Evaluation of Attention and Concentration Using Mobile Computing

Abstract

The educational trend toward personalized learning requires the teacher to monitor the learning process continuously. This article presents mobile computing to administer a battery of cognitive tests based on a standard neuropsychological assessment of attention and concentration derived from Neuropsi ©. Currently, specialists perform this test by observing, measuring time, and taking notes of the process to obtain the final scores. Considering the use of this test as an assessment of students' cognitive abilities in a class, the time required for application and evaluation is a challenge itself. As for overcoming this difficulty, the process has been automated through the development of software. The goal is to provide the test to several users simultaneously on their own mobile devices. Then, it is evaluated both attention and concentration on the subject during the solution of the exercises. Variants of the exercises were provided to extent the Neuropsi options. All the collected information is stored on a server. Moreover, the system provides individual and group profiles to the evaluator, such as a teacher or instructor. Likewise, the provided compendium allows the specialist to identify changes in attention and concentration performance and supports their additional recommendations, as well as to go in deep in the research of the cognitive process providing an initial condition evaluation. This work proved that the concept raised by software specialists, designers, and psychologists is feasible into an interdisciplinary team.

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

“…The following problems have been studied in selected PRISMA research papers: Higher education quality assurance ( Alakbarov 2021 ; Allayarova 2019 ; Asare et al 2021 ); Knowledge-based recommender system ( Barabash et al 2021 ; Barón et al 2015 ; Bin-Noor et al 2021 ; Brunello and Wruuck 2021 ); E-learning and distance learning ( Bukralia et al 2015 ; Burman et al 2021 ; Casselman 2021 ; Chahal et al 2020 ); Inter-professional education ( Cheng 2017 ); Network course recommendation system ( Cinquin et al 2019 ; Dhar and Jodder 2020 ; Dolgikh 2021 ; Ehimwenma and Krishnamoorthy 2021 ); Modern information communication technologies in the higher education sector ( El Gourari et al 2021 ; Elahi et al 2022 ; Ellyatt 2021 ; Elumalai et al 2019 ); Transformation of education during the COVID-19 pandemic ( Erridge 2006 ; Ezz and Elshenawy 2020 ; Fedushko and Ustyianovych 2020 ); Digitalization in higher education ( Gao et al 2021 ; Garay-Jiménez et al 2021 ; Gil et al 2019 ); Personalized recommendation system for learning resources ( Habib et al 2021 ; Ibrahim et al 2019 ; Karan and Asgari 2021 ; Khan and Ramzan 2018 ; Khosravi et al 2021 ); Artificial intelligence techniques for education tasks solving ( Kim Rosemary et al 2014 ; ...…”