Knowledge mining for supporting learning processes

AI technologies for knowledge mining are commonly used in technical environments. Their application for social processes like learning processes, for example, is a quite a new challenge, which is characterized by having "humans in the loop". Humans’ desires, preferences and decisions may be unpredictable and thus, not appropriate for modeling - at a first glance. However, in learning processes didactic variants can be anticipated and can become a subject of AI technologies. A semiformal modeling approach called storyboarding, is outlined here. A storyboard represents various opportunities for composing a learning process according to individual circumstances, such as topical prerequisites (educational history), mental prerequisites (preferred learning styles, etc.), performance prerequisites (a requested success level in former learning activities, etc.), and personal aspects (needs, wishes, talents, aims). By storyboarding, various didactic variants can be validated by considering the average learning success associated with the different paths through a storyboard in a case study. Based on validation results, success chances can be derived for the different paths. Here, a concept and an implementation to pre-estimate success chances of intended (future) learning paths through a storyboard are introduced. They are based on a Data Mining technology, and construct a decision tree by analyzing former learners’ paths and their degrees of success. Furthermore, this technology generates a supplement to a submitted path, which is optimal according to the success chances. This technology has been tested at a Japanese university, in which students had to compose their individual plan (subject sequences) in advance, and the technology helped them by predicting success chances and suggesting alternatives

Knowledge Discovery in Data Mining and Massive Data Mining

Knowledge discovery is a process of non trivial extraction of previously unknown and presently useful information. The rapid advancement of the technology resulted in the increasing rate of data distributions. The data generated from mobile applications, sensor applications, network monitoring, traffic management, weblogs etc. can be referred as a data stream. The data streams are massive in nature. The present work mainly aims at knowledge discovery using data mining and massive data mining techniques. The knowledge discovery process in both the techniques is compared by developing a classification model using Naive bayes classifier. The former case uses Edu-data, a data collected from technical education system and the latter case uses massive online analysis frame work to generate the data streams. Mining data stream is referred as Massive Data Mining. The data streams must be processed under very strict constraints of space and time using sophisticated techniques. The traditional data mining techniques are not advised on this massive data. Therefore the massive online analysis framework is used to mine the data streams. The present work happens to be unique in the literaturein

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

Performance Analysis of Hoeffding Trees in Data Streams by Using Massive Online Analysis Framewor

Present work is mainly concerned with the understanding of the problem of classification from the data stream perspective on evolving streams using massive online analysis framework with regard to different Hoeffding trees. Advancement of the technology both in the area of hardware and software has led to the rapid storage of data in huge volumes. Such data is referred to as a data stream. Traditional data mining methods are not capable of handling data streams because of the ubiquitous nature of data streams. The challenging task is how to store, analyse and visualise such large volumes of data. Massive data mining is a solution for these challenges. In the present analysis five different Hoeffding trees are used on the available eight dataset generators of massive online analysis framework and the results predict that stagger generator happens to be the best performer for different classifiers

Knowledge engineering with didactic knowledge - first steps towards an ultimate goal

Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. A previously introduced modeling approach called storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. 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

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

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

Personalized curriculum composition by learner profile driven data mining

The paper is focused on modeling, processing, evaluating and refining processes with humans involved like (not only, but also e-) learning. A formerly developed concept called storyboarding has been applied at Tokyo Denki University (TDU) to model the various ways to study at this university. Along with this storyboard, we developed a Data Mining Technology to estimate success chances of curricula. Here, we introduce a learner profiling concept that represents the students’ individual properties, talents and preferences personalized data mining

Personalized curriculum composition by learner profile driven data mining

The paper is focused on modeling, processing, evaluating and refining processes with humans involved like (not only, but also e-) learning. A formerly developed concept called storyboarding has been applied at Tokyo Denki University (TDU) to model the various ways to study at this university. Along with this storyboard, we developed a Data Mining Technology to estimate success chances of curricula. Here, we introduce a learner profiling concept that represents the students’ individual properties, talents and preferences personalized data mining