Innovative Techniques for the Implementation of Adaptive Mobile Learning Using the Semantic Web

Adaptive Mobile Learning has constantly faced many challenges in order to make course learning more adaptive. This research presents a conceptual framework for using the Semantic Web to obtain students’ data from other educational institutions, enabling the educational institutions to communicate and exchange students’ data. We then can use this information to adjust the students’ profiles and modify their learning paths. Semantic Web will create a more personalized dynamic course for each student according to his/her ability, educational level, and experience. Through the Semantic Web, our goal is to create an adaptive learning system that takes into consideration previously completed courses, to count the completed topics, and then adjust the leaning path graph accordingly to get a new shortest path. We have applied the developed model on our system. Then, we tested the students on our system and a control system to measure the improvements in the students’ learning. We also have analyzed the results collected from the AML Group and the Control Group. The AML system provided a 44.80% improvement over the Control System. The experimental results demonstrate that Semantic Web can be used with adaptive mobile learning system (AML) in order to enhance the students’ learning experience and improve their academic performance

A Dialogue-based Approach Enhanced with Situation Awareness and Reinforcement Learning for Ubiquitous Access to Linked Data

The main barrier to a mainstream adoption of Semantic Web and Linked Data is the difficulty for users to search and retrieve the required information in this huge network of data. This work proposes a novel approach for Ubiquitous Browsing and Searching Linked Data. The proposed approach lays on a conceptual communication model, namely Interactive Alignment, for disambiguating both users' intentions and requests in the context of an information-seeking dialogue among humans and machines. More in details, the alignment between humans' intentions and machine comprehension is improved by identifying situations the users are involved in and considering users' situated preferences. Situation Awareness techniques are employed to identify and handle perceptions about occurring situations and Reinforcement Learning algorithms are exploited in order to elicit and acquire part of the user's mental model regarding her situated preferences. An ISU-based Dialogue System Architecture has been chosen to handle human-computer interaction and allow interactive alignment. Furthermore, the paper proposes a case study in which users are customers in U-commerce scenarios and they are looking for products or services to purchase