Objectives
This workshop aims at analyzing with an applied and practical perspective different issues and challenges related to learning analytics. Learning analytics can be broadly defined as the methods and techniques to reach conclusions about the learning process in order to improve it. First of all, there is a need of collecting data since you cannot improve what you do not monitor. Nowadays, there are proper technical tools to collect and retrieve all the interactions of the users with the different learning resources and activities. The retrieve of these data enables its analysis and the application of learning analytics for improving the learning process. Scenarios such as MOOCs (Massive Open Online Courses) provides a good case study as it makes even more necessary the application of learning analytics techniques because there is a need of tools for teachers to easily monitor the learning process and students for receiving automatic feedback and awareness. The correct practical application of learning analytics can bring a lot of advantages. Many issues should be considered for a successful application of learning analytics in courses and in the organizations as a whole.
Workshop Format
This is a half-day Workshop that will held on 18 September 2015 within the 10th European Conference on Technology-Enhanced Learning. The workshop will be composed of:
- Oral presentation of selected papers. The Programme Committee members will select the contributions to be presented. Full papers will have an allocated time of 20 min. for presentation, while short papers will have 12 min. for presentation. At least 1/3 of the time should include questions from other workshop participants.
- Hands on Tutorial on exploratory data analysis of educational data sets using Python and Spark. Participants will learn some current techniques in exploratory data analysis of educational data sets (small and large) using the iPython notebook and Apache Spark environment. Knowledge of Python is not a pre-requisite.
- Interactive discussion about current challenges for the practical application of learning analytics.
- Interactive discussion about current software tools for learning analytics.
Call for Papers
- Full papers. 8-10 pages.
- Short papers. 2-4 pages.
Submissions must follow LNCS format. The manuscripts will be evaluated by at least two members of the Program Committee. All accepted papers (full and short) will be invited to make an oral presentation in the workshop. Accepted papers will be published in CEUR workshop proceedings (http://ceur-ws.org/) (pending of confirmation). The manuscripts must be submitted through easy chair: https://easychair.org/conferences/?conf=waplaectel2015
Workshop Topics
The topics of the workshop include but are not limited to the following:
- Experiences of application of learning analytics
- Combination of existing software tools of learning analytics
- Deployment of learning analytics in an institution
- Evaluation results based on learning analytics
- Application of adaptive learning based on learning analytics
- Actuator decisions based on learning analytics
- Design and implementation of useful detectors to infer intelligent infor-
- mation from low level data, e.g. emotion detection, effectiveness, user
- profiles or user behaviors
- Application of personalization and adaptive learning based on learning analytics
- Application of recommenders
- Ethical issues and data protection
- Useful conclusions for the learning process based on techniques such as
- Visual analytics
- Predictive modelling
- Clustering
- Relational mining
Important Dates
- 30 April: Call for Workshop Contributions open
- 20 June: Deadline for paper submission
- 8 July: Notification of acceptance
- 20 July: Camera ready versions of papers due
- Workshop: 18 September 2015
Organizers
Workshop Chairs
- Carlos Delgado Kloos, Universidad Carlos III de Madrid, Spain
- Alfred Essa, McGraw-Hill Education, USA
- Pedro J. Muñoz-Merino, Universidad Carlos III de Madrid, Spain
Programme Committee
- Ryan Baker, University of Columbia, USA
- Simon Buckingham-Shum, University of Technology Sydney, Australia
- Daniel Burgos, Universidad Internacional de la Rioja, Spain
- Miguel Ángel Conde, Universidad de León, Spain
- Carlos Delgado Kloos,Universidad Carlos III de Madrid, Spain
- Erik Duval, Katholieke Universiteit Leuven, Belgium
- Alfred Essa, McGraw-Hill Education, USA
- Rebecca Fergusson, Open University, UK
- Baltasar Fernández-Manjón, Universidad Complutense de Madrid, Spain
- Dragan Gasevic, University of Edinburgh, UK
- Ángel Hernández-García, Universidad Politécnica de Madrid, Spain
- Sharon Hsiao, University of Columbia, USA
- Seiji Isotain, University of Sao Paulo, Brazil
- Marco Kalz, Open Universiteit, The Netherlands
- Tobias Ley, Tallinn University of Technology, Estonia
- Martin Llamas, Universidad de Vigo, Spain
- Pedro J. Muñoz-Merino, Universidad Carlos III de Madrid, Spain
- Mariluz Guenaga, Universidad de Deusto, Spain
- Salvador Ros, UNED, Spain
- Abelardo Pardo, University of Sidney, Australia
- Peter van Rossmalen, Open Universiteit, The Netherlands
- Teresa Sancho, Universitat Oberta de Catalunya, Spain
- Kairit Tammets, Tallinn University of Technology, Estonia
- Anne Tervakari, University of Tampere, Finland
- Fridolin Wild, Open University, UK
- Martin Wolpers, Fraunhofer Institut, Deutschland
Program
| 9:00 – 9-10 | Opening and brief presentation of all workshop participants |
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| 9:10 – 9:40 |
Keynote Talk Dave Pritchard (MIT, RELATE group): Learning Studies in MOOCs Abstract: We have measured learning in several of our introductory Newtonian Mechanics MOOCs (Massive Open Online Courses). Two general findings emerge:
In doing this we checked whether learning on the pretest influenced learning on the post test, finding only a hint of such learning at the 2% level. We also used Item Response Theory to study student skill and relative improvement – improvement relative to the overall “course average”. Then we studied the correlation of skill with time spent using various resources (e-text, concept questions, homework problems, discussion forum..) and found generally positive correlations between resource use and learning, especially for conceptual knowledge. We have also studied the assistance value of individual resources (a problem, page of e-text, etc.) in moving students toward correct answers on particular problems – a revealing approach to learning about the value of various resources. |
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| 9:40 – 11:00 |
Oral presentation of selected papers
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| 11:00 – 11:30 |
Coffee break |
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| 11:30 – 12:30 |
Hands-on Tutorial Al Essa, Shirin Mojarad and Lalitha Agnihotri (McGraw-Hill Education): «Exploratory data analysis of educational data sets using Python and Spark« |
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| 12:30 – 13:00 |
Discussion Discussion among all participants about current software tools for learning analytics, with the purpose of creating a taxonomy of LA tools |
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