Futuristic Trends and Innovations for Examining the Performance of Course Learning Outcomes Using the Rasch Analytical Model

Nasralla, Moustafa M.; Al-Shattarat, Basiem; Almakhles, Dhafer; Abdelhadi, Abdelhakim; Abowardah, Eman Sabry

doi:10.3390/electronics10060727

Cited by 11 publications

(4 citation statements)

References 15 publications

(16 reference statements)

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“…Realizing the importance of evolving educational and pedagogical conditions, universities and colleges are aiming to include extensive upgrades and to develop exciting technological services [ 1 , 2 , 3 , 4 ]. The concept of Smart Campuses in Smart Cities seeks to integrate beneficial technological interventions such as localization and navigation, asset monitoring, smart attendance, and smart parking systems into the fabric of universities in order to improve learning, research, and managerial and administrative efficiency [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

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Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

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Section: Introductionmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

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“…Therefore, to obtain a good data analysis of survey questionnaire, a statistical software for Rasch model analysis named Winstep software V3.72.3 was used. Literature studies showed that the Rasch model has been used in other research fields such as education [32]- [36], food and beverages [23], [37], medical [24], and sport [38] and it was proven capable to measure any subjective assessment. At the same time, Rasch is also capable to convert all ordinal data into ratio data.…”

Section: Discussionmentioning

confidence: 99%

Employee Satisfaction Towards Integration of Quality Management System: A Case Study of Malaysian Automotive Manufacturer

Khalid¹,

Saad²,

Halim³

et al. 2022

JMechE

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This paper presents a case study to analyse the workers’ satisfaction level on the implementation of an integrated quality system between the IATF 16949 and Toyota Production System focusing on internal audit activity in one automotive company in Malaysia. The main goal was to validate and investigate the issues and challenges those employees in the organisation faced before and after the systems were integrated. Rasch Measurement Model was used to analyse the data collected. Initially, pilot data were conducted, and the data were used to construct the survey questionnaire with 3 constructs and 47 items. A total of 30 respondents from various departments were assessed with the demographic traits of position in the organization and years of service. From the pre-implementation survey, most of the respondents agreed that too many documents were involved throughout both audits and lack of training for auditee on both systems was the main cause to the ineffective implementation of both systems, particularly at mass production process. Both items were measured below the person mean value in variable map (pre-implementation). After the integrated system, the post-survey analysis proved that too many documents and lack of training were above person mean value in variable map (post-implementation). The results revealed that most of the respondents agreed the integration of both systems has given an effective result and good impact to the organization’s performance.

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“…The integration of smart technologies and the Internet of Things (IoT) into educational institutions represents a significant step toward meeting the demands of the digital world and preparing students for the future. By embracing these technologies, institutions can enhance learning outcomes, improve resource management, and provide a more efficient and effective educational experience [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Transforming Educational Institutions: Harnessing the Power of Internet of Things, Cloud, and Fog Computing

Badshah,

Rehman,

Farman

et al. 2023

Future Internet

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The Internet of Things (IoT), cloud, and fog computing are now a reality and have become the vision of the smart world. Self-directed learning approaches, their tools, and smart spaces are transforming traditional institutions into smart institutions. This transition has a positive impact on learner engagement, motivation, attendance, and advanced learning outcomes. In developing countries, there are many barriers to quality education, such as inadequate implementation of standard operating procedures, lack of involvement from learners and parents, and lack of transparent performance measurement for both institutions and students. These issues need to be addressed to ensure further growth and improvement. This study explored the use of smart technologies (IoT, fog, and cloud computing) to address challenges in student learning and administrative tasks. A novel framework (a five-element smart institution framework) is proposed to connect administrators, teachers, parents, and students using smart technologies to improve attendance, pedagogy, and evaluation. The results showed significant increases in student attendance and homework progress, along with improvements in annual results, student discipline, and teacher/parent engagement.

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Futuristic Trends and Innovations for Examining the Performance of Course Learning Outcomes Using the Rasch Analytical Model

Cited by 11 publications

References 15 publications

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Employee Satisfaction Towards Integration of Quality Management System: A Case Study of Malaysian Automotive Manufacturer

Transforming Educational Institutions: Harnessing the Power of Internet of Things, Cloud, and Fog Computing

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