Business Intelligence and Learning Analytics as Contributors to a Data Driven Education Industry

2018 ◽  
Author(s):  
Dmitry Apraksin ◽  
Ermina Stylianou ◽  
Nikolay Shcherbinin
Keyword(s):  
Business Intelligence ◽  
Learning Analytics ◽  
Data Driven ◽  
Education Industry

Data-driven Business Intelligence Platform for Smart Retail Stores

2021 ◽  
Author(s):  
D. R. M. R. R. D. R. S. Eheliyagoda ◽  
T.K.G. Liyanage ◽  
D. C. Jayasooriya ◽  
D.P.Y.C.A. Nilmini ◽  
Dasuni Nawinna ◽  
...  
Keyword(s):  
Business Intelligence ◽  
Data Driven ◽  
Retail Stores

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Connecting Teacher-Made Assessments to Course Learning Outcomes Through Learning Analytics

2019 ◽  
pp. 101-125
Author(s):  
Julia Chen ◽  
Dennis Foung
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Structural Equation ◽  
Data Driven ◽  
Equation Modeling ◽  
Curriculum Alignment ◽  
Outcome Based Education ◽  
Data Driven Approach ◽  
The Subject

This chapter explores the possibility of adopting a data-driven approach to connecting teacher-made assessments with course learning outcomes. The authors begin by describing several key concepts, such as outcome-based education, curriculum alignment, and teacher-made assessments. Then, the context of the research site and the subject in question are described and the use of structural equation modeling (SEM) in this curriculum alignment study is explained. After that, the results of these SEM analyses are presented, and the various models derived from the analyses are discussed. In particular, the authors highlight how a data-driven curriculum model can benefit from input by curriculum leaders and how SEM provides insights into course development and enhancement. The chapter concludes with recommendations for curriculum leaders and front-line teachers to improve the quality of teacher-made assessments.

Data-driven problem based learning: enhancing problem based learning with learning analytics

2020 ◽  
Vol 68 (6) ◽  
pp. 3393-3424
Author(s):  
Maria Zotou ◽  
Efthimios Tambouris ◽  
Konstantinos Tarabanis
Keyword(s):  
Learning Analytics ◽  
Problem Based Learning ◽  
Data Driven

scholarly journals Bridging the Gap between Theory and Empirical Research in Evaluative Judgment

2021 ◽  
pp. 1-16
Author(s):  
Hassan Khosravi ◽  
George Gyamfi ◽  
Barbara E. Hanna ◽  
Jason Lodge ◽  
Solmaz Abdi
Keyword(s):  
Higher Education ◽  
Empirical Research ◽  
Learning Analytics ◽  
Learning Activities ◽  
Data Driven ◽  
Educational Tool ◽  
Evaluative Judgment ◽  
Educational Tools ◽  
Education Literature

The value of students developing the capacity to accurately judge the quality of their work and that of others has been widely studied and recognized in higher education literature. To date, much of the research and commentary on evaluative judgment has been theoretical and speculative in nature, focusing on perceived benefits and proposing strategies seen to hold the potential to foster evaluative judgment. The efficacy of the strategies remains largely untested. The rise of educational tools and technologies that generate data on learning activities at an unprecedented scale, alongside insights from the learning sciences and learning analytics communities, provides new opportunities for fostering and supporting empirical research on evaluative judgment. Accordingly, this paper offers a conceptual framework and an instantiation of that framework in the form of an educational tool called RiPPLE for data-driven approaches to investigating the enhancement of evaluative judgment. Two case studies, demonstrating how RiPPLE can foster and support empirical research on evaluative judgment, are presented.

scholarly journals Curricular Design Analysis: A Data-Driven Perspective

2014 ◽  
Vol 1 (3) ◽  
pp. 84-119 ◽  
Author(s):  
Gonzalo Mendez ◽  
Xavier Ochoa ◽  
Katherine Chiluiza ◽  
Bram De Wever
Keyword(s):  
Academic Performance ◽  
Computer Science ◽  
Learning Process ◽  
Learning Analytics ◽  
Data Driven ◽  
Educational Institutions ◽  
Science Program ◽  
Curricular Design ◽  
Micro Level ◽  
Course Difficulty

Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities).  However, another of the key promises of Learning Analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, course impact on the overall academic performance of students, curriculum coherence, dropout paths and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

Sign in / Sign up

Export Citation Format

Share Document