven pattern discovery for analyzing students’ learning activity data in open-ended environments. We present results from an in-depth case study of multiple activity patterns identified in data from the Betty’s Brain learning environment. The results illustrate the benefits of combining model- and data-driven techniques to precisely characterize the learning behavior of students in an open-ended environment. [ABSTRACT FROM PUBLISHER]

Fault detection and isolation schemes are designed to detect the onset of adverse events during operations of complex systems, such as aircraft and industrial processes. The state-of-the-art fault diagnosis systems on aircraft combine an expert-created reference model of the associations between faults and symptoms, and a Naïve Bayes reasoner. For complex systems with many dependencies between components, the expert-generated reference models are often incomplete, which hinders timely and accurate fault diagnosis. Mining aircraft flight data is a promising approach to finding these missing relations between symptoms and data. However, mining algorithms generate a multitude of relations, and only a small subset of these relations may be useful for improving diagnoser performance. In this paper, we adopt a knowledge engineering approach that combines data mining methods with human expert input to update an existing reference model and improve the overall diagnostic performance. We discuss three case studies to demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Digital games can make unique and powerful contributions to K-12 science education, but much of that potential remains unrealized. Research evaluating games for learning still relies primarily on pre- and post-test data, which limits possible insights into more complex interactions between game design features, gameplay, and formal assessment. Therefore, a critical step forward involves developing rich representations for analyzing gameplay data. This paper leverages data mining techniques to model learning and performance, using a metadata markup language that relates game actions to concepts relevant to specific game contexts. We discuss results from a classroom study and identify potential relationships between students’ planning/prediction behaviors observed across game levels and improvement on formal assessments. The results have implications for scaffolding specific activities, that include physics learning during gameplay, solution planning and effect prediction. Overall, the approach underscores the value of our contextualized approach to gameplay markup to facilitate data mining and discovery. [ABSTRACT FROM PUBLISHER]