The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience.
As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student’ s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely.
Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.
1. Introduction: Big Data Analytics in a Learning Environment
I. Data Analytics to Improve the Learning Process
2. Improved Student Feedback with Process and Data Analytics
Johannes De Smedt, Seppe K.L.M. Vanden Broucke, Jan Vanthienen, and Kristof De Witte
3. Toward Data for Development: A Model on Learning Communities as a Platform for Growing Data Use
4. The Impact of Fraudulent Behavior on the Usefulness of Learning Analytics Applications: The Case of Question and Answer Sharing with Medium-Stakes Online Quizzing in Higher Education
Silvester Draaijer and Chris Van Klaveren
II. Data Analytics to Measure Performance
5. Disentangling Faculty Efficiency from Students’ Effort
Cristian Barra, Sergio Destefanis, Vania Sena, and Roberto Zotti
6. Using Data Analytics to Benchmark Schools: The Case of Portugal
Maria C. Andrade E Silva and Ana S. Camanho
7. The Use of Educational Data Mining Procedures to Assess Students’ Performance in a Bayesian Framework
Kristof De Witte, Grazia Graziosi, and Joris Hindryckx
8. Using Statistical Analytics to Study School Performance through Administrative Datasets
Tommaso Agasisti, Francesca Ieva, Chiara Masci, Anna Maria Paganoni, and Mara Soncin
III. Policy Relevance and the Challenges Ahead
9. The Governance of Big Data in Higher Education
Kurt De Wit and Bruno Broucker
10. Evidence-Based Education and Its Implications for Research and Data Analytics with an Application to the Overeducation Literature
Wim Groot and Henriette Maassen Van Den Brink