Human-Computer Interaction: Communication, Cooperation, and Application Design

By Hans-Jörg Bullinger; Jürgen Ziegler | Go to book overview

Especially, this situation is more significant for those students that have better grade of performance in math subject. Such findings indicate that formal sequential instruction design may not be the preferred strategy for knowledge delivery. This research argues that the traditional courseware design facilitates better courseware design that can automatically generate an optimum learning paths. The optimum learning paths not only can assure the learning performance but also can achieve shorten the learning period. To demonstrate this difference, an adaptive learning and testing environment embedded with dynamic instruction strategy is developed with the aid of Genetic Algorithms (GA). A prototype CLS used to aid elemental students in arithmetic operation is provided for discussion.


3 The Proposed Research Methodology

3.1 The Learning/Testing Environment

The experimental platform constructed in this research is an arithmetic addition drilling system for addition of two numbers, each with three digits at most. The testing contents and problem order is based on Chen's classification schema that structures the arithmetic addition problems into a two-perspective dimension ( Chen, 1978). As shown in

Figure 1
, the horizontal dimension (with 7 stages) indicates the number of digits involved in the addition operation; and the vertical dimension (with 5 situations) displays the types of carryover. That is, the level of learning difficulty is determined according to the type of carryover and digits involved. By following the so-called Learning Hierarchy Theory ( Gagne, 1985), all the units are structured in terms of their level of difficulty (i.e., the consideration of carryover status and number of digits involved). For each single unit there exist various different tests that follow the same notation. These tests can be automatically generated by computer program.


3.2 The Modeling of Learning Flows Control

Based on the conceptual framework of Learning Hierarchy for Arithmetic Addition, the distance between two different content units can be defined a weight w(eij) of a directed edge e 〈ui,uj〉, where ul & uj indicate different units. r(eij) is an added learning distance according to a user's actual performance, and can be pre-defined according to instructors. The larger the difference between two units, the higher value the weight is. As shown in Figure 2, since the learning from u1 to u4 is easier that u 1 to u5, the w(e14) (i.e., 5) is less than w(e15) (i.e., 7). These weights and the relationships among units can be determined either by real data analysis or encoded by domain experts. To provide the most suitable learning paths, the proposed system needs to dynamically acquire the

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