Text Mining for Domain Structure Analysis in a Training System for Electrical Procedures

Learning environments themselves constitute a source of knowledge to understand and improve educational settings. We have developed some training systems where instructional content consists of separated electrical procedures stored in text documents. Since, the adequate characterization of the domain is a key factor in learning, we conducted a text mining study to understand relationships between topics, tasks and procedures in the training systems. In turn, this knowledge can help to provide students with an adaptive training, to help instructors to plan courses and to improve the training systems. We rely on a machine learning approach applying bag of words model and a clustering algorithm. Results on mining partial instructional content are encouraging since they show some useful relationships. Here, general proposal and preliminary results are presented.

[1]  Mykola Pechenizkiy,et al.  Handbook of Educational Data Mining , 2010 .

[2]  Peter Brusilovsky,et al.  Evaluation of topic-based adaptation and student modeling in QuizGuide , 2015, User Modeling and User-Adapted Interaction.

[3]  Ted Kwartler,et al.  Text Mining in Practice with R , 2017 .

[4]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[5]  Giner Alor-Hernández,et al.  Designing Empathetic Animated Agents for a B-Learning Training Environment within the Electrical Domain , 2016, J. Educ. Technol. Soc..

[6]  Castro Espinoza,et al.  Clustering Educational Data , 2010 .

[7]  G. Arroyo-Figueroa,et al.  Virtual reality training system for the maintenance of underground lines in power distribution system , 2013, Third International Conference on Innovative Computing Technology (INTECH 2013).

[8]  Gustavo Arroyo-Figueroa,et al.  Virtual reality training system for maintenance and operation of high-voltage overhead power lines , 2015, Virtual Reality.

[9]  Manuel Mejía-Lavalle,et al.  Data-Driven Construction of a Student Model Using Bayesian Networks in an Electrical Domain , 2016, MICAI.

[10]  Ryan Shaun Joazeiro de Baker,et al.  Mining Data for Student Models , 2010, Advances in Intelligent Tutoring Systems.

[11]  David J. Ketchen,et al.  THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE , 1996 .

[12]  Yasmín Hernández,et al.  Virtual Reality Systems for Training Improvement in Electrical Distribution Substations , 2016, 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT).

[13]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Yasmín Hernández,et al.  A B-Learning Model for Training within Electrical Tests Domain , 2014, Res. Comput. Sci..

[15]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..