Academic data provision and analytics services at the UOC

11 November, 2022
Photo by Myriam Jessier a Unsplash

How we collect, provide and analyse academic data



Introducing the services

The goal of the academic data provision and advanced analytics services of the Learning Analytics team at the eLearning Innovation Center (eLinC) is to provide support for teaching innovation by fostering the incorporation of data analytics-based evidence into the decision-making processes of academic staff at our university.


The academic data provision service is entrusted with the mission of providing teaching and research staff at the Universitat Oberta de Catalunya (UOC) with academic data. The service provides data on the teaching staff and students’ daily activities that can be used in experiments, developing and enhancing courses, programmes and bachelor’s degrees, or for carrying out final master’s degree projects, doctoral theses and research projects.


At the academic data advanced analytics service, we work with institutional records to provide evidence that can help in decision-making processes, aiming to respond to questions raised by different bodies and management committees at the university. Deftly leveraging research processes, we design and implement procedures for assessing institutional innovation projects, develop analytical perspectives on issues of strategic importance to the university, and help the different groups involved to take informed decisions starting from the creation of new evidence-based knowledge.



The academic data provision service

The data provision service provides academic data on teaching staff and students’ daily activities, such as information on student access to the campus’s different areas and services, including classrooms, teaching materials and forums, enrolment data, submissions of assessment activities and students’ marks, not to mention figures on the activities of teaching staff (tutors, course instructors and coordinating professors).


The data provision service moves into action when a member of the university teaching or research staff submits a data request. This sets in motion an administrative process consisting of the stages of receipt, execution, closure and follow-up.


To provide these data, we possess a technological infrastructure allowing us to store and organize all the records linked with teaching staff and students’ daily activities, which we call the data mart.


The data held in the data mart are a curated version of the original databases (e.g. the campus database and assessment records), which collects and orders only that information regarded as of use for carrying out subsequent analyses for teaching innovation or research purposes. This makes the data mart a living entity that can grow in line with requirements and the incorporation of fresh data, as we detect new analytics opportunities based on the requests we receive for our service.


It should be noted that all the data we hold in the data mart are anonymized. In other words, they are only associated with a student code, meaning we cannot personally identify the student in question.


Currently, loading data into the data mart follows the academic calendar (after enrolment is concluded). One load is performed at the start of a semester and another at the end. Conceptually, the data are organized in the form of services, employing an abstract data model. The data mart includes data on dozens of different services, with each service expressing an important action or fact from the standpoint of academic activity.



The academic data advanced analytics service

The academic data advanced analytics service begins work when it receives an institutional request. Once this analysis request is received, we implement a standardized methodology for structuring the problem and providing the response based on the data we have available.


The process starts with a preliminary analysis stage, in which we identify the questions to answer and develop a plan to establish what data we require and the kind of analysis called for. We then embark upon the execution stage, in which we use different data exploration, analysis and visualization tools, such as R, Python and PowerBI. Lastly, at the final results delivery stage, we present the results and conclusions of the analysis to inform and support the institutional decision-making process.


By way of illustration, we set out below two real-life examples of academic data advanced analyses performed: the impact of COVID-19 on academic activities and assessment of the ESPRIA project.



The impact of COVID-19 on academic activities

During the pandemic, education institutions had to deal with a period of uncertainty as to what its impact upon the university’s learning processes could be.


Our analysis of the impact of COVID-19 set a number of goals:

  • Assess the potential impact of putting onsite final exams online, to establish what kind of tests could be carried out to complete teaching and to guarantee the necessary technical infrastructure.
  • Assess progress in following up continuous assessment, monitoring students’ submissions in all programmes compared with the previous academic year and analysing the impact of the flexibility measures introduced into the programmes.
  • Assess the patterns of connections to the Virtual Campus by students, course instructors and tutors throughout the university to ensure that everyone was carrying out their activities normally.
  • Assess the impact of changes to assessment models made in courses, in terms of both student performance and their continuing on with studies the following semester and the choice of courses.




Assessing the ESPRIA project

The ESPRIA project is a large-scale institutional intervention for improving support for new students to the university.


ESPRIA has the mission of increasing the retention of students and reducing drop-out rates in bachelor’s degree programmes, establishing a range of measures aimed at helping them settle at the university during the first year of their studies.


To this end, this innovation project actively fosters their balancing online studies with the other personal, family or job-related responsibilities typical of distance learning students, implementing enhancements to the tutoring and enrolment process based on the time new students have available for studying. Additionally, it enacts a raft of measures affecting the design of the associated courses, such as a review of the workload, synchronizing learning activity submission calendars to avoid clashes and introducing procedures for making the assessment model more flexible.


Assessment of the ESPRIA project focused on the impact of student participation on the academic results, taking into account the passing of continuous assessment and courses as a whole and continuing on with studies the following semester. The results obtained allowed us to demonstrate the positive effects of the intervention, leading to its measures being adopted in the student induction process of all the university’s bachelor’s degree programmes.




Further information:

Minguillón, J. [Julià], Meneses, J. [Julio], Calvo, A. [Anna], Serres, J. [Jordi] and Aracil, X. [Xavi]. (2021). Still Open During the COVID-19 Lockdown: An Analysis of Online Students’ Engagement with the Virtual Campus. Proceedings of the 2021 Annual Conference: Lessons from a Pandemic for the Future of the Education (pp. 420-429). European Distance and E-Learning Network, Budapest.


Meneses, J. [Julio], Minguillón, J. [Julià], González, M. D. [María Dolores] and Martínez-Aceituno, J. A. [Josep Antoni]. (2019). ESPRIA. Millora de l’acompanyament dels estudiants de primer any. Universitat Oberta de Catalunya.


Xavier, M. [Marlon] and Meneses, J. [Julio]. (2020, 30 July). El projecte ESPRIA de la UOC es presenta al Congrés Anual 2020 de la xarxa EDEN [blog post]. The eLearning Innovation Center blog.



(Visited 55 times, 1 visits today)
About the authors
Data engineer, expert in providing technological support for data capture and collection.
Data demand service process manager.
Analyst, expert in data extraction, cleaning and advanced analysis for carrying out institutional projects.
Data engineer, expert in data extraction, cleaning, analysis and visualization.
Analyst, expert in understanding UOC processes and sources of information and in data analysis and visualization.
Member of the Faculty of Psychology and Education Sciences and head of Learning Analytics at the eLearning Innovation Center, expert in analytical model development and results reporting.