**3.1 Learning thresholds set up**

Before discussion, the educator needs to set up learning thresholds to enable MALESAbrain to recognize the importance of the discussion-issues. In Fig. 3, the educator has set up "2" as *knowledge qualification-threshold*, when knowledge-weight 2 then the knowledge becomes a

problems. It is not just to give a simple solution from the computer suggestion. The answer

Fig. 2 shows a rapid prototyping and user testing model, called "task-artifact cycle" (Finneran & Zhang, 2003; Vicente, 1999). MALESAassessment borrows the iterative improvement and concepts-in-action design (Sutcliffe & Carroll, 1999) to develop its assessment interface. It iteratively stimulates learners to improve their tasks (troubleshooting plans) and their actions (fixing problems). The four-stage continuous cycles, in MALESAassessment, help the learners to exercise a proto-type in ways that can contribute as much as possible. The open-ended design allows the learners to exercise their plans, rather than provide a raw solution which may be useless in a practical problem. The interface (see Fig. 13) offers advice to the learners to help them to build up a viable working

Fig. 2. Iterating through the task artifact cycle (reprinted from Fig 4.3 Vicente (1999) with

last, we conclude and highlight the contributions of these two algorithms.

**3. Using MALESAbrain in problem-based discussion** 

**3.1 Learning thresholds set up** 

In the following sections, we further discuss these two algorithms. Each algorithm is separated into two sections. The first section applies our experiment result as example to picturize a whole concept to the readers. The second section offers the definitions, the algorithm and the knowledge retained in the system to clarify the previous example. After the algorithm discussions, we evaluate the system based on the participants' comments. At

To illuminate the learning algorithm in MALESAbrain, we use an example to explain the problem-based discussion guided by the learning system. In this section, we explain how an educator set up the learning thresholds, and then discuss the functions in the learning system.

Before discussion, the educator needs to set up learning thresholds to enable MALESAbrain to recognize the importance of the discussion-issues. In Fig. 3, the educator has set up "2" as *knowledge qualification-threshold*, when knowledge-weight 2 then the knowledge becomes a

must be planned by learners and consulted by MALESAassessment.

plan to cope with the difficulty of solving a testing problem.

permission).

qualified knowledge, which is the minimum requirement to join the competition for promotion to a higher order of discussion position; "-3.2" is a *knowledge rejection-threshold*, when knowledge-weight < *-3.2* MALESAbrain would then delete the knowledge; "4.2" is a *solution-maturity threshold*, when the solution-weight 4.2, then MALESAbrain would consider this solution is able to solve a discussion-problem; "-3" is a *solution-disagreement threshold*, if solution-weight < -3 then MALESAbrain would delete the solution. However, to help MALESAbrain decide when to suggest learners to stop the discussion, the educator also needs to set up *learning-rate* "70%" and due-date "4/9/2010".

Fig. 3. Educator set up learning thresholds.

Fig. 4. Welcome page encourages learners to follow the learning rules for discussion.

As shown in Fig. 4, MALESAbrain will keep assessing current learning-rate "0.00%" and checking the due-date "4/9/2010". During discussion, there are three figures shown on the welcome page (see Fig. 4), which help the educator to assess the current retained number of knowledge pieces "5", the current matured number of knowledge pieces "0" and the current

Proposing Two Algorithms to Acquire Learning

"0.1" and then choose 3 to go to "0.1.3".

problem.

his/her preference.

further discussion.

Knowledge in Problem-Based Learning Environment 143

location "0.1" to browse next level "0.1.#" which includes location "0.1.3" or directly click suggestion location "0.1.3". Simply speaking, "0.1.3" is an example of a location address about different level separated with "." Like the "dot" in Internet address, "0.1.3" is an address in MALESAbrain: "0" is the root level address that learner much choose 1 to go to

In Fig. 7, the system asks the learner about his/her preference. The learner then express his/her preference score "0.9" from his own judgment before being allowed to enter chat room for discussion. During exploration, MALESAbrain actively questions the learners about their *preferences* - a numerical measure of the learner's degree of support (or lack of support) for a posted-solution to the problem. Learners should answer these questions prior to moving on to the next piece of content or chat room for discussion. The preference value ranges from 1 for total agreement, to 0 for no comment, and to -1 for total disagreement. Learners must judge or criticize another's proposed solution, for its ability to solve a

Fig. 7. In response to the learner's chosen issue, the system would actively ask its user about

Subsequently, learners can understand each issue better in the chat forum for their discussions. Such a device provides a window of opportunity for individual learners to review other problems from different perspectives and subjectively evaluate the advantages and disadvantages of other learners' works. It is an important mechanism installed on the model, which encourages learners to critically think about a problemsolution from others' suggestions and carefully judge their own preference scores. In certain situations, the educator will encourage each of the learners to pick up a learning issue for further investigation and research, such as, go to library, discuss with an expert, use Internet searching or test an experiment on the laboratory before going back for

In Fig. 8, the user browses deeper into a few levels from a suggested location, and check a problem of interest, such as, "Funny things happened when exchanging screens or dragging stuff from one position to another" which attached a solution of "Incorrect video driver may cause illegal operations when playing a game". By this kind of critical thinking and browsing others' posted problem-solutions, learners would build up their knowledge

regardless of the previous learned or current development.

learning rate "0.00%". They help the educator and learners to understand the progress of the discussion. Nevertheless, at the due date "4/9/2010", if the learning-rate is still lower than the setup learning-rate 70% then the educator will need to decide whether to extend the discussion. The educator may calibrate the learning factors by re-setting the *knowledgequalification-threshold, knowledge-rejection-threshold*, *solution-maturity-threshold*, *solutiondisagreemen- threshold* or *learning-rate* (see Fig.2) and arrange another discussion by extending the due-date. At the end of the learning discussion, the educator will stop the discussion and then assesses the achievement of total retained knowledge and individual learners' contribution for which their suggestions have succeeded in the important area of the learning issues.
