The influence on unaided vision of age, pupil diameter and spherocylindrical refractive error

Background - The aim was to derive equations for the relationship between unaided vision and age, pupil diameter, iris colour and sphero-cylindrical refractive error. Methods - Data were collected from 663 healthy right eyes of white subjects aged 20 to 70 years. Subjective sphero-cylindrical refractive errors ranged from -6.8 to +9.4 D (mean spherical equivalent), -1.5 to +1.9 D (orthogonal component, J0) and -0.8 to 1.0 D (oblique component, J45). Cylinder axis orientation was orthogonal in 46 per cent of the eyes and oblique in 18 per cent. Unaided vision (-0.3 to +1.3 logMAR), pupil diameter (2.3 to 7.5 mm) and iris colour (67 per cent light/blue irides) was recorded. The sample included mostly females (60 per cent) and many contact lens wearers (42 per cent) and so the influences of these parameters were also investigated. Results - Decision tree analysis showed that sex, iris colour, contact lens wear and cylinder axis orientation did not influence the relationship between unaided vision and refractive error. New equations for the dependence of the minimum angle of resolution on age and pupil diameter arose from step backwards multiple linear regressions carried out separately on the myopes (2.91.scalar vector +0.51.pupil diameter -3.14 ) and hyperopes (1.55.scalar vector + 0.06.age – 3.45 ). Conclusion - The new equations may be useful in simulators designed for teaching purposes as they accounted for 81 per cent (for myopes) and 53 per cent (for hyperopes) of the variance in measured data. In comparison, previously published equations accounted for not more than 76 per cent (for myopes) and 24 per cent (for hyperopes) of the variance depending on whether they included pupil size. The new equations are, as far as is known to the authors, the first to include age. The age-related decline in accommodation is reflected in the equation for hyperopes.

The strengths, weaknesses, opportunities and threats of using social software in higher and further education teaching and learning

Social software is increasingly being used in higher and further education to support teaching and learning processes. These applications provide students with social and cognitive stimulation and also add to the interaction between students and educators. However, in addition to the benefits the introduction of social software into a course environment can also have adverse implications on students, educators and the education institution as a whole, a phenomenon which has received much less attention in the literature. In this study we explore the various implications of introducing social software into a course environment in order to identify the associated benefits, but also the potential drawbacks. We draw on data from 20 social software initiatives in UK based higher and further education institutions to identify the diverse experiences and concerns of students and educators. The findings are presented in form of a SWOT analysis, which allows us to better understand the otherwise ambiguous implications of social software in terms of its strengths, weaknesses, opportunities and threats. From the analysis we have derived concrete recommendations for the use of social software as a teaching and learning tool.

From phenomenography study to planning teaching

A phenomenographic study uncovers variations in the way that the subjects are aware of a phenomenon. In the categories of description that represent the variations in awareness there are features that, through their variation, define the characteristics of the categories. Teaching seeks to foster a change in the way that the learner is aware of a phenomenon through opening up a space of learning. This paper outlines the way that the outcome spaces from a phenomenographic study can be used to plan a teaching programme that utilises variations in the features. It discusses a strategy for teaching programming based on a phenomenographic study of practitioner conceptions of an object-oriented program. The strategy covers features related to the nature of an object-oriented program. Copyright 2010 ACM.