Personalized Intelligent Assessment Model for Measuring Initial Students Abilities

Alrajhi, Laila; Salama, Reda; Gamalel-Din, Shehab

doi:10.1145/2643604.2643606

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…e following criteria were used to evaluate the performance of the hybrid AI system [40][41][42][43]:…”

Section: Resultsmentioning

confidence: 99%

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

Tang¹,

Wei²

2022

Journal of Mathematics

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Each individual diversifies in the population with certain characteristics. Thus, diversity is a scientifically proven and widely accepted phenomenon when the human being is a concern. One of the areas where the diversity of human beings is mostly paid attention to is called the learning process, since different forms of responses could be observed. For example, each student perceives, assimilates, and uniquely processes the information when being transmitted to him, which confirms the inherited diversity. In this regard, educational systems are required to deal effectively with students and to apply the principles of personalized learning, which is pertinent to learning processes that meet the individual needs and interests of learners. By doing so, taking into account their unique characteristics, talents, skills, inclinations, and desires are satisfied. This manuscript presents an innovative model to classify college students’ skills. A hybrid artificial intelligence (AI) system that fully automates the process of personalized training is proposed based on individual skills by taking into account the priority of personalized and fully customized learning systems. The process specifically utilizes the Rasch statistical analysis model and an innovative fuzzy Bayesian network. Higher-level reasoning is generated for the automated and personalized learning process in which college students are automatically classified into a certain category based on their skills.

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“…e following criteria were used to evaluate the performance of the hybrid AI system [40][41][42][43]:…”

Section: Resultsmentioning

confidence: 99%

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

Tang¹,

Wei²

2022

Journal of Mathematics

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show abstract

“…Based on the approach of learning theory used, most authors refer to FSLSM learning theory (22%) to construct e-learning model, as has been expressed by 6 (six) papers [3], [6], [9], [12], [22] and [26]. Meanwhile, of all the papers studied, 10 (ten) papers use one or two approaches to the model of learning theory.…”

Section: Table IV Use Of Algorithms For E-learningmentioning

confidence: 99%

“…For example through the proposed intelligent and adaptive learning methods to serve learners fairly, and also appreciate the uniqueness of each individual learner. In addition to the learner or student, in terms of appraisal, there is the role of the instructor involved in the diagnostic module of the electronic The strengths and weaknesses of the learner are assessed, then the instructor makes the best decision in drawing up their learning concepts [3]. Another flexible and innovative concept offers an e-learning model with a social learning approach that creates interaction among learners in a global space.…”

Section: Introductionmentioning

confidence: 99%

Personalized E-learning Model: A systematic literature review

Jando

Meyliana

Hidayanto

et al. 2017

2017 International Conference on Information Management and Technology (ICIMTech)

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The development of electronic learning models, especially in developing countries such as Indonesia has grown well as information technology applications designed for learning purposes. Community colleges and schools seek to complement the traditional teaching system with e-learning systems. However, we found there are differences in individual learning styles in terms of speed and learning styles. Serving or teaching students with one mechanism of the same teaching method will ignore individual rights while reducing the meaning of education broadly from the humanity dimension. Such situations will affect the target of increasing competence, the growth of knowledge and the value expected for some individuals to fail. The electronic learning model then undergoes a shift away from a mere system, now evolving into a personalized learning model, where learning processes are oriented toward the students' abilities. Under these conditions, models and other techniques are needed to help personalized adaptive learning as they need it. The purpose of this study was to identify the general criteria of personalized electronic learning model to meet the needs, interests and objectives of the learner in a more personal sense in a broader sense. This research was conducted through literature study on papers published in the last five years (2012 -2017). The results show the common components, techniques or tools that are commonly used, as well as the support of the theoretical basis used as the platform for the development of a personalized elearning model.

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“…Fortunately, many Adaptive e-Learning researches assumed that personalizing or presentation of the question to match the student's learning preferences, especially the learning style, would aid the student's understanding. Several systems have been developed that provide adaptive presentation for assessment materials, such as: INSPIRE System [18] and PIAT [19].…”

Section: Adaptive Presentation Of Questionsmentioning

confidence: 99%

ASSA: Adaptive E-Learning Smart Students Assessment Model

Aljohany¹,

Salama²,

Saleh³

2018

ijacsa

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Adaptive e-learning can be improved through measured e-assessments that can provide accurate feedback to instructors. E-assessments can not only provide the basis for evaluation of the different pedagogical methods used in teaching and learning but they also can be used to determine the most suitable delivered materials to students according to their skills, abilities, and prior knowledge's. This paper presents the Adaptive Smart Student Assessment (ASSA) model. With ASSA instructors worldwide can define their tests, and their students can take these tests on-line. ASSA determines the students' abilities, skills and preferable Learning Style (LS) with more accuracy and then generates the appropriate questions in an adaptive way then presents them in a preferable learning style of student. It facilitates the evaluation process and measures the students' knowledge level with more accuracy and then store it in the student's profile for later use in the learning process to adapt course material content appropriately according to individual student abilities.

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Personalized Intelligent Assessment Model for Measuring Initial Students Abilities

Cited by 11 publications

References 6 publications

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

Personalized E-learning Model: A systematic literature review

ASSA: Adaptive E-Learning Smart Students Assessment Model

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