**2.1 The learning model of MALESAbrain<sup>1</sup>**

The algorithm one is called MALESAbrain. The design of its learning model is to form a critical thinking methodology for problem-based discussion. Accordingly, we develop an intelligent system to help learners think critically and learn topics like computer troubleshooting through an Internet workshop. The model takes an active role in sharpening the learners' contributions towards viewpoints on the discussion issues. In discussion, the learning system would highlight the importance of those issues which help the learners pay more attention to consensus solutions for better discussion and problem solution. The model consists of three main stages (Fig. 1) to facilitate learners in problembased discussion:

<sup>1</sup> The acronym for "Machine-Learning-Expert-System Algorithm for brainstorming"

Fig. 1. The design model.

138 E-Learning – Organizational Infrastructure and Tools for Specific Areas

more control to the system, by using statistics for decision-making. Thus the learning system becomes more intelligent and reduces the workload of educator. On the other hand, we relegate some of the problem-based learning principles into the development of the on-line

Internet forums allow users to post their topics/learning-issues for discussion. In a well facilitated web site, after having chosen a topic, users can post their suggestions or switch to chat room for on-line discussion. Our system is designed to accept a broader view of problems within the learning domain and deeper discussion. This means that we can allow different problem-descriptions to be posted from individual learners within the set up learning domain. We are not confined to single problem and may achieve superior

This chapter introduces two algorithms/models to design the MALESA learning system – one is MALESAbrain for learning discussion and another is MALESAassessment for learning assessment. The former is a discussion system in facilitating a PBL classroom. It encourages learners to judge or criticize the solutions posted by others before exploring further knowledge-content. The system then sums up the judgment scores as its knowledgeweight in order to pass the thresholds set up for ranking/arranging the learning issues. The latter MALESAassessment is an assessment system for evaluating learners' performances after learning discussion. It uses a real world practical problem to assess the learners' solving-plan and fixing-processes as the educator's marking standard. These two systems co-operate to transform chat forum technologies for the problem-based learning in IT

The first system learning design has borrowed the threshold and knowledge-weight concepts from machine learning (Mitchell, 1997) to build up the intelligent learning tool. The educator needs only to give a learning domain and a few beginning questions (even beginning questions are not compulsory) to the system before the learning discussion starts. The system then asks learners to judge (or criticize) other's proposed solutions. Through threshold evaluation of the knowledge-weights, knowledge pieces are automatically ranked and arranged by the intelligent learning system. This kind of teamwork learning would then integrate and synthesize previous and current learning to increase the knowledge base.

The algorithm one is called MALESAbrain. The design of its learning model is to form a critical thinking methodology for problem-based discussion. Accordingly, we develop an intelligent system to help learners think critically and learn topics like computer troubleshooting through an Internet workshop. The model takes an active role in sharpening the learners' contributions towards viewpoints on the discussion issues. In discussion, the learning system would highlight the importance of those issues which help the learners pay more attention to consensus solutions for better discussion and problem solution. The model consists of three main stages (Fig. 1) to facilitate learners in problem-

The acronym for "Machine-Learning-Expert-System Algorithm for brainstorming"

intelligent learning tool.

education.

based discussion:

 1

**2. MALESA learning system** 

**2.1 The learning model of MALESAbrain<sup>1</sup>**

understanding of the problem in the discussions.

	- giving personal preference or judgment on solutions posted by others;
	- contributing personal problem-solution suggestions for the feedback of preference from the judgments of others.
	- Learners need to pay attention and think about why certain issues accumulate higher scores than others.
	- The highly-scored issues are highlighted by the system to stimulate more discussions on them.
	- Those extensively discussed issues therefore end up with more meaningful content to help solve the problems.
	- An indicator will show in the learning tool to help the educator and learners perceive what percentage of the discussion-problems has resulted in consensus;
	- also how many discussion-problems did not result in consensus.
	- How many discussion-problems with consensus results enable the educator and learners to understand the progress of the discussion and the learning issues on demand.
