Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Learning sentiment from students’ feedback for real-time interventions in classrooms

Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded

Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data

Knowledge about the culture of a user is especially important for the design of e-learning applications. In the experiment reported here, questionnaire data was used to build machine learning models to automatically predict the culture of a user. This work can be applied to automatic culture detection and subsequently to the adaptation of user interfaces in e-learning

Inclusive Education: The Forms of Violation of Children’s Rights and School Dropouts in the Kadey Division: East Region of Cameroon

Article 8 of the African Aspirations for 2063 stipulates that the African people are confident that their countries have the ability and competence to realize or accomplish their full potential in development, culture, and peace. The vast majority of countries in Africa have worked toward establishing flourishing, inclusive, successful and prosperous societies by eradicating any forms of violation of children’s rights (African Union Commission, 2015). Nevertheless, violation of children’s rights remains present in most developing countries including the country of Cameroon. This research aims to explore the forms of violation of children’s rights having a dramatic incident in school attendance in the Kadey Division of Cameroon, East Region of Cameroon. This research work is inductive, values bias and uses the grounded theory of the qualitative method approach. 15 participants have been selected from 3 major focused groups of different stakeholders in the Kadey Division, East Region of Cameroon. The theoretical saturation code was used to explain the relevance of the sample size. Data were examined using the open, axial, and selective coding processes. The results were tested for internal and external validity based on credibility, dependability, conformability, and transferability consideration. The philosophical focused on subjectivism ontology and interpretivism perspective. The study is an investigative case study model. The study showed that the forms of violation of children’s rights in the Kadey division include the recruitment and use of children, the denial of humanitarian access, the sexual violence against children and the killing and hurting of children. This study encourages school leaders in the Kadey Division to acknowledge that schools are not meant to function apart from the local community. Promoting strong collaborative work ethics between the major educational stakeholders may help prevent and reduce violence against children within and outside the school milieu and therefore duplicate school attendance. (UNICEF Regional Office for South Asia, 2016)

Using Random Forests to Describe Equity in Higher Education: A Critical Quantitative Analysis of Utah’s Postsecondary Pipelines

The following work examines the Random Forest (RF) algorithm as a tool for predicting student outcomes and interrogating the equity of postsecondary education pipelines. The RF model, created using longitudinal data of 41,303 students from Utah\u27s 2008 high school graduation cohort, is compared to logistic and linear models, which are commonly used to predict college access and success. Substantially, this work finds High School GPA to be the best predictor of postsecondary GPA, whereas commonly used ACT and AP test scores are not nearly as important. Each model identified several demographic disparities in higher education access, most significantly the effects of individual-level economic disadvantage. District- and school-level factors such as the proportion of Low Income students and the proportion of Underrepresented Racial Minority (URM) students were important and negatively associated with postsecondary success. Methodologically, the RF model was able to capture non-linearity in the predictive power of school- and district-level variables, a key finding which was undetectable using linear models. The RF algorithm outperforms logistic models in prediction of student enrollment, performs similarly to linear models in prediction of postsecondary GPA, and excels both models in its descriptions of non-linear variable relationships. RF provides novel interpretations of data, challenges conclusions from linear models, and has enormous potential to further the literature around equity in postsecondary pipelines

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

Submission to the ALRC in response to Issues Paper 42: copyright and the digital economy

The response submission by the Australian Publishers Association in regards to the current Australian copyright law.The APA’s members are active participants in the digital economy. Further, publishers and other creators are at the forefront of new and innovative digital business models. In relation to sales of books and ebooks, such models include not just sales through bookstores (including online stores) but also direct licensing of ebooks. Whatever their source licences offered include (but are not limited to):   licences specifically designed for individuals and organisations including site licences, licences that allow off-site access and licences developed for sales to and lending by libraries;   bundling and subscription models; payments based on actual use rather than flat fees; delivery systems that allow a certain number of backups or the unlimited transfer of the relevant title to devices owned by the customer; licences for customers (such as educational institutions) to provide their own clients with access to copyright material through Learning Management Systems (“LMS”); and access via cloud storage services. &nbsp