Thushari Atapattu

Automated Extraction of Semantic Concepts from Semi-structured Data: Supporting Computer-Based Education through the Analysis of Lecture Notes

Computer-based educational approaches provide valuable supplementary support to traditional classrooms. Among these approaches, intelligent learning systems provide automated questions, answers, feedback, and the recommendation of further resources. The most difficult task in intelligent system formation is the modelling of domain knowledge, which is traditionally undertaken manually or semi-automatically by knowledge engineers and domain experts. However, this error-prone process is time-consuming and the benefits are confined to an individual discipline. In this paper, we propose an automated solution using lecture notes as our knowledge source to utilise across disciplines. We combine ontology learning and natural language processing techniques to extract concepts and relationships to produce the knowledge representation. We evaluate this approach by comparing the machine-generated vocabularies to terms rated by domain experts, and show a measurable improvement over existing techniques.

Educational Question Answering Motivated by Question-Specific Concept Maps

Question answering (QA) is the automated process of answering general questions submitted by humans in natural language. QA has previously been explored within the educational context to facilitate learning, however the majority of works have focused on text-based answering. As an alternative, this paper proposes an approach to return answers as a concept map, which further encourages meaningful learning and knowledge organisation. Additionally, this paper investigates whether adapting the returned concept map to the specific question context provides further learning benefit. A randomised experiment was conducted with a sample of 59 Computer Science undergraduates, obtaining statistically significant results on learning gain when students are provided with the question-specific concept maps. Further, time spent on studying the concept maps were positively correlated with the learning gain.

A Framework for Topic Generation and Labeling from MOOC Discussions

This study proposes a standardised open framework to automatically generate and label discussion topics from Massive Open Online Courses (MOOCs). The proposed framework expects to overcome the issues experienced by MOOC participants and teaching staff in locating and navigating their information needs effectively. We analysed two MOOCs -- Machine Learning and Statistics: Making Sense of Data offered during 2013 and obtained statistically significant results for automated topic labeling. However, more experiments with additional MOOCs from different MOOC platforms are necessary to generalise our findings.

An Evaluation Methodology for Concept Maps Mined from Lecture Notes: An Educational Perspective

Concept maps are effective tools that assist learners in organising and representing knowledge. Recent efforts in the area of concept mapping work toward semi- or fully automated approaches to extract concept maps from various text sources such as text books. The motivation for this research is twofold: novice learners require substantial assistance from experts in constructing their own maps, introducing additional hurdles, and alternatively, the workload required by academics in manually constructing expert maps is substantial and repetitive. A key limitation of an automated concept map generation is the lack of an evaluation framework to measure the quality of concept maps. The most common evaluation mechanism is measuring the overlap between machine-generated elements (e.g. concepts) with expert maps using relevancy measures such as precision and recall. However, in the educational context, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information being retrieved for knowledge organisation. Therefore, this paper introduces a machine-based approach to evaluate the relative importance of knowledge by comparing with human judgment. We introduce three ranking models and conclude that the structural features are positively correlated with human experts (rs ~ 1) for courses with rich content and good structure (well-fitted).

Categorizing the Content of GitHub README Files

README files play an essential role in shaping a developer’s first impression of a software repository and in documenting the software project that the repository hosts. Yet, we lack a systematic understanding of the content of a typical README file as well as tools that can process these files automatically. To close this gap, we conduct a qualitative study involving the manual annotation of 4,226 README file sections from 393 randomly sampled GitHub repositories and we design and evaluate a classifier and a set of features that can categorize these sections automatically. We find that information discussing the ‘What’ and ‘How’ of a repository is very common, while many README files lack information regarding the purpose and status of a repository. Our multi-label classifier which can predict eight different categories achieves an F1 score of 0.746. To evaluate the usefulness of the classification, we used the automatically determined classes to label sections in GitHub README files using badges and showed files with and without these badges to twenty software professionals. The majority of participants perceived the automated labeling of sections based on our classifier to ease information discovery. This work enables the owners of software repositories to improve the quality of their documentation and it has the potential to make it easier for the software development community to discover relevant information in GitHub README files.

Automated generation of practice questions from semi-structured lecture notes

This paper describes the ad-hoc generation of study and practice questions from lecture notes, designed to assist computer science students in developing their understanding of lecture content.