Narrow Results

Being a decision-making process, software evaluation and selection is complicated, time-consuming, costly and also critical to the success of the project/work/organization. In this study, evaluation and selection of customized software which are used to identify the physical effect (explosion, fire and toxic emission) distances for industrial establishments containing, using or storing hazardous chemicals has been considered. These software are called the “Hazard Analysis and Consequence Modeling” (HACM) software in the literature. This study first prepared Likert scale-based questionnaires which were distributed to Environmental Health and Safety (EHS) professionals to obtain their opinions. Likert scale was used with Analytic Hierarchy Process (AHP) method in the processing of data obtained from questionnaires in order to get over the inconsistency problem of pairwise comparison matrices. The Likert scale-based AHP method was used to determine the weights of the criteria, and Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) method was used to obtain the final ranking. PROMETHEE method was preferred for having the opportunity of evaluating the binary “Yes–No” questions in the solution process. Finally, application results were given to illustrate the proposed method by using the PROMETHEE software “Visual PROMETHEE 1.4.0.0”.

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.

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.