Applying Semantic Analysis to Training, Education, and Immersive Learning

The last decade has seen major advances in the areas of natural language processing and semantic analysis. Theoretical advances and increased computational power have resulted in applications that detect topics and sentiments in communications, automatically classify unstructured data in enterprise settings, and win Jeopardy contests. This paper surveys how these same methods apply to a variety of problems in education and training. Applications include automatic grading and question generation, guiding the behavior of intelligent tutoring systems, aligning content to competencies and educational standards, and improving search in digital repositories. This paper describes the methods, explains how they are applied and evaluated, and discusses their potential for use virtual worlds and immersive learning environments.

[1]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[2]  Rada Mihalcea,et al.  Using Wikipedia for Automatic Word Sense Disambiguation , 2007, NAACL.

[3]  Evgeniy Gabrilovich,et al.  Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis , 2007, IJCAI.

[4]  Brandon Pincombe,et al.  Comparison of Human and Latent Semantic Analysis (LSA) Judgements of Pairwise Document Similarities for a News Corpus , 2004 .

[5]  T. Landauer.  Automatic Essay Assessment , 2003 .

[6]  D. Wiliam.  Assessment in Education: Principles, Policy & Practice , 2008 .

[7]  Arthur C. Graesser,et al.  Simulating Smooth Tutorial Dialogue with Pedagogical Value , 1998, FLAIRS.

[8]  Anne Diekema,et al.  Computer-assisted assignment of educational standards using natural language processing , 2011, J. Assoc. Inf. Sci. Technol..

[9]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[10]  Carlo Strapparava,et al.  Automatic Assessment of Students' Free-Text Answers Underpinned by the Combination of a BLEU-Inspired Algorithm and Latent Semantic Analysis , 2005, FLAIRS Conference.

[11]  Benno Stein,et al.  A Wikipedia-Based Multilingual Retrieval Model , 2008, ECIR.

[12]  Luo Si,et al.  A statistical model for scientific readability , 2001, CIKM '01.

[13]  Ian H. Witten,et al.  Clustering Documents Using a Wikipedia-Based Concept Representation , 2009, PAKDD.

[14]  Byron Marshall,et al.  Exploring educational standard alignment: in search of 'relevance' , 2008, JCDL '08.

[15]  Patrick F. Reidy.  An Introduction to Latent Semantic Analysis , 2009 .

[16]  A. Graesser,et al.  Improving an intelligent tutor ’ s comprehension of students with Latent Semantic Analysis ∗ , 1999 .

[17]  Michael Collins,et al.  Discriminative Reranking for Natural Language Parsing , 2000, CL.

[18]  Arthur C. Graesser,et al.  Automatic detection of learner’s affect from conversational cues , 2008, User Modeling and User-Adapted Interaction.

[19]  Andrew Olney,et al.  Interactive Concept Maps and Learning Outcomes in Guru , 2012, FLAIRS.

[20]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[21]  Valerie J. Shute,et al.  Conceptual Framework for Modeling , Assessing and Supporting Competencies within Game Environments , 2010 .

[22]  Phil Barker,et al.  IMS meta-data best practice guide for IEEE 1484.12.1-2002 Standard for Learning Object Metadata , 2006 .

[23]  Peter W. Foltz,et al.  The intelligent essay assessor: Applications to educational technology , 1999 .

[24]  Erik Duval,et al.  Automatic evaluation of metadata quality in digital repositories , 2009, International Journal on Digital Libraries.

[25]  Arthur C. Graesser,et al.  Strengths, Limitations, and Extensions of LSA , 2007 .

[26]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[27]  R. Mitkov,et al.  Computer-Aided Generation of Multiple-Choice Tests , 2003, International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003.

[28]  Ramesh Nallapati,et al.  Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[29]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[30]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[31]  Jiangping Chen,et al.  Experimenting with the automatic assignment of educational standards to digital library content , 2005, Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '05).

[32]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[33]  Ramesh Nallapati,et al.  Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability , 2007 .

[34]  Le An Ha,et al.  A computer-aided environment for generating multiple-choice test items , 2006, Natural Language Engineering.

[35]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[36]  Noah A. Smith,et al.  Question Generation via Overgenerating Transformations and Ranking , 2009 .

[37]  Sunghwan Mac Kim,et al.  Evaluation of Unsupervised Emotion Models to Textual Affect Recognition , 2010, HLT-NAACL 2010.

[38]  Arthur C. Graesser,et al.  Computational Analyses of Multilevel Discourse Comprehension , 2011, Top. Cogn. Sci..

[39]  Roberto Navigli,et al.  Word sense disambiguation: A survey , 2009, CSUR.

[40]  David A. Hull,et al.  A Detailed Analysis of English Stemming Algorithms , 2006 .

[41]  Arthur C. Graesser,et al.  Emotions During the Learning of Difficult Material , 2012 .

[42]  Ziqi Zhang,et al.  A Comparative Evaluation of Term Recognition Algorithms , 2008, LREC.

[43]  Maxine Eskénazi,et al.  Automatic Question Generation for Vocabulary Assessment , 2005, HLT.

[44]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.