Lecture Notes in Computer Science- P90 doc

In or-der to overcome problems mentioned above, a new framework for Computer Science Courses, which support the personal learning, was proposed and the al-gorithms about the intelligent

F Li et al (Eds.): ICWL 2008, LNCS 5145, pp 434 – 445, 2008

© Springer-Verlag Berlin Heidelberg 2008

Learning System (WBPCLS) for Computer Science

Courses

Zhenlong Li1 and Xiaoming Zhao1,2

1 Computer Science Department, Taizhou University, Linhai 317000, P.R China

li_zhenlong@163.com

2 Information engineering institute, zhejiang Industrial University, Hangzhou 310014,

P.R China tzxyzxm@yahoo.com.cn

Abstract The Web-based collaborative learning system (WBCLS), which is

considered a highly effective teaching method by most theory researchers, could not achieve the goals that can be obtained by traditional in-class teaching method in the teaching practice Therefore, some problems thatare harbored in the operational process of the current popular WBCLS were pointed out In or-der to overcome problems mentioned above, a new framework for Computer Science Courses, which support the personal learning, was proposed and the al-gorithms about the intelligent course recommendation, optimal group forma-tion, and the optimal collaborative partner discovery arediscussed in this paper

Keywords: Web-based Collaborative Learning, Personal Learning, Optimal group formation, Optimal Collaborative partner discovery

1 Introduction

Currently, Web-based learning systems are one of the most interesting topics in the area of the application of computers to education Using computer technology, espe-cially distributed computing technology, teaching resources share has become reality Web-based collaborative learning system (WBCLS) , which is considered a highly effective teaching method by most of theory researchers[1, 3], could not achieve the goals which can be obtained by traditional in-class instruction in the teaching

prac-tice[2] The operational process for the WBCLS can be shown in the Fig 1

According to the operational process mentioned above, we analyzed its reasons for its lower effectiveness than the traditional teaching mode The reasons are as follow:

The first problem for the WBCLS is that improvement of hardware environment has been heavily emphasized, whereas the improvement of learning organization has been heavily ignored in the course of WCBS design The grouping is one key step of the WBCLS, and is often randomly done without considering the characteristics of individual learners Therefore, the quality of learning in the collaborative platform is not well achieved as desired

The second problem is to emphasize excessively the collaborative learning, but ignore the personal learning process The effectiveness of collaborative learning is different according to the different studying tasks The effectiveness for some tasks that cannot be partitioned depends on the personal learning process (such as under-standing) Under this situation, it is important to provide appropriate studying re-sources and learning methods for different learners

Learner Login Select learning content

Sample grouping

Group preparation

Group learning

Learning evalution

Register

Learning resources

Learner document

Learner Characteristic

Collaborative strategy

Characteristic Extraction

Fig 1 The common operational process for WBCLS

The third, the present WBCLS could not intelligently manage the studying record

or satisfy learners enough Because the different courses need different intelligent strategies, the expectation that takes a WBCLS as a common learning environment for all courses is unrealistic

In this paper, the design of the WBPCLS for computer science course will be pre-sented and discussed The WBPCLS target is to support intelligent grouping and per-sonal learning

2 The System Design

In order to improve the effectiveness of the web-based teaching and overcome the problems in the WBCLS, the operational process of WBPCLS has been designed

(Fig 2) by referencing to the relevant literatures [4-6]

The operational process was summarized in:

1) To login or register: Once a student’s identity is verified by comparison with the corresponding ID in the WBPCLS, the student can enter the WBPCLS environment The student, who first uses the WBPCLS, needs to fill in some tables for login The system extracts the student characteristic from the login information; and then builds the personal model database of the student In the end it adds special measurement mechanisms for measuring the student's characteristic

2) To select learning content: After the student enters the environment, the system gives student a catalogue of learning content extracted from the studying resource warehouse Then the student selects one’s learning content

Learner Login Select learning content

Intelligent grouping Group preparation

Group learning

Learning evaluation

Register

Learning resources

Learner

document

Learner

Characteristic

Collaborative

strategy

Characteristic

Extraction

Learning over?

Optimal partner discover

Select studying way

Need help?

Personal learning module

Learning over

partner learning

Group learning

yes

no

no Personal learning

yes

Fig 2 The process flowchart for our system

3) To select the learning way: System provides two kinds of learning ways, e.g the group learning and personal learning

If the student selects the group learning way, steps which students should follow are: (ⅰ) Intelligent group formation System divides the members into groups and as-signs learning tasks to them based on the studying target, the learner characteristic, and the learning strategy

(ⅱ) The group learning preparation The learner not only gets acquaintance with other members, environment, and overall target, but also with the roles that the member plays and the collaborative rules At the same time, the learner extracts the learning target (group and the member target) information from the resource warehouse

(ⅲ) The group learning The learner studying appointed content, accomplishing the appointed role tasks The learning resource warehouse provides fundamental material, implements and corresponding function software so that the learning tasks can be completed System will record interactive behavior in group learning process, stores the information to the corresponding documents, which can be used for the evaluation about member’s learning

If learner chooses personal learning way , the steps which learners should follow are :

(ⅰ) Begin to personal study;

(ⅱ) In the learning process, learner, if needs help, can start Optimal Collaborative partner discovery mechanism, which will find out the optimal collaborative partner for current help-seeker;

(ⅲ) The collaborative learning with partner

4)Learning over;

5)Learning evaluation The WBPCLS modify the corresponding database accord-ing to evaluation result of learner’s behavior in entire learnaccord-ing process, for instance, the learner feature model, the collaborative document

3 The Learner Feature Selection

The learner feature model is the important database, on the basis of which the system

is able to provide personal learning and help, realize collaborative learning tasks, and find out the optimal collaborative partner

Learner feature can be acquired by register and characteristic measurement The register generates the learner’s fundamental information, whereas the characteristic measurement generates the learner’s characteristic warehouse Both of them consti-tute the learner feature model

For learner's fundamental information, except ID (system assigned), sex, and age,

we pay more attention to student's course selecting situation The system records the courses which the learner has studied, at the same time records student’s score if learner has participated in examination

When learner first enter the collaborative learning system (login), the system re-cords the learner’s selected course that includes course name and course conception first, Then it measures the learner’s characteristics (Table 1) automatically, and store the result to the corresponding learner’s feature model The data can be corrected continually according to the learner’s learning situation

Table 1 Learner personal characteristic

value

meaning

characteristic

much

not Be both will do be Be very much

collaborative ability very low low medium high very high

8~10

We have chosen four characteristics which include the character, the knowledge grade, collaborative ability and studying style to build a learner feature model The values belong to the range (0~10)

System divides "character" into 5 grades: Be ready to help others very much , be ready to help others , both will do not being ready to help others, not being ready to help others very much The knowledge grade and collaborative ability are also di-vided into 5 grades: high, very high, medium, low, very low The studying style is divided into field independent tendency or field dependent tendencies If the value of studying style is in range (4~6), the learner has not obvious field independent and dependent tendency, thus is called "medium "

The knowledge grade was acquired by measuring some knowledge points with grade label, whereas others were measured using the test scale that is composed of an example and 30 tests problem

4 The Intelligent Course Recommendation

In our system, when the learner searches for a course, the system not only can get the relevant information from local resource warehouse or internet, but can also recom-mend the courses according to the learner’s selected courses records Exampling stu-dent A, we introduce the realization of course recommendation

1 Course classification

In order to reduce course matrix size, we classify the computer science courses using the method of conception extraction The classification result of all computer science

courses and their corresponding conceptions are shown in the Table 2

Table 2 Classification of computer science courses

Network Programming

Network Management& Security

Distributing System

Software Engineering

Systems Analysis & Design

Multimedia System Design

Database Management

E-Commerce

Programming

Management Information System

Computer Architecture

Multimedia Introduction

Data Structure

Database Management System Introduction

Artificial Intelligence

{network,software,method}

{network,hardware,management } {network,system,management}

{software,method,management}

{software,database,method,system}

{software,system}

{network,database,management}

{software,database,method,system,management} { software,system}

{ software,database,method,system,management} {hardware,method }

{software,system } {software,system}

{ Network, hardware, software, database,method,system,management } { software,method }

2 The course conception favor mining

Once student's records of selected courses were taken out from the learning database, the course conception favor can be found by using the Apriori algorithm For example,