**3.2 Problem-based learning discussion**

After the educator sets up the learning thresholds, learners can start to discuss their problems. Fig. 5 shows a learner entering his/her problem descriptions by completing a 'question enquiry form'. Once submitted, the query will be matched with MALESAbrain's retained knowledge according to the chosen keywords. Firstly the query will be matched by the keywords. If none is found, it will be seconded to match the problem description details. As a result, the locations of the matched knowledge will be the output to which the learner is being advised to advance.


Fig. 5. A learner "Philip" enters his discussion-issue for joining MALESAbrain discussion.

In Fig. 6, the system suggests some locations for learners to join discussion. The suggestion location "0.1.3" weight "5.3" is on the lower/next level of location "0.1". Learners can click


Fig. 6. After matching, the system suggests some locations for learners to join discussion.

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

After the educator sets up the learning thresholds, learners can start to discuss their problems. Fig. 5 shows a learner entering his/her problem descriptions by completing a 'question enquiry form'. Once submitted, the query will be matched with MALESAbrain's retained knowledge according to the chosen keywords. Firstly the query will be matched by the keywords. If none is found, it will be seconded to match the problem description details. As a result, the locations of the matched knowledge will be the output to which the learner

Fig. 5. A learner "Philip" enters his discussion-issue for joining MALESAbrain discussion. In Fig. 6, the system suggests some locations for learners to join discussion. The suggestion location "0.1.3" weight "5.3" is on the lower/next level of location "0.1". Learners can click

Fig. 6. After matching, the system suggests some locations for learners to join discussion.

which their suggestions have succeeded in the important area of the learning issues.

**3.2 Problem-based learning discussion** 

is being advised to advance.

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

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 problem.

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

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 further discussion.

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.

Proposing Two Algorithms to Acquire Learning

Knowledge in Problem-Based Learning Environment 145

Fig. 10. MALESAbrain sends the user's posted problem to other learners.

Fig. 11. The system adds the problem to the location "0.1.4".

In this section, we explain the first algorithm – MALESAbrain. The definitions precisely define the symbols used in the algorithm. The algorithm shows how an educator set up the learning thresholds; and how MALESAbrain facilitates an on-line discussion for knowledge acquisition. The calculation example discusses the knowledge-weights of the retained

*j*

*<sup>i</sup>* in MALESAbrain for problem-based

*si,j* is the collection of suggested solutions

**4. The algorithm of MALESAbrain** 

**4.1 Definitions of MALESAbrain** 

**Definition 1-1**. A piece of *knowledge-content*

discussion is defined as a pair of problem and solutions:

*si,j*) where *pi* is a *problem* 

knowledge.

*<sup>i</sup>*= (*pi , j*

associated with *pi*


Fig. 8. shows a learner browsing deeper into a few levels from a suggested location.

However, if there are no suitable learning issues for their discussions, learners might post their own problems. Fig. 9 shows a learner posting his/her own problem, about "Error happened when running on a computer game. It did not change to the next screen but came out with memory-error message".


Fig. 9. A learner posts his/her own problem for joining discussion.

Fig. 10 shows MALESAbrain sending the user's posted problem to other learners. This is a *broadcasting function* that gives the posted problems a chance to be discussed from other learners' viewpoints or judgments. In this process of a problem-solution discussion, the learners who do not agree with a problem might propose another problem to clarify the original problem; and those who do not agree with a solution might contribute another solution to clarify or specify the original solution. However, when any new knowledge is added, MALESAbrain would notify other learners to encourage them to join the discussion.

This broadcasting function can also be considered as a feedback mechanism, which stimulates the learners to brainstorm more knowledge among participants. In Fig. 11, the system adds the problem to the location "0.1.4". The user now needs to wait for the feedback from the other learners' judgments on his/her proposal. These broadcasts and discussions will continue until MALESAbrain can identify the knowledge according to the set up thresholds. The more discussions which are returned the more changes occur in individual knowledge-weights because of the contribution from the learners' preferences.

Fig. 8. shows a learner browsing deeper into a few levels from a suggested location.

Fig. 9. A learner posts his/her own problem for joining discussion.

out with memory-error message".

discussion.

However, if there are no suitable learning issues for their discussions, learners might post their own problems. Fig. 9 shows a learner posting his/her own problem, about "Error happened when running on a computer game. It did not change to the next screen but came

Fig. 10 shows MALESAbrain sending the user's posted problem to other learners. This is a *broadcasting function* that gives the posted problems a chance to be discussed from other learners' viewpoints or judgments. In this process of a problem-solution discussion, the learners who do not agree with a problem might propose another problem to clarify the original problem; and those who do not agree with a solution might contribute another solution to clarify or specify the original solution. However, when any new knowledge is added, MALESAbrain would notify other learners to encourage them to join the

This broadcasting function can also be considered as a feedback mechanism, which stimulates the learners to brainstorm more knowledge among participants. In Fig. 11, the system adds the problem to the location "0.1.4". The user now needs to wait for the feedback from the other learners' judgments on his/her proposal. These broadcasts and discussions will continue until MALESAbrain can identify the knowledge according to the set up thresholds. The more discussions which are returned the more changes occur in individual knowledge-weights because of the contribution from the learners' preferences.


Fig. 10. MALESAbrain sends the user's posted problem to other learners.


Fig. 11. The system adds the problem to the location "0.1.4".
