In many eLearning contexts, materials are designed to be self-paced, with the content being available anytime, anywhere for learners to study independently. Commonly, without the presence and immediate feedback of an instructor, distance learners are left to their own devices to negotiate their learning path and to monitor their own progress. Furthermore, learning a complex topic structured in terms of various media and learning materials requires learners to make certain instructional decisions concerning what to learn and how to go about their learning. In other words, self-paced learning requires learners to self-regulate their own learning(Hadwin and Winne, 2001). Very often, learners have difficulty regulating learning in higher education when topics are complex and unfamiliar and it is not always clear to the learners if their instructional decisions are optimal.(Azevedo, Cromley, Seibert, and Tron, 2003) Research into adaptive eLearning systems has attempted to facilitate this process by providing recommendations, classifying learners into different preferred learning styles, or highlighting suggested learning paths(Brusilovsky, 1998).The aim of this research is to explore how learners can self-directed and self-regulate their online learning both in terms of domain knowledge and meta knowledge in the subject of computer science with a flexible and adaptive eLearning system. Two educational theories: experiential learning theory (ELT) and self-regulated learning (SRL) theory are used to aid learners' in their learning paths. As a result, changes in domain-knowledge, meta-knowledge, learner experience, learner satisfaction, perceived controllability, and system usability are being measured. All in all, this paper sums up the research work being done on the initial development of the system, instructional design framework based on the two theories, experimental design plan and course material examples as well as related issues.