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Visitor: Andrea Horbach, Automatic scoring

Andrea Horbach is visiting San Sebastian within the enetCollect network on crowdsourcing for language learning, as part of an ongoing collaborating with Itziar Aldabe, Oier Lopez de Lacalle and Montse Maritxalar about evaluating manually as well as automatically generated reading comprehension questions.

Andrea Horbach is a researcher at the Language Technology Lab headed by Prof. Torsten Zesch at the University of Duisburg-Essen, Germany. Last year, she defended her PhD thesis in computational linguistics, titled “Analyzing Short-Answer Questions and their Automatic Scoring: Studies on Semantic Relations in Reading Comprehension and the Reduction of Human Annotation Effort“ at Saarland Universityl. Her main research interests include educational NLP, such as automatic scoring and exercise generation, as well as the processing of non-standard language.

Last Tuesday (2019-09-17 ) she pesented us a talk entitles “Automated and Assisted Content Scoring in Mono- and Cross-Lingual Educational Settings
Summary:

Automatic content scoring of free-text answers has the goal to reduce the scoring workload of teachers and to provide consistency in scoring. In high-stakes tests, fully automatic scoring is often not an option. Nevertheless teachers can benefit from assisted scoring, where they are supported by NLP but are still in control of the scoring process.This talk presents ongoing work of two research projects related to educational scoring: First, we investigate content scoring in a cross-lingual setup, where a model trained on data in one language is applied to new data in a different language in order to foster educational equality as well as to overcome data sparseness. We present our cross-lingual data collection, as well as machine learning experiments using machine translation to bridge the language gap.

In the second part of the talk we present work on assisted scoring of listening comprehension data from language proficiency testing. We show assisted scoring studies where teachers are supported in scoring answers by the use of clustering techniques.

 

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