In this study, educational social learning theory and a statistical multiple-criteria decision-making (MCDM) methodology are creatively cross-employed to comprehensively cross-evaluate online courses and sensor technologies. This was accomplished by means of an in-depth survey of large-scale current online-course users and professional experts with the highest research reliability, validity, accuracy, and representativeness. The three most valuable and contributive conclusions of this study are as follows: (1) The repurposing technology function (RTF) of online-course technology can combine software sensor (SS), motion sensor (MS), and environment sensor (ES) technologies to not only detect moving objects but also achieve cognition in the environment (e.g., by using a face sensor) to extract emotions of course participants in response to words and phrases during lectures to increase online-course learning performance. (2) The course professionalization technology function (CPTF) of online-course technology can merge SS, MS, and ES technologies to control online-course hardware sensor devices and equipment to control the depth and span of online-course content to strengthen online-course learning performance. (3) The course evaluation technology function (CETF) of online-course technology can consolidate SS, MS, and ES technologies to not only empirically evaluate online-course implementation but also indirectly appraise online-course learning performance.