Computer-related gender differences are examined using survey responses from 651 college students. Issues studied include gender differences regarding interest and enjoyment of both using a computer and computer programming. Interesting gender differences with implications for teaching are examined for the groups (family, teachers, friends, others) that have the most influence on students' interest in computers. Traditional areas such as confidence, career understanding and social bias are also discussed. Preliminary results for a small sample of technology majors indicate that computer majors have unique interests and attitudes compared to other science majors.
In this work, we show that the underlying inclusion measure used by fuzzy lattice neurocomputing classifiers can be extended to various similarity and distance measures often used in cluster analysis. We show that for some similarity measures, we can modify the measure to weigh the contribution of each attribute found in the data set. Furthermore, we show that evolutionary algorithms such as genetic algorithms, tabu search, particle swarm optimization, and differential evolution can be used to weigh the importance of each attribute and that this weighting can provide additional improvements over simply using the similarity measure. We provide evidence that these new techniques provide significant improvements by applying them to the Cleveland heart data.
Finding the optimal teaching strategy for an individual student is difficult even for an experienced teacher. Identifying and incorporating multiple optimal teaching strategies for different students in a class is even harder. This paper presents an Adaptive tutor for online Learning, AtoL, for Computer Science laboratories that identifies and applies the appropriate teaching strategies for students on an individual basis. The optimal strategy for a student is identified in two steps. First, a basic strategy for a student is identified using rules learned from a supervised learning system. Then the basic strategy is refined to better fit the student using models learned using an unsupervised learning system that takes into account the temporal nature of the problem solving process. The learning algorithms as well as the initial experimental results are presented.
Often when designing an educational tool, the focus is primarily on how well the tool helps the student learn a concept. However, always in educational research there is an underlying desire to determine what factors actually influence student learning. This is because an understanding of these factors can lead to the design of more effective tools/techniques. The focus of our research has been on developing a tool to help students learn algorithm design. The ability to design an algorithm for a given problem is one of the most important, and unfortunately one of the most difficult to accomplish, learning outcomes of computer science courses. It has previously been shown [13] that students who use AlgoTutor, a Web-based algorithm development tutor, are significantly more likely to think that algorithm design prior to coding is important and to have confidence in their own ability to design an algorithm. From follow up studies, we have found that students who have used AlgoTutor in introductory computer science classes are not only more confident in their ability to design an algorithm, but also more likely to design a correct algorithm than those who have not used AlgoTutor. Additionally, we show that the course management utility for the AlgoTutor system can be used to investigate questions about factors that influence student learning. As an example we investigate the question, "how much is too much help and how much is not enough help if a student is having difficulty solving a problem?"
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