Wednesday, March 15, 2017 - 11:00am Chemistry Building, Room 400 Analytical Seminar The textbook is a multipurpose reference to find problem-solving algorithms or to gain conceptual understanding of the lecture content.1 The instructor-chosen textbook often drives the curriculum, especially for instructors with heavy teaching loads. Over the past 90 years, a traditional hard-copy text has been used to accompany the training of future chemists as the curriculum has evolved from purely descriptive chemistry to a “theory-first” presentation.2,3 However, an adaptation of the textbook to meet the current pedagogical revolution has been slow.4 Since its earliest proposal in 2000, more STEM instructors have embraced a “flipped” classroom model,5,6 in which formal class-time is used to provide active learning and problem solving with the instructor as a guide.7 Until recently, this flipped model has relied on traditional, hard-copy textbooks and instructor-generated videos to deliver the content traditionally found in lectures.5 Textbook publishers have responded to the recent demand for adaptive, data-driven learning technologies with complementary interactive delivery formats for traditional lecture material, such as electronic textbooks (eBooks).4,6,7 As the textbook becomes one of the main content-delivery tools, it becomes increasingly important to evaluate the function and value of a textbook and its features as the STEM community transitions to new teaching models and learning technologies. This presentation has two main areas of inquiry: a survey on textbooks to function as a pilot/overarching theme and a mixed methods study involving eye-tracking to analyze the conceptual and algorithmic knowledge imbedded in worked example problems. Understanding the results of the textbook survey, as well as current research in the field, we can now optimize worked examples in eBooks so that they will not only be beneficial to students’ conceptual understanding of chemistry but will also motivate students to utilize the tools because they are modeled around the students’ learning preferences. (1) Pienta, N. J. In Investigating Classroom Myth through Research on Teaching and Learning; Bunce, D. M., Ed.; ACS: Washington, D.C., 2011, p 121. (2) Lloyd, B. W. J. Chem. Ed. 1992, 69, 633. (3) Bell, J. A. In Sputnik to Smartphones: A Half Century of Chemistry Education; Orna, M. V., Ed.; ACS: Washington, D.C., 2015, p 25. (4) Allen, G.; Guzman-Alvarez, A.; Smith, A.; Gamage, A.; Molinaro, M.; Larsen, D. S. Chem. Educ. Res. Pract. 2015, 16, 939. (5) Ryan, M. D.; Reid, S. A. J. Chem. Ed. 2016, 93, 13. (6) Salami, T. O.; Omiteru, E. O. In The Promise of Chemical Education: Addressing Our Students' Needs; Daus, K., Rigsby, R., Eds.; ACS: Washington, D.C., 2015, p 45. (7) Eubanks, I. D. In Sputnik to Smartphones: A Half Century of Chemistry Education; Orna, M. V., Ed.; ACS: Washington, D.C., 2015, p 339.