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https://ceur-ws.org/Vol-2188/Paper1.pdf
Learning Analytics Summer Institute Spain 2018
Miguel Á. Conde1[0000-0001-5881-7775], Camino Fernández-Llamas1[0000-0002-8705-4786], Ángel
M. Guerrero-Higueras1[0000-0001-8277-0700], Francisco J. Rodríguez-Sedano1[0000-0001-5909-
1566]
, Ángel Hernández-García2[0000-0002-6549-9549] and Francisco J. García-Peñalvo3[0000-
0001-9987-5584]
1
Department of Mechanical, Computer Science and Aerospace Engineering, University of
León. Campus de Vegazana, S/N. 24071-León (Spain)
{mcong,camino.fernandez, am.guerrero, fjrods}@unileon.es
2
Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
angel.hernandez@upm.es
3
GRIAL Research Group, Department of Computer Science, Research Institute for Educational
Sciences. University of Salamanca. Paseo de Canalejas 169, 37008. Salamanca, Spain
fgarcia@usal.es
Preface to the Conference Proceedings
The pervasive and extended use Information and Communication Technologies (ICTs)
in Education is already a reality. Students and instructors can choose a wide range of
technologies and tools in order to improve their teaching and learning. A common char-
acteristic of ICT-supported learning is that student and teacher interactions in digital
spaces leave a trail of data that allows collection and identification of learning evidence.
The analysis of such evidence may lead to a better understanding of the teaching-learn-
ing process, facilitating educational decision-making, and to the improvement of edu-
cational processes and student achievement. Learning analytics groups together differ-
ent techniques aiming to extract, collect and analyze educational data, gaining increas-
ing relevance as a tool to support and improve learning in recent years. The different
aspects of learning analytics have been covered so far in many scholarly works and
academic conferences [1-10].
Learning analytics covers a wide range of analysis techniques and methods over dif-
ferent sources of data (content repositories, interactive systems, multimedia) and tech-
nologies (learning management systems, educational and serious games, virtual learn-
ing environments, mobile devices), including data mining and visualization, among
others. The different techniques and methods focus on different key aspects of learning,
such as detection of at-risk students, reduction of attrition rates, adaptive learning, or
support to instructional design [8, 11-16].
Copyright © 2018 for this paper by its authors. Copying permitted for private and academic purposes
Learning Analytics Summer Institute Spain – LASI 2018
The sixth1 edition of the Learning Analytics Summer Institute Spain, LASI Spain
18 was held in León on June 18th and 19th, 2018. The conference was organized by
2
University of León, in collaboration with the SNOLA (Spanish Network of Learning
Analytics) research network of excellence. The LASI worldwide events, sponsored by
SoLAR (Society for Learning Analytics Research), have become in the past years an
awareness-raising, capacity-building, international network of events, where research-
ers and practitioners can meet and share their work, and envision the future of learning
analytics. The programme of LASI Spain 18 comprised a great variety of activities that
gathered representatives of academia and industry, including keynotes by international
experts on learning analytics in Europe, academic paper presentation sessions, discus-
sion panels and workshops.
The different activities gave attendants the opportunity to review the state of learning
analytics in a global context, and also to showcase innovative pieces of research on the
field that contribute new advances and knowledge to this field, mainly–but not only–
from Spanish research groups.
The keynotes of LASI Spain 18 provided a broad but comprehensive overview of
trending topics on learning analytics. More precisely, the keynotes of the conference
were as follows:
• In “Learning Analytics based tools for improving assessment and interven-
tion in academic contexts”, Rebeca Cerezo (University of Oviedo) dis-
cussed about the application of learning analytics tools in different educa-
tional contexts.
• In “Modeling Education with Learning Analytics: Problems and Opportu-
nities”, David Griffiths (University of Bolton) provided an overview of the
different issues we should consider when adopting learning analytics poli-
cies.
In addition to the academic keynotes, LASI Spain 18 has held four different sessions:
1) The Academic Session, which included the presentation of the scientific contribu-
tions included in these proceedings; 2) one session on “Application of Learning Ana-
lytics”; 3) one panel that gathered together different contributions from the Industry;
and 4) two Workshops on Learning Analytics.
The presentation of scientific contributions to the Academic Session includes the
twelve contributions included in these proceedings. The contributions share some com-
mon topics. Three studies present innovative learning analytics applications: “Auto-
matic Group Formation in MOOCs Based on Homogeneous Engagement Criteria”
1
The previous editions of LASI Spain, as 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
2
https://lasi18.snola.es
Learning Analytics Summer Institute Spain – LASI 2018
(Luisa Sanz-Martínez, Alejandra Martínez-Monés, Miguel L. Bote-Lorenzo, and Yan-
nis Dimitriadis) proposes methods to achieve higher effectiveness in group formation
in MOOCs; “Learning Analytics to Assess Students’ Behaviour With Scratch Through
Clickstream” (Daniel Amo, Marc Alier, Francisco J. García-Peñalvo, David Fonseca,
and María J. Casañ) analyses the behavior of students using Scratch for learning; “To-
wards a Methodology and a Toolkit to Analyse Data for Novices in Computer Pro-
gramming” (Tatiana Person, Iván Ruiz-Rube, and Juan M. Dodero) presents a toolkit
for learning analytics; and “Learning Analytics and Recommender Systems Toward Re-
mote Experimentation” (Alexandre L. Gonçalves, Gustavo R. Alves, Lucas M. Carlos,
Juarez B. da Silva, and João B. da M. Alves) shows how to provide students with rec-
ommendations about which remote labs can better suit their needs. The remaining
group of contributions pivot around the idea of using learning analytics to facilitate
decision making, including competence-based learning in “Supporting Competence-
based Learning in Blended Learning Environments” (Mikel Villamañe, Ainhoa Álva-
rez, Mikel Larrañaga, Jessica Caballero, and Oscar Hernández-Rivas) and “Design of
an Extraction, Transform and Load Process for Calculation of Teamwork Indicators in
Moodle” (Ángel Hernández-García, Emiliano Acquila-Natale, Santiago Iglesias-Pra-
das, and Julián Chaparro-Peláez), assessment of student performance in “Learning An-
alytics to Improve the Effectiveness of Continuous Assessment” (Martín Liz-
Domínguez, Martín Llamas-Nistal, Manuel Caeiro-Rodríguez, and Fernando Mikic-
Fonte), “SPEET: Visual Data Analysis of Engineering Students Performance from Ac-
ademic Data” (Manuel Domínguez, Ramón Vilanova, Miguel Á. Prada, José Vicario,
Marian Barbu, Maria J. Varanda, Michal Podpora, Umberto Spagnolini, Paulo Alves,
and Anna Paganoni) and “Model for Evaluating Student Performance through their
Interaction with Version Control Systems” (Angel M. Guerrero-Higueras, Vicente
Matellán-Olivera, Gonzalo Esteban-Costales, Camino Fernández-Llamas, Francisco J.
Rodríguez-Sedano, and Miguel Á. Conde), or data visualization, such as the visualiza-
tion of resource used described in “Visualization Index for Educational Resources by
Learning Analytics” (Noemí DeCastro-García, and Ángel L. Muñoz-Castañeda), or
dashboards with information about employability in “Generation of Customized Dash-
boards Through Software Product Line Paradigms to Analyse University Employment
and Employability Data” (Andrea Vaquez-Ingelmo, Francisco J. García-Peñalvo and
Roberto Therón). The presentation of these contributions took place during the two
days of the conference.
The session on Application of Learning Analytics was also distributed in the two
days of the conference. The session included the participation of several experts in the
field, who shared their expertise and concerns, as well as the main problems they have
faced when applying learning analytics and the benefits derived from the use of such
applications. The researchers involved in this discussion were Manuel Caeiro (Univer-
sity of Vigo), Mikel Villamañe (University of the Basque Country), Alejandra Martínez
(University of Valladolid), Pedro J. Muñoz (Carlos III University of Madrid) and Mi-
guel Ángel Conde (University of León).
The Industry panel of LASI 2018 offered a different perspective on uses and appli-
cations of learning analytics from the business side. Four experts from the industry
shared their expertise about the application of learning analytics in businesses: Nora M.
Learning Analytics Summer Institute Spain – LASI 2018
Villanueva from Gradient presented Applying Learning Analytics to enhance learning
experiences in secondary education and corporate environments”; Camino Antón from
SAP Bussiness One discussed the use of learning analytics in large companies in
“Learning Analytics – Challenges and Oportunities in bussiness”; Agustín Cuenca
from ASPGems showed examples of application of learning analytics in “NeuroK:
what we measure and why”; and Luis A. Oliva from Brambles shared his views and
experience in learning analytics in “Companies learning requirements and learning
analytics”.
Finally, the conference included the celebration of two workshops that put confer-
ence participants in direct contact with learning analytics technologies: ElasticSearch
and Kibana in “Data Analysis with ElasticSearch and Kibana”, by Antonio Robles and
Llanos Tovarra (National Distance University of Madrid), and the ETL and data mining
application RapidMiner in “RapidMiner”, by second one, by Ángel Hernández-García
(Universidad Politécnica de Madrid).
Acknowledgements
The authors thank the Spanish Ministry of Economy, Industry and Competitiveness for
the funding and support of the SNOLA Network of Excellence (TIN2015-71669-
REDT) to LASI Spain 18.
The authors also thank the members of the Programme Committee for their dedica-
tion and knowledge, as well as all the authors who submitted their valuable contribu-
tions to LASI Spain 18.
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