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Learning Analytics Summer Institute
Spain 2019: Learning Analytics in
Higher Education
Vigo, Spain, June 27-28, 2019
Editors:
Manuel Caeiro-Rodríguez
Universidad de Vigo, España
Ángel Hernández-García
Universidad Politécnica de Madrid, España
Pedro J. Muñoz-Merino
Universidad Carlos III de Madrid, España
ISBN: 978-84-16829-40-8
Learning Analytics Summer Institute Spain 2019:
Learning Analytics in Higher Education
Manuel Caeiro-Rodríguez1 [0000-0002-2784-6060], Ángel Hernández-García2 [0000-0002-6549-
9549]
and Pedro J. Muñoz-Merino3 [0000-0002-2552-4674]
1 Department of Telematics Engineering, Universidade de Vigo, Campus Lagoas-Marcosende,
36310 Vigo, Spain
mcaeiro@det.uvigo.es
2 Departamento de Ingeniería de Organización, Administración de Empresas y Estadística,
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040
Madrid, Spain
angel.hernandez]@upm.es
3 Department of Telematics Engineering, Universidad Carlos III de Madrid, Av. Universidad,
30, 28911 Leganés, Madrid, Spain
pedmume@it.uc3m.es
Preface to the Conference Proceedings
The seventh1 edition of the Learning Analytics Summer Institute Spain, LASI Spain
192 was held in Vigo on June 27th and 28th, 2019. Under the main theme of “Learning
Analytics in Higher Education”, the conference was organized by Universidade de
Vigo, in collaboration with the SNOLA (Spanish Network of Learning Analytics) re-
search network and TELGalicia. LASI Spain 19 is conceived as a platform to catalyze
educators, technologists, researchers, enterprise and policymakers around shaping the
next generation of learning infrastructures to truly serve the needs now facing the edu-
cation sector. LASI Spain 19 is part of the Learning Analytics Summer Institute locals;
LASI worldwide events, sponsored by SoLAR (Society for Learning Analytics Re-
search), are strategic events that bring the right mix of people together for an intensive
‘summer camp’ that serves as an intellectual and social springboard to accelerate the
maturation of learning analytics.
This year’s edition of LASI Spain focused on a specific context of application of
learning analytics: Higher Education. Learning Analytics has been called to improve
1 The previous editions of LASI Spain as official LASI-local event include the following:
• LASI Spain 2013 in Madrid: http://www.emadridnet.org/index.php/es/eventos2/312-
seminario-emadrid-learning-analytics-summer-institue
• LASI Spain 2014 in Madrid: https://canal.uned.es/serial/index/id/1303
• LASI Spain 2015 in Bilbao: https://blogs.deusto.es/lasi2015Bilbao
• LASI Spain 2016 in Bilbao: http://lasi16.snola.es
• LASI Spain 2017 in Madrid: http://lasi17.snola.es
• LASI Spain 2018 in León: http://lasi18.snola.es
2 https://lasi19.snola.es
Copyright © 2019 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
2 LASI Spain 2019: Learning Analytics in Higher Education
learning practice by transforming the ways we develop and carry out learning and teach-
ing activities and processes. In the particular context of Higher Education, the pervasive
integration of digital technology is influencing both teaching and learning practices,
allowing access to new resources, functionality and data. Nowadays, existing online
learning environments are used to communicate with students, distribute educational
resources and perform learning activities. These technologies have already been
adopted and their use is common practice in education. Now it is time to move forward
and to put the focus on increasing “quality”. This is a central idea shared by the scien-
tific contributions and keynotes presented in LASI Spain 19: that learning analytics can
play a key role in this new landscape and contribute to a significant change.
The programme of LASI Spain 19 comprised a wide range of activities that brought
together representatives of academia, practitioners and policymakers, including key-
notes by international experts on learning analytics in Europe, presentation sessions of
scientific studies on learning analytics in Higher Education, discussion panels and
workshops. The different activities gave attendants the opportunity to have a compre-
hensive view of the state of affairs in learning analytics in Higher Education, share
experiences in the design and application of learning analytics techniques and showcase
innovative pieces of research.
The keynotes of LASI Spain 19 provided a thorough overview of learning analytics
in Higher Education and gave attendees insight about new and promising analysis tech-
niques to improve the design and implementation of learning processes in IT-mediated
education. More precisely, the keynotes of LASI Spain 19 were as follows:
• In “Can Learning Analytics Transform Higher Education?”, Mar Pérez-
Sanagustín (Université Paul Sabatier Tolouse III) set out to review the past,
present and future of learning analytics in Higher Education. From the per-
spective of the era of ‘big data’, Dr. Pérez-Sanagustín explained how ‘big
data’ were introduced in Higher Education through the emerging discipline
of learning analytics. Owing to the introduction of information technologies
in Higher Education systems and learning processes, we are living a transi-
tion from a time of data scarcity, with students’ grades being the central
data, to a time of abundance. Digital environments that collect the students’
digital “fingerprints” in different contexts, generate massive datasets that
offer great opportunities for research and to support students’, teachers’ and
managers’ tasks, and only by analyzing and understanding the student’s
learning process it is possible to provide adequate support and improve it.
The presentation emphasized that learning analytics facilitates the provi-
sion of aggregated information to every agent involved in the learning pro-
cess, in order to act at the most appropriate time, define learning strategies
and plan and execute interventions. Dr. Pérez-Sanagustín went through the
different stages of learning analytics —descriptive, diagnostic, predictive
and prescriptive— and highlighted the need of a ‘learning analytics culture’
within Higher Education institutions for an effective change that allows
Universities to benefit from the advantages made possible by learning ana-
lytics. It was argued that such change is, however, context-dependent, and
Copyright © 2019 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
LASI Spain 2019: Learning Analytics in Higher Education 3
requires both bottom-up and top-down approaches to foster diffusion of ef-
fective learning analytics practices. The presentation also discussed differ-
ent challenges faced by the learning analytics community as a whole. Ques-
tions such as ‘In what context and with what objective are Higher Education
institutions incorporating learning analytics into their processes?’, ‘What is
the impact of the use of learning analytics in our institutions for students,
teachers and managers?’ need to be addressed. Finally, the presentation of
examples of application of learning analytics from research projects in Eu-
rope and Latin America invited reflection on the potential of learning ana-
lytics as the catalyst for the transformation of Higher Education.
• In “Process mining in Education: Current state and opportunities”, Manuel
Lama (University of Santiago de Compostela) focused on process mining
techniques in Higher Education. Process mining aims to understand what is
really happening in a process from the data generated over time. In the last
decade these techniques have been successfully applied to several applica-
tion domains, such as industry, public administrations or finance. Nonethe-
less, the use of process mining in education has been relatively low due,
among other reasons, to the need to adapt and to make flexible the educa-
tional processes to the profile and behavior of the students. The presentation
focused on presenting the main opportunities that process mining techniques
offer to facilitate decision making by teachers and managers in Education.
• In “Big data-based technology implmenetation at UNED: Ethical consider-
ations”, José Luis Aznarte (The National Distance Education University,
UNED) brought forward some of the most up-to-date topics in learning an-
alytics: privacy, ethics of data collection, handling and analysis, and limits
and good practices in learning analytics. Dr. Aznarte shared his experience
at UNED and described a roadmap, strategies and an evidence-based frame-
work for the definition and implementation of ethically responsible learning
analytics in Higher Education. Using the ongoing ED 3 project at UNED as
an example, Dr. Aznarte advocated for a participative process involving all
learning agents to design an ethical framework of data use, collection and
curation, analysis, intervention and predictive modelling.
The keynote by Dr. Aznarte served as starting point for discussion of pending issues
and the future of learning analytics in a roundtable under the title “Learning Analytics
in Higher Education: Opportunities, threats, strengths and weaknesses”, sponsored by
the IEEE Spain Section and promoted by the Spanish Chapter of the IEEE Education
Society. The discussion panel included key representatives of European Higher Educa-
tion institutions, bringing together academic research and policymaking: Manuel
Caeiro-Rodríguez (Universidade de Vigo), José Luis Aznarte (The National Distance
Education University, UNED), Óscar Rubiños, (Universidade de Vigo), Pedro Muñoz-
Merino (Universidad Carlos III de Madrid), Ángel Hernández-García (Universidad
Politécnica de Madrid) and Mar Pérez-Sanagustín (Université Paul Sabatier Tolouse
III). Beginning with an overview of ethical principles for the application of learning
analytics in the discussants’ institutions, the talk then shifted to the differentiation be-
tween ethical and legal frameworks and the need for definition of transparent ethical
Copyright © 2019 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
4 LASI Spain 2019: Learning Analytics in Higher Education
frameworks in learning analytics. At this point, the discussants debated about the dif-
ference between educational data (given) and capta (captured or collected), and the
necessity to address this difference and only use data in learning analytics. The discus-
sion then moved to the potential of (but also difficulty in) data integration across the
institutions to perform smart and responsible use of educational data, and the need to
adapt any valid framework to the specific context of the Higher Education institution.
LASI Spain 19 also welcomed the celebration of a workshop directed by Pedro J.
Muñoz-Merino and Mar Pérez-Sanagustín under the theme “LALA Project: Connect-
ing Europe and Latin America for Learning Analytics”. The workshop presented the
results and ongoing research within the LALA project3 and included the presentation
of the LALA framework, which aims to guide the design, implementation and use of
learning analytical tools in Higher Education institutions in Latin America. Participants
had the chance to have a hands-on practice of the application of the LALA framework
to the specific context of their institutions.
Finally, the academic community attending LASI Spain 19 had the opportunity to
describe and discuss recent scholar developments in the field in two sessions that in-
cluded selected research studies from the open call for papers for the conference, three
of which were presented the first day of the conference with the remaining five being
presented on the second day. These proceedings include the 8 selected contributions
after double-blinded peer review. The remainder of this preface summarizes the studies
presented at LASI Spain 2019 in order of presentation, and shows the diversity of ap-
proaches to learning analytics in Higher Education:
“Application of Learning Analytics techniques on blended learning environments
for university students” (Sheila Lucero Sánchez-López, Rebeca P. Díaz-Redondo and
Ana Fernández-Vilas) presents an exploratory analysis of student activity in different
Moodle modules. The data uses log-data of three cohorts of university students in a
programming course, and differentiates between actions related to content or class notes
and actions related to interpersonal activities. The analysis consists on a category-based
classification that differentiates between ‘code’, ‘content’ and ‘course administration’,
and applies content analysis using two corpora —code and content. The authors con-
clude that the analysis of messages can provide insightful feedback to instructors and
help identify topics of interest or those that are not being completely learnt, information
that may be used for remediation practices.
“Using Simva to evaluate serious games and collect game learning analytics data”
(Cristina Alonso-Fernández, Iván José Perez-Colado, Antonio Calvo-Morata, Manuel
Freire-Morán, Ivan Martínez-Ortiz and Baltasar Fernández-Manjón) describes Simva,
a tool that may be employed to validate serious games using pre-post experiments. The
study describes the application of Simva in three different serious games, after present-
ing the architecture of the tool. The study proposes a pragmatical approach to run con-
trolled experiments, orchestrating conditions and data collection instruments embedded
in serious games. The authors argue about the potential of Simva to simplify the vali-
dation of serious games and student assessment, but they also identify key issues when
3 https://www.lalaproject.org
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LASI Spain 2019: Learning Analytics in Higher Education 5
conducting experiments in real settings to validate serious games: ensuring users pri-
vacy, the heterogeneity of data sources and the difficulty in transitioning from pre-post
experiments to Game Learning Analytics.
“Extending a dashboard meta-model to account for users' characteristics and
goals for enhancing personalization” (Andrea Vázquez-Ingelmo, Francisco José
García-Peñalvo, Roberto Therón and Miguel Ángel Conde) presents the extension of a
meta-model for dashboard personalization. The meta-model extends a generic
dashboard to account for users’ characteristics (preferences, disabilities, knowledge
about different domains, visualization literacy and bias, including action, perceptual or
social bias) and goals, which can be broken down into individual and more specific
tasks. The conceptual study discusses the pivotal role of characteristics and goals in
user-personalization of dashboards for learning analytics in order to provide users with
a complete view of the data necessary for decision making and to foster self-regulated
learning and improve academic achievement.
“Predicting student performance over time. A case study for a blended-learning en-
gineering course” (Juan Antonio Martínez, Joaquim Campuzano and Teresa Sancho-
Vinuesa) proposes a comparison of prediction models to test their accuracy as estima-
tors of at-risk students. The study uses data from out-of-school activities of a first-year
engineering course that follows a flipped-classroom methodology. The results show
that the longer the period of analysis, the more accurate the models are, but also suggest
that early periods lack accuracy and would not be optimal for interventions.
“Analyzing Students’ Persistence using an Event-Based Model” (Pedro Manuel
Moreno-Marcos, Pedro J. Muñoz-Merino, Carlos Alario-Hoyos and Carlos Delgado-
Kloos) proposes a way to measure students' persistence when solving specific exer-
cises. The study considers that students show persistence when they do not give up
after failing an exercise. The analysis uses data from different courses and divides
students into two groups: high-persistence and mid-persistence students. The study
finds a positive correlation between this type of persistence and average grades, but
concludes that there is no relationship between persistence and dropout or video
visualizations in the educational scenarios under analysis.
“A Data Value Chain to Support the Processing of Multimodal Evidence in Authen-
tic Learning Scenarios” (Shashi Kant Shankar, Adolfo Ruiz-Calleja, Sergio Serrano-
Iglesias, Alejandro Ortega-Arranz, Paraskevi Topali and Alejandra Martínez-Monés)
presents and examines four different multimodal learning analytics (MMLA) scenarios
under the lens of the data value chain (DVC). The four scenarios provide a wide view
of the complexity and heterogeneity of applying MMLA in different learning environ-
ments. The proposal of the data value chain identifies a total of seven multimodal data
processing activities, divided into three groups: (i) data discovery related to the collec-
tion, annotation, curation, structuring and transformation of heterogeneous datasets
(collect & annotate, prepare, and organize); (ii) data fusion, focused on integration of
different datasets to generate a coherent view of multimodal evidence; and (iii) data
exploitation, which includes analysis-related activities (analysis, visualization and de-
cision-making).
“Predictors and Early Warning Systems in Higher Education - A Systematic Liter-
ature Review” (Martín Liz-Domínguez, Manuel Caeiro-Rodríguez, Martín Llamas-
Copyright © 2019 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
6 LASI Spain 2019: Learning Analytics in Higher Education
Nistal and Fernando Mikic-Fonte) introduced a review of the literature of the use of
learning analytics techniques applied to early-warning systems. Liz-Domínguez et al.
conclude that most of the existing research focuses on predictive algorithms and tools
to detect and analyze at-risk students; e.g. a student failing or dropping out a course.
The study reviews the defining characteristics of existing predictive models considering
input data, prediction goal and key aspects related to their use in practice, and shows
that this is a hot topic in the current learning analytics landscape.
“Predicting early dropout student is a matter of checking completed quizzes: the
case of an online statistics module” (Josep Figueroa-Cañas and Teresa Sancho-Vi-
nuesa) deals with the prediction of student dropout. Predictive learning is one of the
most popular application of learning analytics, while dropout is one of the most studied
problems in learning and instruction in Higher Education. This study proposes easy-to-
use classifiers based on decision trees to detect students at risk of dropout. The predictor
variables include quiz results and forum activity, but only quiz results —particularly,
completion of quizzes along the course period— were significant for the prediction.
Acknowledgements
The authors want to thank the financial support provided by TELGalicia (under project
ED431D 2017/12) and the IEEE Spain section. We also thank the members of the Or-
ganization Committee and Scientific Programme Committee for their dedication and
knowledge, as well as all the authors who submitted their valuable contributions to
LASI Spain 19.
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LASI Spain 2019: Learning Analytics in Higher Education 7
LASI Spain 19 Committees
General Chair
Manuel Caeiro-Rodríguez
Universidade de Vigo, Spain
Programme Committee Chairs
Pedro J. Muñoz-Merino
Universidad Carlos III de Madrid, Spain
Ángel Hernández-García
Universidad Politécnica de Madrid, Spain
Organization Chairs
Martín Llamas Nistal
Universidade de Vigo, Spain
Fernando Mikic-Fonte
Universidade de Vigo, Spain
Martín Liz Domínguez
Universidade de Vigo, Spain
Andrea Vázquez-Ingelmo
Universidad de Salamanca, Spain
Juan Manuel Santos-Gago
Universidade de Vigo, Spain
Scientific Committee
Ainhoa Álvarez
Euskal Herriko Unibertsitatea, Spain
Miguel L. Bote-Lorenzo
Universidad de Valladolid, Spain
Ruth Cobos
Universidad Autónoma de Madrid, Spain
Miguel Á. Conde
Universidad de León, Spain
Juan Cruz-Benito
Universidad de Salamanca, Spain
Davinia Hernández-Leo
Universitat Pompeu Fabra, Spain
Mikel Larrañaga
Euskal Herriko Unibertsitatea, Spain
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8 LASI Spain 2019: Learning Analytics in Higher Education
Alejandra Martínez-Monés
Universidad de Valladolid, Spain
Rafael Pastor-Vargas
Universidad Nacional de Educación a Distancia, Spain
Cristóbal Romero
Universidad de Córdoba, Spain
Salvador Ros
Universidad Nacional de Educación a Distancia, Spain
Teresa Sancho-Vinuesa
Universitat Oberta de Catalunya, Spain
Andrea Vázquez-Ingelmo
Universidad de Salamanca, Spain
Mikel Villamañe
Euskal Herriko Unibertsitatea, Spain
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