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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

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.

Lecture videos are amongst the most widely used instructional methods in present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, students’ engagement behaviour with videos directly impacts the students’ success or failure and accordingly, in-video dropouts positively correlate with dropout from MOOCs. The primary focus of previous video learning analytics studies is on analysing video engagement behaviour using explicit factors (i.e. views or annotations). Limited research studies focus on implicit video learning analytics (e.g. pause, seek, content type) and their impact on students’ success, with existing studies addressing video interactions and their relationship with visual transitions. We aim to explore the association between video interactions and non-visual (i.e. verbal or discourse) transition. This research focuses on text (e.g. cohesion and syntactic complexity) and spoken (e.g. speaking rate) discourse features of lecture videos. We conduct a fine-grained analysis of 3.4 million video interactions of two AdelaideX MOOCs – Programming (Code101x) and Cyber, Surveillance and Security (Cyber101x). According to our results, some discourse features (e.g. lexical diversity and causal connectives) demonstrate statistically significant correlation with video interactions. We present insights for educational video design implications based on discourse processing theories.

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.

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 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.