Artificial Intelligence and Machine Learning in Higher Education

2021 ◽  
pp. 1-17
Author(s):  
Ibrahim Eren Bisen ◽  
Emin Alp Arslan ◽  
Kamil Yildirim ◽  
Yetkin Yildirim
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Online Learning ◽  
Learning Experiences ◽  
Curriculum Planning ◽  
Virtual Classrooms ◽  
The Future

Artificial intelligence and machine learning have the potential to address many of the problems that have emerged in higher education due the rapid and haphazard transition to online learning brought about by the coronavirus pandemic. These problems include students' struggle to self-regulate their learning, the increase in curriculum planning and administrative workload for teachers, and the loss of personalized interaction between students and teachers. This chapter explores how artificial intelligence can be used to help students and teachers to adapt to the new realities of online learning, and how these technologies could further transform higher education in the future. By providing more personalized, flexible, inclusive, and engaging learning experiences, artificial intelligence has the potential to re-invigorate students and teachers both and to make virtual classrooms more meaningful and productive.

scholarly journals COVID-19 pandemic’s disruption on university teaching and learning and competence cultivation: Student evaluation of online learning experiences in Hong Kong

2021 ◽  
Vol 10 (1) ◽  
pp. 221258682110070
Author(s):  
Ka Ho Mok ◽  
Weiyan Xiong ◽  
Hamzah Nor Bin Aedy Rahman
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Hong Kong ◽  
Social Presence ◽  
Online Teaching ◽  
Teaching And Learning ◽  
Student Evaluation ◽  
Learning Experiences ◽  
University Teaching ◽  
Digital Learning

The COVID-19 pandemic outbreak has forced online teaching and learning to be the primary instruction format in higher education globally. One of the worrying concerns about online learning is whether this method is effective, specifically when compared to face-to-face classes. This descriptive quantitative study investigates how students in higher education institutions in Hong Kong evaluated their online learning experiences during the pandemic, including the factors influencing their digital learning experiences. By analysing the survey responses from 1,227 university students in Hong Kong, this study found that most of the respondents felt dissatisfied with their online learning experiences and effectiveness. Meanwhile, this study confirms that respondents’ household income level and information technology literacy affected their online learning effectiveness. Moreover, this study highlights the significant contributions of the community of inquiry, which places social presence on the promotion of a whole person development that could not be achieved when relying mainly on online learning. Findings encourage university leaders and instructors to search for multiple course delivery modes to nurture students to become caring leaders with the 21st century skills and knowledge set.

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Online Learning and Metacognition

2020 ◽  
pp. 310-331
Author(s):  
Geraldine Torrisi-Steele
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Instructional Design ◽  
Student Success ◽  
Learning Environments ◽  
Learning Experiences ◽  
Great Emphasis ◽  
Education Students ◽  
Online Learning Environments ◽  
Metacognitive Scaffolding

Online learning experiences are becoming the norm for an increasing number of higher education students. Although there are clear advantages to online learning in terms of flexibility and access, many students struggle to succeed, especially in purely online learning environments. To a large extent student success in online learning environments is dependent on students' ability to self-regulate and ‘learn for themselves'- both abilities related to academic metacognition. Unfortunately, even at university, many students do not have well developed metacognition. It is therefore important to consider carefully metacognitive scaffolding in the design of online learning experiences. However, the models of instructional design commonly used in online learning tend not to place great emphasis on the importance of metacognitive scaffolding. The aim of the present chapter is therefore to increase awareness of metacognition, as one of the important considerations in the design of online learning environments that can help to maximize chances of student success. Towards this end, a framework of instructional design that is more sensitive to metacognition is developed.

Online Learning and Metacognition

2016 ◽  
pp. 221-241
Author(s):  
Geraldine Torrisi-Steele
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Instructional Design ◽  
Student Success ◽  
Learning Environments ◽  
Learning Experiences ◽  
Great Emphasis ◽  
Education Students ◽  
Online Learning Environments ◽  
Metacognitive Scaffolding

Online learning experiences are becoming the norm for an increasing number of higher education students. Although there are clear advantages to online learning in terms of flexibility and access, many students struggle to succeed, especially in purely online learning environments. To a large extent student success in online learning environments is dependent on students' ability to self-regulate and ‘learn for themselves'- both abilities related to academic metacognition. Unfortunately, even at university, many students do not have well developed metacognition. It is therefore important to consider carefully metacognitive scaffolding in the design of online learning experiences. However, the models of instructional design commonly used in online learning tend not to place great emphasis on the importance of metacognitive scaffolding. The aim of the present chapter is therefore to increase awareness of metacognition, as one of the important considerations in the design of online learning environments that can help to maximize chances of student success. Towards this end, a framework of instructional design that is more sensitive to metacognition is developed.

scholarly journals Transparency and Fairness in Machine Learning Applications

2018 ◽  
Vol 4 (5) ◽  
pp. 443-463
Author(s):  
Jim Shook ◽  
Robyn Smith ◽  
Alex Antonio
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reading Comprehension ◽  
New Technology ◽  
Human Intervention ◽  
Daily Work ◽  
Comprehension Test ◽  
The Future ◽  
Machine Learning Applications ◽  
Self Driving Cars

Businesses and consumers increasingly use artificial intelligence (“AI”)— and specifically machine learning (“ML”) applications—in their daily work. ML is often used as a tool to help people perform their jobs more efficiently, but increasingly it is becoming a technology that may eventually replace humans in performing certain functions. An AI recently beat humans in a reading comprehension test, and there is an ongoing race to replace human drivers with self-driving cars and trucks. Tomorrow there is the potential for much more—as AI is even learning to build its own AI. As the use of AI technologies continues to expand, and especially as machines begin to act more autonomously with less human intervention, important questions arise about how we can best integrate this new technology into our society, particularly within our legal and compliance frameworks. The questions raised are different from those that we have already addressed with other technologies because AI is different. Most previous technologies functioned as a tool, operated by a person, and for legal purposes we could usually hold that person responsible for actions that resulted from using that tool. For example, an employee who used a computer to send a discriminatory or defamatory email could not have done so without the computer, but the employee would still be held responsible for creating the email. While AI can function as merely a tool, it can also be designed to act after making its own decisions, and in the future, will act even more autonomously. As AI becomes more autonomous, it will be more difficult to determine who—or what—is making decisions and taking actions, and determining the basis and responsibility for those actions. These are the challenges that must be overcome to ensure AI’s integration for legal and compliance purposes.

scholarly journals Automating Cyber Attacks

2020 ◽  
Author(s):  
Ben Buchanan ◽  
John Bansemer ◽  
Dakota Cary ◽  
Jack Lucas ◽  
Micah Musser
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Attacks ◽  
Learning Systems ◽  
Depth Analysis ◽  
The Future

Based on an in-depth analysis of artificial intelligence and machine learning systems, the authors consider the future of applying such systems to cyber attacks, and what strategies attackers are likely or less likely to use. As nuanced, complex, and overhyped as machine learning is, they argue, it remains too important to ignore.

scholarly journals Systemic Changes in Higher Education

in education ◽  
2012 ◽  
Vol 16 (1) ◽  
Author(s):  
George Siemens ◽  
Kathleen Matheos
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Open Access ◽  
Educational Change ◽  
Information Access ◽  
Educational Resources ◽  
Research And Teaching ◽  
Power Shift ◽  
The Future

A power shift is occurring in higher education, driven by two trends: (a) the increased freedom of learners to access, create, and re-create content; and (b) the opportunity for learners to interact with each other outside of a mediating agent. Information access and dialogue, previously under control of the educator, can now be readily fulfilled by learners. When the essential mandate of universities is buffeted by global, social/political, technological, and educational change pressures, questions about the future of universities become prominent. The integrated university faces numerous challenges, including a decoupling of research and teaching functions. Do we still need physical classrooms? Are courses effective when information is fluid across disciplines and subject to continual changes? What value does a university provide society when educational resources and processes are open and transparent?Keywords: higher education; freedom of learners; open access; online learning

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