C-TOOLS automated grading for online concept maps works well with a little help from Wordnet

Criterion concept maps developed by instructors in the C-TOOLS project contain numerous expert-generated or “correct” propositions manually created by expert users connecting two object or subject phrases together with a linking phrase. WordNet, an electronic lexical database, was then used to construct additional proposition derivatives by supplying different linking phrases in place of those originally specified by users. Derived propositions were made from original propositions by substituting linking phrase verbs with troponyms, synonyms, and antonyms. During the past year some 1298 students created concept maps (with 35404 propositions) aided by automatic grading and the WordNet® propositions. We have now studied how successful WordNet was at creating valid additional linking words like those generated by experts. By comparing manual assessments of derived propositions to manual assessments of original propositions, the persistence of correctness was evaluated for determinative factors such as frequencies and senses of usage. Results from data analyses are compared to parameters for WordNet usage in order to better determine the potential for the refining of concept map grading algorithms. An empirical approach to word sense relationships is presented as an iterating step to progressively prototype and test optimizations.

[1]  A. M. White.  The Process of Education , 1994 .

[2]  J. Novak.  Concept mapping: A useful tool for science education , 1990 .

[3]  Kathleen M. Fisher.  SemNet Software as an Assessment Tool , 2005 .

[4]  M. Wittrock.  Generative Learning Processes of the Brain , 1992 .

[5]  John L. Casti,et al.  Searching for Certainty: What Scientists Can Know about the Future , 1992 .

[6]  D. Ausubel.  The psychology of meaningful verbal learning. , 1963 .

[7]  Allan Collins,et al.  Design Research: Theoretical and Methodological Issues , 2004 .

[8]  Larry E. Suter,et al.  Guiding Principles for Mathematics and Science Education Research Methods: Report of a Workshop , 2000 .

[9]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[10]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[11]  Ted Pedersen,et al.  Extended Gloss Overlaps as a Measure of Semantic Relatedness , 2003, IJCAI.

[12]  Douglas B. Luckie,et al.  Introduction to C-TOOLS: concept mapping tools for online learning , 2004 .

[13]  C A Nelson,et al.  Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.

[14]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[15]  John D. Bransford,et al.  Toward a Learning Technologies knowledge network , 1999 .

[16]  Alberto J. Cañas,et al.  Using WordNet for Word Sense Disambiguation to Support Concept Map Construction , 2003, SPIRE.