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doi: 10.3934/bdia.2017019

User perceived learning from interactive searching on big medical literature data

Xiangmin Zhang

School of Information Sciences, Wayne State University, 106 Kresge Library, Detroit, MI 48202, USA

Published  April 2018

As in other fields, search engines have been heavily used as an information accessing tool for massive amount of medical literature data. This research investigates the user's learning during interactive searching process with the PubMed data, to find out what search behaviors would be associated with the user's perceived learning, and whether or not the user's perceived learning could be reflected in the existing search performance measures, so that such measures could also be used for indicating learning during searching process. The research used a data set collected by a research project on searching, which involved 35 participants at a major US university. The results show that the number of documents saved is significantly correlated with perceived learning for all search topics. None of the classical search performance measures is correlated with perceived learning. However, for specific topics, one of the performance measures, Recall, is significantly correlated with perceived learning. The results and the implications of the findings are discussed.

Keywords: Information interactions, learning by searching, searching as learning, perceived learning, searching behavior.
Mathematics Subject Classification: Primary: 97R50; Secondary: 97R71.
Citation: Xiangmin Zhang. User perceived learning from interactive searching on big medical literature data. Big Data & Information Analytics, doi: 10.3934/bdia.2017019
References:
[1]

S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett and M. K. Norman, How Learning Works: Seven Research-Based Principles for Smart Teaching, Jossey-Bass, A Wiley Imprint. P. 3, 2010.Google Scholar

[2]

L. W. Anderson and D. A. Krathwohl, A Taxonomy for Learning, Teaching, and assessing: A Revision of Bloom's Taxonomy of Educational Objectives, New York: Longman, 2001.Google Scholar

[3]

N. J. Belkin, R. N. Oddy and H. M. Brooks, ASK for information retrieval: Part 1: Background and theory, Journal of Documentation, 38 (1982), 61-71. doi: 10.1108/eb026722. Google Scholar

[4]

Bransford, J. D., Brown, A. L. and R. R. Cocking, How People Learn: Brain, Mind, Experience, and School, Washington: National Academies Press, 2000.Google Scholar

[5]

C. Bruce and H. Hughes, Informed learning: A pedagogical construct attending simultaneously to information, use and learning, Library & Information Science Research, 32 (2010), A2-A8. doi: 10.1016/j.lisr.2010.07.013. Google Scholar

[6]

Downes, Learning Objects: Resources For Distance Education Worldwide, International Review of Research in Open and Distance Learning, 2001.Google Scholar

[7]

G. B. Duggan and S. J. Payne, Knowledge in the head and on the web: Using topic expertise to aid search, CHI '08 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008, 39-48. doi: 10.1145/1357054.1357062. Google Scholar

[8]

D. C. Edelson, D. N. Gordin and R. D. Pea, Addressing the challenges of inquiry-based learning through technology and curriculum design, Journal of the Learning Sciences, 8 (1999), 391-450. Google Scholar

[9]

R. Farwick, O. J. L. Hester and W. H. Teale, Where do you want to go today? Inquiry-based learning and technology integration, The Reading Teacher, 55 (2002), 616-625. Google Scholar

[10]

S. R. Goldman, J. Braasch, J. Wiley, A. Graesser and K. Brodowinska, Comprehending and learning from internet sources: Processing patterns of better and poorer learners, Reading Research Quarterly, 47 (2012), 356-381. Google Scholar

[11]

W. Hersh and E. Voorhees, TREC genomics special issue overview, Information Retrieval, 12 (2009), 1-15. doi: 10.1007/s10791-008-9076-6. Google Scholar

[12]

B. J. Jansen, D. Booth and B. Smith, Using the taxonomy of cognitive learning to model online searching, Information Processing and Management, 45 (2009), 643-663. doi: 10.1016/j.ipm.2009.05.004. Google Scholar

[13]

C. Kuhlthau, Seeking Meaning, 2nd ed., Libraries Unlimited, Westport, CT, 2004.Google Scholar

[14]

S. K. MacGregor and Y. Lou, Web-based learning: How task scaffolding and web site design support knowledge acquisition, Journal of Research on Technology in Education, 37 (2004), 161-175. doi: 10.1080/15391523.2004.10782431. Google Scholar

[15]

G. Marchionini, Exploratory search: From finding to understanding, Communications of the ACM, 49 (2006), 41-46. Google Scholar

[16]

G. Marchionini and H. Maurer, The roles of digital libraries in teaching and learning, Commnication of the ACM, 38 (1995), 67-75. doi: 10.1145/205323.205345. Google Scholar

[17]

J. Ormrod, Educational Psychology? Developing Learners, 7th Ed., Pearson, New York, 2011.Google Scholar

[18]

S. Y. Rieh, K. Collins-Thompson, P. Hansen and H. J. Lee, Towards searching as a learning process: A review of current perspectives and future directions, Journal of Information Science, 42 (2016), 19-34. doi: 10.1177/0165551515615841. Google Scholar

[19]

J.-L. Shih, C.-W. Chuang and G.-J. Hwang, An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness, Educational Technology & Society, 13 (2010), 50-62. Google Scholar

[20]

P. Vakkari, Searching as learning: A systematization based on literature, Journal of Information Science, 42 (2016), 7-18. doi: 10.1177/0165551515615833. Google Scholar

[21]

C. J. Van Rijsbergen, Information Retrieval (2nd ed.), Butterworth, 1979.Google Scholar

[22]

A. Walraven, S. Brand-Gruwel and H. P. A. Boshuizen, How students evaluate information and sources when searching the World Wide, Web for information. Computers & Education, 52 (2009), 234-246. Google Scholar

[23]

T. Willoughby, S. A. Anderson, E. Woodc, J. Mueller and C. Ross, Fast searching for information on the Internet to use in a learning context: The impact of domain knowledge, Computers & Education, 52 (2009), 640-648. doi: 10.1016/j.compedu.2008.11.009. Google Scholar

[24]

C. Yin, H.-Y. Sung, G.-J. Hwang, S. Hirokawa, H.-C. Chu, B. Flanagan and Y. Tabata, Learning by Searching: A Learning Environment that Provides Searching and Analysis Facilities for Supporting Trend Analysis Activities, Educational Technology & Society, 14 (2013), 1865-1889. Google Scholar

[25]

X. Zhang, J. Liu, C. Liu and M. Cole, Factors influencing users? perceived learning during online searching, in: Proceedings of the 9th International Conference on e-Learning (ICEL-2014), 2014, 200-210.Google Scholar

show all references

References:
[1]

S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett and M. K. Norman, How Learning Works: Seven Research-Based Principles for Smart Teaching, Jossey-Bass, A Wiley Imprint. P. 3, 2010.Google Scholar

[2]

L. W. Anderson and D. A. Krathwohl, A Taxonomy for Learning, Teaching, and assessing: A Revision of Bloom's Taxonomy of Educational Objectives, New York: Longman, 2001.Google Scholar

[3]

N. J. Belkin, R. N. Oddy and H. M. Brooks, ASK for information retrieval: Part 1: Background and theory, Journal of Documentation, 38 (1982), 61-71. doi: 10.1108/eb026722. Google Scholar

[4]

Bransford, J. D., Brown, A. L. and R. R. Cocking, How People Learn: Brain, Mind, Experience, and School, Washington: National Academies Press, 2000.Google Scholar

[5]

C. Bruce and H. Hughes, Informed learning: A pedagogical construct attending simultaneously to information, use and learning, Library & Information Science Research, 32 (2010), A2-A8. doi: 10.1016/j.lisr.2010.07.013. Google Scholar

[6]

Downes, Learning Objects: Resources For Distance Education Worldwide, International Review of Research in Open and Distance Learning, 2001.Google Scholar

[7]

G. B. Duggan and S. J. Payne, Knowledge in the head and on the web: Using topic expertise to aid search, CHI '08 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008, 39-48. doi: 10.1145/1357054.1357062. Google Scholar

[8]

D. C. Edelson, D. N. Gordin and R. D. Pea, Addressing the challenges of inquiry-based learning through technology and curriculum design, Journal of the Learning Sciences, 8 (1999), 391-450. Google Scholar

[9]

R. Farwick, O. J. L. Hester and W. H. Teale, Where do you want to go today? Inquiry-based learning and technology integration, The Reading Teacher, 55 (2002), 616-625. Google Scholar

[10]

S. R. Goldman, J. Braasch, J. Wiley, A. Graesser and K. Brodowinska, Comprehending and learning from internet sources: Processing patterns of better and poorer learners, Reading Research Quarterly, 47 (2012), 356-381. Google Scholar

[11]

W. Hersh and E. Voorhees, TREC genomics special issue overview, Information Retrieval, 12 (2009), 1-15. doi: 10.1007/s10791-008-9076-6. Google Scholar

[12]

B. J. Jansen, D. Booth and B. Smith, Using the taxonomy of cognitive learning to model online searching, Information Processing and Management, 45 (2009), 643-663. doi: 10.1016/j.ipm.2009.05.004. Google Scholar

[13]

C. Kuhlthau, Seeking Meaning, 2nd ed., Libraries Unlimited, Westport, CT, 2004.Google Scholar

[14]

S. K. MacGregor and Y. Lou, Web-based learning: How task scaffolding and web site design support knowledge acquisition, Journal of Research on Technology in Education, 37 (2004), 161-175. doi: 10.1080/15391523.2004.10782431. Google Scholar

[15]

G. Marchionini, Exploratory search: From finding to understanding, Communications of the ACM, 49 (2006), 41-46. Google Scholar

[16]

G. Marchionini and H. Maurer, The roles of digital libraries in teaching and learning, Commnication of the ACM, 38 (1995), 67-75. doi: 10.1145/205323.205345. Google Scholar

[17]

J. Ormrod, Educational Psychology? Developing Learners, 7th Ed., Pearson, New York, 2011.Google Scholar

[18]

S. Y. Rieh, K. Collins-Thompson, P. Hansen and H. J. Lee, Towards searching as a learning process: A review of current perspectives and future directions, Journal of Information Science, 42 (2016), 19-34. doi: 10.1177/0165551515615841. Google Scholar

[19]

J.-L. Shih, C.-W. Chuang and G.-J. Hwang, An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness, Educational Technology & Society, 13 (2010), 50-62. Google Scholar

[20]

P. Vakkari, Searching as learning: A systematization based on literature, Journal of Information Science, 42 (2016), 7-18. doi: 10.1177/0165551515615833. Google Scholar

[21]

C. J. Van Rijsbergen, Information Retrieval (2nd ed.), Butterworth, 1979.Google Scholar

[22]

A. Walraven, S. Brand-Gruwel and H. P. A. Boshuizen, How students evaluate information and sources when searching the World Wide, Web for information. Computers & Education, 52 (2009), 234-246. Google Scholar

[23]

T. Willoughby, S. A. Anderson, E. Woodc, J. Mueller and C. Ross, Fast searching for information on the Internet to use in a learning context: The impact of domain knowledge, Computers & Education, 52 (2009), 640-648. doi: 10.1016/j.compedu.2008.11.009. Google Scholar

[24]

C. Yin, H.-Y. Sung, G.-J. Hwang, S. Hirokawa, H.-C. Chu, B. Flanagan and Y. Tabata, Learning by Searching: A Learning Environment that Provides Searching and Analysis Facilities for Supporting Trend Analysis Activities, Educational Technology & Society, 14 (2013), 1865-1889. Google Scholar

[25]

X. Zhang, J. Liu, C. Liu and M. Cole, Factors influencing users? perceived learning during online searching, in: Proceedings of the 9th International Conference on e-Learning (ICEL-2014), 2014, 200-210.Google Scholar

Table 1.  Search Topics and Their Specificity
TREC topic # Topic title keywords MeSH category Specificity
2 Generating transgenic mice Genetic structure Specific (4)
7 DNA repair and oxidative stress Genetic processes General (1)
42 Genes altered by chromosome translocations Genetic phenomena Specific (4)
45 Mental Health Wellness-1 Genetic phenomena General (1)
49 Glyphosate tolerance gene sequence Genetic structure General (1)
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TREC topic # Topic title keywords MeSH category Specificity
2 Generating transgenic mice Genetic structure Specific (4)
7 DNA repair and oxidative stress Genetic processes General (1)
42 Genes altered by chromosome translocations Genetic phenomena Specific (4)
45 Mental Health Wellness-1 Genetic phenomena General (1)
49 Glyphosate tolerance gene sequence Genetic structure General (1)
Table 2.  Behavior Variables
Behavior Variables Description
# of Qs The total number of queries submitted to the search system for a specific search task
q-Length Query length is the number of words contained in a query. Here query length is the average length of multiple queries for a search task
# of Docs. saved Number of documents/abstracts saved form the search results for a task
# of Docs. viewed Number of documents/abstracts opened and viewed from the search results for a topic
Ratio-of-DocsSaved/Viewed The ratio of documents saved and the documents opened/viewed
# of Actions task The total number of actions during working on a search topic. The actions include both keyboard and mouse actions
# of SERPs viewed Number of search result pages viewed or checked that were returned by the search system
Time for the Task The total time spent on tasks
Ranking on SERPs The average ranking position of the documents opened in SERPs. "1" is the top ranking, most related by the system and the larger the number, the lower the ranking is.
Average dwell time Average time spent on viewing document/abstract
Querying time Average time spent on working on queries
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Behavior Variables Description
# of Qs The total number of queries submitted to the search system for a specific search task
q-Length Query length is the number of words contained in a query. Here query length is the average length of multiple queries for a search task
# of Docs. saved Number of documents/abstracts saved form the search results for a task
# of Docs. viewed Number of documents/abstracts opened and viewed from the search results for a topic
Ratio-of-DocsSaved/Viewed The ratio of documents saved and the documents opened/viewed
# of Actions task The total number of actions during working on a search topic. The actions include both keyboard and mouse actions
# of SERPs viewed Number of search result pages viewed or checked that were returned by the search system
Time for the Task The total time spent on tasks
Ranking on SERPs The average ranking position of the documents opened in SERPs. "1" is the top ranking, most related by the system and the larger the number, the lower the ranking is.
Average dwell time Average time spent on viewing document/abstract
Querying time Average time spent on working on queries
Table 3.  Correlations between Perceived Learning and Behavior Measures
Behavior Variables Correlation with Perceived Learning ($n=140$)
# of Qs Correlation -.085
Sig. (2-tailed) .320
q length Correlation .060
Sig. (2-tailed) .482
# of Docs Saved Correlation .1801
Sig. (2-tailed) .034
# of Docs opened or viewed Correlation .082
Sig. (2-tailed) .336
Ratio of Docs Saved or viewed Correlation .3112
Sig. (2-tailed) .000
# of Actions task Correlation .107
Sig. (2-tailed) .206
# of SERPs viewed Correlation .086
Sig. (2-tailed) .314
Time for task Correlation .031
Sig. (2-tailed) .714
Ranking on SERPs Correlation .1681
Sig. (2-tailed) .047
Average dwell time Correlation -.047
Sig. (2-tailed) .584
Query time Correlation -.049
Sig. (2-tailed) .567
1Correlation is significant at the 0.05 level (2-tailed).
2Correlation is significant at the 0.01 level (2-tailed).
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Behavior Variables Correlation with Perceived Learning ($n=140$)
# of Qs Correlation -.085
Sig. (2-tailed) .320
q length Correlation .060
Sig. (2-tailed) .482
# of Docs Saved Correlation .1801
Sig. (2-tailed) .034
# of Docs opened or viewed Correlation .082
Sig. (2-tailed) .336
Ratio of Docs Saved or viewed Correlation .3112
Sig. (2-tailed) .000
# of Actions task Correlation .107
Sig. (2-tailed) .206
# of SERPs viewed Correlation .086
Sig. (2-tailed) .314
Time for task Correlation .031
Sig. (2-tailed) .714
Ranking on SERPs Correlation .1681
Sig. (2-tailed) .047
Average dwell time Correlation -.047
Sig. (2-tailed) .584
Query time Correlation -.049
Sig. (2-tailed) .567
1Correlation is significant at the 0.05 level (2-tailed).
2Correlation is significant at the 0.01 level (2-tailed).
Table 4.  GLM/ANOVA Results of Effects of Behaviors on Perceived Learning
Dependent Variable Perceived Learning
Source Type Ⅲ Sum of Squares df Mean Square F Sig.
Corrected Model 44.296a 11 4.027 2.102 .024
Intercept .221 1 .221 .115 .735
# of Qs 3.299 1 3.299 1.722 .192
q length .011 1 011 .006 .939
# of Docs Saved 1.068 1 1.068 .558 .457
# of Docs opened viewed 1.967 1 1.967 1.027 .313
Ratio of Docs Saved Viewed 20.760 1 20.760 10.838 .001
#of Actions task .309 1 .309 .161 .688
# of SERPs viewed 1.426 1 1.426 .745 .390
Time for Task 2.610 1 2.610 1.362 .245
Ranking on SERPs .505 1 .505 .264 .608
Average dwell time 6.798 1 6.798 3.549 .062
Query time 7.339 1 7.339 3.831 .052
Error 245.175 128 1.915
Total 2434.500 140
Corrected Total 289.471 139
aR Squared =.153 (Adjusted R Squared =.080)
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Dependent Variable Perceived Learning
Source Type Ⅲ Sum of Squares df Mean Square F Sig.
Corrected Model 44.296a 11 4.027 2.102 .024
Intercept .221 1 .221 .115 .735
# of Qs 3.299 1 3.299 1.722 .192
q length .011 1 011 .006 .939
# of Docs Saved 1.068 1 1.068 .558 .457
# of Docs opened viewed 1.967 1 1.967 1.027 .313
Ratio of Docs Saved Viewed 20.760 1 20.760 10.838 .001
#of Actions task .309 1 .309 .161 .688
# of SERPs viewed 1.426 1 1.426 .745 .390
Time for Task 2.610 1 2.610 1.362 .245
Ranking on SERPs .505 1 .505 .264 .608
Average dwell time 6.798 1 6.798 3.549 .062
Query time 7.339 1 7.339 3.831 .052
Error 245.175 128 1.915
Total 2434.500 140
Corrected Total 289.471 139
aR Squared =.153 (Adjusted R Squared =.080)
Table 5.  Correlations between Perceived Learning and Performance Measures
Performance Measures Correlation with Perceived Learning (n=140)
Precision Correlation -.069
Sig. (2-tailed) .416
Recall Correlation .052
Sig. (2-tailed) .545
F2Score Correlation .047
Sig. (2-tailed) .579
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Performance Measures Correlation with Perceived Learning (n=140)
Precision Correlation -.069
Sig. (2-tailed) .416
Recall Correlation .052
Sig. (2-tailed) .545
F2Score Correlation .047
Sig. (2-tailed) .579
Table 6.  Correlations between Perceived Learning and Behavior measures for General and Specific Topics Separately
Behavior Variables Correlation with Perceived Learning
General Topics (n=90) Specific Topics (n=50)
# of Qs Correlation -.085 -.015
Sig. (2-tailed) .320 .915
q length Correlation .045 .084
Sig. (2-tailed) .671 .563
# of Docs Saved Correlation .126 .338
Sig. (2-tailed) .235 .016
# of Docs opened or viewed Correlation .045 .179
Sig. (2-tailed) .671 .213
Ratio of Docs Saved or viewed Correlation .248 .457
Sig. (2-tailed) .018 .001
# of Actions task Correlation .058 .0234
Sig. (2-tailed) .589 .102
# of SERPs viewed Correlation .037 .199
Sig. (2-tailed) .731 .165
Time for task Correlation -.014 .109
Sig. (2-tailed) .899 .453
Ranking on SERPs Correlation .192 .122
Sig. (2-tailed) .069 .399
Average dwell time Correlation -.017 -.105
Sig. (2-tailed) .871 .469
Query time Correlation -.092 .011
Sig. (2-tailed) .390 .942
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Behavior Variables Correlation with Perceived Learning
General Topics (n=90) Specific Topics (n=50)
# of Qs Correlation -.085 -.015
Sig. (2-tailed) .320 .915
q length Correlation .045 .084
Sig. (2-tailed) .671 .563
# of Docs Saved Correlation .126 .338
Sig. (2-tailed) .235 .016
# of Docs opened or viewed Correlation .045 .179
Sig. (2-tailed) .671 .213
Ratio of Docs Saved or viewed Correlation .248 .457
Sig. (2-tailed) .018 .001
# of Actions task Correlation .058 .0234
Sig. (2-tailed) .589 .102
# of SERPs viewed Correlation .037 .199
Sig. (2-tailed) .731 .165
Time for task Correlation -.014 .109
Sig. (2-tailed) .899 .453
Ranking on SERPs Correlation .192 .122
Sig. (2-tailed) .069 .399
Average dwell time Correlation -.017 -.105
Sig. (2-tailed) .871 .469
Query time Correlation -.092 .011
Sig. (2-tailed) .390 .942
Table 7.  Correlations between Perceived Learning and Different Types of Topics
Performance Measures Perceived Learning
General (n=90) Specific (n=50)
Precision r=-.083, p=.439 r=-.040, p=.785
Recall r=.021, p=.842 r=.296, p=.037
$F_2$ Score r=.015, p=.888 r=.295, p=.037
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Performance Measures Perceived Learning
General (n=90) Specific (n=50)
Precision r=-.083, p=.439 r=-.040, p=.785
Recall r=.021, p=.842 r=.296, p=.037
$F_2$ Score r=.015, p=.888 r=.295, p=.037
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