A method for measuring students' power skills automatically

Abstract

The academic performance of the university students is measured with the help of the Grade Point Average (GPA). Further, the marks of a subject of a degree program are accumulated to identify the final academic performance of the degree. Unfortunatel y, the achievements of special power skills such as knowledge levels, evaluation skills and application development skills are not highlighted in the current GPA. Therefore, this research was mainly designed focusing on measuring students’ power skills aut omatically which reflect specific performances of the students. Cognitive levels of the Blooms taxonomy are identified as the categories of power skills. Knowledge, comprehension, application, analysis, synthesis and evaluation are the main cognitive skill levels of the Blooms taxonomy. Typically, summative and formative assessments are held to cover the Intended Learning Outcome (ILO) of the subject. The questions of the final exam papers of the Computer Science stream of the Wayamba University were used a s the dataset. First, a preliminary research was conducted to categorize exam questions automatically. Natural Language Processing (NLP) techniques such as tagging, spell correction, lemmatization, parse tree generation and semantic similarity analysis tec hniques were used to derive the features for summative assessments classification. Based on the extracted features, rule set was identified to categorize the questions automatically. Once the questions were categorized, the portion of the marks allocated f or each Blooms taxonomy performance level was identified. Based on the assigned marks for each category, students’ achievements for each category were calculated separately to measure the power skills levels of students. This identification would immensely be helpful to academics and universities to develop the best graduates with high power skills.