A Conceptual Framework for Students’ Data-Driven Decision Making Process in Higher Education Institutions

2015 ◽  
Vol 21 (7) ◽  
pp. 2256-2260 ◽  
Author(s):  
Semiu Akanmu ◽  
Zulikha Jamaludin
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Conceptual Framework ◽  
Higher Education Institutions ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

scholarly journals Analysis of Decision Making Process in Moneyball: The Art of Winning an Unfair Game

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 57
Author(s):  
Mochamad Sandy Triady ◽  
Ami Fitri Utami
Keyword(s):  
Decision Making ◽  
Excellent Result ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
General Manager ◽  
Use Of Data ◽  
Bottom Position ◽  
Using Data ◽  
Baseball Team

Billy Beanes’s success in using data-driven decision making in baseball industry is wonderfully written by Michael Lewis in Moneyball. As a general manager in baseball team that were in the bottom position of the league from the financial side to acquire the players, Beane, along with his partner, explored the use of data in choosing the team’s player. They figured out how to determine the worth of every player.The process was not smooth, due to the condition of baseball industry that was not common with using advanced statistic in acquiring   players. Many teams still use the old paradigm that rely on experts’ judgments, intuition, or experience in decision making process. Moneyball approached that using data-driven decision making gave excellent result for Beane’s team. The team won 20 gamessequently in the 2002 season and also spent the lowest cost per win than other teams.This paper attempts to review the principles of Moneyball – The Art of Winning an Unfair Game as a process of decision making and gives what we can learn from the story in order to win the games, the unfair games.

An Examination of Organization Learning and Data Driven Decision Making within Two University Enrollment Management Programs

2021 ◽  
Vol 8 (4) ◽  
pp. 150-168
Author(s):  
Barbara N Martin ◽  
J. D. Gragg
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Organizational Learning ◽  
Best Practices ◽  
Enrollment Management ◽  
Effective Means ◽  
Data Driven ◽  
Organization Learning ◽  
Data Driven Decision Making ◽  
Effective Strategies

This paper investigated the declining number of students in higher education institutions, and the effective strategies universities can use to recruit students who will graduate. From this research, it appeared that data-driven decision-making can be an effective means to create a successful enrollment management office. Through a practice of data-driven decision-making, organizational learning can happen, which will allow an organization to maintain success and build a culture that sustains that success. Three key themes emerged from the research that will help to inform best practices for other colleges and universities to implement ideas from this research. The three themes are: an organization must have good, usable data; an organization should strive to create a culture of teamwork to sustain success, and an organization needs a successful enrollment leader.

Lip Service or Actionable Insights? Linking Student Experiences to Institutional Assessment and Data-Driven Decision Making in Higher Education

2017 ◽  
Vol 88 (6) ◽  
pp. 835-862 ◽  
Author(s):  
Bradley E. Cox ◽  
Robert D. Reason ◽  
Barbara F. Tobolowsky ◽  
Rebecca L. Brower ◽  
Shawna Patterson ◽  
...  
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Data Driven ◽  
Student Experiences ◽  
Data Driven Decision Making ◽  
Institutional Assessment

Decision Making and Problem Solving in Higher Educational Institutions

2016 ◽  
pp. 226-241 ◽  
Author(s):  
Muhamadi Kaweesi ◽  
Farooq Miiro
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Economic Development ◽  
Problem Solving ◽  
Higher Education Institutions ◽  
Educational Institutions ◽  
Decision Making Process ◽  
Complex World ◽  
Decision Making Factors ◽  
To Come

As it is stated that the economy of a country can never be greater than the education of its people, today parents and several organizations are investing a lot of money in education to better the future of their children and also meet the demands of the several generations to come. Through this process, the level of competition among higher education institutions is too high for them to survive and continuously attract parents, citizens and funders to meet demands of both institutional and community challenges. The aim of this paper, therefore, is to explore the concept of decision making, factors that determine the decision making process, the effectiveness of decision making and problem solving in higher education institutions deemed to be largely chaotic and complex. World over, government and states are investing in higher education to transform their communities in terms of knowledge, skills, culture and economic development.

Data-Driven Decision Making in Higher Education

2012 ◽  
Vol 7 (1) ◽  
pp. 78-97 ◽  
Author(s):  
Liz Hollingworth ◽  
Anne M. Sullivan ◽  
Chris Condon ◽  
Monica Bhatt ◽  
W. Christopher Brandt
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Data Driven ◽  
Data Driven Decision Making

scholarly journals Academic Staff Direct Participation in Managing Higher Education Institutions – Pilot Study Report

Management ◽  
2019 ◽  
Vol 23 (2) ◽  
pp. 188-204
Author(s):  
Katarzyna Szelągowska-Rudzka
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Pilot Study ◽  
Questionnaire Survey ◽  
Higher Education Institutions ◽  
Academic Staff ◽  
Survey Method ◽  
Decision Making Process ◽  
The Real ◽  
Direct Participation

Abstract The aim of the study is to present the results of pilot study on academic staff direct participation (ASDP) in managing higher education institutions (HEI) (content, scope, intensity, practicality of impact), in key areas of its activity and its advantages and risks. The research was carried out under questionnaire survey method among academic staff of selected HEIs, in autumn 2018. It was found that ASDP applies to all areas of activity of these HEIs (content), primarily educating students; is full (academics participate in all stages of the decision-making process), has mainly operational scope (related to basic tasks in the position at work), less often strategic; it is perceived participation because the real impact of the respondents in managing HEIs is limited (passive form of participation dominates). The identified main benefits from participation indicate that ASDP can be an effective tool for creating commitment of academic staff in managing HEIs in handling their key areas of activity. The results presented are not representative (pilot study), but interesting. Therefore, the author is planning to carry out in-depth research on ASDP in managing HEI in the future.

scholarly journals Assuring Academic Integrity of Supervisor Appointment in Post-Graduate Program with Data-Driven Decision Making Strategy: A Proposal

2021 ◽  
Vol 5 (1) ◽  
pp. 141-154
Author(s):  
Aziman Abdullah ◽  
Asar A.K
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Academic Integrity ◽  
Graduate Program ◽  
Data Driven ◽  
Research Projects ◽  
Data Driven Decision Making ◽  
Post Graduate ◽  
Research Supervision ◽  
Research Profile

Research supervision is one of the important aspect in academic quality assurance and the sustainbility of the science itself. However, there is lack of attention based on research literature and evidence of good practice on research supervision from the context of academic integrity in higher education. This study aims to develop a data-driven decision making strategy in supervisor selection for post-graduate program based using research projects data. Apart of that, the researchers reviewed the indicator of academic integrity in research supervisory from program standards in masters and doctoral degree by Malaysia Qualification Agency (MQA), international recommendation by UNESCO and Islamic principles according to the roles of the supervisor, administrator and student in the context of research supervisory. This study adopted data analytics and visualization technique using cloud-based collaborative platform as a research method for data acqusition, processing and analyzing the data. The researchers acquired the research projects profile data registered in the institutional database in Universiti Malaysia Pahang from Department of Research and Innovation as a case study. We categorized and mapped the research profile according to Malaysian Research and Development Classification System (MRDCS) code. The combined data was been analyzed and visualized to specific online dashboard to indicate the research experience in fraction of years as a metric. The researchers evaluate the characteristics of the dashboard based on the academic integrity indicators from MQA, UNESCO and Islamic principles as our measures. The result shows that there is a potential usefulness of the proposed strategy in assuring academic integrity for supervisor selection in post-graduate programmes. This novel approach has a potential impact on academic integrity in higher education which can be adopted at larger scale by higher education institution in Malaysia.

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