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Digital Textbook Usage and Course Outcomes
With the advent of technology in education, one thing that has changed significantly is the way academic content is accessed. Students and instructors have been increasingly adopting digital textbooks. While this helps in more ways than one, such as ease to carry and ability to search within content, one important thing it does is provide us with the textbook usage data which is not the case with traditional paper books. This paper attempts to find how individual textbook usage data can be correlated with students’ performance in the given course and how such data can be predictive of course outcomes. The predictive data is of immense use for the instructor as it can help plan the course better and to address the needs of at-risk students. For this purpose, Linear regression analyses can be conducted using data from students to determine whether digital textbook usage metrics predicted final course grades (Junco, R., & Clem, C., 2015).
Prior Research and Significance
The area of research that we are dealing with here is called Learning Analytics. Learning analytics 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. (Siemens, G., & Gasevic, D., 2012)” Instead of relying on direct input of data from students, learning analytics collects data unobtrusively from various sources where students perform some sort of academic activity. The major source of such data collection is usually Learning and Course Management Systems (LCMSs). This real time data collection and analysis gives instructors the ability to identify students at risk of academic failure.
Campbell and Oblinger (2007) identified five steps of the learning analytics process (Campbell, J. P., & Oblinger, D. G., 2007). These five steps can be summarised as: 1) Capturing 2) Reporting 3) Predicting 4) Acting 5) Refining. Though these analytics systems show great promise for education, the data they collect is largely limited. For instance, the data for LMS is collected based only on the number of time a student logs in, engages in a discussion forum or perform any other activity on the platform. It does not account for the time spent in learning.
This is where the role of collecting data from digital textbooks comes into play. Since, books are a major learning source the time spent by a student on digital textbook has a direct correlation with students performance. Also, a major advantage of digital textbooks over traditional books is it offers actionable insights into the user behaviour. To capitalize on these insights, textbook companies are acquiring learning analytics startups. For example, in 2013 Pearson acquired Learning Catalytics, a company that uses predictive analytics to help provide feed- back to faculty and improve student engagement (Pearson, 2013).
The data used in the analysis is obtained from CourseSmart and CourseSmart Analytics Platform. CourseSmart provides a large percentage of books in digital format. The analytics platform obtains multiple data from user behaviour and converts it into an Engagement Index and reports the information to the faculty. Since Engagement Index is a corporate for profit product, the exact algorithm is not publically available. A study was conducted with texas A&M University (Junco, R., & Clem, C., 2015) with 236 students which aimed to answer three major questions:

What are the digital reading patterns of students?
How is the CourseSmart Engagement Index related to course performance?
How are the individual components of the CourseSmart Engagement Index related to course performance?


Results show that students read an average of slightly over 7 h and 20 min over 11 days throughout the entire 16-week
It also showed that the Engagement Index Score was a significant positive predictor of course GPA when taking into account student demographics, course enrollment, and prior academic
The only component of the Engagement Index predictive of course grades was number of days students read during the semester.

Junco, R., & Clem, C. (2015). Predicting course outcomes with digital textbook usage data. The Internet and Higher Education, 27, 54-63.
Siemens, G., & Gasevic, D. (2012). Guest editorial-Learning and knowledge analytics. Educational Technology & Society, 15(3), 1-2.
Campbell, J. P., & Oblinger, D. G. (2007). Academic analytics. EDUCAUSE review, 42(4), 40-57.
Pearson, 2013. http://www.pearsoned.com/news/pearson-acquires-ed-tech-startup-learning-catalytics/

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