=Paper=
{{Paper
|id=Vol-2704/invited1
|storemode=property
|title=An Overview of the LALA project (invited paper)
|pdfUrl=https://ceur-ws.org/Vol-2704/invited1.pdf
|volume=Vol-2704
|authors=Pedro J. Muñoz-Merino,Carlos Delgado Kloos,Yi-Shan Tsai,Dragan Gasevic,Katrien Verbert,Mar Pérez-Sanagustín,Isabel Hilliger,Miguel Angel Zúñiga-Prieto,Margarita Ortiz-Rojas,Eliana Scheihing
|dblpUrl=https://dblp.org/rec/conf/ectel/Munoz-MerinoKTG20
}}
==An Overview of the LALA project (invited paper)==
https://ceur-ws.org/Vol-2704/invited1.pdf
An Overview of the LALA project
Pedro J. Muñoz-Merino1, Carlos Delgado Kloos1, Yi-Shan Tsai2, Dragan Gasevic3,
Katrien Verbert4, Mar Pérez-Sanagustín5,9, Isabel Hilliger5, Miguel Ángel Zúñiga-
Prieto6, Margarita Ortiz-Rojas7, Eliana Scheihing8
1 Department of Telematics Engineering, Universidad Carlos III de Madrid, Av. Universidad,
30, 28911 Leganés, Madrid, Spain
pedmume@it.uc3m.es, cdk@it.uc3m.es
2 School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
Yi-Shan.Tsai@ed.ac.uk
3 Department of Data Science and Artificial Intelligence, Faculty of Information Technology,
Monash University, 29 Ancora Imparo Way , Clayton, VIC 3800, Australia
dragan.gasevic@monash.edu
4 Department of Computer Science, KU Leuven, Celestijnenlaan 200A, B-3001 Leuven,
Belgium
katrien.verbert@kuleuven.be
5 Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago, Chili
{ihillige,mar.perez}@uc.cl
6 Department Computer Science, Universidad de Cuenca, Av. 12 de Abril y Av. Loja, 010203
Cuenca, Ecuador
miguel.zunigap@ucuenca.edu.ec
7 Information Technology Center, Escuela Superior Politécnica del Litoral, ESPOL, Km 30.5
Vía Perimetral, 09-01-5863, Guayaquil, Ecuador.
margarita.ortiz@cti.espol.edu.ec
8 Universidad Austral de Chile, Valdivia, Chile
escheihi@inf.uach.cl
9 Institute de Recherce en Informatique de Toulouse, Université Paul Sabatier Toulouse III, 118
Route de Narbonne, F-31062 Toulouse
mar.perez-sanagustin@irit.fr
Abstract. The LALA project (“Building Capacity to Use Learning Analytics to
Improve Higher Education in Latin America”) is a project that aims at building
capacity about the use of data in education for improving education in Latin
America. This article presents a general overview of the LALA project including
the LALA framework (as a set of guidelines, recommendations and patterns for
enabling adoption of learning analytics), the adaptation of learning analytics tools
(mainly three different tools used in Europe) and the pilots with learning analytics
experiences. The results of this project could serve as an example for other
institutions in the Latin American region or other under-represented regions to
adopt Learning Analytics as part of their processes.
Keywords: learning analytics, Latin America, adoption
Copyright 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2
1 Introduction
The LALA project (http://lalaproject.org) is an initiative funded by the European Union
with the purpose of transferring knowledge from Europe to Latin America for the
application of learning analytics in a practical manner in Higher Education Institutions
in Latin America so as to improve their education by means of data-driven approaches.
Since the project started in October 2018, the LALA project has already produced
different outcomes, some of the most important are:
• The LALA framework. A set of guidelines to support Higher Education
Institutions in Latin America in the adoption of learning analytics.
• The adapted or adopted learning analytics tools in Latin America, which
includes a counselling system, an early dropout prediction system, a system
for supporting self-regulation in online and blended learning environments,
and the on-task tool.
• Pilots with Learning Analytic tools. At present, different pilots are running
in Latin America institutions and others have already been done in order to
test and validate the learning analytic tools designed during the project.
• Dissemination actions. As part of the dissemination activities, we have
developed different training activities, workshops, publications,
presentations and the annual LALA conference. In addition, the LALA
community has been set up with many institutions interested in the project.
Moreover, there are twitter and Facebook accounts, a distribution list and a
bulletin. Institutions in Latin America can join the LALA project following
the instructions at https://www.lalaproject.org/be-part-of-lala/ and
according to the statutes at https://www.lalaproject.org/statutes/
2 LALA framework
The LALA framework [1] was conceived as a set of guidelines and instruments to
facilitate and promote the design, implementation and adoption of learning analytics
tools in Higher Education Institutions in Latin America. The LALA framework is
inspired by the SHEILA framework [2] which was established in the SHEILA Erasmus
+ project. The LALA framework is composed of four dimensions that could be applied
independently or in an integrated manner.
• Institutional. This dimension aims to promote the participation and
commitment of key actors - or stakeholders - (students, academic staff, and
leaders) in the adoption of LA tools, anticipating political and strategic
aspects. Specifically, it proposes activities to understand what the current
state and the desired state of the institution is in relation to the incorporation
of LA tools, as well as the policies and strategies established for the
management of educational data. It proposes a series of instruments and
phases for answering the following question: What are the institutional
considerations to adopt a a learning analytics tool or process? An important
part of the result of applying the instruments and methods proposed in this
3
dimension is a list of needs of the main actors of an institution in the form of
strategic guidelines to reach a desired state in terms of adoption of LA. The
identification of needs for the different Latin American partners of the project
can be seen in [3].
• Technical. This dimension proposes a set of guidelines and recommendations
for supporting the design of learning analytic tools. These guidelines aim at
providing a basis for ensuring the adequate collection and administration of
educational metadata, as well as the management of the adequate
infrastructure and technical capacities to support them. This dimension will
answer the question: What steps do I need to follow to work on the deisgn,
implementation and/or adaptation and evaluation of a learning analytics tool
athat is asapted to the needs of the main actors in the institution?
• Ethical. This dimension aims to promote the adoption of ethical and privacy
considerations in the design implementation and adoption of learning
analytics tools. Specifically, it proposes a collection of papers and instruments
on ethical considerations for learning analytics that interested institutions
could take as a reference. The application of this dimension will answer the
following question: What are the ethical and privacy considerations that the
institution should take into account in order to adopt or implement a learning
analytics tool?
• Community. This dimension provides the guidelines for the creation of a
Learning Analytcis community for promoting the exchange and experiences
between higher education insttitutions in Latin America. The aim is to favour
collaboration among institutions without compromising internal information.
Applying this dimension institutions could answer the following question:
What steps should we follow to to become an active member of the Learning
Analytics community in Latin America (LALA community)?
3 Adoption of LALA tools
The LALA project has adapted or adopted mainly four tools that have been
developed (at least partially) in Europe: a counselling tool, an early dropout prediction
tool, a system for supporting self-regulation in online and blended learning
environments and the on-task tool. Detailed information about the adaptation and
adoption of these learning analytics tools can be found at [4].
A counselling tool developed in KU Leuven has been adapted to Latin American
partners. Universidad Austral de Chile, Escuela Politecnica Superior del Litoral and
Universidad de Cuenca adapted the counselling tool providing a set of dashboards at
the academic level such as the grades of students in different courses, their present call
in each course, etc. An example of this tool can be seen in [5] for Universidad Austral
de Chile. Pontificia Universidad Católica de Chile provided also a tool at the course
level named NoteMyProgress, which enabled students and teachers to self-monitor
their learning process in a course including information such as self-regulated learning
strategies.
4
An early dropout prediction tool developed in Universidad Carlos III de Madrid has
been adapted to Latin American partners. Universidad Austral de Chile, Escuela
Politecnica Superior del Litoral and Universidad de Cuenca used an early dropout
prediction tool for predicting dropout at the degree levels, while Pontificia Universidad
Católica de Chile adapted it for courses in a MOOC [6].
The Pontificia Universidad Católica de chile developed the NoteMyProgress tool, a
tool for supporting students self-regulation strategies in both online and blended
learning environment. It is composed by a pluggin currently available for the MOOC
platform Courser and the Moodle Learning Management system that provides
interactive visualisations about students activities. Both plugins are available here:
https://git.cti.espol.edu.ec/LALA-Project/PUC .
The on-task tool, which is contributed in part by University of Edinburgh, is being
adopted in Escuela Politecnica Superior del Litoral, Universidad de Cuenca, and
Universidade Federal Rural de Pernambuco (UFRPE)(an associate partner in Brazil)
for providing personalization and feedback.
4 Pilot experiences
All four regular Latin American partners (Pontificia Universidad Católica de Chile,
Universidad Austral de Chile, Universidad de Cuenca and Escuela Politécnica Superior
del Litoral) have already run some pilot experiences with learning analytics tools
developed during the project. Other pilot experiences are running or will run during this
year. In addition, other associate partners from Latin America have or will run pilots
with the tools of the project.
Regarding the pilot experiences, depending on the context, the target groups are
different. In Pontificia Universidad Católica de Chile, a total of 638 students from a
MOOC in Coursera downloaded the tool, although the analysis of their behavior with
the tool was conducted with 236 that answered all questionnaires related with the pilot.
In Escuela Superior Politécnica del Litoral (ESPOL), more than 315 academic
counselors (teachers) used the new LA visualizations in their current counselling
system. Thus, involving approximately 7700 students. However, the analysis was
carried out with only 152 teachers who answered all the questionnaires related with the
pilot. In Universidad de Cuenca, where LA has no been previously applied, 31
academic counselors (teachers) used the counseling dashboard LA. Thus, involving
approximately 522 students whose academic performance was analyzed by the
academic counselors. Additionally, 178 of those students had an academic counseling
session to analyze their academic performance. While Universidad Austral de Chile
the main stakeholders using the tools are the managers. In any case, students are the
target group to improve their educational process. The first version of the project
deliverable describing all pilots will be publicly available soon in the webpage of the
project.
5
Acknowledgements
Work partially funded by the LALA project (grant no. 586120-EPP-1-2017-1-ES-
EPPKA2-CBHE-JP). The LALA project has been funded with support from the
European Commission. his publication reflects the views only of the authors, and the
European Commission and the EACEA agency cannot be held responsible for any use
which may be made of the information contained therein.
The authors want to thank the work of all team members by 7 institutions of the
LALA project.
References
1. Sanagustín, M. P., Hilliger, I., Maldonado, J., Pérez, R., Ramírez, L., Muñoz-Merino, P. J.,
Tsai, Y.,Ortiz-Rojas, M., Broos, T.,Pesantez, P., Sheihing, E., Whitelock-Wainright, Al,
(2018), LALA Framework version 2.0, https://www.lalaproject.org/wp-
content/uploads/2019/01/LALS-FW-2.0.pdf
2. Tsai, Y-S, Gasevic, D, Whitelock-Wainwright, A, Muñoz-Merino, P.J., Moreno-Marcos,
P.M., Rubio Fernández, A., Delgado Kloos, C., Scheffel, M., Jivet, I., Drachsler, H.,
Tammets, K., Calleja, A.R., Kollom, K.. 2018, SHEILA: Supporting Higher Education to
Intergrate Learning Analytics Research Report. The University of Edinburgh, Edinburgh,
UK, https://www.research.ed.ac.uk/portal/files/77883596/SHEILA_research_report.pdf
3. Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y. S., Muñoz-
Merino, P. J., Broos, T., Whitelock-Wainwright, A. & Pérez-Sanagustín, M. (2020).
Identifying needs for learning analytics adoption in Latin American universities: A mixed-
methods approach. The Internet and Higher Education, 45, 100726.
4. Ortiz-Rojas, M., Jimenez, A., Maya, R., Muñoz-Merino, P. J., Moreno-Marcos, P. M.,
Marín, J. I., Delgado Kloos, C.,Zuñiga Prieto, M.A., Ulloa, M., Pérez, R., Pérez-Sanagustín,
M., Henriquez, V., Guerra, J., Ferreira, R., Broos, T., & Millecamp, M., WPD3. O. 4 (2019),
“Design for Learning Analytics tools for LALA”, https://www.lalaproject.org/wp-
content/uploads/2019/04/Deliverable-WP3_English_April12.pdf
5. Chevreux, H., Henríquez, V., Guerra, J., & Scheihing, E. (2019). Agile Development of
Learning Analytics Tools in a Rigid Environment like a University: Benefits, Challenges
and Strategies. In European Conference on Technology Enhanced Learning (pp. 705-708).
Springer, Cham.
6. Moreno-Marcos, P. M., Muñoz-Merino, P. J., Maldonado-Mahauad, J., Pérez-Sanagustín,
M., Alario-Hoyos, C., & Delgado Kloos, C. (2020). Temporal analysis for dropout
prediction using self-regulated learning strategies in self-paced MOOCs. Computers &
Education, 145, 103728.