Toward making didactics a subject of knowledge engineering

Learning systems suffer from a lack of an explicit and adaptable didactic design. A way to overcome such deficiencies is (semi-) formally representing the didactic design. A modeling approach, storyboarding, is outlined here. Storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify, validate the didactics behind a learning process. As a vision, didactics can be refined according to revealed weaknesses and proven excellence. Furthermore, suc-cessful didactic patterns can be inductively inferred by analyzing the particular knowledge processing and its alleged contribution to learning success

Tecnologias do conhecimento de engenharia para processos de aprendizagem

De um modo geral, os sistemas de aprendiza- gem sofrem com a falta de um projeto didático de design explícito e adaptável. Considerando serem os sistemas de e-learning digitais, pela sua própria natureza, a sua apre- sentação levanta a questão da modelagem do projeto didá- tico de forma a implicar a possibilidade de aplicar técnicas de IA. Uma abordagem de modelagem apresentada ante- riormente chamada storyboarding constitui-se no palco de aplicação do conhecimento de tecnologias de engenharia, para verificar e validar a didática nos processos de aprendi- zagem. Além disso, a didática pode ser refinada de acordo com as deficiências reveladas e a excelência comprovada. Os padrões didáticos bem sucedidos podem ser explorados mediante a aplicação de técnicas de coleta mining para as várias formas utilizadas pelos alunos no storyboard e os níveis de sucesso a eles associados

Toward Making Didactics a Subject of Knowledge Engineering

Learning systems suffer from a lack of an explicit and adaptable didactic design. A way to overcome such deficiencies is (semi-) formally representing the didactic design. A modeling approach, storyboarding, is outlined here. Storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify, validate the didactics behind a learning process. As a vision, didactics can be refined according to revealed weaknesses and proven excellence. Furthermore, suc-cessful didactic patterns can be inductively inferred by analyzing the particular knowledge processing and its alleged contribution to learning success

Dynamic learning need reflection system for academic education and its applicability to intelligent agents

This paper suggests a new concept DLNR (Dynamic Learning Need Reflection) and its system practically used in the education at Japanese University. The effects, particularly on the learning of software agents, are analyzed. DLNR’s goal is to increase students' learning motivation through dynamically clarifying and reflecting their learning need. To achieve this goal, DLNR includes “prerequisite conditions”, “no compulsory subjects”, “payment for each learning subject”, and “GPA (Grade Point Average)” for estimating learning results. Using a tool developed for realizing DLNR, students design their learning need, namely their own graduation timeline by themselves to achieve their academic goal towards their job after graduation. Through taking classes, students dynamically modify the timeline reflectively according to the intermediate results such as shown by GPA. DLNR’s effects are evaluated. Particularly, DLNR was found applicable to the learning of software agents for intelligent system assistants, through incorporating more general tool such as Story board

Personalization in learning by knowledge engineering with didactic knowledge

The paper proposes an approach to model, process, evaluate and refine learning processes. A formerly-developed concept to visualize learning paths called storyboarding has been applied at Tokyo Denki University (TDU) to model the various curricula for students to progress in their studies at this university. Along with this storyboard, we developed a data mining technology to estimate chances for success for the students following each curricular path. This paper introduces a concept (we call "personalized data mining") of learner profiling. This learner profile represents the students’ individual properties, talents and preferences constructed through mining personal log data

Knowledge engineering technologies for learning processes

Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. Since e-learning systems are digital by their very nature, their introduction rises the issue of modeling the didactic design in a way that implies the chance to apply AI Techniques. A previously introduced modeling approach called storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics learning processes. Moreover, didactics can be refined according to revealed weaknesses and proven excellence. Successful didactic patterns can be explored by applying Mining techniques to the various ways students went through the storyboard and their associated level of success

Toward knowledge engineering with didactic knowledge

Learning systems suffer from a lack of an explicit and adaptable didactic design. A way to overcome such deficiencies is (semi-) formally representing the didactic design. A modeling approach, storyboarding, is outlined here. Storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify, validate the didactics behind a learning process. As a vision, didactics can be refined according to revealed weaknesses and proven excellence. Furthermore, successful didactic patterns can be inductively inferred by analyzing the particular knowledge processing and its alleged contribution to learning success