Software Support for Time Management

To mitigate difficulties in time management, software can play an important role. Several approaches have been made in the context of calendar scheduling and study planning. Firstly, traditional personal information manager or groupware applications such as Mi- crosoft Outlook or Lotus Notes can be used. However, these out-of-the-box software solutions lack adaptation capabilities and have been specifically designed for use in the professional workplace. Furthermore,Blandford and Green[2001] found that users prefer the use of a “battery of tools as an ensemble” for time management.

Mitchell et al.[1994] emphasise the importance of adaptation for such applications and

developed an interactive learning assistant called CAP (Calendar Apprentice). CAP passively learns patterns (duration, location, day of week, and times) for calendar ap- pointments from user input by using decision trees. It is also suggested that time-related individual differences (mono/poly-chronicity, see section2.3.2) should also be considered in such applications [Lee, 1999, 2003], and that temporal behaviour of workers in an organisation can change dependent on the current context or activity.

Similarly, Rebenich and Gravell [2008] presented an adaptive time management system for student learning. It takes the student’s learning style using the Index of Learning Styles [Felder and Silverman, 1988; Soloman and Felder, 2001], matches it with the teaching style of a module, and creates an individual study plan based on the differences between the two styles and a user-defined set of learning tasks. While following the plan, the student gives regular feedback about task progress. Using this feedback, the system automatically adapts the study plan by means of a multi-layered neural network and an iterative back-propagation learning algorithm. The desktop application is complemented by a mobile application using GPS data to issue position-related reminders, however, this mechanism was found to be unreliable and very power-intensive. While the practical applicability of the system framework was shown, a thorough evaluation and research study were not conducted.

Context-based resource discovery and reminder services have also been attempted by

Byun and Cheverst[2001]. In their Personal Digital Secretary (PDS) system architecture,

a context model containing data retrieved from sensors is used in combination with a traditional user model. The system tries to predict the user’s possible future behaviour based on the context, past events which happened in that context, and a predefined schedule. Here, context refers to the user’s current location and all available information about resources associated with it. The system then launches reminders based on that prediction and uses user feedback upon these reminders for system adaptation. At the same time, the user can access a history of past activities or events performed in the current context.

In an educational domain, Leung and Li [2003] use a dynamic conceptual network of programmes, courses, and credit units to develop a personalised study plan for students. Personalisation in this context means that the system presents the subset of the concep- tional network best fitting the student’s academic background and learning goals.

Martin et al.[2006b] utilise the user’s context information in combination with a learning

activity agenda, which helps determining learner availability in a particular situation, and their idle time in order to propose situational learning activities to the learner. Learner characteristics such as learning style and collaborative learning aspects are also considered. However, the main focus of their architecture is on context-aware learning activity adaptation. This mechanism is based on rules such that activities are only suggested if certain context-specific or general conditions apply.

Sharples[2000] proposes the use of mobile technology for lifelong learning, that is, learn-

ing “from cradle to grave” [Johnston, 2003]. His framework, which later led to the development of concrete learning organiser software [Holme and Sharples,2002;Vavoula

and Sharples,2002;Corlett et al.,2004;Chan et al.,2005], is based on the idea of con-

structivism, enabling a dialogue between teacher and learner in order to enable reflection. It addresses the key requirements of lifelong learning and suggests that software should take the role of a mentor, providing dictionaries, bibliographies, concept maps, learn- ing organisers and schedulers, visualisation tools, and project management capabilities. Based onSharples’s theory, a number of prototypes were developed.

First,Vavoula and Sharples[2002] presented KLeOS, an application focussing on learning projects, learning episodes taking place within them, and learning activities associated with an episode. As a student performs learning activities, the newly gained knowledge is visualised on a knowledge map and time line. The former is linked to the episodes and projects, so that the user can trace which episodes contributed to a particular knowledge aspect.

Then,Holme and Sharples [2002] and laterCorlett et al.[2004] worked on another stu- dent learning organiser software, developed for a Microsoft Windows Mobile compatible PDA, harnessing the functionality of existing personal information manager applications (here: Microsoft Pocket Outlook). The tool consists of a time manager displaying course

timetables and deadlines, a course manager which is used to access course material, a communication tool, and a concept-mapping tool for organising notes and documents. The latter had usability issues, partly due to a lack of concept mapping skills [Corlett

et al., 2005] on the part of the user. Student feedback at the end of a ten-month trial

revealed that timetable and communication tools were used significantly, but also that more research is required on the integration of study organisation tools into traditional personal information managers, and on adaptation to learner model and context. Finally, Bull et al. [2005] present TenseITS, a system aimed at enabling their users to learn whenever and wherever they want by pointing out so called learning oppor- tunities fitting into their daily schedule. This is combined with traditional intelligent tutoring techniques. Rather than detecting the current learner context through sensors (for example a GPS receiver), the system expects this data (location type, concentra- tion level, interruption likelihood, and available time) to be provided manually by the learner. Moreover, the system contains a learner model-like structure denoting the user’s knowledge level. In the course of user-system interaction, instruction and concepts to be taught are adapted to learner context and the learner model.

In project-based groupwork contexts, Mochizuki et al. [2008] use groupware techniques to enhance student project planning. They found that undergraduate students often struggle to come together and discuss their progress on groupwork assignments. As a result, it often happens that some group members are more active than others, and that project deadlines are missed due to some members’ inactivity. To address these issues, they developed a web-based groupware application for project-based learning called “ProBo”, and a complementary mobile version “ProBo Portable” which can be used on mobile phones. The system allows students to (a) organise division of labour on group tasks and provide feedback on their progress, (b) display a task tree showing task interdependencies, (c) schedule tasks on the time line, and (d) share electronic resources associated with these tasks. The mobile application keeps individual group members informed about the progress of each member on their project tasks. Mochizuki

et al. [2008] found that the use of this system increased students’ progress awareness,

encouraged them to work on their tasks since other users would immediately notice when they fall behind, and improved in-group communication. In their experiment, the group using ProBo Portable also reported an increased “sense of learning community”, higher connectedness, and enhanced learning.