Arguments in final course project reports written by engineering students are an essential feature of academic texts. Further revisions of instructors reveal to the students their mistakes, such as a lack of argumentation. However, the depth of revisions is often limited by the time availability of the instructor. In this paper, we present a system for argument assessment embedded in an Internet‐based Moodle course with Learning Tools Interoperability (LTI) standard, to help students improve argumentation in the problem statement, justification and conclusion sections of their final project report. The system identifies argumentative paragraphs along with argumentation level and provides recommendations to improve their writing. The analysis is achieved employing machine learning techniques with lexical features trained using an annotated collection of student writings. We performed a pilot test to compare control and experimental groups. Both groups consisted of undergraduate students of computer engineering programs from two different universities. We observed that using the argument assessment system increased the number of arguments in the experimental group. We discuss some further improvements for the system.

Agile global software engineering challenges architectural knowledge (AK) management since face-to-face interactions are preferred over comprehensive documentation, which causes AK loss over time. The AK condensation concept was proposed to reduce AK losing, using the AK shared through unstructured electronic media. A crucial part of this concept is a classification mechanism to ease AK recovery in the future. We developed a Slack complement as a classification mechanism based on social tagging, which recommends tags according to a chat/message topic, using natural language processing (NLP) techniques. We evaluated two tagging modes: NLP-assisted versus alphabetical auto-completion, in terms of correctness and time to select a tag. Fifty-two participants used the complement emulating an agile and global scenario and gave us their complement’s perceptions about usefulness, ease of use, and work integration. Messages tagged through NLP recommendations showed fewer semantic errors, and participants spent less time selecting a tag. They perceived the component as very usable, useful, and easy to be integrated into the daily work. These results indicated that a tag recommendation system is necessary to classify the shared AK accurately and quickly. We will improve the NLP techniques to evaluate AK condensation in a long-term test as future work.

In undergraduate theses, a good methodology section should describe the series of steps that were followed in performing the research. To assist students in this task, we develop machine-learning models and an app that uses them to provide feedback while students write. We construct an annotated corpus that identifies sentences representing methodological steps and labels when a methodology contains a logical sequence of such steps. We train machine-learning models based on language modeling and lexical features that can identify sentences representing methodological steps with 0.939 f-measure, and identify methodology sections containing a logical sequence of steps with an accuracy of 87%. We incorporate these models into a Microsoft Office Addin, and show that students who improved their methodologies according to the model feedback received better grades on their methodologies.

Lexical Richness is a competence that students acquire while advancing in education. This article presents results analyzing lexical richness of drafts of students, in terms of lexical density, variety, and sophistication. A computational model allows reviewing some essential elements in drafts. Results show that there exists a difference in lexical richness between graduate and undergraduate texts, and that sophistication is the measure that best differentiates them. We also report results of a pilot test. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:638–644, 2015; View this article online at http://wileyonlinelibrary.com/journal/cae; DOI