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5 contributions from KMi presented LAK 2017

Rachel Coignac-Smith, Monday 27 March 2017 | Annotate
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As every year, KMi presented the current results at the top-level Learning Analytics conference, Learning Analytics and Knowledge (LAK17). This year, the conference was held in Vancouver, Canada (13-17 March) and co-organised with ex-KMier Marek Hatala. Even though the acceptance rate was quite low, KMi was proud to have 5 papers accepted: 1 full paper, 3 short papers and 1 poster in the research sections and one also in the practitioners track.

On the first day of the conference, Martin Hlosta presented the novel model that is used at The Open University for identifying at-risk students, when no historical data are available for learning the model. 

Michal Huptych showed an early version of our study materials recommender, which is based on analysing the information about successful students in the previous presentation of the same course.

The joint paper between KMi, IET and LTS, introduced by Thea Herodotou, showed the teachers perspective of learning analytics at large scale. This research is based on the results and interviews with tutors from the previous year.

Moreover, in the practitioner track, organised together with the poster section, we shared our results in scaling up the OUAnalyse from various perspectives: both with the focus on including as many courses and as many users at OU.

Tracie Farrell Frey, one KMi’s PhD students, participated in the poster section with her contribution about Seeking Relevance: Affordances of Learning Analytics for Self-Regulated Learning.

Still fresh from the conference, the OU Analyse team hope to come back to present their new results next year down under in Sydney, 


Additional Media

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Zdenek Zdrahal Photograph

Zdenek Zdrahal

Emeritus Professor

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Martin Hlosta Photograph

Martin Hlosta

Visiting Fellow

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Michal Huptych Photograph

Michal Huptych

Research Associate

Tracie Farrell Photograph

Tracie Farrell

Research Fellow

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OU Analyse

The OU Analyse project is piloting new machine learning based methods for early identification of students who are at risk of failing.

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