Treffer: A context-aware mobile learning system for adapting learning content and format of presentation: design, validation and evaluation.

To date, the growth usage of mobile technologies and devices as well as the ubiquitous wireless communication have led to the development of new systems and applications in many fields and areas including education. This technological progress can be used to facilitate the students' lives by creating smart and personalized solutions considering their personal and academic profiles as well as their real environments. This is because mobile technologies have the capacity to detect the contextual dimensions of learners through different sensors, and also because of the available software that have known a big improvement in the recent years. In this way, this article focuses on proposing a model called Dynamic Mobile Adaptive Learning Content and Format (D-MALCOF) that considers the learner's knowledge level and learning styles in order to provide the suitable learning for each and every student. The paper introduces the design of our approach as well as its conception and modelling activities. In addition, we validated this model in real life learning settings by developing an Android mobile application that has been tested in the context of Moroccan higher education system. The evaluation of the model was conducted with undergraduate students, and examined the impact of D-MALCOF on the students' knowledge level and learning achievements. The results showed that our solution has the potential to enhance the students' knowledge level in the java programming language by comparing the tests' results of the participants who used our mobile app with those who have learned through a traditional e-learning platform. Moreover, the students showed encouraging feedback, general satisfaction, positive perceptions and good intentions towards the application after completing their learning activities and responding to the survey. [ABSTRACT FROM AUTHOR]

Copyright of Education & Information Technologies is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)