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2020 | OriginalPaper | Chapter

Exposing Students to New Terminologies While Collecting Browsing Search Data (Best Technical Paper)

Authors : Omar Zammit, Serengul Smith, David Windridge, Clifford De Raffaele

Published in: Artificial Intelligence XXXVII

Publisher: Springer International Publishing

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Abstract

Information overload is a well-known problem that generally occurs when searching for information online. To reduce this effect having prior knowledge on the domain and also a searching strategy is critical. Obtaining such qualities can be challenging for students since they are still learning about various domains and might not be familiar with the domain-specific keywords. In this paper, we are proposing a framework that aims to assist students to have a richer list of keyphrases that are pertinent to a domain under study and provide a mechanism for lectures to understand what search strategies their students are adopting. The proposed framework includes a Google Chrome Extension, a background and a remote server. The Google Chrome Extension is utilized to collect, process browsing data and generate reports containing keyphrases searched by students. The results of the user evaluation were compared with a similar framework (TextRank). The results indicate that our framework performed better in terms of accuracy of keyphrases and response time.

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Maes, P.: Agents that reduce work and information overload. Commun. ACM 37, 30–40 (1994). https://doi.org/10.1145/176789.176792CrossRef

Kilbride, J., Mangina, E.: Automated keyphrase extraction: assisting students in the search for online materials. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 225–230. Springer, Heidelberg (2005). https://doi.org/10.1007/11495772_35CrossRef

Usta, A., Altingovde, I.S., Vidinli, I.B., Ozcan, R., Ulusoy, Ö.: How K-12 students search for learning? Analysis of an educational search engine log. In: SIGIR 2014 - Proceedings of 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1151–1154. ACM (2014). https://doi.org/10.1145/2600428.2609532

Barr, E., Bird, C., Hyatt, E., Menzies, T., Robles, G.: On the shoulders of giants. In: Proceedings of FSE/SDP Workshop on Future of Software Engineering Research FoSER 2010, vol. 41(4), pp. 23–27 (2010). https://doi.org/10.1145/1882362.1882368

Sheeja, N.K.: An analytical study of medical students’ interaction with internet and online resources. Int. J. Inf. Dissem. Technol. 5(3), 167–170 (2015)

Kroustallaki, D., Kokkinaki, T., Sideridis, G.D., Simos, P.G.: Exploring students’ affect and achievement goals in the context of an intervention to improve web searching skills. Comput. Hum. Behav. 49, 156–170 (2015). https://doi.org/10.1016/j.chb.2015.02.060CrossRef

Wu, D., Cai, W.: An empirical study on Chinese adolescents’ web search behavior. J. Doc. 72, 435–453 (2016). https://doi.org/10.1108/JD-04-2015-0047CrossRef

Zhou, M.: SCOOP: a measurement and database of student online search behavior and performance. Brit. J. Educ. Technol. 46(5), 928–931 (2015). https://doi.org/10.1111/bjet.12290CrossRef

Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: Practical automated keyphrase extraction. In: Design and Usability of Digital Libraries: Case Studies in the Asia Pacific, pp. 129–152. IGI Global (2005)

10.

Monchaux, S., Amadieu, F., Chevalier, A., Mariné, C.: Query strategies during information searching: effects of prior domain knowledge and complexity of the information problems to be solved. Inf. Process. Manag. 51(5), 557–569 (2015). https://doi.org/10.1016/j.ipm.2015.05.004CrossRef

11.

Sanchiz, M., Chin, J., Chevalier, A., Fu, W.T., Amadieu, F., He, J.: Searching for information on the web: impact of cognitive aging, prior domain knowledge and complexity of the search problems. Inf. Process. Manag. 53(1), 281–294 (2017). https://doi.org/10.1016/j.ipm.2016.09.003CrossRef

12.

Chen, K.T.C.: University EFL students’ use of online English information searching strategy. Iran. J. Lang. Teach. Res. 8(1), 111–127 (2020)

13.

Tsai, M.J.: Online Information Searching Strategy Inventory (OISSI): a quick version and a complete version. Comput. Educ. 53(2), 473–483 (2009). https://doi.org/10.1016/j.compedu.2009.03.006MathSciNetCrossRef

14.

Zammit, O., Smith, S., De Raffaele, C., Petridis, M.: Exposing knowledge: providing a real-time view of the domain under study for students. In: Bramer, M., Petridis, M. (eds.) SGAI 2019. LNCS (LNAI), vol. 11927, pp. 122–135. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34885-4_9CrossRef

15.

Ribeiro, J., Henrique, J., Ribeiro, R., Neto, R.: NoSQL vs relational database: a comparative study about the generation of the most frequent N-grams. In: 2017 4th International Conference on System and Informatics, ICSAI 2017, vol. 2018-January, pp. 1568–1572. Institute of Electrical and Electronics Engineers Inc., June 2017. https://doi.org/10.1109/ICSAI.2017.8248535

16.

Ahmad, A., Rub Talha, M., Ruhul Amin, M., Chowdhury, F.: Pipilika N-Gram viewer: an efficient large scale N-Gram model for Bengali. In: 2018 International Conference on Bangla Speech and Language Processing, ICBSLP 2018. Institute of Electrical and Electronics Engineers Inc., November 2018. https://doi.org/10.1109/ICBSLP.2018.8554474

17.

Gledec, G., Soic, R., Dembitz, S.: Dynamic N-Gram system based on an online croatian spellchecking service. IEEE Access 7, 149988–149995 (2019). https://doi.org/10.1109/ACCESS.2019.2947898CrossRef

18.

Irfan, R., Khan, S., Khan, I.A., Ali, M.A.: KeaKAT - an online automatic keyphrase assignment tool. In: Proceedings of 10th International Conference on Frontiers of Information Technology FIT 2012, pp. 30–34 (2012). https://doi.org/10.1109/FIT.2012.14

19.

Mihalcea, R.: Graph-based ranking algorithms for sentence extraction, applied to text summarization. In: Proceedings of EMNLP, vol. 85, p. 20 (2004). https://doi.org/10.3115/1219044.1219064

20.

Kim, J.Y., Collins-Thompson, K., Bennett, P.N., Dumais, S.T.: Characterizing web content, user interests, and search behavior by reading level and topic. In: WSDM 2012 - Proceedings of 5th ACM International Conference on Web Search Data Mining, pp. 213–222. ACM, New York (2012). https://doi.org/10.1145/2124295.2124323

21.

Chaithanya, K., Reddy, P.V.: A novel approach for document clustering using concept extraction. Int. J. Innov. Res. Adv. Eng. 3 (2016)

22.

Mesquita, D.P.P., Gomes, J.P.P., Souza Junior, A.H., Nobre, J.S.: Euclidean distance estimation in incomplete datasets. Neurocomputing 248, 11–18 (2017)

23.

Niwattanakul, S., Singthongchai, J., Naenudorn, E., Wanapu, S.: Using of Jaccard coefficient for keywords similarity. Lect. Notes Eng. Comput. Sci. 1, 380–384 (2013)

24.

Flowers, C., Mansour, A., Al-Khateeb, H.M.: Web browser artefacts in private and portable modes: a forensic investigation. Int. J. Electron. Secur. Digit. Forensics 8(2), 99–117 (2016). https://doi.org/10.1504/IJESDF.2016.075583CrossRef

25.

Fan, J., Fan, Y.: HIGH-Dimensional classification using features annealed independence rules. Ann. Stat. 36(6), 2605–2637 (2008). https://doi.org/10.1214/07-AOS504MathSciNetCrossRefMATH

26.

Havrlant, L., Kreinovich, V.: A simple probabilistic explanation of term frequency-inverse document frequency (TF-IDF) heuristic (and variations motivated by this explanation). Int. J. Gen. Syst. 46(1), 27–36 (2017). https://doi.org/10.1080/03081079.2017.1291635MathSciNetCrossRef

27.

Joorabchi, A., Mahdi, A.E.: Automatic subject metadata generation for scientific documents using Wikipedia and genetic algorithms. In: ten Teije, A., et al. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 32–41. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33876-2_6CrossRef

28.

Gollapalli, S.D., Caragea, C.: Extracting keyphrases from research papers using citation networks. In: Proceedings of the National Conference on Artificial Intelligence, vol. 2, pp. 1629–1635 (2014)

29.

Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, pp. 216–223. Association for Computational Linguistics (2003). https://doi.org/10.3115/1119355.1119383

30.

Aquino, G., Lanzarini, L.: Keyword identification in Spanish documents using neural networks. J. Comput. Sci. Technol. 15(2), 55–60 (2015)

31.

Kim, S.N., Medelyan, O., Kan, M.Y., Baldwin, T.: SemEval-2010 task 5: automatic keyphrase extraction from scientific articles. In: ACL 2010 - SemEval 2010–5th Proceedings of International Workshop on Semantic Evaluation, pp. 21–26. Association for Computational Linguistics (2010)

32.

Krapivin, M.: Large Dataset for Keyphrase Extraction. Technical Report May 2008, University of Trento (2008)

33.

Nguyen, T.D., Kan, M.-Y.: Keyphrase extraction in scientific publications. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 317–326. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77094-7_41CrossRef

34.

Schutz, A.T.: Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical methods.Master of Applied Science (MAppSc) (2008)

Title: Exposing Students to New Terminologies While Collecting Browsing Search Data (Best Technical Paper)
Authors: Omar Zammit
Serengul Smith
David Windridge
Clifford De Raffaele
Publisher: Springer International Publishing
Book: Artificial Intelligence XXXVII
Print ISBN: 978-3-030-63798-9

Electronic ISBN: 978-3-030-63799-6

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63799-6_1

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