Student progress assessment with the help of an intelligent pupil analysis system

Kaklauskas, Artūras; Vlasenko, Andrej; Raudonis, Vidas; Zavadskas, Edmundas Kazimieras; Gudauskas, Renaldas; Seniut, Mark; Juozapaitis, Algirdas; Jackute, Ieva; Kanapeckienė, Loreta; Rimkuvienė, Silva; Kaklauskas, Gabrielius

doi:10.1016/j.engappai.2012.01.006

Cited by 20 publications

(9 citation statements)

References 36 publications

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“…Tai et al [4] explored the ability of eye tracking to recognize differences in problemsolving approaches within a group of testers with different levels of knowledge and expertise in science disciplines. Kaklauskas et al [5] investigated student progress assessments through the analysis of pupil size (approach also adopted by Porta et. al.…”

Section: Related Workmentioning

confidence: 99%

Gaze behavior analysis in multiple-answer tests: An Eye tracking investigation

Nugrahaningsih

Porta

Ricotti

2013

2013 12th International Conference on Information Technology Based Higher Education and Training (ITHET)

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In this paper we present an eye tracking study aimed at examining learners' behaviors while searching for the right answer in multiple-choice tests. In particular, the analysis is focused on a geometry problem characterized by four graphical solutions. The data gathered through several experiments have allowed us to find interesting relationships between answers and testers' performance (especially with regard to time spent watching the available options and gaze wavering between the two most plausible choices), as well as to identify otherwise hidden similarities among testers themselves. Providing a better understanding of students' learning processes, the obtained results can potentially be exploited to improve the design of assessment tasks and to train intelligent tutoring systems.

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Section: Related Workmentioning

confidence: 99%

Gaze behavior analysis in multiple-answer tests: An Eye tracking investigation

Nugrahaningsih

Porta

Ricotti

2013

2013 12th International Conference on Information Technology Based Higher Education and Training (ITHET)

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“…Gaze tracking is the process of locating the point of a person's gaze [15]. Applications of gaze tracking are numerous and include cognitive studies [16], medical research (ophthalmology, neurology, and related areas) [17] for diagnosing diseases such as Attention Deficit Hyperactivity Disorder (ADHD), Schizophrenia, Parkinson's and Alzheimer's disease, psychological evaluation [18], website usability [19] and advertisement studies [20], consumer product research [21], driver fatigue and drowsiness detection [22], reading studies [23], evaluation of image quality [24], learning process assessment [25], and human-computer interfaces for impaired [26].…”

Section: Overview Of Related Workmentioning

confidence: 99%

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

2023

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Eye gaze interfaces are an emerging technology that allows users to control graphical user interfaces (GUIs) simply by looking at them. However, using gaze-controlled GUIs can be a demanding task, resulting in high cognitive and physical load and fatigue. To address these challenges, we propose the concept and model of an adaptive human-assistive human–computer interface (HA-HCI) based on biofeedback. This model enables effective and sustainable use of computer GUIs controlled by physiological signals such as gaze data. The proposed model allows for analytical human performance monitoring and evaluation during human–computer interaction processes based on the damped harmonic oscillator (DHO) model. To test the validity of this model, the authors acquired gaze-tracking data from 12 healthy volunteers playing a gaze-controlled computer game and analyzed it using odd–even statistical analysis. The experimental findings show that the proposed model effectively describes and explains gaze-tracking performance dynamics, including subject variability in performance of GUI control tasks, long-term fatigue, and training effects, as well as short-term recovery of user performance during gaze-tracking-based control tasks. We also analyze the existing HCI and human performance models and develop an extension to the existing physiological models that allows for the development of adaptive user-performance-aware interfaces. The proposed HA-HCI model describes the interaction between a human and a physiological computing system (PCS) from the user performance perspective, incorporating a performance evaluation procedure that interacts with the standard UI components of the PCS and describes how the system should react to loss of productivity (performance). We further demonstrate the applicability of the HA-HCI model by designing an eye-controlled game. We also develop an analytical user performance model based on damped harmonic oscillation that is suitable for describing variability in performance of a PC game based on gaze tracking. The model’s validity is tested using odd–even analysis, which demonstrates strong positive correlation. Individual characteristics of users established by the damped oscillation model can be used for categorization of players under their playing skills and abilities. The experimental findings suggest that players can be categorized as learners, whose damping factor is negative, and fatiguers, whose damping factor is positive. We find a strong positive correlation between amplitude and damping factor, indicating that good starters usually have higher fatigue rates, but slow starters have less fatigue and may even improve their performance during play. The proposed HA-HCI model and analytical user performance models provide a framework for developing an adaptive human-oriented HCI that enables monitoring, analysis, and increased performance of users working with physiological-computing-based user interfaces. The proposed models have potential applications in improving the usability of future human-assistive gaze-controlled interface systems.

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“…La IA debe ser entendida como una disciplina científica que configura máquinas para que sean inteligentes y capaces de resolver problemas al anticipar la acción del entorno gracias a su adaptabilidad y aprendizaje de patrones (Tuomi, 2018;Wang et al, 2015;Ma et al, 2014). En el contexto actual, algunas instituciones educativas han aprovechado la IA en su vertiente de chatbots o tutores virtuales para interactuar con el alumnado y optimizar su aprendizaje al poder controlar su progreso, evaluar las tareas o prestarles apoyo de manera instantánea (Wang et al, 2018;Yang, 2018;Kaklauskas, 2015). Otra rama de la IA utilizada en el ámbito educativo es el Machine Learning, entendido como un sistema de IA que construye modelos matemáticos, a partir de datos registrados como muestra, para hacer predicciones o tomar decisiones emulando la inteligencia humana sin necesidad de pre-programación (Zhang, 2020;Naqa y Murphy, 2015).…”

Section: Introductionunclassified

La Inteligencia Artificial como recurso educativo durante la formación inicial del profesorado

Puerto

Esteban

2022

RIED

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La Inteligencia Artificial (IA) se presenta como una tecnología emergente que facilita la personalización del aprendizaje y prepara a la juventud para un cambiante mercado laboral marcado por nuevos requerimientos sociales. Este trabajo describe cómo se diseñó y desarrolló una formación virtual destinada a ampliar el conocimiento sobre la IA de 76 profesores en formación inicial que cursaban la asignatura TIC aplicadas a la Educación del Grado de Infantil de la Universidad de Extremadura. A través de un enfoque mixto, se analizan las respuestas ofrecidas por las participantes en el cuestionario diseñado ad hoc para este estudio y en su propio porfolio digital. Para las preguntas cerradas, de escala Likert, se calcularon estadísticos descriptivos y para las preguntas abiertas, se utilizó la técnica de análisis cualitativa de codificación. Los resultados revelan que el alumnado percibe que la IA tiene un impacto positivo en el aprendizaje y se ven capacitadas para diseñar sus propios recursos educativos si cuentan con el apoyo y acompañamiento del profesorado universitario. Finalmente, consideramos que es necesario revisar los planes docentes de las asignaturas del Grado de Educación Infantil para que contemplen el uso de la IA en el diseño del proceso de enseñanza del profesorado en formación inicial.

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Student progress assessment with the help of an intelligent pupil analysis system

Cited by 20 publications

References 36 publications

Gaze behavior analysis in multiple-answer tests: An Eye tracking investigation

Gaze behavior analysis in multiple-answer tests: An Eye tracking investigation

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

La Inteligencia Artificial como recurso educativo durante la formación inicial del profesorado

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