Evaluation of Attention and Concentration Using Mobile Computing

Laura-Ivoone Garay-Jiménez, Elena Fabiola Ruiz Ledesma, Enrique Carmona-García, Asucena Lozano Gutiérrez, Feggy Ostrosky Shejet

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.


Keywords


Neuropsychological test; automated evaluation; cognitive skills; education.

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References


H. Crompton, “Moving toward a mobile learning landscape: presenting a mlearning integration framework,†Interact. Technol. Smart Educ., 2017.

W. Suryasa, R. Zambrano, J. Mendoza, M. Moya, and M. Rodríguez, “Mobile devices on teaching-learning process for high school level.,†Int. J. od Psychosoc. Rehabil., vol. 24, no. 4, pp. 330–340, 2020.

Cognifit, “CogniFit, Test Neuropsicológicos y Estimulación Cognitiva,†2017.

P. Studios, “Towi: Desarrollo de habilidades del aprendizaje a través del juego.,†SAPI de CV, 2020.

K. B. Casaletto and R. K. Heaton, “Neuropsychological assessment: Past and future,†J. Int. Neuropsychol. Soc. JINS, vol. 23, no. 9–10, p. 778, 2017.

A.-L. Schubert and G. T. Frischkorn, “Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences,†Curr. Dir. Psychol. Sci., vol. 29, no. 2, pp. 140–146, 2020.

C. Cognition, “CANTAB,†Cambridge Cognition Ltd.

T. M. Roebuck-Spencer et al., “Cognitive screening tests versus comprehensive neuropsychological test batteries: a national academy of neuropsychology education paper,†Arch. Clin. Neuropsychol., vol. 32, no. 4, pp. 491–498, 2017.

M. Ostrosky-Solis, F., A. Ardila A., Roselli, NEUROPSI: Evaluación Neuropsicológica Breve en Español. Mexico: Publingenio, 1998.

F. O. Solís, E. G. Pérez, R. C. Dichy, and J. C. F. Lázaro, ¿Problemas de atención?: un programa para su estimulación y rehabilitación. American Book Store, 2004.

C. B. Shearer and J. M. Karanian, “The neuroscience of intelligence: Empirical support for the theory of multiple intelligences?,†Trends Neurosci. Educ., vol. 6, pp. 211–223, 2017.

D. Schunk, Learning Theories: An Educational Perspective. Pearson, 2019.

B. Byrne, R. K. Olson, and S. Samuelsson, “Behavior-Genetic Studies of Academic Performance in School Students: A Commentary for Professionals in Psychology and Education BT- Reading Development and Difficulties: Bridging the Gap Between Research and Practice,†D. A. Kilpatrick, R. M. Joshi, and R. K. Wagner, Eds. Cham: Springer, 2019, pp. 213–232.

M. A. Pievsky and R. E. McGrath, “The neurocognitive profile of attention-deficit/hyperactivity disorder: A review of meta-analyses,†Arch. Clin. Neuropsychol., vol. 33, no. 2, pp. 143–157, 2018.

M. Causse, Z. Chua, V. Peysakhovich, N. Del Campo, and N. Matton, “Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS,†Sci. Rep., vol. 7, no. 1, pp. 1–15, 2017.

F. Dolcos et al., “Neural correlates of emotion-attention interactions: From perception, learning, and memory to social cognition, individual differences, and training interventions.,†Neurosci. Biobehav. Rev., vol. 108, pp. 559–601.

Y. Luo, D. Weibman, J. M. Halperin, and X. Li, “A review of heterogeneity in attention deficit/hyperactivity disorder (ADHD),†Front. Hum. Neurosci., vol. 13, p. 42, 2019.

D. Stringer et al., “Trajectories of emotional and behavioral problems from childhood to early adult life,†Autism, p. 1362361320908972, 2020.

D. R. Coghill, T. Banaschewski, C. Soutullo, M. G. Cottingham, and A. Zuddas, “Systematic review of quality of life and functional outcomes in randomized placebo-controlled studies of medications for attention-deficit/hyperactivity disorder,†Eur. Child Adolesc. Psychiatry, vol. 26, no. 11, pp. 1283–1307, 2017.

P. Julayanont and D. Ruthirago, “The illiterate brain and the neuropsychological assessment: From the past knowledge to the future new instruments,†Appl. Neuropsychol. Adult, vol. 25, no. 2, pp. 174–187, 2018.

D. Ostrosky-Solis, F., Gómez, E., Matute, E., Roselli, M., Ardila, A., Pineda, Neuropsi, Atención y Memoria. Manual e instructivo. Mexico: American Book Store, 2007.

T. Valentine, C. Block, K. Eversole, L. Boxley, and E. Dawson, “Wechsler Adult Intelligence Scaleâ€IV (WAISâ€IV),†Wiley Encycl. Personal. Individ. Differ. Meas. Assess., pp. 457–463, 2020.

D. Ostrosky-Solís, F., Gómez, E. Matute, E., M. Roselli, M. Ardila A, Pineda, Neuropsi, Atencion Y Memoria. Manual, Protocolos, Láminas, Tablas Puntuaciones Totales y Perfilesitle. Mexico: American Book Store, 2003.

J. Kamiński, S. Sullivan, J. M. Chung, I. B. Ross, A. N. Mamelak, and U. Rutishauser, “Persistently active neurons in human medial frontal and medial temporal lobe support working memory,†Nat. Neurosci., vol. 20, no. 4, pp. 590–601, 2017.

D. L. Woods, J. M. Wyma, T. J. Herron, E. W. Yund, and B. Reed, “The Dyad-Adaptive Paced Auditory Serial Addition Test (DA-PASAT): Normative data and the effects of repeated testing, simulated malingering, and traumatic brain injury,†PLoS One, vol. 13, no. 4, p. e0178148, 2018.

D. P. M., Web2py, Complete Reference Manual. Web2py, 2020.

S. Bradshaw, E. Brazil, and K. Chodorow, MongoDB: The Definitive Guide: Powerful and Scalable Data Storage. O’Reilly Media, 2019.

G. Fraser and J. M. Rojas, “Software Testing,†in Handbook of Software Engineering, Springer, 2019, pp. 123–192.

M. K. Abd Ghani, M. M. Jaber, S. A. Mostafa, A. Mustapha, and M. Abed, “Proper Software Engineering Process in Developing an Integrated Telehealth System,†Int. J. Eng. Technol., vol. 7, no. 3.20, pp. 441–450, 2018.

P. Ramya, V. Sindhura, and P. V. Sagar, “Testing using selenium web driver,†in 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2017, pp. 1–7.

S. Matam and J. Jain, Pro Apache JMeter: web application performance testing. Pleasonton, California: Apress, 2017.

D. Lakens, “Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses,†Soc. Psychol. Personal. Sci., vol. 8, no. 4, pp. 355–362, May 2017, doi: 10.1177/1948550617697177.

P. M. Dixon, P. F. Saint-Maurice, Y. Kim, P. Hibbing, Y. Bai, and G. J. Welk, “A Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement.,†Med. Sci. Sports Exerc., vol. 50, no. 4, pp. 837–845.




DOI: http://dx.doi.org/10.18517/ijaseit.11.1.12631

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