Since time is the most limited ressource students have, an efficient use of it is neccessary. To get the most out of it, time management is a crucial process which itself unfortunately counsumes too much time and therefore is often neglected by students. However, research shows that good time management results in less stress and an increased academic performance of students. The process of time management can be split into three major parts, which should be conducted periodically, e.g. each day: (i) defining goals and subgoals, (ii) prioterizing these and (iii) monitoring which goals were achieved and how much time was spent achieving them. An automatization of single parts of this process implies lower time consumption for the whole process. To assist students in time management, a system capable of automatically detecting students’ daily activities, like listening to a Lecture, is needed. Such a system would simplify part (iii) of the time management process.
Activity recognition systems can be split into two groups: (a) systems to detect simple activities like sitting, walking or running and (b) systems to detect complex activities like listening to a Lecture. Student activities refer to complex activities. Hence, they can only be detected with systems of group (b). Current activity recognition systems commonly use sensor data at personal scale to detect simple activities. These systems focus on users’ physical activities and do not consider the actual context. Nevertheless, they are able to detect simple activities like sitting, walking or running with an accuracy above 90 %. Systems bother with detecting complex activities – like student activities – instead, do not gain such a high value of accuracy. They often try to gather more than smartphone-based sensor data by deploying additional sensors resulting in increased costs of the overall system. Albeit the ubiquitous sensors, the social context of the user is not considered. For example, it cannot be used to determine the number of people around the user, nor the number of friends nearby. However, this information is crucial to derive a deep insight into the actual situation and to correctly classify a conducted activity.
The goal of this thesis is to build a system capable to automatically detect the following seven student activities: Lecture, Learning, Teamwork, Transition, Leisure, Sleeping and Job.