Analysis of usage statistics in the Virtual Learning Environment Moodle shows that provision of learning resources significantly improves student grades in basic microbiology Materials and Methods BE102 is a lecture module covering basic Microbiology and Genetics given to all those students enrolled in first year programmes in Biotechnology, Analytical Science, Chemical and Pharmaceutical Sciences, Environmental Science and Health, Common Entry in Science, and Science Education. Microbiology is covered first (6 weeks of 3 lectures per week plus one tutorial per week) followed by an equivalent content of Genetics. Support materials developed for the section on Microbiology (Section A) were delivered through Moodle. We expect the more motivated and able students, who would score highly, to use Moodle extensively. There is thus an important confounding factor that must be accounted for in the analysis. The section on Genetics (Section B) was handled by a different lecturer in a conventional format, and without the use of Moodle, and was used as a control in this study. At the start of the BE102 lecture course students were given access to the lecture notes for Section A of the course, and at the end of each week, were given access to MCQs (10 questions) and model examination questions on topics covered in that week. Usage was automatically logged in Moodle and at the end of the course the entire database of access records was downloaded to XL. From this database (over 31,000 records), information on student access to individual elements was extracted and combined in a Microsoft ACCESS database with data on student performance in the terminal examination in BE102. All statistical analysis was carried out using SPSS for Windows V11.01 and by AnswerTree. Results Increase in usage typically led to improved grades. Results for Section B are similar, but less pronounced. This shows the important confounding effect of student aptitude and ability, which must be controlled for. Multiple regression analysis shows highly significant effects of factors on student grades: Mark on Section A = (10.9% * Mark on Section B) + (0.93* No. of weeks of model exam Q's accessed) + (3.4* No. of MCQs attempted). Conclusions Moodle is an excellent means for providing usage statistics to examine the effect of factors on student grades, and given its open source nature could usefully be extended to make collation of data easier and more effective. Introduction It can be problematical to assess the impact of factors that may influence student performance as reliable data on usage can be difficult to obtain. However, the Virtual Learning Environment Moodle automatically records all student usage. I detail here how it may be a useful means for assessing the impact of factors which contribute to student performance. Factorial ANOVA on Section A marks with the levels of each factor defined by these sub-groups, and with Section B mark as a covariate shows a significant effect of access to MCQs (p=0.009), lectures (p=0.022) and model exam Qs (p-0.007). The most influential factor was score on Section B (p=0.001). CHAID (Chi-squared Automated Interaction) analysis was carried out on pass rate for Section A. CHAID carries out Chi-squared analysis with all possible permutations of each factor, combines sub-groups which are not significantly different, selects the most significant factor, then repeats the process at the next level. It is thus an immensely powerful method for analysing classification data. There were highly significant effects (p<0.001) of access of lecture notes, MCQs and model exam Q’s on pass rate, and of passing Section B. Sub-groups with similar pass rates were identified to define the levels of each factor for Factorial ANOVA. MCQ’s: No Usage/ Usage Lecture usage: 0-9/ 10-17/ 18 lectures accessed Model exam Q usage: 0/ 1-2/ >2 sets used.