Demircioglu Diren, D. & Horzum, M. B. (2022). Artificial intelligence based adaptive learning model for distance learning readiness. In D. Ifenthaler & S. Seufert (Eds.), Artificial intelligence education in the context of work (pp. 139–154). Springer. https://doi.org/10.1007/978-3-031-14489-9_8

Abstract:

“In learning models that require the use of technology, such as distance learning, readiness is very important. The emergency distance learning process revealed that the instructors with high readiness for distance learning conduct the lessons efficiently. Otherwise, the lessons were found to be inefficient. In the chapter, a training model aimed at ensuring the readiness of instructors in higher education is presented for this problem. Readiness will be provided within the framework of the Technological Pedagogical Content Knowledge (TPACK) model. TPACK will be adapted in accordance with distance learning. In addition, the model will be designed in an adaptive structure to reduce the cognitive load in education processes. Adaptation in the model will be in two stages. The first will be carried out at the beginning of the training. For this, the education needs of the instructors will be determined before the training and the navigation adaptation will be offered through direct guidance, tailored to the needs. The training will start with different navigation for every instructor according to these adaptations. The second stage includes the process after the training has started. This phase is dynamic and will continue with the monitoring of users’ real-time system data flows throughout the process. At this stage, both navigation and content adaptation will be carried out. Expert systems will be used in the first stage of the model developed within the scope of design-based research, and machine learning and learning analytics will be used in the second stage.”