This research paper examines the experience of Computer Science students during their period of study in higher education institutions, based on the surrounding ecological system, using Bronfenbrenner's theory of human development as a guiding principle. The objective of the study was to uncover the significant elements of the student experience when the student environment is regarded and categorised as an ecology. We focused on eight distinct student development lifestyle categories that could have an influence on their experience: the student's own awareness of their experience as a concept, their pre-university phase, their transition to the university phase, their university peers and colleagues, their social background outside the university, their hosting department, their extracurricular activities, and their post-university phase, if any. The study involved the participation of 206 computer science students, yielding a suitable population for analysis. The survey contained 42 questions to address the aspects of the eight categories discussed above, with a range of questions for each category. The questions employed a mixed-methods methodology using open-ended and closed questions. For the open-ended questions, we followed a grounded theory coding analysis technique to identify common codes in the student's responses. Word fre-quencies and figures were also analyzed to gain insights into students' perspectives using sentiment analysis techniques. For the quantitative questions, response rates and summary statistics were calculated. The findings shed light on the pivotal factors that influence the student experience of Computer Science students, offering valuable insights to students, educators, administrators, and decision-makers. The study highlights the significance of environmental factors in shaping student experience in the CS discipline and proposes potential directions for future research in this field. The implications of these findings for the creation of more supportive and effective learning environments for Computer Science students are also discussed.

This systematic literature review (SLR) critically examines the published literature since 2012 on the applications of machine learning (ML) in higher education. These applications include student performance classification, retention prediction, experience enhancement, and educational data mining. Machine Learning research has developed at a rapid pace to enhance different aspects of our lives. The educational sciences are at a pivotal point due to the potential influence that ML techniques have and will have on the field. We have aggregated the findings from a range of data sources, offering a thorough and objective perspective on the advancements, challenges, and future directions in this area. The volume of work that needed to be considered in the review clearly indicates the importance and relevance of this application of ML. In this SLR, we collect, analyse, and present the existing literature on ML applications in higher education. We utilise a comprehensive search strategy across multiple data sources, including 11 data sources, identifying all relevant studies published from 2012 to 2024. The inclusion criteria focused on peer-reviewed articles using ML-derived tools in areas such as student performance, retention, and experience. Exclusion criteria were rigorously applied to filter out studies that did not align with the focused domain of higher education and machine learning. The initial results yielded a pool of 16,581 articles, then refined to a substantive selection of 15,788 by removing duplicates, screening for false positives, and conducting quality assessment based on a structured quality control (QC) scoring system. The final set of papers was analysed to extract insights into ML applications' methodologies, effectiveness, and trends in higher education. The insights gained have allowed us to identify key trends, methodologies, and areas for future research. In addition, the surveying process will enable us to highlight publication patterns and critical research areas in the space.

People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person’s choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent),consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including ‘mental illness’ (e.g., depression) and emotions (e.g., ‘sadness’, ‘anger’). EmoMent corpus achieved ‘very good’ inter-annotator agreement of 98.3% (i.e. % with two or more agreement) and Fleiss’ Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.

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.

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.

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 academics. 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 components. However, in the educational context, particularly in course materials, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information that has to be organised. Therefore, this paper introduces a machine-based approach which studies the relative importance of knowledge components and organises them hierarchically. We compare machine-extracted maps with human judgment, based on expert knowledge and perception. This paper describes three ranking models to organise domain concepts. The results show that the auto-generated map positively correlates with human judgment (rs~1) for well-structured courses with rich grammar (well-fitted contents).

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.