## Volume 13, Number 5

#### Difference of Probability and Information Entropy for Skills Classification and Prediction in Student Learning

### Authors

Kennedy Efosa Ehimwenma^{1}, Safiya Al Sharji^{2} and Maruf Raheem^{3}, ^{1}Wenzhou-Kean University, China, ^{2}University of Technology and Applied Sciences, Oman, ^{3}Department of Statistics University of Uyo, Nigeria

### Abstract

The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes’ theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].

### Keywords

Complement of probability, Bayes’ rule, computational education, pre-learning assessment, information theory, SQL ontology.