It is the Holy Grail of understanding student progress: whether tutors can predict student outcome. It was, until recently, more unusual to use textbooks as a method of assessment but the digital era has changed all that. Now academic achievement progress can be pinned down to percentages, charts and reports throughout the year.
The advent of digital textbooks is a relatively new phenomenon that is revolutionising the publishing world, as authors go straight to electronic format, before any print books are published. This gives the publishers some indicative analysis whether they’re going to sell or not, and inform the decision to publish in hard copy.
Digital textbooks are also an ideal platform to uncover a plethora of learning analytics (which is the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” according to Siemens 2010, cited in Junco & Clem, 2015, 54) such as formative assessment. How do they work? Naturally, reading textbooks is an integral part of study, but the particular gift of digital textbooks is that they record quiz scores, student engagement (completing exercises, et al), significantly the number of annotations and highlighting, time spent reading outside of office hours, and time spent re-reading (i.e. the retention of knowledge). Their interactivity provides a welcome contrast to a traditional assessment model that is primarily summative; marking essays at the end of the term, or taking exams and so forth. It is a form of academic monitoring, particularly understandable in the context when electronic registers for seminars are so commonplace, and electronic surveillance is routine. More research needs to be carried out to find reliable data on learning analytics and digital textbooks, but I find it a fascinating area and one that will no doubt become more and more popular across universities as tutors become more aware of their capability. Where does that leave libraries? Hopefully involved.
Reference list
Junco, R. & Clem, C. (2015). Predicting course outcomes with digital textbook usage data. Internet and Higher Education. Vol. 27, 54–63.
Siemens, G. (2010). 1st international conference on Learning Analytics and Knowledge. Available from: https://tekri.athabascau.ca/analytics/. [accessed 21st March 2017].
