scholarly journals La universidad inteligente/ The Smart University

2020 ◽  
Author(s):  
Miguel Zapata-Ros
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Instructional Design ◽  
Learning Outcomes ◽  
Institutional Policies ◽  
Pedagogical Model ◽  
Group Situation ◽  
Adaptive Support

As it happens in other fields, also in Higher Education it is detectedthat the use of Artificial Intelligence (AI) can be, and in some cases is being, a powerful medium of inclusion through adaptive support in pedagogical help and in the delivery of resources. And to do it in a sensitive, relevant and pertinent way with the personal and group situation of students' learning, in response to their demand for knowledgeand for the development of their skills.There is a need for a framework of pedagogical model and instructional design that integrates students and guides this help to common and desirable learning outcomes. We also raise the need for an analysis of the conditions necessary for its validation. Finally, we propose, through analysis based on experiences, concrete answers to the insufficiency of institutional policies that contemplate modalities of integration and their repercussions.

Educational Responsibility in the Deepfake Era

2021 ◽  
pp. 1-23
Author(s):  
Rebecca J. Blankenship
Keyword(s):  
Artificial Intelligence ◽  
Instructional Design ◽  
Student Learning ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Web Based ◽  
Student Work ◽  
Integrated Classrooms ◽  
The Right ◽  
Design Challenges

Choosing the right technologies to match student learning outcomes in today's technology-integrated classrooms presents educators with multiple instructional design challenges including selecting appropriate technologies to match desired student learning outcomes. As students continue to have broad access to information from a variety of web-based platforms, teachers are increasingly tasked with ensuring the information used to complete key assignments is authentic and from a verifiable resource. As such, the era of deep fakes in images, audio, videos, and digital texts is more prevalent than ever as numerous programs using artificial intelligence (AI) can significantly alter original content to fundamentally change the intent of original content. A discussion of educational and pedagogic responsibility in the era of deep fakes will serve as the primer for reform of the TPACK construct with recommendations for remediating student work in which deep fake resources were utilized.

scholarly journals The future of internationalization of higher education in challenging global contexts

2020 ◽  
Vol 22 (3) ◽  
pp. 538-545 ◽  
Author(s):  
Hans De Wit
Keyword(s):  
Higher Education ◽  
Learning Outcomes ◽  
Political Context ◽  
Internationalization Of Higher Education ◽  
Institutional Policies ◽  
The Past ◽  
The Future ◽  
Internationalization At Home ◽  
The Impact ◽  
At Home

Internationalization has been over the past three decades one of the key focus points of (inter)national and institutional policies for higher education, with two related components: internationalization abroad, and internationalization at home. The 'abroad' component: mobility of students, faculty and programs, has been more predominant than the 'at home' component: internationalization of the curriculum and learning outcomes, perceived as a neoliberal and western paradigm. What will be the future of internationalization? Do we see a return from competition to cooperation?  What will be the impact of the changing global economic, ecological and political context? These questions will be addressed in a critical analytical way in this paper, taking into account the impact of Covid-19 on the internationalization of higher education.

Artificial intelligence for decision making in higher education

2021 ◽  
pp. 1-2
Author(s):  
Carlos Enrique Montenegro Marin ◽  
Paulo Alonso Gaona Garcia ◽  
Edward Rolando Nuñez Valdez
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Decision Making

scholarly journals Evaluating Age-Friendly Intergenerational Classrooms: Insights From Instructors and Students

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 543-543
Author(s):  
Joann Montepare
Keyword(s):  
Higher Education ◽  
Learning Outcomes ◽  
Interest Group ◽  
Teaching And Learning ◽  
Intergenerational Learning ◽  
Older Learners ◽  
Diverse Classrooms ◽  
Intergenerational Exchange ◽  
Learning Research ◽  
Student Reflections

Abstract Age-friendly University (AFU) campuses are reshaping how we think about teaching and learning in higher education. In particular, intergenerational classrooms are on the rise as shifting age demographics call for institutions to create new opportunities for older learners and encourage intergenerational exchange. Age diverse classrooms have distinctive needs and dynamics that instructors, and students, will need to learn how to navigate. This presentation will describe outcomes of one AFU institution’s attempt to identify the challenges and triumphs of intergenerational classrooms through facilitated instructor and student reflections in different classrooms over the course of several semesters. Recommendations will be offered for enhancing intergenerational exchange in classrooms across disciplines, as well as evaluating attitudes, logistics, and learning outcomes. Part of a symposium sponsored by Intergenerational Learning, Research, and Community Engagement Interest Group.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

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