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The vast nature of scientific publications brings out the importance of Literature-Based Discovery (LBD) research that is highly beneficial to accelerate knowledge acquisition and the research development process. LBD is a knowledge discovery workflow that automatically detects significant, implicit knowledge associations hidden in fragmented knowledge areas by analysing existing scientific literature. Therefore, the LBD output not only assists in formulating scientifically sensible, novel research hypotheses but also encourages the development of cross-disciplinary research. In this systematic review, we provide an in-depth analysis of the computational techniques used in the LBD process using a novel, up-to-date, and detailed classification. Moreover, we also summarise the key milestones of the discipline through a timeline of topics. To provide a general overview of the discipline, the review outlines LBD validation checks, major LBD tools, application areas, domains, and generalisability of LBD methodologies. We also outline the insights gathered through our statistical analysis that capture the trends in LBD literature. To conclude, we discuss the prevailing research deficiencies in the discipline by highlighting the challenges and opportunities of future LBD research.

Literature-Based Discovery (LBD) which is a sub-discipline of text mining, aims to detect meaningful implicit knowledge linkages in digital libraries that have the potential in generating novel research hypotheses. The input can be considered as one of the most critical components of the LBD process as the entire knowledge discovery is solely dependent on the content and quality of input. However, there is no uniform selection of the input since different LBD studies have picked different input types (e.g., titles, abstracts, keywords). This emphasises the need for assessing the information richness of inputs to decide the most suited input type for LBD workflow. Therefore, this study focuses on a large-scale assessment of the information richness of different variants of popular LBD input types. Our observations are consistent with all of the five golden test cases in the discipline.

Literature-Based Discovery (LBD) refers to the process of detecting implicit, novel knowledge linkages hidden in scientific digital libraries and its contribution in accelerating research innovations is widely recognised. Despite significant advances, almost all the prior research efforts suffer from a major research deficiency which is lack of portability. That is, the existing LBD models are highly dependent on specialised and domain-dependent knowledge resources that restrict their applicability to limited problem areas or domains. However, LBD, the process of discovering new knowledge from unstructured text that potentially leads to novel research innovations is crucial despite the domain. Thus, this study proposes an interdisciplinary LBD framework by circumventing the existing impediments in the LBD workflow towards promoting portable scientific problem solving. To this end, we considered the revolutionary opportunities offered through Semantic Web by employing DBpedia for the first time in the LBD workflow. The suitability of our proposals to overcome the prevailing limitations are evaluated by comparing them with commonly used domain specific resources.

Good software documentation encourages good software engineering, but the meaning of "good" documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information quality community to propose a framework that decomposes documentation quality into ten dimensions of structure, content, and style. To demonstrate its application, we recruited technical editors to apply the framework when evaluating examples from several genres of software documentation. We summarise their assessments -- for example, reference documentation and README files excel in quality whereas blog articles have more problems -- and we describe our vision for reasoning about software documentation quality and for the expansion and potential of a unified quality framework., to appear in the Visions and Reflections Track of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2020

Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.

This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks – offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C). Our team has participated in all the three subtasks. We have developed machine learning and deep learning-based ensembles of models. We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively. While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still remain challenging to classify.

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.

Detecting interesting, cross-disciplinary knowledge associations hidden in scientific publications can greatly assist scientists to formulate and validate scientifically sensible novel research hypotheses. This will also introduce new areas of research that can be successfully linked with their research discipline. Currently, this process is mostly performed manually by exploring the scientific publications, requiring a substantial amount of time and effort. Due to the exponential growth of scientific literature, it has become almost impossible for a scientist to keep track of all research advances. As a result, scientists tend to deal with fragments of the literature according to their specialisation. Consequently, important and hidden associations among these fragmented knowledge that can be linked to produce significant scientific discoveries remain unnoticed. This doctoral work aims to develop a novel knowledge discovery approach that suggests most promising research pathways by analysing the existing scientific literature.

Lecture videos are amongst the most commonly used instructional methods in present Massive Open Online Courses (MOOCs). As the main form of instruction, students' engagement behaviour with MOOC videos directly impacts the students' success or failure. This research focuses on an in-depth analysis of 1.5 million video interactions (e.g. pause, seek video) of a Programming MOOC. Our video-by-video analysis explores the rationale behind the time-wise variation of video interactions. We aim to analyse discourse features (e.g. syntactic simplicity of text, and speaking rate) and their correlation with the video interaction patterns. This paper presents preliminary results and educational video design implications.

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.

This paper investigates the effect of different forms of concept maps as scaffolding techniques to support answering quizzes in an online learning environment. Concept maps which represent a course topic have being utilised as a scaffolding technique for learning the subject matters and problem solving. However, due to the typical amount of information presented in the topic concept maps, learners might feel overwhelmed, reducing their motivation and increasing the learners' disorientation. In order to overcome this issue, a study was conducted with 59 undergraduates of a Software Engineering course to measure the effect of different forms of concept maps on learning. The study obtained statistically significant results when using concept maps adapted to given quizzes (known as task-adapted concept maps). Students' reflections collected through a questionnaire were very positive towards task-adapted concept maps as a scaffolding technique.

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.

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).

Concept maps are commonly used tools for organising and representing knowledge in order to assist meaningful learning. Although the process of constructing concept maps improves learners’ cognitive structures, novice students typically need substantial assistance from experts. Alternatively, expert-constructed maps may be given to students, which increase the workload of academic staff. To overcome this issue, automated concept map extraction has been introduced. One of the key limitations is the lack of an evaluation framework to measure the quality of machine-extracted concept maps. At present, researchers in this area utilise human experts’ judgement or expert-constructed maps as the gold standard to measure the relevancy of extracted knowledge. However, in the educational context, particularly in course materials, the majority of knowledge presented is relevant to the learner, resulting excess of information for knowledge organisation. Therefore, this paper introduces a novel machine-based approach which studies the relative importance of knowledge and organise knowledge hierarchically according to their importance. We compare machine’s prediction with human judgment based on experts’ knowledge and perception. Our ranking model utilise linguistic and structural features such as nouns, degree of co-occurrence and proximity. The results show that the machine’s predication positively correlates with human (rs~1) for well-fitted courses.

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.