Seminar on Educational Data Mining and Learning Analytics

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Educational Data Mining (EDM) and Learning Analytics (LA) are research fields that focus on developing methodologies and tools to explore and analyze data gathered from educational settings. The roots of EDM are in constructing, studying and improving intelligent tutoring systems that guide and teach students, while LA has roots in the analysis of data originating from different types of learning management systems with the goal of improving offered education. Methods and practices in both fields are very similar, and various forms of machine learning and data mining practices are in the very core of both fields.
Vuosi Lukukausi Päivämäärä Periodi Kieli Vastuuhenkilö
2015 kevät 13.03-24.04. 4-4 Englanti Hannu Toivonen

Luennot

Aika Huone Luennoija Päivämäärä
Pe 10-12 B222 Arto Vihavainen 13.03.2015-24.04.2015
Ti 10-16 B119 Hannu Toivonen 28.04.2015-28.04.2015

Information for international students

This seminar will be held in English.

Yleistä

Educational Data Mining (EDM) and Learning Analytics (LA) are research fields that focus on developing methodologies and tools to explore and analyze data gathered from educational settings. The roots of EDM are in constructing, studying and improving intelligent tutoring systems that guide and teach students, while LA has roots in the analysis of data originating from different types of learning management systems with the goal of improving offered education. Methods and practices in both fields are very similar, and various forms of machine learning and data mining practices are in the very core of both fields.
 
This seminar focuses on both research and practice papers in recent Educational Data Mining (http://educationaldatamining.org/), Learning Analytics and Knowledge (e.g. http://lak14indy.wordpress.com/) and Learning @ Scale (e.g. http://learningatscale.acm.org/las2015/detailed/) conferences as well as relevant journals such as the Journal of Educational Data Mining and International Journal of Artificial Intelligence in Education. The recent Computers in Human Interaction journal also had an issue on learning analytics and educational data mining. Other venues can be also considered based on suggestions.
 
Learning objectives
  • Improve scientific and technical writing skills
  • Improve scientific and technical presentation skills
  • Learn about educational data mining and learning analytics

Kurssin suorittaminen

Participants must have completed the course Scientific writing or have equivalent skills. Students complete this seminar by actively participating in its work: the work methods include studying scientific sources, writing reports and giving presentations, reading the reports of other participants and evaluating them, and actively following presentations.
 
The grading will be based on each student's own written work (1/2), oral presentation (1/4), and commentary on the reports of others as well as activeness in general (1/4). To pass the seminar, each of these components must be passed. (Active) attendance of seminar meetings is mandatory.
 
During the course, you will (1) write an abstract describing your topic, (2) give a short presentation based on the abstract, (3) participate in discussions on relevant papers in the course, (4) write a seminar report, and (5) give a "conference-style" presentation that is based on your report.
 
Schedule
  • First meeting on 13.3.
  • Select a seminar topic by 17.3. -- send your selection to avihavai@cs.helsinki.fi
  • Write an abstract (max 200 words) of your topic; submit it to avihavai@cs.helsinki.fi by the end of 19.3.
  • Give a short presentation on your topic (5 minutes / 3 slides) at the second seminar meeting on 20.3.
  • Refine your topic to focus your work by 3.4.
  • Submit your seminar report by 21.4. -- ATTN!
  • Give a 25 min presentation on your work on 28.4.
  • Review a given number of articles from other participants by 30.4.

In addition, participate in the weekly meetings: You are expected to read two articles each week for the meetings, and to introduce one article in one of the meetings. More details during the first lecture.

On the seminar report

The seminar report should use 2 to 4 most relevant articles as references. In addition, you can have a few articles to provide more insight and e.g. areas where your topic has been used. The overall length of the report should be around 12-15 pages, using the CS department template for seminar papers.

Kirjallisuus ja materiaali

Read the article "Educational Data Mining and Learning Analytics" by Ryan S.J. Baker and George Siemens (link).

Ideas for topics, you will refine your topic later on

Once you have selected a topic, mail avihavai@cs.helsinki.fi and it will be tagged for you. 

Topics for meetings -- will be announced based on the selected topics

In each meeting, we will discuss two articles. Read them beforehand -- if your name is after the article, you are in charge for providing a brief introduction to the topic (no presentation, no slides, just a short recap).