Digital game based learning (DGBL) system is a promising area of research and debate, as it promotes contextualized learning, creates and harnesses motivation in learning capacities and encourages curiosity. DGBL takes place in a technological-mediated environment while engaging players in a learning activity through the support of computers. Several digital game based learning systems have been deployed for educational purposes to aid or support students in hands-on-learning experiences and constructive knowledge that involves mental reasoning processes. Hence, the research paper presented a development of digital game based fraction algebra learning system which is associated with game principle of snakes and ladders with underpinned concept of step-by-step procedure of solving mathematical problems. The system was tested by participants of FUTA staff primary school and the results of performance evaluation showed that the system could greatly support students to learn effectively the fraction algebra through game technology and would increase the students’ thinking process.

Personalised learning is a way of organising the learning content and to be accessed by the individual learner in a manner that is suitable to learner’s requirements. There are existing related works on personalised e-learning systems that focused on learner’s preference without considering the difficulty level and the relationship degree that exists between various course concepts. Hence, these affect the learning ability and the overall performance of learners. This research paper presents a genetic algorithm-based curriculum sequencing model in a personalised e-learning environment. It helps learners to identify the difficulty level of each of the curriculum or course concepts and the relationship degree that exists between the course concepts in order to provide an optimal personalised learning pattern to learners based on curriculum sequencing to improve the learning performance of the learners. The result of the implementation showed that the genetic algorithm is suitable to generate the optimal learning path using the values of difficulty level and relationship degree of course concepts. Furthermore, the system classified the learners into three different understanding levels of the course concepts such as partially, moderately and highly successful.