Usage

This page shows a usage example of the toolset, as a means to provide a better understanding of how the learning knowledge cycle is supported and to show the implemented user interface. It is assumed that the framework learner has access to the wiki and has already sign in with her credentials, which gives permission to view the documentation and use the components.

As depicted in Figure 1, the learner has access to a small menu on the top left side of the wiki. This menu allows her to signal she’s about to start her learning, so the system can begin capturing her navigation steps.

After entering the capturing state, the system provides the following new actions to the learner while she is browsing the wiki:

• Stop your Learning Path. Used to signal the system that she no longer wants her browsing steps to be captured. The system moves to the not capturing state and the menu resumes its previous set of actions.
• Mark as Landmark. This action enables the learner to mark a page as of noticeable importance to her learning experience. If she reaches a partial conclusion (not yet the final answer), or realises something important to her knowledge needs (that she wants to point out and share), this feature allows her to do so.
• Ignore Page. This action marks the current page as non-capturable, i.e., every time the user returns or browses through this page, it is not added to the existing browsing steps. This is useful if the learner is recurrently going through a certain page, probably because it is a central page with many links or an index page.

Besides these new actions, the learner is presented, in the top of the page and at all times, with a trail of the last steps she took (and that were captured) while browsing the wiki. These steps can have associated markings or icons, as explained in Figure 2.

Each cell or step is composed of an icon (optional) and the captured wiki page title. Both these elements are clickable anchors to their respective pages, so that the user can directly navigate to them. In the case of sections, the section title appears first, followed by an abbreviation of the page the section belongs to (subscript and bracketed). The tooltip shows the full name of the page.

Capturing a page being browsed is easy because the dokuwiki allows for the capturing of the navigation between wiki pages. The same is not true for sections. In the case of sections, the user has to manually mark the section as read. For this, each section has a like action link at the end of its contents. Consequently, the user can add that particular section to the captured learning path. Usually this enables parts of a documentation artifact to be emphasised, instead of the whole document. Later on, the learner can re-arrange the final learning path to indicate only these sections and not the whole document. This like action link only appears when the system is in capturing state (Figure 3).

At any time during the capturing state, the learner can filter and store her learning path. For that purpose, she uses the Prune/Graft component, accessible by clicking on a tab on the right side of the wiki. This tab is visible at all times, despite the state the system. If the system is at a not capturing state, the available learning steps remain from the last capturing session.

The Prune/Graft component opens up on a different layer from the wiki itself, providing a set of features without conflicting with the current work session. The user interface can be seen in Figure 4.

As such, the following functionalities are allowed:

• Drag and drop learning steps. The user is presented with all the learning steps she took during the learning session¹⁾. These can be dragged into a different area where she can compose the effective learning path she wishes to store and share. The steps can be re-arranged at will.
• Preview learning step. The learning steps can be previewed in an auxiliary pane so that the user can confirm, if her memory fails, the contents of that step. The user just have to open the preview pane (Show Previewer button on top of the page), click on the name of the step, which, at this point, loads that page into the pane. This functionality can be seen in Figure 5.

• View similar existing learning paths. While the user is dragging steps into the effective learning path area, the system automatically searches²⁾ for similar learning paths that already exist in the knowledge-base. This allows the user to check for relevant discrepancies between the learning path she is building and existing others. If the system encounters an exact match of the learning path the user is constructing, it does not duplicate when saving. Instead, it will merge the tags the user assigns to her learning path with the existing ones. This functionality can be seen in Figure 6.

• Tagging. At the bottom of the page, the user can tag the effective learning path using, again, a drag and drop behaviour. She has a list of tags that were present in the captured wiki pages and from where she can drag the tags she wants to assign the learning path. There is also the possibility of adding new tags through a text box.
• Save. The user can save the effective learning path she has composed, tagged accordingly.
• Save unprunned. The user can save the learning path as is, i.e., exactly as it was captured, having only to tag it accordingly.

The quickest way to access the knowledge-base of learning paths is to use the Search component. For that, the learner just have to open the respective tab (visible at all times) on the right side of the wiki, or clicking on the search option on the menu on the left sidebar.

The interface is quite simple and straightforward (see Figure 7), if you are used to the popular web search engines (Google³⁾, Yahoo⁴⁾ or Bing⁵⁾). The user just have to enter⁶⁾ the tags by which she wants to query the knowledge-base and the matched learning paths will be listed.

The search heuristics finds all learning paths that have those tags, where the learning paths with the most matched tags appear first, sorted descendent by rating.

Each result item has 5 constituent elements:

• Steps. This shows the learning path steps, conveying to the standardised form of showing learning paths as depicted in Figure 2.
• Tags. On the top left corner of each result item, the tags associated with that learning path are listed, where the matched tags are shown in bold.
• Overall rating. On the top right corner of each result item, the overall rating of that learning path is shown. The rating value varies between 0 and 5 and follows a star-like graphical representation, complemented by its numerical value in brackets.
• User rating. On the lower right corner of each result item, the user can view and change her personal rating of that learning path. The Rate button commits the user’s rating, entered by using a similar star-like interface.
• Previewer. On the lower left corner of each result item, there is a Show Previewer button that allows the user to preview the steps of the learning path, similarly to the same operation on the Prune/Graft component. The only difference is that the preview pane appears below the respective learning path.

As such, the Search component not only allows finding a suitable learning path, but enables the community to give their feedback on the usefulness of the learning paths, improving the effectiveness of the learning knowledge.

While in the capturing state, the system can provide the learner with directions on possible next steps in her learning path. This is done using the Hint component in Figure 8. The user has access to a tab at the top of the page that slides open to show a list of recommended next steps, based on the existing learning paths in the knowledge-base.

The list item is composed of clickable links to the page or section to where the user can directly navigate. Prefixed to each item is the number of learning paths (in brackets) that have the current page as the previous step.

This enables the learner to rely on the knowledge captured from the community to assist on her learning process. Generally, this only happens when the learner is at a loss, and seems to be disoriented in her quest for knowledge that might help her. The common behaviour is to fall back to the last known point, i.e. documentation artifact that appears to lead somewhere, and try to proceed from there. The Hint component can, then, provide an educated guidance to possible solution directions. Of course, if the learner is hacking her way into virgin land, i.e. paving new learning directions, the Hint component won’t be of much use.

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