Abstract
Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is personalized based on learning and solving problem styles. The purposed algorithm, based on ACO, generates the adaptive optimal learning path. The algorithm describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The algorithm parameters are tuned dynamically to conform to the actual pedagogical process. The article includes the results of implementation and experiment represent this algorithm is able to provide its main purpose which is finding optimal learning paths based on learning styles and improved performance of previous adaptive tutoring systems.
Highlights
In recent years, we have seen exponential growth of Internet-based learning
75.36 75.34 75.09 75.28 75.46 75.58 75.16 74.66 75.01 75.35 75.48 75.15 75.58 75.00 75.15 74.56 adaptive e-learning systems based on Ant Colony Optimization (ACO), a new method was proposed that benefits from the advantages of these systems, and which minimizes the drawbacks of previous methods
Course content is personalized based on the VARK learning style, and MyersBriggs Type Indicator (MBTI) problem-solving styles have been used to design learning exercises to meet the needs of learners
Summary
We have seen exponential growth of Internet-based learning. The transition to online technologies in education provides opportunities to use new learning methodologies and more effective methods of teaching (Georgieva et al.Educ Inf Technol (2017) 22:1067–10872003). In web-based educational systems, the structure of the domain and content are usually presented in a static way, without taking into account the learners’ goals, their experiences, their existing knowledge, and their abilities (Huang et al 2007). This is known as insufficient flexibility (Xu and Wang 2006). This lack of interactivity means there is less opportunity for receiving instant responses and feedback from the instructor when online learners need support. Adding interactivity and intelligence to Web educational applications is considered an important direction of research (Hamdi 2007)
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