The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

Artificial Intelligence (AI) has emerged from its traditional domain of computer science research to be a management reality. This can be seen in the remarkable increase in the adoption of AI technology in organizations resulting in increased revenue, reduced costs and improved business efficiency [19]. Despite this trend, there are still many organizations that are facing the decision whether to adopt AI. Thus, to evaluate the adoption of AI at organizational-level, we draw on two-grounded theories:Technology-Organizations-Environment (TOE) framework and Diffusion of Innovation theory (DOI) to identify factors that influence the adoption of AI. Survey data collected from 208 large, mediumsized and small organizations in Australia is used to test the proposed framework. We offer a method of how examining AI over a set of organizations. Besides offering several important recommendations for AI adoption future directions for research in this area are also included in this paper.

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This paper introduces an extended set of Haarlike features beyond the standard vertically and horizontally aligned Haar-like features [Viola and Jones, 2001a;2001b] and the 45 o twisted Haar-like features [Lienhart and Maydt, 2002;Lienhart et al., 2003a;2003b]. The extended rotated Haar-like features are based on the standard Haar-like features that have been rotated based on whole integer pixel based rotations. These rotated feature values can also be calculated using rotated integral images which means that they can be fast and efficiently calculated with just 8 operations irrespective of the feature size. In general each feature requires another 8 operations based on an identity integral image so that appropriate scaling corrections can be applied. These scaling corrections are needed due to the rounding errors associated with scaling the features. The errors introduced by these rotated features on natural images are small enough to allow rotated classifiers to be implemented using a classifier trained on only vertically aligned images. This is a significant improvement in training time for a classifier that is invariant to the rotations represented in the parallel classifier. Figure 1. Standard Haar-like features.

Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The robot soccer game presents an uncertain and dynamic environment for cooperating agents [1] [2]. Dynamic role switching and formation control are crucial for a successful game. The fuzzy logic based strategy described in this paper employs an arbiter which assigns a robot to shoot or pass the ball.

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