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

Instructor Performance Prediction Model Using Artificial Intelligence for Higher Education Systems

2021 ◽  
Author(s):  
Shuping Xiao ◽  
A. Shanthini ◽  
Deepa Thilak
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Decision Making ◽  
Prediction Model ◽  
Education Systems ◽  
Higher Education Systems ◽  
Routine Tasks ◽  
Instructor Performance ◽  
The Individual

Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers’ performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program’s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor’s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor’s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods.

scholarly journals Of ‘black boxes’ and algorithmic decision-making in (higher) education – A commentary

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172093399
Author(s):  
Paul Prinsloo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Decision Making ◽  
Strategic Planning ◽  
Real Time ◽  
Higher Education Institutions ◽  
Learning Needs ◽  
Effective Learning ◽  
Student Data ◽  
Black Boxes

Higher education institutions have access to higher volumes and a greater variety and granularity of student data, often in real-time, than ever before. As such, the collection, analysis and use of student data are increasingly crucial in operational and strategic planning, and in delivering appropriate and effective learning experiences to students. Student data – not only in what data is (not) collected, but also how the data is framed and used – has material and discursive effects, both permanent and fleeting. We have to critically engage claims that artificial intelligence and the ever expansive/expanding systems of algorithmic decision-making provide speedy, accessible, revealing, panoramic, prophetic and smart analyses of students' risks, potential and learning needs. We need to pry open the black boxes higher education institutions (and increasingly venture capital and learning management system providers) use to admit, steer, predict and prescribe students’ learning journeys.

Evaluation of the Academic Setting in Spain 1This study was financed by the University of Oviedo.

1995 ◽  
Vol 11 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Juan Fernández ◽  
Miguel A. Mateo ◽  
José Muñiz
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Social Climate ◽  
Three Dimensions ◽  
Academic Setting ◽  
Teaching And Research ◽  
Evaluation Questionnaire ◽  
University Teachers ◽  
The University

The conditions are investigated in which Spanish university teachers carry out their teaching and research functions. 655 teachers from the University of Oviedo took part in this study by completing the Academic Setting Evaluation Questionnaire (ASEQ). Of the three dimensions assessed in the ASEQ, Satisfaction received the lowest ratings, Social Climate was rated higher, and Relations with students was rated the highest. These results are similar to those found in two studies carried out in the academic years 1986/87 and 1989/90. Their relevance for higher education is twofold because these data can be used as a complement of those obtained by means of students' opinions, and the crossing of both types of data can facilitate decision making in order to improve the quality of the work (teaching and research) of the university institutions.

To Investigate the Critical Influencing Factors for Decision Making Process of Students Who Want to Study Abroad

2019 ◽  
Vol 118 (11) ◽  
pp. 619-624
Author(s):  
JueJueMyint Toe ◽  
Ali Abdulbaqi Ameen ◽  
Sui Reng Liana ◽  
Amiya Bhaumik
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Study Abroad ◽  
Education Level ◽  
Decision Making Process ◽  
International Level ◽  
Continuing Higher Education ◽  
Clear Trend ◽  
The People ◽  
Study Overseas

Myanmar is the developing country and its education system is not yet to international level. Hence, most of the young adults, who like to upgrade their knowledge global wide and to gain international recognized higher educational certificates, choose to study overseas rather than continuing higher education after their high education nowadays, that becomes the trend of young people to study overseas since the competency among the people is getting intense based on the education level in every industry. The purpose of this research is to understand that students’ decision making process of selecting university. The study will be conducted to see clear trend of Myanmar students’ decision making of studying in abroad. This research will cover the context of what is Myanmar students’ perception of abroad, how they consider among other countries and explaining those factors which determine Myanmar students’ choice and how they decide to study abroad.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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