Genetic algorithms: a pragmatic, non-parametric approach to exploratory analysis of questionnaires in educational research

Data from a survey to determine student attitudes to their courses are used as an example to show how genetic algorithms can be used in the analysis of questionnaire data. Genetic algorithms provide a means of generating logical rules which predict one variable in a data set by relating it to others. This paper explains the principle underlying genetic algorithms and gives a non-mathematical description of the means by which rules are generated. A commercially available computer program is used to apply genetic algorithms to the survey data. The results are discussed

Categorisation of Mental Computation Strategies to Support Teaching and to Encourage Classroom Dialogue

Mental strategies are a desired focus for computational instruction in schools and have been the focus of many syllabus documents and research papers. Teachers though, have been slow to adopt such changes in their classroom planning. A possible block to adoption of this approach is their lack of knowledge about possible computation strategies and a lack of a clear organisation of a school program for this end. This paper discusses a framework for the categorisation of mental computation strategies that can support teachers to make the pedagogical shift to use of mental strategies by providing a framework for the development of school and classroom programs and provide a common language for teachers and students to discuss strategies in use

2009-10 statistics derived from HESA data for monitoring and allocation of funding

Part of series - Core funding/operations: Request for information - "This document describes the process we will use when reconciling 2009-10 data returns made to the Higher Education Statistics Agency (HESA) with other returns made to HEFCE. It also describes how we use HESA data to inform the widening participation and teaching enhancement and student success allocations, and the partial completion weighting, for 2011-12." - Cover

Welcome to OR&S! Where students, academics and professionals come together

In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates