**8. Conclusion**

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

IT education is in a state of change. Similar to nursing education, IT courses have a high content load to teach in the state of the art. However, the large student numbers and limited staff in the computer class often hinder interested educators combining PBL with structured teaching in their curricula. The question is "without an effective and efficient tool, it seems impractical to ask an educator handle with large number of group discussions in the

In this experiment, we test whether these two algorithms in the learning system can support

The evaluation after the experiment, with "56%" learning rate on the due date; and

1. helps the students to think about the pros and cons of the proposed issues before they

2. highlights the issues with different levels of importance to help the students to identify

1. allows educator to monitor learner-groups' discussions on his/her screen to save the

2. allows the educator to coach the meeting progressing and to guide learners in the right

3. allows educator to encourage each of the students to pick up a learning issue for further

However, the strategies to set up the "learning thresholds" in MALESAbrain still remain to

1. Firstly, it is because we are trying to change the learning thresholds setup by observing the number of learners in discussion at that time. In the Internet discussion, sometimes, it is not easy to control the number of participants who are really keen to learn and join discussion. Based on observing the number of participants and the discussion situation, we changed the learning thresholds to control the remaining learning issues to a

2. Second, we do not use "rejection threshold" to prune out worthless issues during discussion until due date for calculating the leaning-rate. It is because we do not want to lose any proposed issues. General speaking, it looks fine in this experiment.

However, we are not sure it is still a proper procedure on the other experiment? In the experiment, the thresholds' setup proves to be challenged by the participants – "why not let them decide the thresholds before the meeting, because it is their discussions and is their responsibility to answer the educator's request". This means we cannot approve a best

thresholds setup at this stage. However, the following table is their suggestion setup.

 Does the system help students organize and synthesize knowledge in discussion? Does the system save educator the efforts in helping discussion group effectively? Does the system efficiently save educator the efforts on the student performance test? The first algorithm, MALESAbrain has been tested in our graduate laboratory, with "70%" learning rate setup and for two-week time of discussion. This experiment invited six of the postgraduate students to discuss the question: "How to fix an illegal operation? An illegal operation is an operation requested to be performed by either the Operating System or CPU

PBL discussion in IT curricula or not? We want to know:

MALESAbrain has received some comments from the participants:

the significance of the learning issues in the problem. . In considering of the educators' benefits, the learning system

that is not understood and therefore is illegal".

go into chat room for discussion.

direction for discussion.

number.

investigation and research.

In considering of the methodology, the learning system

shortage of manpower and time limitation.

be evaluated and estimated. There are two reasons:

**7. Discussion** 

discipline of PBL".

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 discussion more effective and efficient 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

**10** 

*1University of Ljubljana 2Aalborg University* 

> *1Slovenia 2Denmark*

**E-Learning in Architecture:** 

**Professional and Lifelong Learning Prospects** 

E-learning in architectural and spatially related fields can be examined from two different perspectives, each having quite specific and complex implications. By discussing e-learning *in* architecture we inspect the scope of e-learning tools and practices within the architectural domain, the visual nature of education and professional training of architects, and the state of the art of e-learning implementations, together with their practicality and limitations. While these are the first areas that come to mind when considering e-learning in relation to architecture, there is another also very relevant and sometimes overlooked aspect: that of e-learning *about* architecture. In the latter, we introduce not only the professional but also the broader, non-expert public into the process of acting within, and shaping of, their spatial environments. This aspect raises burning questions regarding the communication abilities of the actors involved, holding their attention, ingraining sustainable principles and getting the messages across the invisible, but perennial expert / non-expert divide. E-learning *in* and *about* architecture not only offers opportunities for both sides to learn but also to get to know

The chapter first introduces and highlights the common aspects of e-learning within the architectural domain, followed by e-learning for experts, through what we have named e-learning *in* architecture, describing specifics and presenting an example of one of the e-learning initiatives. It is followed by a subchapter describing aspects of e-learning *about* architecture and sustainable principles of space interventions for broader audience of nonexperts involved in the lifelong learning processes (LLP). Similarly, the subchapter concludes with an example of an e-learning tool in action and the reflections on the research presented. The chapter concludes with discussions of 'lessons learned' and ranking of new opportunities

Architectural education is centrally concerned with individual design creativity among its students and encompasses an important aspect of visual acuity or training in interpretation of visual representation. These are aspects of human articulation, neither easily taught in the

in professional and lifelong e-learning prospects in architecture and its related fields.

**2. Aspects of e-learning within the architectural domain** 

lecture theatre nor transmitted in the computer laboratory.

**1. Introduction** 

each other better.

Matevz Juvancic1, Michael Mullins2 and Tadeja Zupancic1

retrospect their learned knowledge and refine them to solve another problem in test. The test assesses learners' concept in making an action plan and examines their abilities to solve a physical troubleshooting problem.

The design feature of MALESAassessment is based on the principles described on the chapter "From case-based reasoning to problem-based learning" (Eshach & Bitterman, 2003) and four-stage CBR-cycle design on the book of "Applying Case-Based Reasoning: Techniques for Enterprise Systems" (Watson, 1997) (see Fig. 13). The four-stage open-ended cycle and concepts-in-action designs help to refine an existing case and construct a new case from the raw/previous cases. In the assessment, learners retrieve the similar cases from the CBR knowledge base; reuse the cases to attempt to solve the existing problem; revise the proposed solutions to solve the new problem; and retain the new solution as their answer to solve the test problem.
