Higher education institutions in the Arab States: A study of objectives and their achievement

Throughout the world, pressure on resources and emphasis on greater accountability has resulted in higher education institutions' making efforts to define their objectives and to measure their success in achieving them. This paper describes research carried out into this subject in a number of Middle Eastern universities. The methodology employed was based on previous U.S. studies but modified to fit the Middle Eastern context. The results showed that there were significant differences in the ranking of objectives between different universities although not between different academic constituencies. Another important result of the study was the apparently poor performance on highly rated objectives of student intellectual development and progress in research.     

Performance indicators in higher education

Interest has been increasing in many countries in assessing performance of higher education institutions (and also departments within institutions). Two important reports in the U.K. in recent years have strongly advocated the use of performance indicators for this purpose. This paper argues that such indicators cannot be used in a meaningful way without a clear view of institutional goals. Problems of deriving such goal systems are discussed and a critical review of work done in this field is presented. Methodological problems associated with devising and using effective and useful performance indicators are described and research undertaken in this field outlined. Finally the authors outline the conditions under which they believe performance indicators may be used to give valid insights into performance of institutions (or departments).     

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Objective assessment of an athlete’s performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sport-specific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data pre-processing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental set-up, data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).     

Different centre of pressure patterns within the golf stroke II: group-based analysis

Although the golf coaching literature stresses the importance of weight transfer during the swing, research has been conflicting or lacking statistical support. A potential problem with previous studies is that no attempt was made to account for different movement strategies in the golf swing. This study evaluated the relationship between centre of pressure measures and club head velocity within two previously identified swing styles, the "Front Foot" and "Reverse" styles. Thirty-nine Front Foot golfers and 19 Reverse golfers performed swings with a driver while standing on two force plates. From the force plate data, centre of pressure displacement, velocity, range, and timing parameters were calculated. Correlation and regression analysis indicated that a larger range of centre of pressure and a more rapid centre of pressure movement in the downswing was associated with a larger club head velocity at ball contact for the Front Foot group. For the Reverse golfers, positioning the centre of pressure further from the back foot at late backswing and a more rapid centre of pressure transfer towards the back foot at ball contact was associated with a larger club head velocity at ball contact. This study has highlighted the importance of identifying different movement strategies before evaluating performance measures, as different parameters were found to be important for the Front Foot and Reverse styles.     

Comparison of anthropometry to dual energy X-ray absorptiometry: a new prediction equation for women

The purpose of this study was to assess the accuracy of three recommended anthropometric equations for women and then develop an updated prediction equation using dual energy x-ray absorptiometry (DXA). The percentage of body fat (%BF) by anthropometry was significantly correlated (r = .896-.929; p < .01) with DXA, but each equation underestimated %BF (3.2-5.6 %BF; p < .01). The following DXA criterion (DC) equation was created: %BF= -6.40665 + 0.41946(S3SF) - 0.00126(S3SF)2 + 0.12515(hip) + 0.06473 (age); (S3SF = sum of triceps, suprailiac, thigh; hip = circumference in cm; age = years). The predicted residual sum of squares (PRESS) R2 was high (0.86), and the PRESS standard error of estimate (SEE) was low (2.5 %BF) for our sample of 150 women. The DC equation was further crosschecked on a separate sample of women (n = 25) and again showed excellent agreement. The DC equation appears to be a more accurate estimation of %BF in women.