| Abstract [eng] |
Distance education technology is gaining momentum all around the world. Weaknesses and limitations in the technology are exposed more than ever. Activities, such as, stimulation of students’ activity and development of support systems are becoming important in attempts to lower student dropout rates and improve quality of education. This thesis has goals to review existing methodology of analyzing and encouraging student activity in higher education courses based on online technology. As well as to enhance current methods and set guidelines for development of the new ones. Literature analysis has highlighted the astonishing potential of data mining methods in Learning Management Systems. Based on these methods a new model of students’ activity stimulation has been suggested. The new model is based on automatic self tuning system which would analyze the behavior patterns of course users. The results of this analysis would be used to compose and send notifications of course events which are relevant for and desired by the individual user while respecting their learning patterns. Same system could inform instructor of any abnormalities in student learning behavior, unfavorable odds to successfully complete the course or even drop out. A study has been made, which confirmed the initial assumptions and potential usefulness of the proposed model. |
