The academic committees worldwide suggest technical institutions to follow Revised Bloom's Taxonomy (RBT), a framework that helps to develop learning objectives. The model classifies a hierarchy of educational objectives such as cognitive, sensory and affective domains that are not only helping the students to evolve thinking abilities but also to identify the skills they are lacking with. Analysis of students RBT skills through data mining techniques is more valuable and is yet to be explored. This paper employs predictive and descriptive techniques of data mining to analyze the RBT level of each student. The methodology uses a classifier to classify the RBT level of questions under six levels such as remembering, understanding, applying, analyzing, evaluating, creating and performs clustering of students with respect to overall RBT level and lacking RBT skill of each student. The experimentation is carried out with university students. The results show that the proposed classifier is able to achieve 98% accuracy by correctly classifying RBT levels of input questions. The results also shows that the proposed work creates précised and meaningful clusters of overall RBT level/Lacking RBT skill of each student with precision 0.83 and 0.79 which could help the instructor to design different pedagogical approaches to improve students learning.

Cohesion in Object Oriented (OO) modules impact reusability, efficiency and complexity of software. OO Programmers are mandated to create software with high cohesion. The testing phase in Software Development Life Cycle (SDLC) is not only concerned about creating error free software but also assess quality of code through software metrics. The metric'Lack of Cohesion in Methods (LCOM)' is one of the significant OO metric for measuring level of cohesion in software modules. LCOM and its improvised versions of cohesion metrics output degree of cohesion in software modules rather than providing solutions to reconstruct the poorly cohesive modules. Further, the traditional cohesion metrics do not differentiate the possible levels such as high, medium and low cohesions. Thus, in this paper a novel, Variable Frequency-Inverse Method Frequency (VF-IMF) based machine learning metric is proposed to assess the level of cohesion in modules and also to group module methods to instill high cohesion. The proposed metric is experimented over three sample modules represents each level of cohesion. The experimental results show that the proposed metric clearly differentiates the three levels of cohesion and offers a compromised solution for building high cohesive modules than traditional LCOM metrics. The metric is also validated against Weyuker's properties and is proven to be a valid metric as it satisfies all the 9 properties.