Chapter 14: Technologies and Student Feedback Collection and Analysis

Technology-enabled feedback encompasses many significant features including AI. It also allows students greater flexibility in providing feedback, and the university/institute enables them to make data-driven decisions as the collected data are free from bias and data analyses are accurate. There are different technology-centric feedback collections such as Open edX Insight, speech-to-text, Kahoot, Dialogflow, and so on. These technology-centric feedback collections mean collecting more realistic data from the students. Both qualitative and quantitative data could be analyzed by applying technology-centric data analyses tools. AMOS, LABLEAU, SAS, SPSS, Stata, Statista, XLSTAT, and ROOT are a few of the quantitative data analyses software, and Atlas, Airtable, Coda, Condens, MaxQDA, Notion, and NVivo are a few of the qualitative data analyses software. To make the students’ feedback and survey more successful we recommend sharing survey methods/survey questions with other educational institutions, conducting broader surveys, broad topics to ask questions on, technology-based analysis of qualitative feedback comments, and including skills development-focused questions.