Paper Type
Research-in-Progress Paper
Abstract
Implicit user modeling plays an important role in supporting personalized e-learning environment. Its main concern is to unobtrusively and ubiquitously learn from a learner’s previous learning experience about characteristics for enhancing the user model, in order to adapt the services to their personal needs. An investigation for understanding learning behavior patterns forms the basis for establishing stronger implicit user modeling mechanisms. This empirical investigative study aims at getting a better insight into types of learning behavior. The proposed usage of data mining methods and visualization tools elicited some interesting learning behavior patterns. We analyzed these patterns from two perspectives: action frequency and action sequences, based on an expert-designed classification of the learning behavior patterns that helped in ranking the various action categories according to their significance from a user’s perspective. The initial results of the study are promising and suggest possible directions for further improving implicit user modeling.
Recommended Citation
Shi, Lei; Cristea, Alexandra; Awan, Malik Shahzad; Stewart, Craig; and Hendrix, Maurice, "Towards Understanding Learning Behavior Patterns in Social Adaptive Personalized E-Learning Systems" (2013). AMCIS 2013 Proceedings. 6.
https://aisel.aisnet.org/amcis2013/ISEducation/RoundTablePresentations/6
Towards Understanding Learning Behavior Patterns in Social Adaptive Personalized E-Learning Systems
Implicit user modeling plays an important role in supporting personalized e-learning environment. Its main concern is to unobtrusively and ubiquitously learn from a learner’s previous learning experience about characteristics for enhancing the user model, in order to adapt the services to their personal needs. An investigation for understanding learning behavior patterns forms the basis for establishing stronger implicit user modeling mechanisms. This empirical investigative study aims at getting a better insight into types of learning behavior. The proposed usage of data mining methods and visualization tools elicited some interesting learning behavior patterns. We analyzed these patterns from two perspectives: action frequency and action sequences, based on an expert-designed classification of the learning behavior patterns that helped in ranking the various action categories according to their significance from a user’s perspective. The initial results of the study are promising and suggest possible directions for further improving implicit user modeling.