Houston Brown, a chemistry professor at the University of Houston–Downtown, often felt as if he was anything but a professor. Instead of spending the bulk of his time helping introductory and organic chemistry students better understand and comprehend difficult subject matter, Brown regularly found himself tending to a learning management system that laboriously soaked up his time and patience.

Besides Brown, students at UHD were demanding more from the platform that constituted twenty-percent of their grade. It’s one reason Brown was happy to switch to Cengage Learning’s OWLv2, a cloud-based learning system that provides chemistry students with a collection of study and test preparation tools designed to foster engagement and better grades.

One feature that helps Brown and his students focus more on what matters is the on-demand feedback OWLv2 provides students during homework assignments. Besides organizing and prioritizing assignments in visually appealing ways, OWLv2 allows students to identify in real time why exactly they answered a problem incorrectly. It also provides Brown with the analytics required to intervene immediately.

Unlike traditional homework, OWLv2 gives students an array of ways in which they might interact with their assignments and better prepare for tests. OWLv2 offers mastery learning problems and dynamic end-of-chapter questions, and also prompts students to dig deeper into complex subject matter by helping them visualize concepts with distraction-free simulation exercises.

Instructors using OWLv2 gain insight that allows them to act with precision on behalf of students in real time. Its diagnostic tools enable professors to peel back the curtain and identify exactly where a specific student may be struggling, and lets track student performance based on time spent in OWLv2.

In this case study, you’ll read more about the success that Houston Brown saw in his chemistry courses, and see why he says: “I have hard evidence that the amount of time spent in OWLv2 and scores on the tests are highly correlated.”

» Read the OWLv2 Case Study.