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.

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.

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 The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

Rapid, centralised decision-making in a higher education emergency

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brigid Freeman ◽  
Peodair Leihy ◽  
Ian Teo ◽  
Dong Kwang Kim
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Policy Making ◽  
Higher Education Institutions ◽  
Institutional Policy ◽  
Policy Governance ◽  
Content Type ◽  
On Line ◽  
Policy Problems ◽  
Policy Shifts

Purpose This study aims to explain the primacy that rapid, centralised decision-making gained in higher education institutions during the COVID-19 pandemic, with a particular focus on Australian universities. Design/methodology/approach This paper draws on discussions regarding policy problems of an international, purpose-convened on-line policy network involving over 100 registrations from multiple countries. It analyses emerging institutional policy governance texts and documents shared between network participants, applies policy science literature regarding traditional institutional policy-making routines and rapid decision-making, and references media reportage from 2020. The paper traces how higher education institutions rapidly adjusted to pandemic conditions and largely on-line operations. Findings The study finds that higher education institutions responded to the COVID-19 crisis by operationalising emergency management plans and introducing rapid, centralised decision-making to transition to remote modes of operation, learning and research under state-imposed emergency conditions. It highlights the need to ensure robust governance models recognising the ascendance of emergency decision-making and small-p policies in such circumstances, notwithstanding longstanding traditions of extended collegial policy-making routines for big-P (institutional) Policy. The pandemic highlighted practice and policy problems subject to rapid reform and forced institutions to clarify the relationship between emergency planning and decision-making, quality and institutional policy. Practical implications In covering a range of institutional responses, the study advances the possibility of institutions planning better for unexpected, punctuated policy shifts during an emergency through the incorporation of rapid decision-making in traditionally collegial environments. At the same time, the paper cautions against the normalisation of such processes. The study also highlights key practices and policies that require urgent reconsideration in an emergency. The study is designed as a self-contained and freestanding narrative to inform responses to future emergencies by roundly addressing the particularities of the 2020 phase of the COVID-19 pandemic as it affected higher education. Originality/value There is only limited research on policy-making in higher education institutions. This research offers an original contribution on institutional policy-making during a prolonged emergency that deeply changed higher education institution’s governance, operations and outlook. Particularly significant is the synthesis of experiences from a wide range of sector personnel, documenting punctuated policy shifts in policy governance (meta-policy), institutional policy-making routines and quality assurance actions under great pressure. This paper is substantially developed from a paper given at the Association for Tertiary Education Management Institutional Policy Seminar, 26th October 2020.

Decision-making system for higher education university selection: comparison of priorities pre- and post-COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Krishnadas Nanath ◽  
Ali Sajjad ◽  
Supriya Kaitheri
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Higher Education Institutions ◽  
Mode Of Delivery ◽  
Primary Data ◽  
Final Decision ◽  
Content Type ◽  
University Selection ◽  
The University ◽  
The Impact

PurposeUniversity selection in higher education is a complex task for aspirants from a decision-making perspective. This study first aims to understand the essential parameters that affect potential students' choice of higher education institutions. It then aims to explore how these parameters or priorities have changed given the impact of the COVID-19 pandemic. Learning about the differences in priorities for university selection pre- and post-COVID-19 pandemic might help higher education institutions focus on relevant parameters in the post-pandemic era.Design/methodology/approachThis study uses a mixed-method approach, with primary and secondary data (university parameters from the website and LinkedIn Insights). We developed a university selector system by scraping LinkedIn education data of various universities and their alumni records. The final decision-making tool was hosted on the web to collect potential students' responses (primary data). Response data were analyzed via a multicriteria decision-making (MCDM) model. Portal-based data collection was conducted twice to understand the differences in university selection priorities pre- and post-COVID-19 pandemic. A one-way MANOVA was performed to find the differences in priorities related to the university decision-making process pre- and post-COVID-19.FindingsThis study considered eight parameters of the university selection process. MANOVA demonstrated a significant change in decision-making priorities of potential students between the pre- and post-COVID-19 phases. Four out of eight parameters showed significant differences in ranking and priority. Respondents made significant changes in their selection criteria on four parameters: cost (went high), ranking (went low), presence of e-learning mode (went high) and student life (went low).Originality/valueThe current COVID-19 pandemic poses many uncertainties for educational institutions in terms of mode of delivery, student experience, campus life and others. The study sheds light on the differences in priorities resulting from the pandemic. It attempts to show how social priorities change over time and influence the choices students make.

Sign in / Sign up

Export Citation Format

Share Document