An Automated Survey Designing Tool for Indirect Assessment in Outcome Based Education Using Data Mining

Abstract

In today's era, graduates need to acquire the competent skill sets to get a good job. The traditional education model suffers from the limitations of assessing these skills. Outcome Based Education (OBE) model plays an important role in this context by overcoming the above-specified issue. OBE empirically measures the student performance and helps to demonstrate the skills that are clearly articulated to them at the start of the program. Accrediting bodies are focusing more on the assessment of student learning outcomes. Every year huge datasets of assessment data are accumulated using OBE model. If analyzed properly, this data can be used to predict student competencies. Manual record keeping and attainment calculation make it a challenging task to extract meaningful information from this ever-growing data repository. The proposed work implicates the construction of a framework to automate the attainment process using the assessment and mapping data of Program Outcomes and Course Outcomes retrieved from an indirect assessment tool. The attainment data so obtained is used to predict attainment of various programs and courses using the classification techniques. The work done helps the instructors to find meaningful information from assessment data and helps them to take proactive decisions to improve the attainment of low-performing courses. Moreover, with automation of attainment calculation, manual work for the faculty is reduced, resulting in time-saving and better focusing on attainment improvement and various other scholarly activities.