The study explores the relationships between learning outcomes (scientific essays and statistical computer exercises), actions (in a custom made computer-supported learning environment), and profiles (learning motivation, learning strategies and social abilities) of educational science adult learners in a university level applied statistics course. The learning environment used in our study is based on previous research prototypes named EDUCO (Kurhila et al., 2002), EDUCOSM (Miettinen et al., 2003) and OurWeb (Miettinen et al., 2006). Its contents consist of lecture slides, about 50 instructional documents from miscellaneous sources, and about 6000 research papers from the proceedings of conferences on education and educational technology. The research papers are needed in the two assignments, in which the students rely on the system's search engine to find and analyze examples of the use of specific statistical methods. The system provides a semi-transparent graphical view, which floats in the browser window and shows the presence and location of the users in real time. Documents are portrayed as sheets of paper within clusters of related material, and the users appear around the documents as dots. Whenever somebody moves from one document to another, the corresponding dot jumps to the new location. Documents and users can be identified by placing the mouse pointer over the respective icon, which shows the title of the document or the name of the user as a tooltip. Clicking a document icon opens the corresponding page, and clicking a dot opens the instant messaging tool. Several tools for asynchronous communication are also provided. All documents except for the lecture slides can be annotated. Two different types of annotations are supported: highlights and comments. Highlights can be applied to marking important parts of the text, analogously to the way people underline text on paper. In practice, adding a highlight involves selecting a fragment of text with the mouse and clicking a button in a small toolbar that appears near the selection. Comments are added the same way, except that the user types the input in a popup window. A comment appears as a tooltip when the mouse pointer is placed on top of the commented text fragment. If several comments are attached to the same text, they appear one after another as a dialogue. Longer reflections and remarks that may not be associated with any single passage of text can be posted in a threaded discussion located at the bottom of the page. Finally, the system provides an interface for sending e-mail. The messages appear in the receiver's external mailbox, but sending them from the system is convenient and enables partial logging of e-mail communications. In addition to the core functionality described above, there are a number of auxiliary features addressing the practical needs of the users. Solutions to the assignments can be uploaded in the system, for example, and aggregate listings of documents, annotations and discussion postings facilitate navigation as well as monitoring of the progress of interactions. Following three research questions are formulated: 1) Are differences in the learners' profiles statistically related to differences their in learning outcomes? 2) Are the learners' actions in the online learning environment during the course related to their learning outcomes? 3) Are the learners' actions in the online learning environment related to differences in their profiles?

The statistics course started in October 2007 and ended in February 2008. The sample consisted of fourteen adult learners (ten females and four males). In the beginning of the course, students filled up five questionnaires that provided the profiling information: 1) Initial subject knowledge test; 2) Information technology knowledge and experience test; 3) Abilities for Professional Learning Questionnaire (Nokelainen & Ruohotie, 2002); 4) Multiple Intelligences Profiling Questionnaire (Tirri, K., Komulainen, Nokelainen, & Tirri, H., 2003); 5) Self-attitudes Attributes Scale (Campbell, 1996). During the course, students produced two scientific essays in the learning environment. In the last face to face meeting they also completed an examination that measured their real-life statistical analysis skills tasks with the SPSS software. All three learning outcomes were evaluated in a scale from 1 (poor) to 5 (excellent). The learning environment accumulates fine-grained data on the actions of the students. Several interesting questions can be addressed by analyzing the amount of time invested in various kinds of activities (reviewing lecture slides, skimming and reading instructional material, searching for research papers, skimming and reading research papers, annotating, discussing,...). The system monitors the scrolling of pages in the browser window, and is able to detect periods when the user is (probably) not working actively. Therefore, our analyses regarding the use of time will avoid one of the main obstacles to drawing meaningful conclusions. After the course, students were asked to evaluate 1) subject contents of the course; 2) course assignments; 3) the added value of the learning environment.

We expect to learn from data analysis if there is a statistical relation between learners profiles (measured with five questionnaires), learning outcomes (measured with three assignments) and use of computer-assisted learning environment (measured with logfile data of the system). One area of interest lies in the relationship of the amounts and distributions of working time to learning strategies, learning outcomes and social orientations. Analyzing data on annotations and discussion postings enables a complementary perspective based on the amount of contribution observed. Another interesting set of questions concerns the extent to which the continuous visibility of other users draws the students' attention and guides their navigation. Placing the mouse pointer over a dot or a document icon in the graphical view shows the name of the person or the title of the document as a tooltip, and produces an entry in the logs of the system. Although only a small effort is required on part of the subject, it can be interpreted as a sign of attention towards the other person and/or the document attended by the other person. People have also been claimed to engage in social navigation, which would mean that they "follow the crowd" by navigating to documents attended by others (Dieberger et al., 2000). Navigation actions are also logged by the system, and the data therefore enables us to study the occurrence of social navigation in a web-based learning environment.