Model-Based Design of complex software systems is an activity that requires the use of different modeling formalisms, with different perspectives of the system, to cover all relevant aspects of the system, to avoid over-design, to employ manageable models and to support system integration The comprehensive use of models in design has created a set of challenges beyond those of supporting one isolated design task In particular, the need to combine, couple, and integrate models at different levels of abstraction and in different formalisms is posing a set of specific problems that must be tackled Multi-Paradigm Modeling is precisely the research field to focus on developing an appropriate set of concepts and tools to address the challenge of integrating models of different aspects of a software system specified using different formalisms and eventually at different levels of abstraction This paper summarizes the results of the 3rd Workshop on Multi-Paradigm Modeling: Concepts and Tools

We have developed a learning environment where students teach a computer agent, using visual representations, and can monitor the agent’s learning progress by asking her questions and having her take quizzes. The system provides self-regulated learning and metacognitive support via dialog-embedded prompts from Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning of complex science topics in middle school classrooms and facilitate development of metacognitive skills to support future learning. In this paper, we discuss methods that we have employed for detecting and characterizing students’ behavior patterns from their activity sequences on the system. In particular, we discuss a method for learning hidden Markov models (HMM) from the activity logs. We demonstrate that the HMM structure corresponds to students’ aggregated behavior patterns in the learning environment. Overall, the HMM technique allows us to go beyond simple frequency and sequence analyses, such as individual activity and pre-defined pattern counts, instead using exploratory methods to examine how these activities cohere in larger patterns over time. The paper outlines a study conducted in a 5th grade science classroom, presents the models derived from the students’ activity sequences, interprets the model structure as aggregate patterns of their learning behaviors, and links these patterns to students’ use of self-regulated learning strategies. The results illustrate that those who teach an agent demonstrate better learning performance and better use of metacognitive monitoring behaviors than students who only learn for themselves. We also observed more advanced and focused monitoring behaviors in the students who received metacognitive strategy feedback from the mentor agent while they taught the teachable agent.

Large scientific computing data-centers require a distributed dependability subsystem that can provide fault isolation and recovery and is capable of learning and predicting failures to improve the reliability of scientific workflows. This paper extends our previous work on the scientific workflow management systems by presenting a hierarchical dynamic workflow management system that tracks the state of job execution using timed state machines. Workflow monitoring is achieved using a reliable distributed monitoring framework, which employs publish-subscribe middleware built upon OMG Data Distribution Service Standard. Failure recovery is achieved by stopping and restarting the failed portions of workflow directed acyclic graph.

Timed failure propagation graph (TFPG) is a directed graph model that represents temporal progression of failure effects in physical systems. In this paper, a distributed diagnosis approach for complex systems is introduced based on the TFPG model settings. In this approach, the system is partitioned into a set of local subsystems each represented by a subgraph of the global system TFPG model. Information flow between subsystems is achieved through special input and output nodes. A high level diagnoser integrates the diagnosis results of the local subsystems using an abstract high level model to obtain a globally consistent diagnosis of the system.