Online tutor for research writing

English is the most prominent second language used in educational programs throughout the world. Unfortunately, there is a limitation of time and skill to guide students with learning the language and for evaluating their writings. Automated Writing Evaluation (AWE) tools would help in addressing this gap. In this thesis, I document a contribution to the field of Automated Writing Evaluation in the form of a new AWE tool called the Research Writing Tutor (RWT). The system design, user interface design, and features of this tool are introduced first, and then the findings obtained from an user evaluation study are reported. The website has been designed and developed to be user friendly. This tool could be of great use to graduate students and undergraduates in writing research reports, articles, and thesis or dissertations. Unlike most studies that concentrate on the accuracy of the AWE systems, this study aims at the usability and utility of the RWT in addition to the trust on automated systems

A metacognitive feedback scaffolding system for pedagogical apprenticeship

This thesis addresses the issue of how to help staff in Universities learn to give feedback with the main focus on helping teaching assistants (TAs) learn to give feedback while marking programming assignments. The result is an innovative approach which has been implemented in a novel computer support system called McFeSPA. The design of McFeSPA is based on an extensive review of the research literature on feedback. McFeSPA has been developed based on relevant work in educational psychology and Artificial Intelligence in EDucation (AIED) e.g. scaffolding the learner, ideas about andragogy, feedback patterns, research into the nature and quality of feedback and cognitive apprenticeship. McFeSPA draws on work on feedback patterns that have been proposed within the Pedagogical Patterns Project (PPP) to provide guidance on structuring the feedback report given to the student by the TA. The design also draws on the notion of andragogy to support the TA. McFeSPA is the first Intelligent Tutoring System (ITS) that supports adults learning to help students by giving quality feedback. The approach taken is more than a synthesis of these key ideas: the scaffolding framework has been implemented both for the domain of programming and the feedback domain itself; the programming domain has been structured for training TAs to give better feedback and as a framework for the analysis of students’ performance. The construction of feedback was validated by a small group of TAs. The TAs employed McFeSPA in a realistic situation that was supported by McFeSPA which uses scaffolding to support the TA and then fade. The approach to helping TAs become better feedback givers, which is instantiated in McFeSPA, has been validated through an experimental study with a small group of TAs using a triangulation approach. We found that our participants learned differently by using McFeSPA. The evaluation indicates that 1) providing content scaffolding (i.e. detailed feedback about the content using contingent hints) in McFeSPA can help almost all TAs increase their knowledge/understanding of the issues of learning to give feedback; 2) providing metacognitive scaffolding (i.e. each level of detailed feedback in contingent hint, this can also be general pop-up messages in using the system apart from feedback that encourage the participants to give good feedback) in McFeSPA helped all TAs reflect on/rethink their skills in giving feedback; and 3) when the TAs obtained knowledge about giving quality feedback, providing adaptable fading of TAs using McFeSPA allowed the TAs to learn alone without any support

Proceedings of the First European Workshop on Latent Semantic Analysis in Technology Enhanced Learning

Latent Semantic Analysis (LSA) has been successfully deployed in various educational applications to enrich learning and teaching with information-technology. The primary goal of the workshop is to bring together experts in the field in order to share knowledge gained within the scattered research about latent semantic analysis in educational applications, in particular from the context of the IST projects Cooper, iCamp,T enCompetence and ProLearn

Automated analysis of Learner\u27s Research Article writing and feedback generation through Machine Learning and Natural Language Processing

Teaching academic writing in English to native and non-native speakers is a challenging task. Quite a variety of computer-aided instruction tools have arisen in the form of Automated Writing Evaluation (AWE) systems to help students in this regard. This thesis describes my contribution towards the implementation of the Research Writing Tutor (RWT), an AWE tool that aids students with academic research writing by analyzing a learner\u27s text at the discourse level. It offers tailored feedback after analysis based on discipline-aware corpora. At the core of RWT lie two different computational models built using machine learning algorithms to identify the rhetorical structure of a text. RWT extends previous research on a similar AWE tool, the Intelligent Academic Discourse Evaluator (IADE) (Cotos, 2010), designed to analyze articles at the move level of discourse. As a result of the present research, RWT analyzes further at the level of discourse steps, which are the granular communicative functions that constitute a particular move. Based on features extracted from a corpus of expert-annotated research article introductions, the learning algorithm classifies each sentence of a document with a particular rhetorical move and a step. Currently, RWT analyzes the introduction section of a research article, but this work generalizes to handle the other sections of an article, including Methods, Results and Discussion/Conclusion. This research describes RWT\u27s unique software architecture for analyzing academic writing. This architecture consists of a database schema, a specific choice of classification features, our computational model training procedure, our approach to testing for performance evaluation, and finally the method of applying the models to a learner\u27s writing sample. Experiments were done on the annotated corpus data to study the relation among the features and the rhetorical structure within the documents. Finally, I report the performance measures of our 23 computational models and their capability to identify rhetorical structure on user submitted writing. The final move classifier was trained using a total of 5828 unigrams and 11630 trigrams and performed at a maximum accuracy of 72.65%. Similarly, the step classifier was trained using a total of 27689 unigrams and 27160 trigrams and performed at a maximum accuracy of 72.01%. The revised architecture presented also led to increased speed of both training (a 9x speedup) and real-time performance (a 2x speedup). These performance rates are sufficient for satisfactory usage of RWT in the classroom. The overall goal of RWT is to empower students to write better by helping them consider writing as a series of rhetorical strategies to convey a functional meaning. This research will enable RWT to be deployed broadly into a wider spectrum of classrooms

Computer-Assisted Research Writing in the Disciplines

It is arguably very important for students to acquire writing skills from kindergarten through high school. In college, students must further develop their writing in order to successfully continue on to graduate school. Moreover, they have to be able to write good theses, dissertations, conference papers, journal manuscripts, and other research genres to obtain their graduate degree. However, opportunities to develop research writing skills are often limited to traditional student-advisor discussions (Pearson & Brew, 2002). Part of the problem is that graduate students are expected to be good at such writing because if they “can think well, they can write well” (Turner, 2012, p. 18). Education and academic literacy specialists oppose this assumption. They argue that advanced academic writing competence is too complex to be automatically acquired while learning about or doing research (Aitchison & Lee, 2006). Aspiring student-scholars need to practice and internalize a style of writing that conforms to discipline-specific conventions, which are norms of writing in particular disciplines such as Chemistry, Engineering, Agronomy, and Psychology. Motivated by this need, the Research Writing Tutor (RWT) was designed to assist the research writing of graduate students. RWT leverages the conventions of scientific argumentation in one of the most impactful research genres – the research article. This chapter first provides a theoretical background for research writing competence. Second, it discusses the need for technology that would facilitate the development of this competence. The description of RWT as an exemplar of such technology is then followed by a review of evaluation studies. The chapter concludes with recommendations for RWT integration into the classroom and with directions for further development of this tool

Learning Analytics for Academic Writing through Automatic Identification of Meta-discourse

Effective written communication is an essential skill which promotes educational success for undergraduates. Argumentation is a key requirement of successful writing, which is the most common genre that undergraduates have to write particularly in the social sciences. Therefore, when assessing student writing academic tutors look for students’ ability to present and pursue well-reasoned and strong arguments through scholarly argumentation, which is articulated by meta-discourse. Today, there are some natural language processing systems which automatically detect authors’ rhetorical moves in scholarly texts. Hence, when assessing their students’ essays, educators could benefit from the available automated textual analysis which can detect meta-discourse. However, previous work has not shown whether these technologies can be used to analyse student writing reliably. The aim of this thesis therefore has been to understand how automated analysis of meta-discourse in student writing can be used to support tutors’ essay assessment practices. This thesis evaluates a particular language analysis tool, the Xerox Incremental Parser (XIP) as an exemplar of this type of automated technology. The studies presented in this thesis investigates how tutors define the quality of undergraduate writing and suggests key elements that make for good quality student writing in the social sciences, where XIP seems to work best. This thesis also sets out the changes that needs to be made to the XIP and proposes in what ways its output can be delivered to tutors so that they make use of this output to give feedback on student essays. The findings reported also show problems that academic tutors experience in essay assessment, which potentially could be solved by automated support. However, tutors have preconceptions about the use of automated support. The study revealed that tutors want to be assured that they retain the ‘power’ themselves in any decision of using automated support to overcome these preconceptions

D3.1 – First Bundle of Core Social Agency Assets

This deliverable presents and describes the first delivery of assets that are part of the core social agency bundle. In total, the bundle includes 16 assets, divided into 4 main categories. Each category is related to a type of challenge that developers of applied games are typically faced with and the aim of the included assets is to provide solutions to those challenges. The main goal of this document is to provide the reader with a description for each included asset, accompanied by links to their source code, distributable versions, demonstrations and documentation. A short discussion of what are the future steps for each asset is also given. The primary audience for the contents of this deliverable are the game developers, both inside and outside of the project, which can use this document as an official list of the current social agency assets and their associated resources. Note that the information about which RAGE use cases are using which of these assets is described in Deliverable 4.2.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains