Towards Declarative Programming of Conceptual Models

This article introduces some basic functions and architectural issues, that help to build a tool for programming conceptual models, and that is not specific for a particular problem class or problem solving method. Our work is based on the KADS-method, that had to be modified in some points, to enable declarative programming of inference knowledge as well as domain knowledge. It is shown, how knowledge sources can be described as semantic network modules. Knowledge sources are instantiated from generic descriptions. All resulting semantic networks are part of a modular knowledge base, each module representing the knowledge on its own right level of granularity. Functions are introduced, that define views between semantic networks. They help connecting declarative representation of knowledge sources on the inference layer to parts of the domain layer network. We only contemplate the interconnection of domain and inference layer. 1. Introduction 1.1. Notions First, to avoid misconception..

Mitigating knowledge decay from instruction with voluntary use of an adaptive learning system

Knowledge decays across breaks in instruction. Learners lack the metacognition to self-assess their knowledge decay and effectively self-direct review, as well as lacking interactive exercises appropriate to their individual knowledge level. Adaptive learning systems offer the potential to mitigate these issues, by providing open learner models to facilitate learner’s understanding of their knowledge levels and by presenting personalized practice exercises. The current study analyzes differences in knowledge decay between learners randomly assigned to an intervention where they could use an adaptive system during a long gap between courses, compared with a control condition. The experimental condition used the Personal Assistant for Life-Long Learning (PAL3), a tablet-based adaptive learning system integrating multiple intelligent tutoring systems and conventional learning resources. It contained electronics content relevant to the experiment participants, Navy sailors who graduated from apprentice electronics courses (A-School) awaiting assignment to their next training (C-School). The study was conducted over one month, collecting performance data with a counterbalanced pre-, mid-, and post-test. The control condition exhibited the expected decay. The PAL3 condition showed a significant difference from the control, with no significant knowledge decay in their overall knowledge, despite substantial variance in usage for PAL3 (e.g., most of overall use in the first week, with fewer participants engaging as time went on). Interestingly, while overall decay was mitigated in PAL3, this result was primarily through gains in some knowledge offsetting losses in other knowledge. Overall, these results indicate that adaptive study tools can help prevent knowledge decay, even with voluntary usage