Xiaohong Tan

Personalized course generation and evolution based on genetic algorithms

Online learners are individuals, and their learning abilities, knowledge, and learning performance differ substantially and are ever changing. These individual characteristics pose considerable challenges to online learning courses. In this paper, we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning. The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept, but also the changing learning performance of the individual learner during the learning process. We present a layered topological sort algorithm, which converges towards an optimal solution while considering multiple objectives. Our general approach makes use of the stochastic convergence of genetic algorithms. Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner, which results in good learning performance.

The Design and Application of an Automatic Course Generation System for Large-Scale Education

In China, the number of online learners who attend formal education has quadrupled in the last 5 years to 8.2 millions until the end of 2008. How can online teachers build and update web-courses for such a large numbers of online students – courses that ideally respect the different requirements of the students? In this paper we describe an automatic course generation System (ACGS) developed at the Shanghai Jiao Tong University (SJTU). In this ACGS, teachers can build web-based courses according to their own instructional plan and publish the web-based courses without requiring technological support. Teachers can also update the web-courses whenever the instructional objective changes. The web-course learning environment generated from this system takes into account pedagogical scenarios. The ease of use of our system is illustrated by its quickly increasing number of users. About 50 teachers in the SJTU School of Continuing Education (SOCE-SJTU) have developed 45 web-courses in only two months. These 45 web-courses have been provided to about 5800 online learners in the last 2009 autumn semester alone.

Personalized Course Generation Based on Layered Recommendation Systems

Personalized learning aims at providing services that fit the needs, goals, capabilities and interests of the learners. Recommender systems have recently begun to investigate into helping teachers to improve e-learning. In this paper, we propose a personalized course generation system based on a layered recommender system. The aim of this system is to recommend personalized leaning content for online learners based on the personal characteristics of learners, such as the prior knowledge level, learning abilities and learning goals. The recommender algorithm generates a knowledge domain and learning objects in three layers. The generated courses consider both the teaching plan of teachers and the learners’ personal characteristics of the knowledge.