Abstract
Although many areas within the education sector have been subjected to digitalization, including electronic storage and processing of student information, student-administrative tasks are still often handled manually. One such task is the linking of student inaccurate information from different sources such as the task of aligning teachers grade spreadsheets with standardized exam template spreadsheets. Manual linking of records can be tedious, monotonous, and error-prone, especially in large classes with several hundred students. Although automatic robust record linking is common within other areas such as medicine, there are surprisingly few linking tools aimed at educators. The tool FLINK was therefore developed to assist with this task. FLINK was developed over a period of three years through practical experimentation and testing. This paper presents the rationale for the tool, practical use cases, and key design decisions. The tool provides a simple and flexible link between how educators interact with inaccurate student information in practice on one hand, and how they must relate to inflexible administration tools that require exact formal information on the other.
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References
Hall, P.A., Dowling, G.R.: Approximate string matching. ACM Comput. Surv. 12(4), 381–402 (1980)
Navarro, G.: A guided tour to approximate string matching. ACM Comput. Surv. 33(1), 31–88 (2001)
Dorneles, C.F., Gonçalves, R., dos Santos Mello, R.: Approximate data instance matching: a survey. Knowl. Inf. Syst. 27, 1–21 (2011)
Al-Khamaiseh, K., Alshagarin, S.: A survey of string matching algorithms. Int. J. Eng. Res. Appl 4(7), 144–156 (2014)
Winkler, W. E.: String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage. Technical report (1990)
Gomaa, W.H., Fahmy, A.A.: A survey of text similarity approaches. Int. J. Comput. Appl. 68(13), 13–18 (2013)
Chaudhuri, S., Chen, B.C., Ganti, V., Kaushik, R.: Example-driven design of efficient record matching queries. In: VLDB, vol. 7, pp. 327–338 (2007)
Gravano, L., Ipeirotis, P.G., Koudas, N., Srivastava, D.: Text joins in an RDBMS for web data integration. In: Proceedings of the 12th International Conference on World Wide Web, pp. 90–101 (2003)
Jaro, M.A.: Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J. Am. Stat. Assoc. 84(406), 414–420 (1989)
Jaro, M.A.: Probabilistic linkage of large public health data files. Stat. Med. 14(5–7), 491–498 (1995)
Schnell, R., Bachteler, T., Reiher, J.: Privacy-preserving record linkage using Bloom filters. BMC Med. Inform. Decis. Mak. 9(1), 1–11 (2009)
Bachteler, T., Schnell, R., Reiher, J.: An empirical comparison of approaches to approximate string matching in private record linkage. In: Proceedings of Statistics Canada Symposium, vol. 2010. Statistics Canada, Ottawa (2010)
Sandnes, F.E.: HIDE: short IDs for robust and anonymous linking of users across multiple sessions in small HCI experiments. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (2021)
Sandnes, F.E.: CANDIDATE: a tool for generating anonymous participant-linking IDs in multi-session studies. PLoS ONE 16(12), e0260569 (2021)
Sandnes, F.E.: BRIDGE: administering small anonymous longitudinal HCI studies with snowball-type sampling. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12935, pp. 287–297. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85610-6_17
Lin, C.Y., Och, F.J.: Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 605–612 (2004)
Sandnes, F.E.: Reflective text entry: a simple low effort predictive input method based on flexible abbreviations. Procedia Comput. Sci. 67, 105–112 (2015)
Zhang, L., Zhou, M., Huang, C., Pan, H.: Automatic detecting/correcting errors in Chinese text by an approximate word-matching algorithm. In: Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pp. 248–254 (2000)
Liu, B., Han, D., Zhang, S.: Approximate Chinese string matching techniques based on pinyin input method. Appl. Mech. Mater. 513, 1017–1020 (2014)
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Sandnes, F.E. (2023). FLINK: An Educator’s Tool for Linking Inaccurate Student Records. In: Huang, YM., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2023. Lecture Notes in Computer Science, vol 14099. Springer, Cham. https://doi.org/10.1007/978-3-031-40113-8_14
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DOI: https://doi.org/10.1007/978-3-031-40113-8_14
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