Recommending degree studies according to students’ attitudes in high school by means of subgroup discovery

Abstract

The transition from high school to university is a critical step and many students head toward failure just because their final degree option was not the right choice. Both students' preferences and skills play an important role in choosing the degree that best fits them, so an analysis of these attitudes during the high school can minimize the drop out in a posteriori learning period like university. We propose a subgroup discovery algorithm based on grammars to extract itemsets and relationships that represent any type of homogeneity and regularity in data from a supervised context. This supervised context is cornerstone, considering a single item or a set of them as interesting and distinctive. The proposed algorithm supports the students' final degree decision by extracting relations among different students' skills and preferences during the high school period. The idea is to be able to provide advices with regard to what is the best degree option for each specific skill and student. In this regard, the use of grammars is essential since it enables subjective and external knowledge to be included during the mining process. The proposed algorithm has been compared against different subgroup discovery algorithms, achieving excellent results. A real-world experimental analysis has been developed at King Abdulaziz University, one of the most important universities in Saudi Arabia, where there is a special interest in introducing models to understand the students' skills to guide them accordingly.

Frequent itemset mining: A 25 years review

“…To list a few, FIM has been correctly applied to education by analyzing data gathered by e-learning systems with the aim of providing instructors with beneficial or detrimental relationships between the use of educational resources and the student's learning (Romero, Zafra, Luna, & Ventura, 2013). FIM has also been used as a recommender system to support the students' final degree decision according to their skills and preferences during the high school period (Noaman, Luna, Ragab, & Ventura, 2016). In crime against women, the well-known Apriori algorithm has been correctly applied (Bansal & Bhambhu, 2014).…”

Subgroup Discovery on Multiple Instance Data

“…The applicability of SD to real-world problems can be observed throughout the literature widely. For example, in [20,21] descriptions in bioinformatic domains are performed, in medicine [22], in industry [23], or e-learning [24] among others [25].…”

Exceptional in so Many Ways—Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios

“…W E are living in a Golden Age of data science, where data mining techniques designed to discover valuable insights from a collection of records [1] are employed to transform tons of facts into useful information in fields as diverse as education [2], health care [3], and Internet of Things [4]. Nowadays, the quantity of data gathered on different domains is so high that it is a common practice not only to provide specific algorithms for such huge quantity of data [5], but also to reduce such enormous amount of data in order to be able to process it.…”