**2.2 The assessment model of MALESAassessment2**

The second algorithm is called MALESAassessment. The principle of the system design is to reuse learners' discussion knowledge, which retained in MALESAbrain learning system, for answering the follow-up performance test. In this chapter the assessment system consults the learners for organizing their own problem-solving plan for answering their test

 2 The acronym for "Machine-Learning-Expert-System Algorithm for learning assessment"

Proposing Two Algorithms to Acquire Learning

Fig. 3. Educator set up learning thresholds.

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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

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

also needs to set up *learning-rate* "70%" and due-date "4/9/2010".

problems. It is not just to give a simple solution from the computer suggestion. The answer must be planned by learners and consulted by MALESAassessment.

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

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

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