Applications of Machine Learning in Improving Learning Environment

Asthana, Pallavi; Hazela, Bramah

doi:10.1007/978-981-13-8759-3_16

Cited by 37 publications

(13 citation statements)

References 13 publications

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“…The strong correlations pointed out that AI and ML can develop skills among students (AI2) [20] as well as provide a collaborative learning environment in the HEI (AI3) [1]. In other words, through skills development and collaborative learning [30], AI and ML can enhance customised learning [2].…”

Section: Discussion and Further Recommendationsmentioning

confidence: 99%

“…Artificial intelligence (AI) and machine learning (ML) advancements help people go beyond classic computers to simulate and surpass human intelligence. The development of such technologies has already significantly altered the educational world, giving students new skills and providing a collaborative learning environment in the HEI with great implications for the near future [1,2]. Data show that the Republic of Serbia operates 18 universities, of which nine are public, and the rest are privately owned [3].…”

Section: Introductionmentioning

confidence: 99%

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Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

Kuleto¹,

Ilić²,

Dumangiu

et al. 2021

Sustainability

205

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The way people travel, organise their time, and acquire information has changed due to information technologies. Artificial intelligence (AI) and machine learning (ML) are mechanisms that evolved from data management and developing processes. Incorporating these mechanisms into business is a trend many different industries, including education, have identified as game-changers. As a result, education platforms and applications are more closely aligned with learners’ needs and knowledge, making the educational process more efficient. Therefore, AI and ML have great potential in e-learning and higher education institutions (HEI). Thus, the article aims to determine its potential and use areas in higher education based on secondary research and document analysis (literature review), content analysis, and primary research (survey). As referent points for this research, multiple academic, scientific, and commercial sources were used to obtain a broader picture of the research subject. Furthermore, the survey was implemented among students in the Republic of Serbia, with 103 respondents to generate data and information on how much knowledge of AI and ML is held by the student population, mainly to understand both opportunities and challenges involved in AI and ML in HEI. The study addresses critical issues, like common knowledge and stance of research bases regarding AI and ML in HEI; best practices regarding usage of AI and ML in HEI; students’ knowledge of AI and ML; and students’ attitudes regarding AI and ML opportunities and challenges in HEI. In statistical considerations, aiming to evaluate if the indicators were considered reflexive and, in this case, belong to the same theoretical dimension, the Correlation Matrix was presented, followed by the Composite Reliability. Finally, the results were evaluated by regression analysis. The results indicated that AI and ML are essential technologies that enhance learning, primarily through students’ skills, collaborative learning in HEI, and an accessible research environment.

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Section: Discussion and Further Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

Kuleto¹,

Ilić²,

Dumangiu

et al. 2021

Sustainability

205

View full text Add to dashboard Cite

show abstract

“…On the other hand, data must be collected, analyzed, and reported, in order to analyze human learning with ML. Based on this data, predictions can then be made as to how students can be supported in the learning process and the creation of customized teaching and learning environments becomes feasible (Asthana & Hazela, 2020). Prior learning experiences can be taken into account as well as current individual aptitudes and needs.…”

Section: Applicationsmentioning

confidence: 99%

Machine Learning for the Educational Sciences

Hilbert¹,

Coors²,

Kraus³

et al. 2021

Preprint

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Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytical approaches of this millennium and has recently become popular in the behavioral and social sciences. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows as larger and more complex datasets become available through massive open online courses (MOOCs) and large scale investigations.The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. Here, we review the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. In this review, we (1) provide an overview of the types of data suitable for ML, (2) give practical advice for the application of ML methods, and (3) show how ML-based tools and applications can be used to enhance the quality of education. Additionally we provide practical R code with exemplary analyses, available at https: //osf.io/ntre9/?view only=d29ae7cf59d34e8293f4c6bbde3e4ab2.

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“…Machine learning has become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on observations. It is Machine learning which predicts the future nature of the education environment by adapting new advanced intelligent technologies [14].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Online Courses Performance Prediction Using Machine Learning Techniques

Dongardive

Pawar

Mishra

2021

Advances in Intelligent Systems and Computing

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The impact of the pandemic has been felt through all sectors of the global society including the field of education. The pandemic is a strong indication of the fact that things will not be the same as they were before. This impact is also seen in the education community. Several institutions across the globe have vouched for a blended learning approach and have adopted VLEs to disembark education. This has given rise to a number of professionally developed as well as amateur types of courses on these platforms. Through this study, the researchers have applied machine learning algorithms to identify the learners with lower engagement and predicting their reasons. A comparison is made between several such algorithms to fine tune the output and understand the interdependency. This study has emphasised on the gender and predictability of course completion on MOOC and there is a significant difference seen in it.

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Applications of Machine Learning in Improving Learning Environment

Cited by 37 publications

References 13 publications

Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

Machine Learning for the Educational Sciences

Online Courses Performance Prediction Using Machine Learning Techniques

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