Identifying online learners' requirements for an efficient feedback and support system

In distance education learners are encouraged to be autonomous learners by learning at their own place and pace. However, they require relevant supports and timely feedback to motivate them progress effectively in their studies. In absence of face to face interactions, tutors are unable to understand clearly the problems faced by the learners and thus they are not given the right timely feedback. This research is part of a doctoral research and the aim of this paper is to firstly, investigate the types of supports that learners look for in a completely online course and secondly, identify a convenient environment to enable tutors track the learners' progress and give personalized assistance. As methodology, a content analysis of four synchronous tutorial chats, of 998 lines of interactions, was done based on activity theory of Engestrom. An online tutor and his ten learners participated in the chats, enabling them to interact freely with the focus of successfully completing the assignment set. Result depicts the human behaviour of learners enrolled in a completely online course: they queried on the rules, the procedures of the course as well as on the expectations of the tutor. The interactions were focused towards meeting the objectives of the course but they were not dominated by meta-cognitive interactions. Thus, we propose for relevant variables to be logged to enable tutors to understand learner's progress and to give efficient support based on the analysis of learning analytics of learners' past interactions. Learners' behaviours via their interactions, will enable tutors identify lurkers, active and passive learners. This research is a stepping stone in identifying patterns with regards to tutors and learners' interactions.