IoT-school guidance: A holistic approach to vocational self-awareness &amp; career path

Mrabet, Hicham El; Moussa, Abdelaziz Ait

doi:10.1007/s10639-021-10548-6

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…This model is essentially an enhanced BP neural network used to extract students' academic achievement features and continuously enhance the attributes based on the labelling results. In the experiment, to obtain the network with the optimal number of layers, the number of network layers is set to [2,4,8,16,32]. Similarly, the hidden layer space is set to 64, and the embedded size is the same as SPE-TNN.…”

Section: Experiments Settingmentioning

confidence: 99%

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Social-path embedding-based transformer for graduation development prediction

Yang

Ouyang

et al. 2022

Appl Intell

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As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students' learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students' graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student's performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students' features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

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Section: Experiments Settingmentioning

confidence: 99%

“…Only when students have a stable and clear goal, their career orientation can be formed gradually during their university career and future career exploration. Therefore, career guidance is an indispensable part of the university, which can help students to plan their life goals after graduation better [4].…”

Section: Introductionmentioning

confidence: 99%

Social-path embedding-based transformer for graduation development prediction

Yang

Ouyang

et al. 2022

Appl Intell

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“…Mostly, the guidance choices fail when academic choices are based on unsupervised foundations and information or emanate from the desires of the surroundings and the parents' aspirations, which do not necessarily reflect the student's qualifications. An up-and-coming technology to achieve this objective is the use of artificial intelligence, the internet of things (El Mrabet et al 2021a ), machine learning, and data mining (Memiş et al 2022a ). The use of machine learning in an educational environment is more common in smart learning, predicting student performance, and predicting academic orientation.…”

Section: Introductionmentioning

confidence: 99%

A framework for predicting academic orientation using supervised machine learning

Mrabet

Moussa

2022

J Ambient Intell Human Comput

Self Cite

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School guidance is declared an integral part of the education and training process, as it accompanies students in their educational and professional choices. Accordingly, the current situation in light of the Covid-19 epidemic requires a reconsideration of school guidance together with the methods of accompanying the student to choose the field that suits his/her personality, knowledge qualifications, perceptual and intellectual skills in order to achieve an excellent educational level that enables the learner to work in future professions. The current study aims to predict a student's potential and provide support for academic guidance. This paper emphasizes the importance of supervised machine learning and classification algorithms to predict the personality type based on student traits. Based on the information gathered, the results of this study indicate that it contributes significantly to providing a comprehensive approach to support academic self-orientation.

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“…The machine learning boom has emerged many algorithms an tools used in various areas of research [6,7,8,9]. The educational guidance is an example where machine learning is increasingly used as recent works show [5,10,4,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…In [11], the authors proposed a school guidance system named "IoT-School Guidance". They idea is a new approach that includes implementing an adaptive orientation process using smart technologies to guide pre-tertiary students choose and follow the best professional career generated automatically by the IoT-school guidance system.…”

Section: Introductionmentioning

confidence: 99%

Towards a Smart Guidance System in CAMPUSFASO : Simulation Results

Bombiri¹,

Ouedraogo²,

Some³

et al. 2022

Proceedings of the 4th Edition of the Computer Science Research Days, JRI 2021, 11-13 November 2021, Bobo-Dioulasso, Burkina Fa

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Guidance is a complex and multidisciplinary field where the main goal is to help students find their suitable training pathways. The emergence of artificial intelligence has boosted many area of research. Machine learning tools have been used to improve both educational and vocational guidance system. Since 2018, the university guidance system has evolved with the establishment of an online platform named CAMPUSFASO. This platform, presented as an innovation for the guidance, has been strongly criticized. Firstly, we present the academic achievements of the first year students of Université Norbert Zongo after they are guided by CAMPUSFASO. These academic achievements show that more the student is guided at his preferential training path more he succeed. Secondly, we present a machine learning model for the guidance. Unlike CAMPUSFASO, our model uses of high school grades of the student to find the suitable training path. The model reaches auspicious results with simulated data.

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IoT-school guidance: A holistic approach to vocational self-awareness & career path

Cited by 12 publications

References 15 publications

Social-path embedding-based transformer for graduation development prediction

Social-path embedding-based transformer for graduation development prediction

A framework for predicting academic orientation using supervised machine learning

Towards a Smart Guidance System in CAMPUSFASO : Simulation Results

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