Completeness based classification algorithm: a novel approach for educational semantic data completeness assessment

Abstract

Purpose The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment. Design/methodology/approach A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier. Findings The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives. Originality/value The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

“…In profiling and prediction applications, three types of decisions were identified: 1) identification of students at risk of dropping out of a course, study programme, or school [20], [42], [46], [47], [49], [51]- [63], 2) prediction of student performance prior to their admission [21], [64]- [67], 3) identification of students' learning profile [43], [50], [68]. The first two types of decisions have a more significant impact on access to studies than the last one.…”