A Bayesian-based knowledge tracing model for improving safety training outcomes in construction: An adaptive learning framework

Xu, Shirong; Sun, Manfang; Fang, Weili; Chen, Ke; Luo, Hanbin; Zou, Patrick X.W.

doi:10.1016/j.dibe.2022.100111

Cited by 10 publications

(5 citation statements)

References 38 publications

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“…-The BKT [30] model is a classic model proposed in 1990s that assumes that each student's knowledge state is a set of binary variables, and utilizes hidden Markov models to track these variables separately. [31], [32] made some progress in this area recent years.…”

Section: Methodsmentioning

confidence: 99%

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Pei,

Raga Jr.,

Abisado

2023

Int. J. Adv. Intell. Informatics

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Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind the model's predictions for the next problem, and this limits their opportunity to take an active role in improving the learning process. To address this limitation, this article presents a novel personalized exercise question recommendation model based on knowledge tracing. The approach incorporates graph convolutional neural networks to model the student's abilities, thus enhancing the interpretability of the model. By employing Bidirectional gate recurrent unit (Bi-GRU), the model effectively traces fluctuations in students' abilities over time and predicts their responses to exercise questions. Experimental results demonstrate the effectiveness of this model, achieving an accuracy of 90.8% and 92.6% on ASSISTment 2009 and ASSISTment 2017 datasets, containing 4218 and 1709 student records, respectively. Moreover, the experiment was also conducted to validate the model's exercise difficulty setting. Results indicate an acceptable level of effectiveness in generating appropriate difficulty-level recommendations for individual students. The proposed model contributes to advancing personalized exercise recommendations by offering valuable insights that can lead to more efficient and effective student learning experiences.

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Section: Methodsmentioning

confidence: 99%

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Pei,

Raga Jr.,

Abisado

2023

Int. J. Adv. Intell. Informatics

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“…A Bayesian-based knowledge tracking model has been built to suggest training materials based on the learning pace of an employee (Fig. 1) [29]. The model can predict an employee's performance using data such as cognitive abilities and past training records.…”

Section: Ai In the Construction Sectormentioning

confidence: 99%

“…The model can predict an employee's performance using data such as cognitive abilities and past training records. It can calculate the probability of an employee mastering construction safety concepts and generate personalized suggestions for future training to increase training effectiveness [29]. In another study, an Al-facilitated real-time personalized safety training model was put together.…”

Section: Ai In the Construction Sectormentioning

confidence: 99%

Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects

Tang

2024

J. Eng. Res. Rep.

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Artificial intelligence (AI) has gained much popularity in various sectors and has found applications in multiple areas, including occupational health and safety (OHS) risk management of the high-risk construction, mining, and oil and gas sectors. OHS risk management centers on identifying, assessing and controlling occupational risks systematically to prevent work-related injuries, illnesses and deaths. This review presents the advances in AI applications for OHS risk management in these sectors and synthesizes their barriers for better application prospects. In the construction sector, AI can be employed in building information modeling during the design stage to identify and deal with the hazards of building models. AI can be deployed in construction sites through computer vision, sensor networks, knowledge-based systems, and machine learning to capture real-time site conditions, analyze the videos or pictures captured, and provide feedback to workers for appropriate responses. A similar setup involving the same components is also used for managing the OHS risks of surface or underground mining, particularly for monitoring the environmental conditions, detecting the presence of hazardous gases, and identifying hazards in locations that are remote and difficult to assess. Sensors can be attached to personal protective equipment and watches and the signals transmitted via Bluetooth to permit data collection for analysis and response by AI. In the oil and gas sector, sensors are extensively used to collect process safety data from wells, pipelines, valves, etc. for analytical and predictive Al. Al, especially, machine learning is used to create personalized training for workers based on their learning pace and characteristics. However, the major barriers identified are high cost, lack of support and skilled employees, ethical issues, and the uncertainty of AI.

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“…This training can use technology in the form of virtual reality, which is an adaptive and interactive training medium for construction workers. This virtual reality will later feature simulations or games that will train and increase brain endurance [55].…”

Section: Brain Endurance Trainingmentioning

confidence: 99%

Literature review: A study of cognitive aspects in minimizing human error in construction

Zahra Mutmainah,

Pramudya,

Akbar Rufa’i

2024

SHS Web Conf.

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Human error is a mistake due to human negligence, both consciously and unconsciously, which can cause a system to experience disruption and not run as planned. These errors can occur at the managerial, conceptual, or technical level. One of the activities with a high potential for finding human error is construction activities. Construction activities are a series of activities that include the construction, operation, maintenance, demolition, and construction of a building. Infrastructure development is one of the government’s policy focuses in 2023, with development from four main areas: water resources, roads and bridges, settlements, and housing. These infrastructure projects must have a good quality because they are closely related to user safety and security. Based on data from the Social Security Organizing Agency (BPJS) Employment and statements from the Ministry of PUPR, the industrial sector is an enormous contributor to work accidents with an average percentage of 32% each year. The human factor is one of the forerunners of human error apart from equipment and working environment conditions. Viewed from the perspective of Cognitive System Engineering, human error is caused by the failure of human cognitive processes where the process is related to Human Information Processing (HIP). This paper will describe and briefly discuss the problem of human error and its relation to Human Information Processing (HIP). This paper will examine various literature and summarize efforts that can be applied to improve the information processing process in the hope of minimizing the occurrence of human error in the scope of construction work.

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A Bayesian-based knowledge tracing model for improving safety training outcomes in construction: An adaptive learning framework

Cited by 10 publications

References 38 publications

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects

Literature review: A study of cognitive aspects in minimizing human error in construction

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