Glosser: Enhanced Feedback for Student Writing Tasks

Proceedings of the International Conference on Advanced Learning Technologies ICALT 2008

We describe Glosser, a system that supports students in writing essays by 1) scaffolding their reflection with trigger questions, and 2) using text mining techniques to provide content clues that can help answer those questions.
A comparison with other computer generated feedback and scorings systems is provided to explain the novelty of the approach. We evaluate the system with Wiki pages produced by postgraduate students as part of their assessment.

Concept Map Mining: A definition and a framework for its evaluation

Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology WI-IAT 2008

Concept maps are visual representations of knowledge,
widely used in educational contexts. We use the term ”Concept Map Mining” (CMM) to refer to the automatic extraction of Concept Maps from documents such as essays. The principles behind CMM have been proposed for applications such as: information extraction in specific knowledge domains, the measurement of student understanding and misconceptions based on written essays, and as a preliminary step to creating domain ontologies.
Previous work on the automatic extraction of concept maps present two problems: 1) overly simplistic and varying definitions of concept maps, and 2) the lack of an evaluation framework that can be used to measure the quality of the generated maps. In this paper, we propose a formal definition of the term CMM, with a focus on educational applications. We also propose an evaluation framework that will allow other researchers to share a common ground to evaluate the performance of CMM methods.

Concept Extraction from student essays, towards Concept Map Mining

This paper presents a new approach for automatic concept extraction, using grammatical parsers and Latent Semantic Analysis. The methodology is described, also the tool used to build the benchmarking corpus. The results obtained on student essays shows good inter-rater agreement and promising machine extraction performance. Concept extraction is the first step to automatically extract concept maps from student’s essays or Concept Map Mining.

Single document semantic spaces

to appear in Proceedings of the Australian Joint Conference on Artificial Intelligence 2009

Latent Semantic Analysis (LSA) has been successfully used in a number of information retrieval, document visualization and summarization applications. LSA semantic spaces are normally created from large corpora that reflect an assumed background knowledge. However the right size and coverage of the background knowledge for each application are still open research questions. Moreover, LSA's computational cost is directly related to the size of the corpus, making the technique inviable in many cases.
This paper introduces a technique for creating semantic spaces using a single document and no background knowledge, which cuts computational cost and is domain independent. Single document semantic spaces' reliability was evaluated on a collection of student essays. Several semantic spaces generated from large corpora and single documents were used to compare how essays are represented. The distance between consecutive sentences in the essays changes between semantic spaces, but the rank of the distances is preserved. The results show that high correlations (0.7) of ranked distances between sentences can be achieved on the different spaces for the weight schemes evaluated.
This has important implications for the applications discussed.