Evaluating the authenticity of the PowerPoint presentations’ contents using word embedding techniques
In the educational system, assessments are essential for evaluating students’ performance. An evaluation using manual grading is a laborious and time-consuming task and is vulnerable to inconsistencies and inaccuracies. Even though there has been significant research to automate the evaluation of st...
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| Vydáno v: | International journal of information technology (Singapore. Online) Ročník 15; číslo 4; s. 2303 - 2316 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Singapore
Springer Nature Singapore
01.04.2023
Springer Nature B.V |
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| ISSN: | 2511-2104, 2511-2112 |
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| Abstract | In the educational system, assessments are essential for evaluating students’ performance. An evaluation using manual grading is a laborious and time-consuming task and is vulnerable to inconsistencies and inaccuracies. Even though there has been significant research to automate the evaluation of student work, researchers still need to consider PowerPoint presentation grading. In our earlier research, we graded students’ PowerPoint presentations based on the quality features, not the contents. In this study, we have graded PowerPoint presentations based on the text contents to check the students’ expertise on the topic. Our approach consists of two main steps. The first step extracts the text from the PowerPoint presentations using the python-pptx library. PowerPoint presentation text is represented in vectors using various word embedding techniques in the semantic space (SS). In the next step, similarities between students’ and reference PowerPoint presentation vectors are calculated using Cosine Similarity (CS). Depending on the similarity score, the student’s presentation is graded automatically. Experimental results depict that the results gained using the tf-idf word embedding technique are comparable. The system proposed using tf-idf word embedding gives better results than other word embedding techniques. The agreement between the human score and our system score is measured using Quadratic Weighted Kappa (QWK). Our system performs with a QWK score of 0.88 and an accuracy of 82.60%. |
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| AbstractList | In the educational system, assessments are essential for evaluating students’ performance. An evaluation using manual grading is a laborious and time-consuming task and is vulnerable to inconsistencies and inaccuracies. Even though there has been significant research to automate the evaluation of student work, researchers still need to consider PowerPoint presentation grading. In our earlier research, we graded students’ PowerPoint presentations based on the quality features, not the contents. In this study, we have graded PowerPoint presentations based on the text contents to check the students’ expertise on the topic. Our approach consists of two main steps. The first step extracts the text from the PowerPoint presentations using the python-pptx library. PowerPoint presentation text is represented in vectors using various word embedding techniques in the semantic space (SS). In the next step, similarities between students’ and reference PowerPoint presentation vectors are calculated using Cosine Similarity (CS). Depending on the similarity score, the student’s presentation is graded automatically. Experimental results depict that the results gained using the tf-idf word embedding technique are comparable. The system proposed using tf-idf word embedding gives better results than other word embedding techniques. The agreement between the human score and our system score is measured using Quadratic Weighted Kappa (QWK). Our system performs with a QWK score of 0.88 and an accuracy of 82.60%. |
| Author | Kiwelekar, A. W. Netak, L. D. Borade, J. G. |
| Author_xml | – sequence: 1 givenname: J. G. orcidid: 0000-0002-6062-7249 surname: Borade fullname: Borade, J. G. email: jyoti.borade81@gmail.com organization: Department of Computer Engineering, Dr. Babasaheb Ambedkar Technological University – sequence: 2 givenname: L. D. surname: Netak fullname: Netak, L. D. organization: Department of Computer Engineering, Dr. Babasaheb Ambedkar Technological University – sequence: 3 givenname: A. W. surname: Kiwelekar fullname: Kiwelekar, A. W. organization: Department of Computer Engineering, Dr. Babasaheb Ambedkar Technological University |
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| Copyright | The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. |
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| References | Borade JG, Netak LD. Automated grading of essays: a review. In: Singh M, Kang DK, Lee JH, Tiwary US, Singh D, Chung WY (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science, vol 12615, pp 238–249. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_25 Wilson J (2018) Universal screening with automated essay scoring: evaluating classification accuracy in grades 3 and 4. J School Psychol. 68:19–37. https://doi.org/10.1016/j.jsp.2017.12.005 SrihariJCSrihariRSrinivasanHShettySBrutt-GrifflerJAutomatic scoring of short handwritten essays in reading comprehension testsArtif Intell200817230032410.1016/j.artint.2007.06.005ISSN 0004-3702 GoularteFBNassarSMFiletoRSaggionHA text summarization method based on fuzzy rules and applicable to automated assessmentExpert Syst Appl201911526427510.1016/j.eswa.2018.07.047ISSN 0957-4174 BoradeJGKiwelekarAWNetakLDAutomated grading of PowerPoint presentation using latent semantic analysisRevue d'Intelligence Artificielle, Int Inform Eng Technol202236230531110.18280/ria.360215issn: 1958-5748 Surya K, Ekansh G, Kailasanathan, Nallakaruppan (2019) Deep learning for short answer scoring. Int J Recent Technol Eng. 7:1712–1715 ZupancKBosnicZAutomated Essay Evaluation Augmented with Semantic Coherence MeasuresIEEE International Conference on Data Mining201420141133113810.1109/ICDM.2014.21 FazalAnharHFTharamDAn innovative approach for automatically grading spelling in essays using rubric-based scoringJ Comput Syst Sci2013791040105610.1016/j.jcss.2013.01.021 KaurHMainiRAssessing lexical similarity between short sentences of source code based on granularityInt j inf tecnol20191159961410.1007/s41870-018-0213-1 Olowolayemo A, Nawi SD, Mantoro T (2018) Short answer scoring in English grammar using text similarity measurement”, ICCED：131–136. https://doi.org/10.1109/ICCED.2018.00034 Thongyoo T, Saelee S, Krootjohn S (2016) Automated Thai online assignment scoring. Fifth ICT International Student Project Conference (ICT-ISPC), pp. 33–36.doi: https://doi.org/10.1109/ICT-ISPC.2016.7519229. AhamadMAhmadNStudents’ knowledge assessment using the ensemble methodsInt j inf tecnol2021131025103210.1007/s41870-020-00593-8 Chaturvedi B, Basak R (2019) Automatic short-answer grading using corpus-based semantic similarity measurements. Proc ICACIE 2019 2:266–281 Peng X, Ke D, Chen Z, Xu B (2010) Automated Chinese essay scoring using vector space models. 4th International Universal Communication Symposium, Beijing, 149–153. BoradeJGKiwelekarAWNetakLDAutomatic grading of student's presentation skills based on PowerPoint presentation and audioUPorto J Eng202289510710.2484/2183-6493_008.002_0008ISSN 2183-6493 Sagar P, Ziyaan D, Praveen S, Rajdeep D, Amey K, Arnab B (2017) Automatic Grading and Feedback using Program Repair for Introductory Programming Courses. ITiCSE, pp. 92–97 Azmi AqilMAl-JouieMFHussainMAAEE–Automated evaluation of students’ essays in the Arabic languageInf Process Manage20195651736175210.1016/j.ipm.2019.05.008 Anak R, Putri A, Dyah L, Ihsan I, Diyanatul H, Prima P (2018) Automatic essay grading system for Japanese language examination using winnowing algorithm. International Seminar on Application for Technology of Information and Communication (iSemantic), pp 565–569 George N, Sijimol PJ, Varghese S (2019) Grading descriptive answer scripts using deep learning. IJITEE 8(5):991–996 ChandraMABediSSSurvey on SVM and their application in image classificationInt j inf tecnol20211311110.1007/s41870-017-0080-1 SüzenNGorbanANLevesleyJMirkesEMAutomatic short answer grading and feedback using text mining methodsProc Comput Sci202016972674310.1016/j.procs.2020.02.171ISSN 1877-0509 MeshramSAnand KumarMLong short-term memory network for learning sentences similarity using deep contextual embeddingsInt j inf tecnol2021131633164110.1007/s41870-021-00686-y Ajitiono T, Widyani Y (2016) Indonesian essay grading module using Natural Language Processing. International Conference on Data and Software Engineering (ICoDSE), pp. 1–5. https://doi.org/10.1109/ICODSE.2016.7936117 JandaHKPawarADuSMagoVSyntactic, semantic and sentiment analysis: the joint effect on automated essay evaluationIEEE Access2019710848610850310.1109/ACCESS.2019.2933354 SonawaneSSKulkarniPConcept-based document similarity using graph modelInt j inf tecnol20221431132210.1007/s41870-019-00314-w Youfang L, Li Y, Xiong J (2019) DeepReviewer: Collaborative Grammar and Innovation Neural Network for Automatic Paper Review. ICMI, pp. 395–403 Aluizio HF, Hercules P, Edilson F, Jonathan N (2018) An approach to evaluate adherence to the theme and the argumentative structure of essays. Proc Comput Sci 126:788–797 TarandeepWGurpreetJAmarpalSAn efficient automated answer scoring system for the Punjabi language”Egypt Inform J2018208996 Borade JG, Kiwelekar AW, Netak LD (2022) FeatureExtraction for automatic grading of students’ presentations. In: ICT systems and sustainability. Tuba M, Akashe S, Joshi A (eds) Springer Nature Singapore, Singapore vol 321, pp 293–301. https://doi.org/10.1007/978-981-16-5987-4_30. Online ISBN: 978-981-16-5987-4 Chang T, Lee C (2009) Automatic Chinese essay scoring using connections between concepts in paragraphs. 2009 International Conference on Asian Language Processing, pp. 265–268. https://doi.org/10.1109/IALP.2009.63 Borade JG, Kiwelekar AW, Netak LD (2022) Machine learning techniques for grading students’ presentations”. Intelligent Human Computer Interaction (2021), Lecture Notes in Computer Science, Springer, USA 13184:3–15. https://doi.org/10.1007/978-3-030-98404-5_1 M Azmi Aqil (1223_CR14) 2019; 56 1223_CR23 1223_CR25 MA Chandra (1223_CR2) 2021; 13 1223_CR24 JC Srihari (1223_CR22) 2008; 172 FB Goularte (1223_CR19) 2019; 115 H Kaur (1223_CR29) 2019; 11 1223_CR8 1223_CR9 1223_CR6 S Meshram (1223_CR30) 2021; 13 1223_CR7 1223_CR4 1223_CR5 N Süzen (1223_CR21) 2020; 169 1223_CR3 1223_CR1 HF FazalAnhar (1223_CR11) 2013; 79 SS Sonawane (1223_CR28) 2022; 14 1223_CR13 M Ahamad (1223_CR31) 2021; 13 W Tarandeep (1223_CR15) 2018; 20 1223_CR10 K Zupanc (1223_CR20) 2014; 2014 HK Janda (1223_CR12) 2019; 7 JG Borade (1223_CR26) 2022; 8 1223_CR16 1223_CR18 1223_CR17 JG Borade (1223_CR27) 2022; 36 |
| References_xml | – reference: BoradeJGKiwelekarAWNetakLDAutomatic grading of student's presentation skills based on PowerPoint presentation and audioUPorto J Eng202289510710.2484/2183-6493_008.002_0008ISSN 2183-6493 – reference: TarandeepWGurpreetJAmarpalSAn efficient automated answer scoring system for the Punjabi language”Egypt Inform J2018208996 – reference: MeshramSAnand KumarMLong short-term memory network for learning sentences similarity using deep contextual embeddingsInt j inf tecnol2021131633164110.1007/s41870-021-00686-y – reference: George N, Sijimol PJ, Varghese S (2019) Grading descriptive answer scripts using deep learning. IJITEE 8(5):991–996 – reference: KaurHMainiRAssessing lexical similarity between short sentences of source code based on granularityInt j inf tecnol20191159961410.1007/s41870-018-0213-1 – reference: Chaturvedi B, Basak R (2019) Automatic short-answer grading using corpus-based semantic similarity measurements. Proc ICACIE 2019 2:266–281 – reference: Anak R, Putri A, Dyah L, Ihsan I, Diyanatul H, Prima P (2018) Automatic essay grading system for Japanese language examination using winnowing algorithm. International Seminar on Application for Technology of Information and Communication (iSemantic), pp 565–569 – reference: SrihariJCSrihariRSrinivasanHShettySBrutt-GrifflerJAutomatic scoring of short handwritten essays in reading comprehension testsArtif Intell200817230032410.1016/j.artint.2007.06.005ISSN 0004-3702 – reference: Azmi AqilMAl-JouieMFHussainMAAEE–Automated evaluation of students’ essays in the Arabic languageInf Process Manage20195651736175210.1016/j.ipm.2019.05.008 – reference: Aluizio HF, Hercules P, Edilson F, Jonathan N (2018) An approach to evaluate adherence to the theme and the argumentative structure of essays. Proc Comput Sci 126:788–797 – reference: Thongyoo T, Saelee S, Krootjohn S (2016) Automated Thai online assignment scoring. Fifth ICT International Student Project Conference (ICT-ISPC), pp. 33–36.doi: https://doi.org/10.1109/ICT-ISPC.2016.7519229. – reference: Borade JG, Kiwelekar AW, Netak LD (2022) FeatureExtraction for automatic grading of students’ presentations. In: ICT systems and sustainability. Tuba M, Akashe S, Joshi A (eds) Springer Nature Singapore, Singapore vol 321, pp 293–301. https://doi.org/10.1007/978-981-16-5987-4_30. Online ISBN: 978-981-16-5987-4 – reference: BoradeJGKiwelekarAWNetakLDAutomated grading of PowerPoint presentation using latent semantic analysisRevue d'Intelligence Artificielle, Int Inform Eng Technol202236230531110.18280/ria.360215issn: 1958-5748 – reference: Borade JG, Kiwelekar AW, Netak LD (2022) Machine learning techniques for grading students’ presentations”. Intelligent Human Computer Interaction (2021), Lecture Notes in Computer Science, Springer, USA 13184:3–15. https://doi.org/10.1007/978-3-030-98404-5_1 – reference: Borade JG, Netak LD. Automated grading of essays: a review. In: Singh M, Kang DK, Lee JH, Tiwary US, Singh D, Chung WY (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science, vol 12615, pp 238–249. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_25 – reference: ChandraMABediSSSurvey on SVM and their application in image classificationInt j inf tecnol20211311110.1007/s41870-017-0080-1 – reference: Wilson J (2018) Universal screening with automated essay scoring: evaluating classification accuracy in grades 3 and 4. J School Psychol. 68:19–37. https://doi.org/10.1016/j.jsp.2017.12.005 – reference: Ajitiono T, Widyani Y (2016) Indonesian essay grading module using Natural Language Processing. 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