Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM

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dc.authoridTulu, Cagatay Neftali/0000-0002-4462-3707
dc.authoridOzkaya, Ozge/0000-0002-2001-4608
dc.authoridOrhan, Umut/0000-0003-1882-6567
dc.contributor.authorTulu, Cagatay Neftali
dc.contributor.authorOzkaya, Ozge
dc.contributor.authorOrhan, Umut
dc.date.accessioned2025-01-06T17:44:19Z
dc.date.available2025-01-06T17:44:19Z
dc.date.issued2021
dc.description.abstractAutomatic assessment of exams is widely preferred by educators than multiple-choice exams because of its efficiency in measuring student performance, lack of subjectivity when evaluating student response, and faster evaluation time than the time consuming manual evaluation. In this study, a new approach for the Automatic Short Answer Grading (ASAG) is proposed using MaLSTM and the sense vectors obtained by SemSpace, a synset based sense embedding method built leveraging WordNet. Synset representations of the Student's answers and reference answers are given as input into parallel LSTM architecture, they are transformed into sentence representations in the hidden layer and the vectorial similarity of these two representation vectors are computed with Manhattan Similarity in the output layer. The proposed approach has been tested using the Mohler ASAG dataset and successful results are obtained in terms of Pearson (r) correlation and RMSE. Also, the proposed approach has been tested as a case study using a specific dataset (CU-NLP) created from the exam of the Natural Language Processing course in the Computer Engineering Department of Cukurova University. And it has achieved a successful correlation. The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks.
dc.identifier.doi10.1109/ACCESS.2021.3054346
dc.identifier.endpage19280
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85100492934
dc.identifier.scopusqualityQ1
dc.identifier.startpage19270
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2021.3054346
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3003
dc.identifier.volume9
dc.identifier.wosWOS:000619316000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectSemantics
dc.subjectNatural language processing
dc.subjectBenchmark testing
dc.subjectLong short term memory
dc.subjectDeep learning
dc.subjectTask analysis
dc.subjectLearning systems
dc.subjectAutomatic short answer grading
dc.subjectMaLSTM
dc.subjectsemspace sense vectors
dc.subjectsynset based sense embedding
dc.subjectsentence similarity
dc.titleAutomatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM
dc.typeArticle

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