In this article, Prasad et al (2016) make the case for learning analytics as a key ingredient in iterative improvements of open textbooks. As the authors note, "Learning analytics offers (sic) a faster and more objective means of data collection and processing than traditional counterparts, such as surveys and questionnaires" (abstract), and as such offer the potential to provide highly valuable information in assessing and improving textbook quality. To demonstrate this, they conduct a trial at the University of the South Pacific, where 66 students in a post-graduate course agreed to participate in the study that would collect analytics on their interactions with the textbook. The authors report that "The results of the pilot trial were both informative and evaluative as they provide crucial information to undertake more nuanced analysis of the value of open textbooks" (p. 228). For instance, they find that textbook views were greatest in the second week of the course and dropped off steeply there after. They also showed that the majority of students viewed less than five chapters in total. The authors conclude that learning analytics offer opportunities for "optimizing textbook planning and development; monitoring usage type and degree; evaluating breadth and depth of impact and effectiveness; and revision strategies for improvement" (p. 229), as well s supporting open research if datasets themselves are openly available.
Deepak Prasad, Rajneel Totaram, Tsuyoshi Usagawa
Primary educational sector