Computational Methods for Analysis of Language in Graduate and Undergraduate Student Texts

Often, academic programs require students to write a thesis or research proposal. The review of such texts is a heavy load, especially at initial stages. Natural Language Processing techniques are employed to mine existing corpora of research proposals and theses to further assess drafts of college students in information technologies and computer science. In this chapter, we focus on examining specific sections of student writings, first seeking for the connection of ideas identifying the pattern of entities. Subsequently, we analyze the justification and conclusions sections, studying features such as the presence of importance in justification and the level of speculative words in a conclusion section. Experiments and results for the different analyses are explained in detail. Each analysis is independent and could allow the student to analyze their text with a set of tools with the aim of improving their writing.

[1]  Arthur C. Graesser,et al.  The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor , 2005 .

[2]  Moshe Tennenholtz,et al.  Strong mediated equilibrium , 2006, Artif. Intell..

[3]  Mirella Lapata,et al.  Modeling Local Coherence: An Entity-Based Approach , 2005, ACL.

[4]  Regina Barzilay,et al.  Bayesian Unsupervised Topic Segmentation , 2008, EMNLP.

[5]  Donald J. Mash,et al.  The Graduate Student's Guide to Theses and Dissertations: A Practical Manual for Writing and Research , 1974 .

[6]  Milam Aiken,et al.  An Evaluation of the Accuracy of Online Translation Systems , 2009, Communications of the IIMA.

[7]  Yoshua Bengio,et al.  Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.

[8]  Arthur C. Graesser,et al.  Guru: A Computer Tutor That Models Expert Human Tutors , 2012, ITS.

[9]  Danielle S. McNamara,et al.  Using Automatic Scoring Models to Detect Changes in Student Writing in an Intelligent Tutoring System , 2013, FLAIRS Conference.

[10]  Lukás Burget,et al.  Empirical Evaluation and Combination of Advanced Language Modeling Techniques , 2011, INTERSPEECH.

[11]  Vidas Daudaravicius,et al.  Automated Evaluation of Scientific Writing: AESW Shared Task Proposal , 2015, BEA@NAACL-HLT.

[12]  James H. Martin,et al.  Identifying science concepts and student misconceptions in an interactive essay writing tutor , 2012, BEA@NAACL-HLT.

[13]  Rafael A. Calvo,et al.  Analysing Semantic Flow in Academic Writing , 2009, AIED.

[14]  Bonnie L. Webber,et al.  Discourse structure and language technology , 2011, Natural Language Engineering.

[15]  Aurelio López-López,et al.  Analysis of Concept Sequencing in Student Drafts , 2014, EC-TEL.

[16]  Jerome R. Bellegarda Unsupervised document clustering using multi-resolution latent semantic density analysis , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[17]  Daniel Marcu,et al.  A Machine Learning Approach for Identification Thesis and Conclusion Statements in Student Essays , 2003, Comput. Humanit..

[18]  John Bitchener,et al.  Perceptions of the difficulties of postgraduate L2 thesis students writing the discussion section , 2006 .

[19]  Steven Bethard,et al.  Identifying Weak Sentences in Student Drafts: A Tutoring System , 2014, MIS4TEL.