**6.1 The definitions of MALESAassessment**

**Definition 2-1**. The *re-organize(K)* action is used to reorganize the knowledge base, which includes two steps, are defined as follow:


**Definition 2-2**. The *inquiry(x)* function is defined as

$$\text{Inquality:} \xrightarrow[]{} \xrightarrow[]{} \text{matching} \xrightarrow[]{} \bigcup\_{i=0}^{n} k\_i \quad \text{where}$$


**Definition 2-3-1**. The *retrievalQuantity()* is defined as the number of AK-cells being retrieved into consultation screen where

$$|\bigcup\_{i=0}^{n} k\_i| \le \sigma \pm \varepsilon \text{ where}$$


**Definition 2-3-2**. A *consultation-action(ci)* is defined as an action chosen by the participants on consultation screen, where

$$\mathbf{c}\_{i} \leftarrow \begin{cases} \textit{inquivy}(\boldsymbol{x}) \; ; \; or \\ \textit{attention}(k\_{i}) \; \end{cases} \text{ where,}$$


Proposing Two Algorithms to Acquire Learning

definition 2-3\*/

inquiry(x) /\* *Inquiry* Definition 2-2 \*/ INPUT inquiring-question

INPUT participant-email

consultation-action(ci) /\* *Consultation* Definition 2-3\*/

attention-action(ai) /\* *Attention* Definition 2-4\*/

plan-action(pi) /\* *Plan* Definition 2-5\*/

"plan" : DISPLAY-IN plan(*si,j*) "delete": DELETE delete(*ki*)

"clear": CLEAR clearAttentionScreen

"position": DETERMINE position(*si,j*),

"delete": DELETE delete (*si,j*) "clear": CLEAR clearPlanScreen "successful": EXIT exit(successful)

"fail": EXIT exit(fail)

"inquiry": GOTO inquiry(*x*) /\*Definition 2-2\*/

"attention": DISPLAY-IN attention(*ki*) /\*Definition 2-3-2 \*/

INPUT keywords

four operations \*/

three operations \*/

five operations \*/

ENDCASE END consultation-action

ENDCASE END attention-action

ENDCASE END plan-action

ENDCASE END MALESAassessment

END inquiry

Knowledge in Problem-Based Learning Environment 155

*Attention* screen (3): GOTO attention-action(ai) /\* *Attention* screen definition 2-4\*/

*Inquiry* screen (1): GOTO inquiry(x) /\* *Inquiry* screen definition 2-2\*/ *Consultation* screen (2): GOTO consultation-action(ci) /\* *Consultation* screen

*Plan* screen (4): GOTO plan-action(pi) /\* *Plan* screen definition 2-5\*/

CASE operation chosen by participanti OF /\* the participants can choose one of the

"up": INCREMENT retrievalQuantity( + ) /\*Definition 2-3-1 \*/ "down": DECREMENT retrievalQuantity( - ) /\*Definition 2-3-1 \*/

CASE operation chosen by participanti OF /\* the participants can choose one of the

CASE operation chosen by participanti OF /\* the participants can choose one of the

**Definition 2-4**. A *attention-action(ai)* is defined as

*ai*<sup>=</sup> *clearAttentionScreen orkdelete orsplan i ji* ;)( ;)( , where


**Definition 2-5**. A *plan-action(pi)* is defined as

*pi*<sup>=</sup> )( ;)( ; ;)( ;)( , , *failexit exit successful or clearPlanS orcreen orsdelete orsposition ji ji* where


#### **6.2 The algorithm of MALESAassessment**

re-organise(**K**) /\* *Preparation* Definition 2-1 \*/ INPUT keywords(educator, *ki*) CONSTRCT CBR(*K*)

END re-organise

#### MALESAassessment(participanti,*K*)

CASE screen chosen by participanti OF /\* the participants can choose one of the four screens \*/

where

 *plan(si,j)*, means participant chosen a solution on the attention screen for revision, and MALESAassessment then append it to the plan screen. *delete (ki)*, means participant want to delete a chosen AK-cell on the

*clearAttentionScreen*, means participant does not satisfy with the whole

where

 *position(si,j)*, means participant want to organize solution's execution steps; by either click ascending or descending on a chosen solution to the right

*delete(si,j)* , means participant want to delete a chosen solution on the plan

*clearPlanScreen*, means participant does not satisfy with the whole plan

 *exit(successful)*, means participant have successfully made the plan and fixed the problem. MALESAassessment then retains the log file and the successful plan into a new CBR knowledge base as a successful case when

 *exit(fail)*, means participant cannot establish a plan or cannot fix the problem. MALESAassessment then retains the log file and the failure plan

into a new CBR knowledge base as a failure case when s/he exits.

CASE screen chosen by participanti OF /\* the participants can choose one of the four

**Definition 2-4**. A *attention-action(ai)* is defined as

attention screen.

**Definition 2-5**. A *plan-action(pi)* is defined as

*pi*<sup>=</sup>

screen.

s/he exits.

**6.2 The algorithm of MALESAassessment**  re-organise(**K**) /\* *Preparation* Definition 2-1 \*/ INPUT keywords(educator, *ki*)

CONSTRCT CBR(*K*)

MALESAassessment(participanti,*K*)

END re-organise

screens \*/

 

)( ;)(

screen and want to clear it.

*failexit*

;)( ;)(

*exit successful or clearPlanS orcreen orsdelete orsposition ji ji*

, ,

 

execution step.

*clearAttentionScreen orkdelete orsplan i ji* ;)( ;)( ,

attention screen and want to clear them.

;

*ai*<sup>=</sup> 

 

```
Inquiry screen (1): GOTO inquiry(x) /* Inquiry screen definition 2-2*/ 
        Consultation screen (2): GOTO consultation-action(ci) /* Consultation screen 
        definition 2-3*/ 
        Attention screen (3): GOTO attention-action(ai) /* Attention screen definition 2-4*/ 
        Plan screen (4): GOTO plan-action(pi) /* Plan screen definition 2-5*/ 
    ENDCASE 
END MALESAassessment 
inquiry(x) /* Inquiry Definition 2-2 */ 
        INPUT inquiring-question 
        INPUT keywords 
        INPUT participant-email 
END inquiry 
consultation-action(ci) /* Consultation Definition 2-3*/ 
    CASE operation chosen by participanti OF /* the participants can choose one of the 
    four operations */ 
        "inquiry": GOTO inquiry(x) /*Definition 2-2*/ 
        "attention": DISPLAY-IN attention(ki) /*Definition 2-3-2 */ 
        "up": INCREMENT retrievalQuantity( + ) /*Definition 2-3-1 */ 
        "down": DECREMENT retrievalQuantity( - ) /*Definition 2-3-1 */ 
    ENDCASE 
END consultation-action 
attention-action(ai) /* Attention Definition 2-4*/ 
    CASE operation chosen by participanti OF /* the participants can choose one of the 
    three operations */ 
        "plan" : DISPLAY-IN plan(si,j) 
        "delete": DELETE delete(ki) 
        "clear": CLEAR clearAttentionScreen 
    ENDCASE 
END attention-action 
plan-action(pi) /* Plan Definition 2-5*/ 
    CASE operation chosen by participanti OF /* the participants can choose one of the 
    five operations */ 
        "position": DETERMINE position(si,j), 
        "delete": DELETE delete (si,j) 
        "clear": CLEAR clearPlanScreen
```
"successful": EXIT exit(successful)

"fail": EXIT exit(fail)

ENDCASE END plan-action

Proposing Two Algorithms to Acquire Learning

*kq*

**Symbol**

**8. Conclusion** 

Knowledge in Problem-Based Learning Environment 157

**MALESAbrain learning Threshold** 

**Threshold** *qualification threshold rejection threshold maturity threshold disagreement threshold* 

The second experiment, of MALESAassessment, found the keywords in the AK-cells are not well matched the problem issues. It is because learners' assign keywords, during MALESAbrain discussion, will have individuals' viewpoints without integrated into an overview for reusing. It affects the searching results in the test. After we re-assign keywords to each AK-cell, MALESAassessment becomes quite significant for reusing the learned knowledge. The Assessment Report and log file, in section 6.3, show one of the successful retained cases after test. This means there are no difficulties to give a proper mark to a successful answered student

In this chapter, we propose two intelligent algorithms to save the educator the time for acquiring learning knowledge in PBL environment. The first algorithm builds up MALESAbrain as an intelligent system to acquire students' knowledge in PBL discussion. It will help students integrate their knowledge by critical thinking on different angles of a problem through on-line discussion. The second algorithm builds up MALESAassessment as an evaluation system to test students' performance after PBL discussion. These two algorithms work together to reduce educator's efforts for connecting PBL to IT education. MALESAbrain algorithm saves the educator the effort of searching for important knowledge pieces generated by students' discussions through its automatic calculations. It reduces the pressure of time in the teaching schedule for coaching PBL discussions in the IT course. Consequently, MALESAbrain has contributed three notions to make the PBL

1. The first notion is the created data structure – coined as "AK-cell" (see Definition 1-4) – for cooperative learning, which combines knowledge-content and knowledge-weight. It allows the discussion-knowledge to become calculable in a threshold system. The knowledge-contents become mobile because of the combination of knowledge-weight and knowledge-content in the data structure. Knowledge-weight ranks AK-cells into different important locations, based on learners' judgments and the thresholds set up. It helps learners pay attention to consensus knowledge for more discussion; and to think

2. The second notion is the autonomous decision-making mechanism – called "learning threshold" (see Definition 1-5). It helps the learning system automatically arrange and order construction of a hierarchical knowledge base. It helps students to identify the

3. The third notion is the dynamic structure of the knowledge base – called "growth-factor" (see Definition 1-6). It helps the data structure AK-cell to be constructed according to the educator's viewpoint; whatever is right in his/her coaching for knowledge acquisition. The value of the second algorithm MALESAassessment is the assessment design, which uses the student performance test to refine the learned knowledge. It offers learners a chance to

about why certain issues accumulate different scores from other issues.

*kr*

**Setup Value** 2 points -3.2 points 4.2 points -3 points Table 1. A suggestion learning thresholds set up in MALESAbrain with 6 participants.

and give a proper advice to a failure answered student in this experiment.

discussion more effective and efficient for knowledge acquisition:

importance of the issues in a problem.

*sd*

*sm*
