Applications of natural language processing (NLP) for improving classroom learning experiences using student surveys

Abstract

Academic institutions often assess the efficacy of courses by surveying students. These surveys are critical in structuring course content and evaluating instruction. Given the critical function of surveys for academic institutions, it is essential that surveys obtain data which is precise and accurate. Currently most institutions construct surveys employing the Likert scale: questions that require students to map their opinion on precise topics to a discrete, quantitative domain. These surveys uniformly weight response data, irrespective of student interest. We argue greater accuracy may be obtained by building Student-Directed Discussion Surveys (SDDSs) — surveys with several open-ended, student-directed questions, requiring free text responses. SDDSs retain precision by employing several Natural Language Processing (NLP) techniques including word frequency and sentiment analysis. We use SDDSs to improve course content and evaluate survey accuracy by comparing the results of an SDDS to a Likert-scaled survey administered to an overlapping population. We find that the results of these two survey techniques diverge when topics become increasingly significant to respondents. These results, in addition to the documented issues with Likert-scaled surveys, lead to the conclusion that SDDSs may provide more informative and insightful results.