Online Learning

"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 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."

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.

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.

Writing assignments are ubiquitous in higher education. Writing develops not only communication skills, but also higher-level cognitive processes that facilitate deep learning. Cognitive visualizations, such as concept maps, can also be used as part of learning activities including as a form of scaffolding, or to trigger reflection by making conceptual understanding visible at different stages of the learning process. We present Concept Map Miner (CMM), a tool that automatically generates Concept Maps from students’ compositions, and discuss its design and implementation, its integration to a writing support environment and its evaluation on a manually annotated corpora of university essays (N=43). Results show that complete CM, with concepts and labeled relationships, are possible and its precision depends the level of summarization (number of concepts) chosen.

Teachers in fully online courses face the challenge of achieving students’ satisfaction – usually measured using students’ surveys – as it is known to affect engagement and motivation, which in turn improves learning outcomes. This challenge grows in complexity with the number of students enrolled in the course. In this paper we report an attempt to improve students’ perception on their teacher in a massive online academic writing course. In the experimental group the way contents were delivered was changed, the same as the role teachers had. Content was embedded in a story and the teacher acted as the presenter of each chapter during the course. Results showed that students’ perception improved significantly, particularly on how they perceived the teacher’s availability.

"The importance of formative assessment in higher education is agreed among researchers, but marking this type of assessment is a heavy burden. Computer-based assessment and on-screen marking tools are attempts to overcome this burden, but while the former is not suitable for handwritten
answers, the latter requires commercial OCR software. This paper describes the design and development of an eMarking tool to support the printing, digitalization and marking of paper based evaluations based on open source software."

With an always growing number of student enrolment in higher education, providing quality feedback in both digital and paper based assessment becomes a heavy burden for teachers and tutors. The use of assessment rubrics can help overcome this burden, defining several criteria including formative feedback within it. However, current computer-assisted rubric systems present some drawbacks that hinder their adoption.
This paper presents the design of an adaptive interface for an e-Marking tool that uses rubrics, that is able to suggest assessors on the next criterion to be evaluated, based on nearest neighbor approach. An experimental setup showed encouraging results that provide evidence that the use of advanced learning technologies for assessment can help improve efficiency even in ill-defined domains.

Sophisticated Text Mining features such as visualization, summarization, and clustering are becoming increasingly common in software applications. In Text Mining, documents are processed using techniques from different areas which can be very expensive in computation cost. This poses a scalability challenge for real-life applications in which users behavior can not be entirely predicted. This paper proposes a decoupled architecture for document processing in Text Mining applications, that allows applications to be scalable for large corpora and real-time processing. It contributes a software architecture designed around these requirements and presents TML, a Text Mining Library that implements the architecture. An experimental evaluation on its scalability using a standard corpus is also presented, and empirical evidence on its performance as part of an automated feedback system for writing tasks used by real students.

... Email: jorge.villalon@uai.cl, rafa@ee.usyd.edu.au, rodrigo.montenegro2004@alumnos.uai.cl ... In the first activity the students build a concept map without any support, in the second activity the tea-cher builds a map that has some errors and/or missing information that the ...

ABSTRACT The importance of formative assessment in higher education is agreed among researchers, but marking this type of assessment is a heavy burden. Computer-based assessment and on-screen marking tools are attempts to overcome this burden, but while the former is not suitable for handwritten answers, the latter requires commercial OCR software. This paper describes the design and development of an eMarking tool to support the printing, digitalization and marking of paper based evaluations based on open source software.