Educational Data Mining

Learning management systems record colossal sets of data. However, this data is used only for building superficial tables about what tasks are executed. Furthermore, these summaries, in most cases, are done only on individual level. In other words, we have a data with all elements to diagnose individual learners in detailed level but we skip it. We are approaching Web 3.0: the Web with intelligence. How about Learning 3.0?

To solve this, we have built Educational Data Mining tool, called EduMiner. The solution is based on teachable AI (same as is in AnimalClass's AI) that can learn conceptual structures in terms of conceptual learning.

In AnimalClass the learner can teach conceptual structures about mathematics, sciences, languages and arts to virtual pets (teachable agents). A data model behind AnimalClass is an applied semantic network. The data model is formed during the game play. A key term is teaching phase that should be defined in details in order to understand the results of this study. A teaching phase consists of a question creation and evaluation of the answer. Each teaching phase adds new relations into the conceptual structure. Furthermore, if the concept is not taught before, the new concept is also added into the conceptual structure during the teaching phase. The following example (figure 1) briefly describes the development of conceptual structures in the agent’s mind during teaching phases. The understanding of how an agent’s conceptual structure develops during playing is important in order to be able to interpret the results of the study.

According to research results (e.g. Ketamo & Suominen 2010; Ketamo 2009; Ketamo, Alajääski & Kiili 2009), the conceptual structure taught for the AI correlates highly to users’ real knowledge measured by tarditional paper tests. In fact, there are more variance between paper tests that between average paper tests and conceptual structure constructed while playing AnimalClass or using Mathematics Navigator.

Currently, we are building a open interface to import all SCORM tagged data into our system. In fact, we can easily import SCORM data, but the difficulties and challenges are related on how the meta data is constructed. In AnimalClass and Mathematics Navigator, all definitions are done by authors who have instructed to use meta tags in proper way. Though SCORM is well defined, the information inside XML is not standardized. For example keywords, difficulty levels and object outcomes are always more or less subjective.

More: www.gameminer.fi