Argument Component Classification for Classroom Discussions

This paper focuses on argument component classification for transcribed spoken classroom discussions, with the goal of automatically classifying student utterances into claims, evidence, and warrants. We show that an existing method for argument component classification developed for another educationally-oriented domain performs poorly on our dataset. We then show that feature sets from prior work on argument mining for student essays and online dialogues can be used to improve performance considerably. We also provide a comparison between convolutional neural networks and recurrent neural networks when trained under different conditions to classify argument components in classroom discussions. While neural network models are not always able to outperform a logistic regression model, we were able to gain some useful insights: convolutional networks are more robust than recurrent networks both at the character and at the word level, and specificity information can help boost performance in multi-task training

Context-aware Argument Mining and Its Applications in Education

Context is crucial for identifying arguments and argumentative relations in text, but existing argument studies have not addressed context dependence adequately. In this thesis, we propose context-aware argument mining that makes use of contextual features extracted from writing topics and context sentences to improve state-of-the-art argument component and argumentative relation classifications. The effectiveness as well as generality of our proposed contextual features is proven through its application in different argument mining tasks in student essays. We further evaluate the applicability of our proposed argument mining models in automated persuasive essay scoring tasks

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

unsupervised detection of argumentative units though topic modeling techniques

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TEXT MINING DATA FROM STUDENTS TO REVEAL MEANINGFUL INFORMATION FOR EDUCATORS

Academic institutions adopt different advising tools for various objectives. Past research used both numeric and text data to predict students’ performance. Moreover, numerous research projects have been conducted to find different learning strategies and profiles of students. Those strategies of learning together with academic profiles assisted in the advising process. This research proposes an approach to supplement these activities by text mining students’ essays to better understand different students’ profiles across different courses (subjects). Text analysis was performed on 99 essays written by undergraduate students in three different courses. The essays and terms were projected in a 20-dimensional vector space. The 20 dimensions were used as independent variables in a regression analysis to predict a student’s final grade in a course. Further analyses were performed on the dimensions found statistically significant. This study is a preliminary analysis to demonstrate a novel approach of extracting meaningful information by text mining essays written by students to develop an advising tool that can be used by educators

Uma Abordagem para Mineração de Argumentos em Redações do Português Brasileiro

ABSTRACTArgument mining consists of extracting the argumentative structure of a text. The challenge in finding the argumentative structure lies in: identifying the components of the argument and the relationships that occur between them. Approaches that propose to solve both challenges of argument mining together are known as end-to-end methods. In the literature, some papers were found that perform argument mining in essays, but no papers were found for Brazilian Portuguese. Therefore, in this paper, we propose an end-to-end approach for argument mining of Brazilian Portuguese essays in the ENEM model

Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment

Using latent semantic analysis to detect non-cognitive variables of academic performance

This thesis explores the possibilities of using latent semantic analysis to detect evidence of intrapersonal personality variables in post-secondary student essays. Determining student achievement based on non-cognitive variables is a complex process. Automated essay scoring tools are already in use today in grading and evaluating student texts based on cognitive domain traits, but at this time are not utilized to analyze non-cognitive domains such as personality. Could such tools be configured to detect non-cognitive variables in student essays? Key concepts in this proposal—personality traits, latent semantic analysis, automated essay evaluation, and online cinema reviews—are explored followed by a literature review to justify the research. As a proof of concept study, 43 writing samples written to a constructed response task are collected and analyzed by a test model specifically designed to evaluate sentiment in a movie review constructed response format. A test model is created using LightSIDE, a software tool for text assessment, to predict the sentiment of these essays with highly encouraging results. The thesis concludes with a path for future research in the largely unexplored area of automated assessment of non-cognitive variables