Issue No. 04 - Oct.-Dec. (2013 vol. 6)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TLT.2013.21
Rohit Ranchal , Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN, USA
Teresa Taber-Doughty , Dept. of Educ. Studies, Purdue Univ., West Lafayette, IN, USA
Yiren Guo , Dept. of Educ. Studies, Purdue Univ., West Lafayette, IN, USA
Keith Bain , Liberated Learning Consortium, St. Mary's Univ., Halifax, NS, Canada
Heather Martin , Liberated Learning Consortium, St. Mary's Univ., Halifax, NS, Canada
J. Paul Robinson , Dept. of Basic Med. Sci., Purdue Univ., West Lafayette, IN, USA
Bradley S. Duerstock , Weldon Sch. of Biomed. Eng., Purdue Univ., West Lafayette, IN, USA
Speech recognition (SR) technologies were evaluated in different classroom environments to assist students to automatically convert oral lectures into text. Two distinct methods of SR-mediated lecture acquisition (SR-mLA), real-time captioning (RTC) and postlecture transcription (PLT), were evaluated in situ life and social sciences lecture courses employing typical classroom equipment. Both methods were compared according to technical feasibility and reliability of classroom implementation, instructors' experiences, word recognition accuracy, and student class performance. RTC provided near-instantaneous display of the instructor's speech for students during class. PLT employed a user-independent SR algorithm to optimally generate multimedia class notes with synchronized lecture transcripts, instructor audio, and class PowerPoint slides for students to access online after class. PLT resulted in greater word recognition accuracy than RTC. During a science course, students were more likely to take optional online quizzes and received higher quiz scores with PLT than when multimedia class notes were unavailable. Overall class grades were also higher when multimedia class notes were available. The potential benefits of SR-mLA for students who have difficulty taking notes accurately and independently were discussed, particularly for nonnative English speakers and students with disabilities. Field-tested best practices for optimizing SR accuracy for both SR-mLA methods were outlined.
Real-time systems, Speech recognition, Multimedia communication, Education courses, Electronic learning
R. Ranchal et al., "Using speech recognition for real-time captioning and lecture transcription in the classroom," in IEEE Transactions on Learning Technologies, vol. 6, no. 4, pp. 299-311, 2013.