Optimization of Search Environments for Learning Contexts

Jaurès S. H. Kameni, Bernabé Batchakui, Roger Nkambou

Abstract


This article proposes an improvement of search engines in a learning or training context. Indeed, the learner requests resources or learning content in a training or learning situation. The same goes for the trainer, who wishes to select the appropriate resources available to his learners. Unfortunately, existing search engines produce an enormous mass of content but sometimes do not match the learning context, thus causing an enormous loss of time for the learner or the teacher to find the appropriate resources among this important batch. Therefore, we suggest associating a complementary layer with search engines to extract the most relevant information related to learning or training situations from the engine results. For this purpose, an integrated filter eliminates irrelevant results to the current learning or training situation; and performs a weighted reclassification of these results based on Bloom’s taxonomy. In terms of the HMI, this layer allows having more informative result snippets. The experimentation of this environment is based on Google APIs. According to the Bloom hierarchy, the classification of the user question and the classification of the search results are carried out from Natural Language Processing based on Logistic Regression of Machine Learning Algorithms. The result obtained presents an intuitively favorable environment for education, leading to the implementation of a specific search engine capable of collecting, storing, and indexing educational concepts in the next stage of this project. A project to empirically evaluate the results obtained is currently underway.

Keywords


Information retrieval; search engine; search-as-learning; bloom’s taxonomy; natural language processing; question classification.

Full Text:

PDF

References


J. Haider and O. Sundin, Invisible search and online search engines: The ubiquity of search in everyday life. 2019.

F. Zhang, Y. Liu, J. Mao, M. Zhang, and S. Ma, “User behavior modeling for Web search evaluation,” AI Open, vol. 1, pp. 40–56, 2020, doi: 10.1016/j.aiopen.2021.02.003.

T. Vuong, M. Saastamoinen, G. Jacucci, and T. Ruotsalo, “Understanding user behavior in naturalistic information search tasks,” J. Assoc. Inf. Sci. Technol., vol. 70, no. 11, pp. 1248–1261, 2019, doi: 10.1002/asi.24201.

G. Buscher, S. Dumais, and E. Cutrell, “The good, the bad, and the random: An eye-tracking study of ad quality in web search,” in SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010, pp. 42–49, doi: 10.1145/1835449.1835459.

M. Hingoro and H. Nawaz, “A Comparative Analysis of Search Engine Ranking Algorithms,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 10, no. 2, 2021, doi: 10.30534/ijatcse/2021/1081022021.

S. Y. Chen and J. H. Wang, “Individual differences and personalized learning: a review and appraisal,” Universal Access in the Information Society. pp. 1–17, 2020, doi: 10.1007/s10209-020-00753-4.

M. G. Zaragoza, H. K. Kim, and H. J. Hwang, “E-learning adaptation and mobile learning for education,” in Studies in Computational Intelligence, vol. 788, 2019, pp. 27–36.

S. Kausar, X. Huahu, I. Hussain, Z. Wenhao, and M. Zahid, “Integration of Data Mining Clustering Approach in the Personalized E-Learning System,” IEEE Access, vol. 6, pp. 72724–72734, 2018, doi: 10.1109/ACCESS.2018.2882240.

E. Shchedrina, I. Valiev, F. Sabirova, and D. Babaskin, “Providing Adaptivity in Moodle LMS Courses,” Int. J. Emerg. Technol. Learn., vol. 16, no. 2, pp. 95–107, 2021, doi: 10.3991/ijet.v16i02.18813.

U. Gadiraju, R. Yu, S. Dietze, and P. Holtz, “Analyzing knowledge gain of users in informational search sessions on the web,” in CHIIR 2018 - Proceedings of the 2018 Conference on Human Information Interaction and Retrieval, 2018, vol. 2018-March, pp. 2–11, doi: 10.1145/3176349.3176381.

N. Yusuf, M. A. B. M. Yunus, and N. B. Wahid, “A comparative analysis of web search query: Informational vs. navigational queries,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 1, pp. 136–141, 2019, doi: 10.18517/ijaseit.9.1.7578.

R. Yu, R. Tang, M. Rokicki, U. Gadiraju, and S. Dietze, “Topic-independent modeling of user knowledge in informational search sessions,” Inf. Retr. J., vol. 24, no. 3, pp. 240–268, 2021, doi: 10.1007/s10791-021-09391-7.

N. Spirin, A. Kotov, K. Karahalios, V. Mladenov, and P. Izhutov, “A comparative study of query-biased and non-redundant snippets for structured search on mobile devices,” in International Conference on Information and Knowledge Management, Proceedings, 2016, vol. 24-28-Octo, pp. 2389–2394, doi: 10.1145/2983323.2983699.

T. Kanungo and D. Orr, “Predicting the readability of short web summaries,” in Proceedings of the 2nd ACM International Conference on Web Search and Data Mining, WSDM’09, 2009, pp. 202–211, doi: 10.1145/1498759.1498827.

P. Arora, “Promoting user engagement and learning in amorphous search tasks,” in SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 1051–1051, doi: 10.1145/2766462.2767848.

C. Kodama, B. St. Jean, M. Subramaniam, and N. G. Taylor, “There’s a creepy guy on the other end at Google!: engaging middle school students in a drawing activity to elicit their mental models of Google,” Inf. Retr. J., vol. 20, no. 5, pp. 403–432, 2017, doi: 10.1007/s10791-017-9306-x.

S. Qiu, A. Bozzon, and U. Gadiraju, “Conversational interfaces for search as learning,” in CEUR Workshop Proceedings, 2020, vol. 2699.

M. Sandler, “Organizing search results in a topic hierarchy,” 2012.

R. Syed and K. Collins-Thompson, “Optimizing search results for human learning goals,” Inf. Retr. J., vol. 20, no. 5, pp. 506–523, 2017, doi: 10.1007/s10791-017-9303-0.

R. Syed and K. Collins-Thompson, “Retrieval algorithms optimized for human learning,” in SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 555–564, doi: 10.1145/3077136.3080835.

Y. Lu and I. H. Hsiao, “Personalized Information Seeking Assistant (PiSA): from programming information seeking to learning,” Inf. Retr. J., vol. 20, no. 5, pp. 433–455, 2017, doi: 10.1007/s10791-017-9305-y.

S. Karanam, G. Jorge-Botana, R. Olmos, and H. van Oostendorp, “The role of domain knowledge in cognitive modeling of information search,” Inf. Retr. J., vol. 20, no. 5, pp. 456–479, 2017, doi: 10.1007/s10791-017-9308-8.

I. M. Azpiazu, N. Dragovic, M. S. Pera, and J. A. Fails, “Online searching and learning: YUM and other search tools for children and teachers,” Inf. Retr. J., vol. 20, no. 5, pp. 524–545, 2017, doi: 10.1007/s10791-017-9310-1.

Y. Zhao, J. Zhang, X. Xia, and T. Le, “Evaluation of Google question-answering quality,” Libr. Hi Tech, vol. 37, no. 2, 2019, doi: 10.1108/LHT-10-2017-0218.

A. Strzelecki and P. Rutecka, “Direct Answers in Google Search Results,” IEEE Access, vol. 8, pp. 103642–103654, 2020, doi: 10.1109/ACCESS.2020.2999160.

A. Strzelecki and P. Rutecka, “Featured Snippets Results in Google Web Search: An Exploratory Study,” in Smart Innovation, Systems and Technologies, 2020, vol. 167, pp. 9–18, doi: 10.1007/978-981-15-1564-4_2.

P. Airasian, K. A. Cruikshank, R. E. Mayer, P. Pintrich, J. Raths, and M. C. Wittrock, “A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives Complete Edition,” in BOOK REVIEWS, vol. 83, no. 3, 2005.

M. Pikhart and B. Klimova, “Utilization of linguistic aspects of bloom’s taxonomy in blended learning,” Educ. Sci., vol. 9, no. 3, p. 235, 2019, doi: 10.3390/educsci9030235.

M. Thangaraj and M. Sivakami, “Text classification techniques: A literature review,” Interdiscip. J. Information, Knowledge, Manag., vol. 13, 2018, doi: 10.28945/4066.

G. Amati, “Information Retrieval Models,” in Encyclopedia of Database Systems, 2018.

B. Batchakui, J. S. H. Kameni, T. Djotio, and C. Tangha, “An ontology for the search of contents in the Cloud Learning,” Int. J. Eng. Technol. IJET-IJENS, vol. 15, no. 06, pp. 48–57, 2015.

G. Chen, J. Yang, C. Hauff, and G. J. Houben, “LearningQ: A large-scale dataset for educational question generation,” 2018.

M. Mohammedid and N. Omar, “Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec,” PLoS One, vol. 15, no. 3, p. e0230442, 2020, doi: 10.1371/journal.pone.0230442.

M. A. Çinici and A. Altun, “Reusable content matters: a learning object authoring tool for smart learning environments,” Smart Learn. Environ., vol. 5, no. 1, pp. 1–7, 2018, doi: 10.1186/s40561-018-0060-3.




DOI: http://dx.doi.org/10.18517/ijaseit.12.2.15945

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development