He is involved in designing, developing and evaluation of computer-supported learning environments. Miikka Miettinen, Patrik Floréen and Henry Tirri are members Abstract A shared document-based annotation tool was presented, and its usefulness in two different real-life web-based university-level courses (adult learners, n = 27 and adolescent learners, n = 23) was empirically investigated. The study design embodied three data collection phases: (1) a pretest measuring selfrated motivation, learning strategies, and social ability; (2) log file data analysis showing actual use of the system features; and (3) a posttest in a form of an email survey. For both groups, the results showed that the level of motivation has a positive effect on activity in the system and the final grade. The learners, who reported to have good time-management strategies, were the most active users of the system. The level of social ability predicted both the number of consecutive comments in the documents and the threads in document-related newsgroup discussions. Log file data analysis showed that user activity in the system was positively related to the final grade in both samples. Results of the posttest showed that all the respondents agreed when asked: (1) if the system brought added value to the learning process; (2) if the use of the system changed their studying habits favourably; and (3) if they would like to use the system in other courses.

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Abstract

Many of the possibilities of Web-based

The era of modern personal and ubiquitous computers is beset with the problem of fragmentation of the user's time between multiple tasks. Several adaptations have been envisioned that would support the performance of the user in the dynamically changing contexts in which interactions with mobile devices take place. This paper assesses the feasibility of sensor-based prediction of time-sharing, operationalized in terms of the number of glances, the duration of the longest glance, and the total and average durations of the glances to the interaction task. The data used for constructing and validating the predictive models was acquired from a field study (N = 28), in which subjects performing mobile browsing tasks were observed for approximately 1 h in a variety of environments and situations. The predictive accuracy achieved in binary classification tasks was about 70% (about 20% above default), and the most informative sensors were related to the environment and interactions with the mobile device. Implications to the feasibility of different kinds of adaptations are discussed.