Learning benefits of structural example-based adaptive tutoring systems

1. Learning benefits of structural example-based adaptive tutoring systems 指導教授: 陳 明 溥 研 究 生: 許 良 村 Davidovic, A., Warren, J., & Trichina, E. (2003). Learning benefits of structural example-based adaptive tutoring systems. IEEE Transaction on education46(2), 241-251.

2. Introduction • Motivation for the Research • Adaptive CAI systems provide individualized instructionusing different methods, and taking into account that students learn at different rates. • Students get the opportunity to improve their skills and make progress in a safe and noncompetitive environment, following their own individual pace. • The authors believe that greater efficiency can be achieved by basing the system development on thetheoretical background of cognitive knowledge acquisitions.

3. Introduction • In the authors' view, the two key elements to creating better systems are • building systems based on solid theoretical background. • performing extensive evaluations • Examples play a particular role as they demonstrate how principles are applied. • Learning by example is the preferred way of learning for the majority of students • Examples are so crucial that students acquire more knowledge through worked examples than by working the same problems themselves.

4. IntroductionCognitive Skill Acquisition(generalization) • To apply the principle to problem-solving students need to - retrieve the principle -place the parts of the principle into correspondence with parts of the problem -draw inferences based on that correspondence -generalize the problem

5. IntroductionCognitive Skill Acquisition (generalization) • The following are the findings : - generalization does not happen automatically - providing an example to illustrate the principle and explaining it results in little generalization - using two or more examples results in little generalization • For the successful generalization and its application to a range of problems, the students need to understand the structural features and identify them within the set of problems.

6. IntroductionCognitive Skill Acquisition (generalization) • Augmenting the example with an explanation of the principle provides more clarity but does little to improve generalization. • Best results are achieved if students are encouraged to find the common structure of examples through comparison

7. IntroductionCognitive Skill Acquisition (interactivity) • the following requirements for the effective interactivity - immediacy of response - nonsequential access to information - adaptability (the ability of the software to individualize the instruction) - bidirectional communication - appropriate grain size

8. Architecture of Seats - SEATS Components

9. Architecture of Seats - Internal Color Scheme and Extensibility of the System

10. SEATS Modes • C - Control Mode: Electronic book (E-book) • Mode A: Adaptive E-book • Mode B: Structural example-based E-book • Mode A+B: Structural example-based adaptive E-book.

11. The differences between the four modes used in the evaluation of SEATS

12. SEATS- Structure-Showing “Highlighting Devices” B A A D D C

13. Evaluation Method • Goal of the Evaluation 1. Is SEATS an efficient way of teaching electronically? • the gain in knowledge • the speed of learning • students’ satisfaction • user interface 2. To measure how the two key components of SEATS individually contribute to the learning process.

14. Experiment Design • Independent variables - adaptation (component A) - structural-example-based tutoring (component B) • Dependent variables - score improvement (the gain in knowledge) - the speed of learning - students' satisfaction - usability of user interface

15. Results and Discussion • score improvement • The significant gain in knowledge by mode (A+B group)

16. Results and Discussion • the speed of learning

17. Results and Discussion • the speed of learning • the greatest speed of learning by the mode(A+B group) • the adaptive component significantly increases the speed of learning.

18. Results and Discussion • students' satisfaction • Mode(B) and Mode(A+B) expressed significantly more satisfaction than the nonstructural groups • Mode(A+B) were significantly more satisfied than students in the other three groups

19. Results and Discussion • user interface -The students found the instructions on the screen easy to follow, and more so in the structural-example based groups (B and A+B) than the other two. 2. The majority of students found the program layout easy to understand and use. 3.Having many information boxes increased cognitive load. 4. The students “most liked” the system to provide an interactive hands-on code writing experience (twenty votes), where students could write and edit code and then execute it if mistakes were found. 5. the text appeared too small, making it hard to read

20. Results and Discussion • The significantly better results that mode( A+B) scored over the Control were attributed to - multiple structure identifying mechanisms, which accounted for the better identification of structural components, and deeper understanding of the materials. - daptive help with the emphasis on structure, which assisted students in overcoming the difficulties and increased the speed of knowledge acquisition. - adaptive navigation, which gave students an optimal learning path while ensuring that no prerequisites were missed.

21. Conclusion • The results strongly indicate that structural-example-based adaptive tutoring is an efficient way of teaching. • Students gained knowledge after using the system in all modes, but the group using the full structural-example based adaptive tutoring reported the best progress. • Adaptation in interaction with the structural-example-based component produced an effect significantly greater than when the same components were used alone. • Adapting tutoring to individual students significantly increases the speed of learning.