Data Analytics and Training Effectiveness Evaluation

Smith, Michael de; Dass, Susan; Dillon, Clarence; Long, Rodney A.

doi:10.1201/9780429445491-5

Cited by 2 publications

(3 citation statements)

References 1 publication

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“…In our case, the training potential of our model is high, indicating that our model could constitute the pillar of an early warning system, accentuating our model's dynamics, a finding that is also reported in some studies based on predictive analytics (Izenman, 2013;Jia & Maloney, 2015;Manly et al, 2002;Smith et al, 2018;Xanthopoulos et al, 2013;Ye et al, 2004).…”

Section: Discussionsupporting

confidence: 76%

“…In the context of e-learning lab courses, predictive analytics can identify students at risk by analyzing various parameters such as previous academic performance, level of engagement and interaction with course material (Bradenberger & Dumer, 2007;DeBerd et al, 2004;Jia & Maloney, 2015;Liz-Domínguez et al, 2019). In a case study by Smith et al (2018), predictive analytics was applied to an e-learning business IT lab course to identify low achieving students. By analyzing data from previous cohorts, including grades, assignment submissions, and online activities, a predictive model was developed to predict student performance.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Framework to Identify Non-Achievers in eLearning Business Informatics Lab Courses

Zakopoulos,

Georgakopoulos,

Dalakouras

et al. 2024

JCBAR

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Learning Management Systems store valuable data in their repositories. Analyzing such data could contribute to identifying non-achievers in eLearning courses. This study presents an integrated framework (encompassing stages from a risk management process) to predict non-achievers in eLearning Business Informatics Lab Courses through a discriminant function analysis of student engagement data. In detail, data regarding with student interaction with the learning activities were elicited from the Moodle LMS log files. The paper also presents a specific eLearning Business Informatics Lab Course, designed upon Business Informatics competencies, tailored to a Business Informatics Curriculum for undergraduate Accounting Students. A discriminant function analysis was used to develop a competent prediction model. Linear discriminant functions were generated for achievers and non-achievers respectively. Students were classified into non-achievers or achievers according to the maximum score of the discriminant functions. The high classification percentage of our model indicates that our framework could be used to identify non-achievers in any eLearning Business Informatics Lab course sharing the same structure. A linear discriminant analysis (LDA) was also employed to indicate the training potential of our model. The evaluation metrics of our trained model indicate that our model could potentially be used to develop an alert system.

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Section: Discussionsupporting

confidence: 76%

Section: Literature Reviewmentioning

confidence: 99%

A Framework to Identify Non-Achievers in eLearning Business Informatics Lab Courses

Zakopoulos,

Georgakopoulos,

Dalakouras

et al. 2024

JCBAR

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“…Incorporating cloud storage in learning analytics allows educational institutions to efficiently manage, analyze, and derive insights from the vast amount of data generated within the learning environment. This ultimately supports evidencebased decision-making, personalized learning experiences, and continuous improvement in education [18].…”

Section: Cloud-based Storagesupporting

confidence: 61%

A Mini Review on Technologies and Storage Platforms in Supporting Learning Analytics

Lim,

Tan,

Chan

et al. 2024

Int J Innov Comp

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Learning Analytics (LA) has emerged as a pivotal field in education, harnessing the power of data to enhance the learning experience and improve educational outcomes. This review paper delves into the multifaceted landscape of LA, focusing on the significance and the pivotal role of LA played by technology and storage platforms. An exploration is undertaken into the varied spectrum of platforms and storage solutions that underlie the foundation of LA. Ranging from traditional storage systems to advanced database storage, this review elucidates the distinctive roles and functionalities these technologies offer within the LA ecosystem. Additionally, a comprehensive examination of how data storage profoundly influences the outcomes of LA is conducted, with an emphasis on the pivotal aspects of efficient data management, security, and scalability. In addition, this paper includes insights into future trends and technological advancements in data storage for LA. As the field continues to evolve, understanding the central importance of technology and storage platforms becomes increasingly crucial. Therefore, this review paper serves as a valuable resource for educators, researchers, and technologists seeking to unlock the full potential of LA through the implementation of optimized data storage solutions.

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Data Analytics and Training Effectiveness Evaluation

Cited by 2 publications

References 1 publication

A Framework to Identify Non-Achievers in eLearning Business Informatics Lab Courses

A Framework to Identify Non-Achievers in eLearning Business Informatics Lab Courses

A Mini Review on Technologies and Storage Platforms in Supporting Learning Analytics

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