Computers and Education vol:57 issue:3 pages:2135-2144
The popularity of today’s blended courses in higher education is driven by the assumption that students are provided with a rich toolset that supports them in their learning process. However, little is known on how students actually use these tools and how this affects their performance for the course. The current study investigates how students use the entire toolset at their disposal, whether tool-use patterns can be found and if these patterns affect performance for the course. Logging students (n ¼ 156) actions throughout the content management system and registering students’ use of the face-to-face support in an undergraduate course, the study reveals large student differences and an underuse for some tooltypes.
Further to this, K-means cluster analysis reveals three distinct tool-use patterns or user profiles: the no-users, the intensive users and the incoherent users. These patterns are characterized by different tool-choices and even different use intensity among students. Evidence is retrieved that these tool-use differences are problematic since multivariate analysis of variance reveals significant performance effects. Hence, these results imply that not all students seem to profit from the learning affordances that are provided. Similar as evidence in controlled settings, the results suggest that learner control in using
tools cannot be taken for granted. Consequently, this study legitimates more research into the influencing (student and context) variables that can explain these differences.