Effects of the Drewlite CSCL Platform on Students' Learning Outcomes · · · Level C: Multi-Structural Subcategories: List/enumerate/number, describe/organize, classify Level D: Uni-Structural Subcategories: Identify/define Level E: Pre-Structural (no subcategories), i.e. irrelevant answers. To assess the quality of the learning outcomes, the contributions of students (both in pre-test and post-test) were segmented into meaningful units. Each unit was scored according to the coding scheme. Student contributions were given points according to their level in the coding scheme: 1 point for category E contributions, 2 points for D, 3 for C, 4 for B, and 5 for A-level contributions. Subsequently, the points for the contributions of each student were added together and this number was then divided by the number of meaningful units, which resulted in an individual mean score (on exposure assessment in nutrition and health research). The main teacher of the course and her assistant evaluated students' contributions to give marks to students and determine which students passed or failed the post-test in the same way that teachers usually assess their students. Both intra-analyses and the reliability were calculated for various signifiers and levels of knowledge construction. Cohen's kappa was employed as a reliability index of inter-rater agreement, which was 0.78 for pre-test and 0.81 for post-test. Moreover, intra-coder test-retest reliability was calculated for 20% of the contributions. This resulted in identical scores in 85% of the contri- butions. For both inter- and intra-analyses, the reliability was deemed sufficient. ANOVA was used to assess the prior knowledge of students in both conditions (OD and PD) in terms of quality of knowledge construction and students' regular marks by teachers as measured by the pre-test.