Performance Improvement Using Adaptive Learning Itineraries

Abstract

In this paper, Bayesian‐Networks (BN) and Ant Colony Optimization (ACO) techniques are combined to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work is that the amount of pheromones released is variable. This amount is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required. By using ACO and BN, a fitness function, responsible of automatically selecting the next course in the learning graph, is defined. This is done by generating a path that maximizes the probability of each user's success in the course. Therefore, the path can change to improve learners’ average performance, taking into account the pedagogical weight of each learning unit and the social behavior of the system. Furthermore, a discrete dynamical system is obtained and its stability is studied. How to wrap an existing Learning Management System is also described in this work. Finally, an experiment compares this approach with the old on‐line learning system being used previously.

Design and evaluation of planning and mathematical models for generating learning paths

“…Durand et al and Vazquez et al consider graph theory to represent curricula and heuristic algorithms to find the best path in the graph that represents a learning sequence. Instead, our representation is closer to a tree rather than a graph.…”

Automatic generation of assessment objects and Remedial Works for MOOCs

“…It is necessary to rethink technologies for supporting development and being fulfilled in the social or classroom learning contexts. The combination of Massive Open Online Courses with Adaptive Learning delivery platform [44] [34], where the delivery happens by mean of individual learning preferences and providing them continuous intelligent tutoring with self-graded content, examination and assessment, opens new perspectives for the adaptive learning for the adult education. The Adaptive MOOCs (A-MOOCs) analyse students' interactions with the learning environment in order to adapt it to individual students' behaviours.…”

“…More flexible models and open approaches will encourage more mature students to participate in higher education and gain qualifications to further their careers. The fast development of the Massive Open Online Courses (MOOC) is asking to the Universities to reformulate their formative offers by exploring new educational methods [46] able to value massive models and flexible learning paths to hold up lifelong and adults learning [44]. The most used educational method is the "monitorial method" where the teacher should "fill students' heads with knowledge and provide them with the information that they needed in order to improve cognitive and metacognitive process" [6].…”