Data Practices: building blocks of data cultures in Higher Education

A study covering data practices in two Higher Education Institutions

Researcher: Juliana E. Raffaghelli

in collaboration with Albert Sangrà, Montse Guitert, Valentina Grion and Marina De Rossi

Six types of Data Practices

The study

We analysed six types of data practices in teaching and learning, connecting them with the participants’ identity (age, sex, disciplinary field, professional experience).

We also considered participants’ motivations to learn and the way they cultivate their professional learning ecologies to learn more about data(practices).

Data practices are the key components of data culturs in HE

1- Educational Data use for Management and Quality

Use of the data of diverse sources (from surveys to digital data extracted from platforms and own research data) to inform decision taking ad institutional development and teaching level, as well as for the collaboration and the construction of academic knowledge to support teaching.

Code: EdManag&Quality

2- Data as Educational Resources

Use of data as learning resources. Adoption of data from own research or public open data to guide and support learning activities.

Code: DataER

5- Students’ Empowerment through Data

Involvement of students in understanding their own participation at the education institution generating learning data ecosystems. Engage in activism towards decision taking at the education institution to adopt their own data in a smart way and to the benefit and care of the several stakeholders.

Code: StudEmpower

3- Data supporting Teaching and Learning

Informing teaching and learning processes through the analogical and digital data which is produced along the workflows. Teachers decision taking might be connected to learning design, monitoring learning, feedback, supporting learners’ self-regulation, or the use of digital data from the systems adopted for teaching.

Code: Teach&Learn

4- Data supporting Assessment

Assessment and Evaluation of learning informed by digital data. Teachers analysis and usage of students’ data (both analogical and digital) to support assessment. Sharing data with the students in order to reflect about the assessment process and supporting assessment for learning. The data usage include analogical and digital data, and moves from grades to data-driven practices within learning management systems.

Code: Assess

6- Supporting Students’ Data Literacy

Teaching to achieve technical abilities as well as aesthetical and political capabilities to promote students participation in a datafied society.

Code: StudDataLit

Case Studies

A tale of two universities.

Take a look at their structural diversities and get surprised by the commonalities in the results about data practices.

You might also find interesting differences in the participants motivations and the learning ecologies they cultivate to engage in data practices.

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Case Study A: A global teaching university

A young, private Spanish University with focus on teaching and professional development. With nearly 80.000 students, it counts over nearly 5000 teaching staff. Within this workforce, nearly 10% is devoted to research activities and half of them as full time researchers. However, the university is internationally recognised by its flexible and international model, serving about 6500 international students from nearly 145 countries.

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Wikimedia Commons

Case Study B: A traditional research university

A traditional and ancient public Italian university, with most undergraduates entering into the university from the schooling system. With nearly 65000 students, it counts almost 4500 teaching staff, from which nearly 35% are tenured and divide their time between research and teaching duties. The university is well-known at regional and national level. International students are about 2500, but in a good number can be considered mostly residents (second generations of immigrants in Italy).

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The Data was collected using a Survey of 21 Questions, which takes about 12-15 minutes to accomplish. The participants were invited via mailing list. All participants signed voluntarily to such mailing lists. The case A was distributed to 4215 participants and got 795 responses; the case B was distributed to 3700 participants and got 370 responses.

The Data visualizations are organised as follows:

  • Participants Professional Profile
    • Gender, Age, Disciplinary Field (Percentages of total)
    • Professional Experience in teaching and research (Percentages)
  • Data Practices in Teaching
    • The six types of practices: Frequency of practice declared through a Likert Scale, transformed into 1-5 score. The scores represented in the radar chart are the mean of all the questions within the specific data practice.
  • Learning about Data Practices
    • Relevance: given by the participant to a certain data practice (percentages of response per relevance assigned in a ranking)
    • Professional Learning Ecologies (LE): the component of a LE (activities, resources and relationships) cultivated to learn about data practices, which can be classified (from 1 to 5) as Digital/Analogical; Isolated/Networked; Restricted/Open. The scores are the mean of the questions for each LE component
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