Raising the Dimensions and Variables for Searching as a Learning Process: A Systematic Mapping of the Literature
Resumo
Search engines are great allies in our daily educational tasks. However, usually, these tools are prepared only for factual learning and are less effective when dealing with more complex learning tasks. Thus, in recent years, Searching as Learning (SAL) research area has been developing from proposals that target the main challenges involving learning during the search process. The effectiveness of educational technologies in providing appropriate instructions depends directly on the input information. Gathering information on what should be taken into account in a search as a learning process can support the development of specialized search engines to support learning. Therefore, we performed a systematic mapping of the literature in order to gather this information, raising the dimensions and their associated variables.Referências
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Azpiazu, I. M., Dragovic, N., Pera, M. S., and Fails, J. A. (2017). Online searching and learning: Yum and other search tools for children and teachers. Information Retrieval Journal, 20:524–545.
Bates, M. J. et al. (1989). The design of browsing and berrypicking techniques for the online search interface. Online review, 13(5):407–424.
Bhattacharya, N. and Gwizdka, J. (2019). Measuring learning during search: Differences in interactions, eye-gaze, and semantic similarity to expert knowledge. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, pages 63–71. ACM.
Biletskiy, Y., Baghi, H., Keleberda, I., and Fleming, M. (2009). An adjustable personalization of search and delivery of learning objects to learners. Expert Systems with Applications, 36(5):9113–9120.
Crescenzi, A. (2016). Metacognitive knowledge and metacognitive regulation in timeconstrained in information search. In SAL@ SIGIR, volume 1647.
Fails, J. A., Pera, M. S., Anuyah, O., Kennington, C., Wright, K. L., and Bigirimana, W. (2019). Query formulation assistance for kids: What is available, when to help & what kids want. In Proceedings of the 18th ACM International Conference on Interaction Design and Children, pages 109–120.
Ghosh, S., Rath, M., and Shah, C. (2018). Searching as learning: Exploring search behavior and learning outcomes in learning-related tasks. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pages 22–31.
Gossen, T., Hempel, J., and Nurnberger, A. (2013). Find it if you can: usability case study of search engines for young users. Personal and Ubiquitous Computing, 17(8):1593–1603.
Gwizdka, J., Hansen, P., Hauff, C., He, J., and Kando, N. (2016). Search as learning (sal) workshop 2016. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pages 1249–1250. ACM.
Hoppe, A., Holtz, P., Kammerer, Y., Yu, R., Dietze, S., and Ewerth, R. (2018). Current challenges for studying search as learning processes. In 7th Workshop on Learning & Education with Web Data (LILE2018), in conjunction with ACM Web Science 2018, Amsterdam, NL, 27 May, 2018.
Ibieta, A., Hinostroza, J. E., and Labbe, C. (2019). Improving students’ information problem-solving skills on the web through explicit instruction and the use of customized search software. Journal of Research on Technology in Education, 0:1–22.
Inhelder, B. and Piaget, J. (1958). The Growth of Logical Thinking from Childhood to Adolescence: An Essay on the Construction of Formal Operational Structures. Developmental psychology]. Routledge.
Jansen, B. J., Booth, D., and Smith, B. (2009). Using the taxonomy of cognitive learning to model online searching. Information Processing & Management, 45(6):643–663.
Karanam, S. and van Oostendorp, H. (2016). Integrating domain knowledge differences into modeling user clicks on search result pages. In SAL@ SIGIR.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Information and software technology.
Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory into practice, 41(4):212–218.
Lu, Y. and Hsiao, I.-H. (2017). Personalized information seeking assistant (pisa): from programming information seeking to learning. Information Retrieval Journal, 20(5):433–455.
Lucchese, C., Nardini, F. M., Perego, R., Trani, R., and Venturini, R. (2018). Efficient and effective query expansion for web search. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 1551–1554.
Machado, M., Pinelli, C., and Siqueira, S. (2019). A evolução da área de busca como um processo de aprendizagem com base em um mapeamento sistemático. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação, pages 833-842. http://dx.doi.org/10.5753/cbie.wcbie.2019.833
Machado, M. d. O. C., Bravo, N. F. S., Martins, A. F., Bernardino, H. S., Barrere, E., and de Souza, J. F. (2020). Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature. Artificial Intelligence Review, pages 1–44.
Mao, J., Liu, Y., Zhang, M., and Ma, S. (2016). How does domain expertise affect users’ search processes in exploratory searches? In SAL@ SIGIR.
Marchionini, G. (2006). Exploratory search: from finding to understanding. Communications of the ACM, 49(4):41–46.
Maxwell, D., Azzopardi, L., and Moshfeghi, Y. (2019). The impact of result diversification on search behaviour and performance. Information Retrieval Journal, 22(5):422–446.
Moraes, F., Putra, S. R., and Hauff, C. (2018). Contrasting search as a learning activity with instructor-designed learning. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 167–176. ACM.
Moreno-Marcos, P. M., Pong, T.-C., Munoz-Merino, P. J., and Kloos, C. D. (2020). Analysis of the factors influencing learners’ performance prediction with learning analytics. IEEE Access, 8:5264–5282.
Pereira, C. K., Medeiros, J. F., Siqueira, S. W., and Nunes, B. P. (2019). How complex is the complexity of a concept in exploratory search. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), volume 2161, pages 17–21. IEEE. https://doi.org/10.1109/ICALT.2019.00008
Pirolli, P. and Card, S. (1999). Information foraging. Psychological Review, 106:643–675.
Rieh, S. Y., Collins-Thompson, K., Hansen, P., and Lee, H.-J. (2016). Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science, 42(1):19–34.
Rieh, S. Y., Kim, Y.-M., and Markey, K. (2012). Amount of invested mental effort (aime) in online searching. Information Processing & Management, 48(6):1136–1150.
Sendurur, E., Efendioglu, E., Senturk, H., and Calıskan, N. (2019). High achievers’ web searching behaviors and patterns in two different task types. Journal of Educational Multimedia and Hypermedia, 28(2):217–238.
Shi, J., Otto, C., Hoppe, A., Holtz, P., and Ewerth, R. (2019). Investigating correlations of automatically extracted multimodal features and lecture video quality. In Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information, pages 11–19.
Smith, C. L. and Rieh, S. Y. (2019). Knowledge-context in search systems: toward information-literate actions. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, pages 55–62.
Soulier, L. and Tamine, L. (2017). On the collaboration support in information retrieval. ACM Computing Surveys, 50(4):1–34.
Syed, R. and Collins-Thompson, K. (2016). Optimizing search results for educational goals: Incorporating keyword density as a retrieval objective. In SAL@ SIGIR.
Syed, R. and Collins-Thompson, K. (2017). Retrieval algorithms optimized for human learning. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 555–564.
Syed, R. and Collins-Thompson, K. (2018). Exploring document retrieval features associated with improved short-and long-term vocabulary learning outcomes. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pages 191–200.
Taibi, D., Fulantelli, G., Marenzi, I., Nejdl, W., Rogers, R., and Ijaz, A. (2017). Sarweb: a semantic web tool to support search as learning practices and cross-language results on the web. In 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pages 522–524. IEEE.
Tibau, M., Siqueira, S., and Nunes, B. P. (2019a). A comparison between entity-centric knowledge base and knowledge graph to represent semantic relationships for searching as learning situations. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação, volume 8, pages 823-832. http://dx.doi.org/10.5753/cbie.wcbie.2019.823
Tibau, M., Siqueira, S. W., Nunes, B. P., Bortoluzzi, M., Marenzi, I., and Kemkes, P. (2018). Investigating users’ decision-making process while searching online and their shortcuts towards understanding. In International Conference on Web-Based Learning, pages 54–64. Springer. https://doi.org/10.1007/978-3-319-96565-9_6
Tibau, M., Siqueira, S. W., Nunes, B. P., Nurmikko-Fuller, T., and Manrique, R. F. (2019b). Using query reformulation to compare learning behaviors in web search engines. In 2019 IEEE 19th International Conference on Advanced Learning Technologies, volume 2161, pages 219–223. IEEE.
Tolmachova, T., Xu, L., Marenzi, I., and Gadiraju, U. (2019). Visualizing search history in web learning. In International Conference on Web-Based Learning, pages 229–240. Springer.
Vakkari, P. (2016). Searching as learning: A systematization based on literature. Journal of Information Science, 42(1):7–18.
Vakkari, P., Volske, M., Potthast, M., Hagen, M., and Stein, B. (2019). Modeling the usefulness of search results as measured by information use. Information Processing & Management, 56:879–894.
Weingart, N. and Eickhoff, C. (2016). Retrieval techniques for contextual learning. In SAL@ SIGIR.
Wilson, M. J. and Wilson, M. L. (2013). A comparison of techniques for measuring sensemaking and learning within participant-generated summaries. Journal of the American Society for Information Science and Technology, 64:291–306.
Wilson, M. L., Ye, C., Twidale, M. B., Grasse, H., Rosenthal, J., and McKittrick, M. (2016). Search literacy: Learning to search to learn. In SAL@ SIGIR, volume 1647.
Yilmaz, T., Ozcan, R., Altingovde, I. S., and Ulusoy, H. (2019). Improving educational web search for question-like queries through subject classification. Information Processing & Management, 56:228–246.
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., and Dietze, S. (2018). Predicting user knowledge gain in informational search sessions. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 75–84.
Azpiazu, I. M., Dragovic, N., Pera, M. S., and Fails, J. A. (2017). Online searching and learning: Yum and other search tools for children and teachers. Information Retrieval Journal, 20:524–545.
Bates, M. J. et al. (1989). The design of browsing and berrypicking techniques for the online search interface. Online review, 13(5):407–424.
Bhattacharya, N. and Gwizdka, J. (2019). Measuring learning during search: Differences in interactions, eye-gaze, and semantic similarity to expert knowledge. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, pages 63–71. ACM.
Biletskiy, Y., Baghi, H., Keleberda, I., and Fleming, M. (2009). An adjustable personalization of search and delivery of learning objects to learners. Expert Systems with Applications, 36(5):9113–9120.
Crescenzi, A. (2016). Metacognitive knowledge and metacognitive regulation in timeconstrained in information search. In SAL@ SIGIR, volume 1647.
Fails, J. A., Pera, M. S., Anuyah, O., Kennington, C., Wright, K. L., and Bigirimana, W. (2019). Query formulation assistance for kids: What is available, when to help & what kids want. In Proceedings of the 18th ACM International Conference on Interaction Design and Children, pages 109–120.
Ghosh, S., Rath, M., and Shah, C. (2018). Searching as learning: Exploring search behavior and learning outcomes in learning-related tasks. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pages 22–31.
Gossen, T., Hempel, J., and Nurnberger, A. (2013). Find it if you can: usability case study of search engines for young users. Personal and Ubiquitous Computing, 17(8):1593–1603.
Gwizdka, J., Hansen, P., Hauff, C., He, J., and Kando, N. (2016). Search as learning (sal) workshop 2016. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pages 1249–1250. ACM.
Hoppe, A., Holtz, P., Kammerer, Y., Yu, R., Dietze, S., and Ewerth, R. (2018). Current challenges for studying search as learning processes. In 7th Workshop on Learning & Education with Web Data (LILE2018), in conjunction with ACM Web Science 2018, Amsterdam, NL, 27 May, 2018.
Ibieta, A., Hinostroza, J. E., and Labbe, C. (2019). Improving students’ information problem-solving skills on the web through explicit instruction and the use of customized search software. Journal of Research on Technology in Education, 0:1–22.
Inhelder, B. and Piaget, J. (1958). The Growth of Logical Thinking from Childhood to Adolescence: An Essay on the Construction of Formal Operational Structures. Developmental psychology]. Routledge.
Jansen, B. J., Booth, D., and Smith, B. (2009). Using the taxonomy of cognitive learning to model online searching. Information Processing & Management, 45(6):643–663.
Karanam, S. and van Oostendorp, H. (2016). Integrating domain knowledge differences into modeling user clicks on search result pages. In SAL@ SIGIR.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Information and software technology.
Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory into practice, 41(4):212–218.
Lu, Y. and Hsiao, I.-H. (2017). Personalized information seeking assistant (pisa): from programming information seeking to learning. Information Retrieval Journal, 20(5):433–455.
Lucchese, C., Nardini, F. M., Perego, R., Trani, R., and Venturini, R. (2018). Efficient and effective query expansion for web search. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 1551–1554.
Machado, M., Pinelli, C., and Siqueira, S. (2019). A evolução da área de busca como um processo de aprendizagem com base em um mapeamento sistemático. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação, pages 833-842. http://dx.doi.org/10.5753/cbie.wcbie.2019.833
Machado, M. d. O. C., Bravo, N. F. S., Martins, A. F., Bernardino, H. S., Barrere, E., and de Souza, J. F. (2020). Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature. Artificial Intelligence Review, pages 1–44.
Mao, J., Liu, Y., Zhang, M., and Ma, S. (2016). How does domain expertise affect users’ search processes in exploratory searches? In SAL@ SIGIR.
Marchionini, G. (2006). Exploratory search: from finding to understanding. Communications of the ACM, 49(4):41–46.
Maxwell, D., Azzopardi, L., and Moshfeghi, Y. (2019). The impact of result diversification on search behaviour and performance. Information Retrieval Journal, 22(5):422–446.
Moraes, F., Putra, S. R., and Hauff, C. (2018). Contrasting search as a learning activity with instructor-designed learning. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 167–176. ACM.
Moreno-Marcos, P. M., Pong, T.-C., Munoz-Merino, P. J., and Kloos, C. D. (2020). Analysis of the factors influencing learners’ performance prediction with learning analytics. IEEE Access, 8:5264–5282.
Pereira, C. K., Medeiros, J. F., Siqueira, S. W., and Nunes, B. P. (2019). How complex is the complexity of a concept in exploratory search. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), volume 2161, pages 17–21. IEEE. https://doi.org/10.1109/ICALT.2019.00008
Pirolli, P. and Card, S. (1999). Information foraging. Psychological Review, 106:643–675.
Rieh, S. Y., Collins-Thompson, K., Hansen, P., and Lee, H.-J. (2016). Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science, 42(1):19–34.
Rieh, S. Y., Kim, Y.-M., and Markey, K. (2012). Amount of invested mental effort (aime) in online searching. Information Processing & Management, 48(6):1136–1150.
Sendurur, E., Efendioglu, E., Senturk, H., and Calıskan, N. (2019). High achievers’ web searching behaviors and patterns in two different task types. Journal of Educational Multimedia and Hypermedia, 28(2):217–238.
Shi, J., Otto, C., Hoppe, A., Holtz, P., and Ewerth, R. (2019). Investigating correlations of automatically extracted multimodal features and lecture video quality. In Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information, pages 11–19.
Smith, C. L. and Rieh, S. Y. (2019). Knowledge-context in search systems: toward information-literate actions. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, pages 55–62.
Soulier, L. and Tamine, L. (2017). On the collaboration support in information retrieval. ACM Computing Surveys, 50(4):1–34.
Syed, R. and Collins-Thompson, K. (2016). Optimizing search results for educational goals: Incorporating keyword density as a retrieval objective. In SAL@ SIGIR.
Syed, R. and Collins-Thompson, K. (2017). Retrieval algorithms optimized for human learning. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 555–564.
Syed, R. and Collins-Thompson, K. (2018). Exploring document retrieval features associated with improved short-and long-term vocabulary learning outcomes. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pages 191–200.
Taibi, D., Fulantelli, G., Marenzi, I., Nejdl, W., Rogers, R., and Ijaz, A. (2017). Sarweb: a semantic web tool to support search as learning practices and cross-language results on the web. In 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pages 522–524. IEEE.
Tibau, M., Siqueira, S., and Nunes, B. P. (2019a). A comparison between entity-centric knowledge base and knowledge graph to represent semantic relationships for searching as learning situations. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação, volume 8, pages 823-832. http://dx.doi.org/10.5753/cbie.wcbie.2019.823
Tibau, M., Siqueira, S. W., Nunes, B. P., Bortoluzzi, M., Marenzi, I., and Kemkes, P. (2018). Investigating users’ decision-making process while searching online and their shortcuts towards understanding. In International Conference on Web-Based Learning, pages 54–64. Springer. https://doi.org/10.1007/978-3-319-96565-9_6
Tibau, M., Siqueira, S. W., Nunes, B. P., Nurmikko-Fuller, T., and Manrique, R. F. (2019b). Using query reformulation to compare learning behaviors in web search engines. In 2019 IEEE 19th International Conference on Advanced Learning Technologies, volume 2161, pages 219–223. IEEE.
Tolmachova, T., Xu, L., Marenzi, I., and Gadiraju, U. (2019). Visualizing search history in web learning. In International Conference on Web-Based Learning, pages 229–240. Springer.
Vakkari, P. (2016). Searching as learning: A systematization based on literature. Journal of Information Science, 42(1):7–18.
Vakkari, P., Volske, M., Potthast, M., Hagen, M., and Stein, B. (2019). Modeling the usefulness of search results as measured by information use. Information Processing & Management, 56:879–894.
Weingart, N. and Eickhoff, C. (2016). Retrieval techniques for contextual learning. In SAL@ SIGIR.
Wilson, M. J. and Wilson, M. L. (2013). A comparison of techniques for measuring sensemaking and learning within participant-generated summaries. Journal of the American Society for Information Science and Technology, 64:291–306.
Wilson, M. L., Ye, C., Twidale, M. B., Grasse, H., Rosenthal, J., and McKittrick, M. (2016). Search literacy: Learning to search to learn. In SAL@ SIGIR, volume 1647.
Yilmaz, T., Ozcan, R., Altingovde, I. S., and Ulusoy, H. (2019). Improving educational web search for question-like queries through subject classification. Information Processing & Management, 56:228–246.
Yu, R., Gadiraju, U., Holtz, P., Rokicki, M., Kemkes, P., and Dietze, S. (2018). Predicting user knowledge gain in informational search sessions. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 75–84.
Publicado
24/11/2020
Como Citar
MACHADO, Marcelo de Oliveira Costa; GIMENEZ, Paulo Jose de Alcantara; SIQUEIRA, Sean Wolfgand Matsui.
Raising the Dimensions and Variables for Searching as a Learning Process: A Systematic Mapping of the Literature. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1393-1402.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1393.
