Smart Analyzer: Assisting College Management through Machine Learning and Data Analysis - et. al., Seema Kedar, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
This work explores various opportunities to improvise regular tasks done by college faculty viz. Exam Result Analysis, Daily Student Attendance Analysis and Lecture Schedule Storage. Result Analysis becomes a tedious task when handled through traditional pen-paper methods and spreadsheets. This can be simplified by using Classification and Regression techniques. Through Regression, module-wise clarity of subjects can be foretold for students. Classification and Clustering algorithms can help to segregate students in various groups so that additional efforts can be taken for slow learners. It can also be used for classifying modules of a specific subject based on their complexities and course outcomes. The usage of register files for daily student attendance can be improved in a digital approach through Android and Django Framework. Through this approach, attendance can be tracked regularly and lecture (session) wise analysis can be done without the clutter of traditional pen-paper approach. Besides, for storing Lecture schedules and relevant timelines in the Realtime database furnishes additional benefits involving access to multiple users simultaneously. Technologies like Django Framework, Android OS, Realtime Database Systems and Machine Learning algorithms make these tasks simplified and less time-consuming. Data Analysis of Exam Results can be used for classifying student response to the teaching-learning process and can help in strategic outlining for future enhancements. Results of the proposed system consists of graphical representation of analysis done on input data and real time analysis of attendance data.