Student Assessment Based on Affective Factors in English Learning Using Fuzzy Inference

Bachtiar, Fitra A.; Cooper, Eric W.; Sulistyo, Gunadi Harry; Kamei, Katsuhiko

doi:10.5057/ijae.ijae-d-15-00037

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…In our opinion, the effectiveness of the conducted studies in the field of computational judgment and reasoning would be more significant if for the solution of this problem would be used the functions of computational inference, decision-making, systems thinking, situational awareness, understanding and mind. ▪ In study [16], a method is proposed for determining student performance, based on affective factors of students when learning English using fuzzy sets. A model of understanding of students' progress and a system of fuzzy inference of students' success in learning English are considered.…”

Section: Artificial Intelligencementioning

confidence: 99%

A Self-Developing Computational System of Full Awareness and Understanding of Reality

Khayut¹,

Fabri²,

Avikhana³

2018

ISASE

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The manuscript represents an approach to create a self-developing computational system of full awareness and understanding of reality through of the perception, processing, memorization and reproduction of languages, images, signals, sounds, feelings and emotions. This system is oriented on using in Kansei Engineering systems as a plug-in. The functionality of this system is realized by its processes of processing of data, information, knowledge, objects, models and modules in current situation. These processes are self-controlled and self-organized themselves under uncertainty of the changing environment with using of computational modeling of the: a

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Section: Artificial Intelligencementioning

confidence: 99%

A Self-Developing Computational System of Full Awareness and Understanding of Reality

Khayut¹,

Fabri²,

Avikhana³

2018

ISASE

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“…This is reinforced by Rimland [11] that the cause is that the assessment of the affective domain is quite difficult to apply. Measuring the affective domain there are factors that make it difficult to measure directly [12]. In addition, research on affective assessment in particular is still rare because it is difficult to evaluate [10].…”

Section: Introductionmentioning

confidence: 99%

Emotions and gesture recognition using affective computing assessment with deep learning

Artanto

Arifin

2023

IJ-AI

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Emotions have an important role in education. Affective development, attitudes, and emotions in learning are measured using affective assessment. This method is the right way to determine the student’s affective development. However, the process did not run optimally because the teacher found it difficult to collect student’s affective data. This paper describes the development of a system that can assist teachers in carrying out affective assessment. The system was developed using a v-model that aligns the verification phase with the validation. The use of the system is carried out during learning activities. The emotion detection system detects through body gestures using PoseNet to generate emotional data for each student. The detection results are then processed and displayed on an information system in the form of a website for affective assessment. The accuracy of emotion detection got validation values of 84.4% and 80.95% after being tested at school. In addition, the acceptance test with the usability aspect of the system by the teacher got a score of 77.56% and a score of 79.85% by the students. Based on several tests carried out, this developed system can assist the process of implementing affective assessment.

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“…The conceptual strategy is approached using expert validation/review; meanwhile, the empirical one is performed by involving a tryout to collect data. Expert validation may be carried out using three ways: think aloud, independent panel, focused group discussion [1], p. 207, in which a series of questions that [8] develop may be utilized. The form provides a useful review form with which to analyze whether an item is biased or not.…”

Section: Detecting Biasmentioning

confidence: 99%

Detecting Anomalies in Assessment Devices for Cross-Cultural Use: Fairness Concerns

Sulistyo¹

2017

KSS

Self Cite

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Assessment devices are developed with the undertaking of collecting data that will inform a valid and reliable crux of interest. Such information is mandatory as it will constitute a basis for responsible decision-making. In order to meet the projected function appropriately, any assessment device, let alone those for cross-cultural use, needs to be anomaly-free. While the use of assessment devices across nations has been ubiquitous worldwide, scrutinizing assessment devices for any existing covert defects is unavoidably imperative if fairness in the result interpretation is to be envisioned. This paper reviews briefly several concepts related with nuisances that potentially cause anomaly in assessment devices particularly for cross-cultural use.

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Student Assessment Based on Affective Factors in English Learning Using Fuzzy Inference

Cited by 7 publications

References 25 publications

A Self-Developing Computational System of Full Awareness and Understanding of Reality

A Self-Developing Computational System of Full Awareness and Understanding of Reality

Emotions and gesture recognition using affective computing assessment with deep learning

Detecting Anomalies in Assessment Devices for Cross-Cultural Use: Fairness Concerns

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